Upload tryon_inference.py

tryon_inference.py

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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,
+    garment_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/catvton-flux-alpha", 
+            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)
+    garment = load_image(garment_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 = transform(garment)
+
+    # 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([garment_mask, mask_tensor], 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('--garment', required=True, help='Path to the garment image')
+    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=768, help='Width')
+    parser.add_argument('--height', type=int, default=576, 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,
+        garment_path=args.garment,
+        output_garment_path=args.output_garment,
+        output_tryon_path=args.output_tryon,
+        num_steps=args.steps,
+        guidance_scale=args.guidance_scale,
+        seed=args.seed,
+        size=(args.width, args.height)
+    )
+    output_garment_path=args.output_garment,
+    output_tryon_path=args.output_tryon,
+    
+    if output_garment_path is not None:
+        garment_result.save(output_garment_path)
+    tryon_result.save(output_tryon_path)
+    
+    print("Successfully saved garment and try-on images")
+
+if __name__ == "__main__":
+    main()