sdxl-fp16
1
—
by
wangkanai
Image Model
OTHER
New
0 downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
Unknown
Mobile
Laptop
Server
Quick Summary
AI model with specialized capabilities.
Code Examples
Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")Usage Examplespythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL base model from local path
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate image with standard settings
image = pipe(
prompt="a beautiful mountain landscape at sunset, photorealistic, highly detailed",
negative_prompt="blurry, low quality, distorted",
num_inference_steps=50,
guidance_scale=7.5,
width=1024,
height=1024
).images[0]
image.save("output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")SDXL Turbo (Fast Generation)pythonpytorch
from diffusers import DiffusionPipeline
import torch
# Load SDXL Turbo for fast inference
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-turbo.safetensors",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate with minimal steps (1-4 steps)
image = pipe(
prompt="a futuristic cityscape at night, neon lights, cyberpunk",
num_inference_steps=4, # Turbo optimized for 1-4 steps
guidance_scale=0.0, # Turbo works best with guidance_scale=0
width=1024,
height=1024
).images[0]
image.save("turbo_output.png")Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Turbo works best with guidance_scale=0pythonpytorch
import torch
from diffusers import DiffusionPipeline
# Enable memory-efficient attention
pipe = DiffusionPipeline.from_single_file(
"E:/huggingface/sdxl-fp16/checkpoints/sdxl/sdxl-base.safetensors",
torch_dtype=torch.float16
)
# Apply optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe.to("cuda")
# Generate with optimized memory usage
image = pipe(
prompt="your prompt here",
num_inference_steps=30
).images[0]Deploy This Model
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