controlnet-canny-sdxl-1.0
17.1K
186
license:apache-2.0
by
xinsir
Image Model
OTHER
Fair
17K downloads
Community-tested
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Quick Summary
AI model with specialized capabilities.
Code Examples
How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")How to Get Started with the Modelpythonpytorch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
controlnet_conditioning_scale = 1.0
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
"xinsir/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
scheduler=eulera_scheduler,
)
# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
controlnet_img = cv2.imread("your image path")
height, width, _ = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = cv2.Canny(controlnet_img, 100, 200)
controlnet_img = HWC3(controlnet_img)
controlnet_img = Image.fromarray(controlnet_img)
images = pipe(
prompt,
negative_prompt=negative_prompt,
image=controlnet_img,
controlnet_conditioning_scale=controlnet_conditioning_scale,
width=new_width,
height=new_height,
num_inference_steps=30,
).images
images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")Deploy This Model
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