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Merge pull request #19 from nftblackmagic/anzh/tryoff
Browse files- .gitignore +3 -0
- README.md +34 -3
- example/tryoff_result/restored_garment1.png +0 -0
- example/tryoff_result/restored_garment2.png +0 -0
- example/tryoff_result/restored_garment3.png +0 -0
- example/tryoff_result/restored_garment4.png +0 -0
- example/tryoff_result/restored_garment5.png +0 -0
- example/tryoff_result/restored_garment6.png +0 -0
- requirements.txt +1 -0
- test.png +0 -0
- tryoff.sh +7 -0
- tryoff_inference.py +117 -0
- tryon.sh +7 -0
.gitignore
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# Gradio cache
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.gradio/example/github.mp4
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# Gradio cache
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.gradio/example/github.mp4
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aws/
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checkpoints/
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README.md
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@@ -22,6 +22,9 @@ Running it now on website: [CATVTON-FLUX-TRY-ON](https://huggingface.co/spaces/x
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---
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**Latest Achievement**
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(2024/12/1):
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- Community comfyui support [here](https://github.com/lujiazho/ComfyUI-CatvtonFluxWrapper). Thanks to [lujiazho](https://github.com/lujiazho)
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---
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## Showcase
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| Original | Garment | Result |
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|----------|---------|---------|
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|  |  |  |
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## Model Weights
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Fine-tuning weights in Hugging Face: 🤗 [catvton-flux-alpha](https://huggingface.co/xiaozaa/catvton-flux-alpha)
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LORA weights in Hugging Face: 🤗 [catvton-flux-lora-alpha](https://huggingface.co/xiaozaa/catvton-flux-lora-alpha)
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The model weights are trained on the [VITON-HD](https://github.com/shadow2496/VITON-HD) dataset.
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## Prerequisites
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## Usage
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Run the following command to try on an image:
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LORA version:
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```
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Gradio demo:
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-
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<!-- Option 2: Using a thumbnail linked to the video -->
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[](https://upcdn.io/FW25b7k/raw/uploads/github.mp4)
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- [x] Add gradio demo
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- [x] Release updated weights with better performance
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- [x] Train a smaller model
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-
- [
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-
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## Citation
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```bibtex
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---
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**Latest Achievement**
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(2024/12/6):
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- Released a new weights for tryoff. The model named [cat-tryoff-flux](https://huggingface.co/xiaozaa/cat-tryoff-flux) can extract and reconstruct the front view of clothing items from images of people wearing them. [Showcase examples](#try-off-examples) is here.
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(2024/12/1):
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- Community comfyui support [here](https://github.com/lujiazho/ComfyUI-CatvtonFluxWrapper). Thanks to [lujiazho](https://github.com/lujiazho)
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---
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## Showcase
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### Try-on examples
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| Original | Garment | Result |
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|----------|---------|---------|
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|  |  |  |
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|  |  |  |
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### Try-off examples
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| Original clothed model | Restored garment result |
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|------------------------|------------------------|
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|  |  |
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## Model Weights
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### Tryon
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Fine-tuning weights in Hugging Face: 🤗 [catvton-flux-alpha](https://huggingface.co/xiaozaa/catvton-flux-alpha)
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LORA weights in Hugging Face: 🤗 [catvton-flux-lora-alpha](https://huggingface.co/xiaozaa/catvton-flux-lora-alpha)
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### Tryoff
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Fine-tuning weights in Hugging Face: 🤗 [cat-tryoff-flux](https://huggingface.co/xiaozaa/cat-tryoff-flux)
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### Dataset
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The model weights are trained on the [VITON-HD](https://github.com/shadow2496/VITON-HD) dataset.
