DarkIdol-1.0

Name: DarkIdol-1.0
Author: aifeifei798

1 language

license:apache-2.0

aifeifei798

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Quick Summary

DarkIdol-1.0 - Online Test https://huggingface.co/spaces/aifeifei798/DarkIdol-1.0

Code Examples

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

Inference codepythonpytorch

from diffusers import FluxPipeline
import torch
import numpy as np

MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)

pipeline = FluxPipeline.from_pretrained(
    "aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")

# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()

image = pipeline(
    prompt="bikini model at sea",
    guidance_scale=0,
    num_inference_steps=4,
    height=1792,
    width=1024,
    max_sequence_length=512,
    generator=generator,
).images[0]
image.save("DarkIdol.png")

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