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import argparse
import torch
from diffusers.utils import load_image, check_min_version
from diffusers import FluxPriorReduxPipeline, FluxFillPipeline
from diffusers import FluxTransformer2DModel
import numpy as np
from torchvision import transforms
def run_inference(
image_path,
mask_path,
size=(576, 768),
num_steps=50,
guidance_scale=30,
seed=42,
pipe=None
):
# Build pipeline
if pipe is None:
transformer = FluxTransformer2DModel.from_pretrained(
"xiaozaa/cat-tryoff-flux",
torch_dtype=torch.bfloat16
)
pipe = FluxFillPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
transformer=transformer,
torch_dtype=torch.bfloat16
).to("cuda")
else:
pipe.to("cuda")
pipe.transformer.to(torch.bfloat16)
# Add transform
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]) # For RGB images
])
mask_transform = transforms.Compose([
transforms.ToTensor()
])
# Load and process images
# print("image_path", image_path)
image = load_image(image_path).convert("RGB").resize(size)
mask = load_image(mask_path).convert("RGB").resize(size)
# Transform images using the new preprocessing
image_tensor = transform(image)
mask_tensor = mask_transform(mask)[:1] # Take only first channel
garment_tensor = torch.zeros_like(image_tensor)
image_tensor = image_tensor * mask_tensor
# Create concatenated images
inpaint_image = torch.cat([garment_tensor, image_tensor], dim=2) # Concatenate along width
garment_mask = torch.zeros_like(mask_tensor)
extended_mask = torch.cat([1 - garment_mask, garment_mask], dim=2)
prompt = f"The pair of images highlights a clothing and its styling on a model, high resolution, 4K, 8K; " \
f"[IMAGE1] Detailed product shot of a clothing" \
f"[IMAGE2] The same cloth is worn by a model in a lifestyle setting."
generator = torch.Generator(device="cuda").manual_seed(seed)
result = pipe(
height=size[1],
width=size[0] * 2,
image=inpaint_image,
mask_image=extended_mask,
num_inference_steps=num_steps,
generator=generator,
max_sequence_length=512,
guidance_scale=guidance_scale,
prompt=prompt,
).images[0]
# Split and save results
width = size[0]
garment_result = result.crop((0, 0, width, size[1]))
tryon_result = result.crop((width, 0, width * 2, size[1]))
return garment_result, tryon_result
def main():
parser = argparse.ArgumentParser(description='Run FLUX virtual try-on inference')
parser.add_argument('--image', required=True, help='Path to the model image')
parser.add_argument('--mask', required=True, help='Path to the agnostic mask')
parser.add_argument('--output_garment', default='flux_inpaint_garment.png', help='Output path for garment result')
parser.add_argument('--output_tryon', default='flux_inpaint_tryon.png', help='Output path for try-on result')
parser.add_argument('--steps', type=int, default=50, help='Number of inference steps')
parser.add_argument('--guidance_scale', type=float, default=30, help='Guidance scale')
parser.add_argument('--seed', type=int, default=0, help='Random seed')
parser.add_argument('--width', type=int, default=576, help='Width')
parser.add_argument('--height', type=int, default=768, help='Height')
args = parser.parse_args()
check_min_version("0.30.2")
garment_result, tryon_result = run_inference(
image_path=args.image,
mask_path=args.mask,
num_steps=args.steps,
guidance_scale=args.guidance_scale,
seed=args.seed,
size=(args.width, args.height)
)
output_tryon_path=args.output_tryon
output_garment_path=args.output_garment
tryon_result.save(output_tryon_path)
garment_result.save(output_garment_path)
print("Successfully saved garment and try-on images")
if __name__ == "__main__":
main() |