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## Prerequisites
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## Usage
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### Tryoff
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Run the following command to restore the front side of the garment from the clothed model image:
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```bash
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python tryoff_inference.py \
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--image ./example/person/00069_00.jpg \
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--mask ./example/person/00069_00_mask.png \
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--seed 41 \
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--output_tryon test_original.png \
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--output_garment restored_garment6.png \
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--steps 30
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```
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### Tryon
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Run the following command to try on an image:
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LORA version:
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```
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Gradio demo:
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Hugging Face: 🤗 [CATVTON-FLUX-TRY-ON](https://huggingface.co/spaces/xiaozaa/catvton-flux-try-on)
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<!-- Option 2: Using a thumbnail linked to the video -->
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[](https://upcdn.io/FW25b7k/raw/uploads/github.mp4)
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- [x] Add gradio demo
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- [x] Release updated weights with better performance
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- [x] Train a smaller model
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- [x] Support comfyui
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- [x] Release tryoff weights
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## Citation
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```bibtex
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example/tryoff_result/restored_garment1.png
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example/tryoff_result/restored_garment2.png
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example/tryoff_result/restored_garment3.png
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example/tryoff_result/restored_garment4.png
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example/tryoff_result/restored_garment5.png
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example/tryoff_result/restored_garment6.png
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requirements.txt
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peft==0.13.2
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huggingface-hub
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spaces
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peft==0.13.2
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huggingface-hub
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spaces
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protobuf
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test.png
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Binary file (337 kB)
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tryoff.sh
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python tryoff_inference.py \
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--image ./example/person/00069_00.jpg \
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--mask ./example/person/00069_00_mask.png \
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--seed 41 \
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--output_tryon test_original.png \
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--output_garment restored_garment6.png \
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--steps 30
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tryoff_inference.py
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import argparse
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import torch
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from diffusers.utils import load_image, check_min_version
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from diffusers import FluxPriorReduxPipeline, FluxFillPipeline
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from diffusers import FluxTransformer2DModel
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import numpy as np
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from torchvision import transforms
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def run_inference(
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image_path,
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mask_path,
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size=(576, 768),
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num_steps=50,
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guidance_scale=30,
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seed=42,
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pipe=None
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):
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# Build pipeline
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if pipe is None:
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transformer = FluxTransformer2DModel.from_pretrained(
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"xiaozaa/cat-tryoff-flux",
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torch_dtype=torch.bfloat16
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)
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pipe = FluxFillPipeline.from_pretrained(
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"black-forest-labs/FLUX.1-dev",
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transformer=transformer,
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torch_dtype=torch.bfloat16
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).to("cuda")
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else:
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pipe.to("cuda")
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pipe.transformer.to(torch.bfloat16)
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# Add transform
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transform = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize([0.5], [0.5]) # For RGB images
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])
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mask_transform = transforms.Compose([
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transforms.ToTensor()
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])
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# Load and process images
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# print("image_path", image_path)
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image = load_image(image_path).convert("RGB").resize(size)
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mask = load_image(mask_path).convert("RGB").resize(size)
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# Transform images using the new preprocessing
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image_tensor = transform(image)
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mask_tensor = mask_transform(mask)[:1] # Take only first channel
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garment_tensor = torch.zeros_like(image_tensor)
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image_tensor = image_tensor * mask_tensor
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# Create concatenated images
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inpaint_image = torch.cat([garment_tensor, image_tensor], dim=2) # Concatenate along width
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garment_mask = torch.zeros_like(mask_tensor)
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extended_mask = torch.cat([1 - garment_mask, garment_mask], dim=2)
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prompt = f"The pair of images highlights a clothing and its styling on a model, high resolution, 4K, 8K; " \
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f"[IMAGE1] Detailed product shot of a clothing" \
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f"[IMAGE2] The same cloth is worn by a model in a lifestyle setting."
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generator = torch.Generator(device="cuda").manual_seed(seed)
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result = pipe(
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height=size[1],
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width=size[0] * 2,
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image=inpaint_image,
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mask_image=extended_mask,
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num_inference_steps=num_steps,
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generator=generator,
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max_sequence_length=512,
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guidance_scale=guidance_scale,
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prompt=prompt,
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).images[0]
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# Split and save results
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width = size[0]
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garment_result = result.crop((0, 0, width, size[1]))
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tryon_result = result.crop((width, 0, width * 2, size[1]))
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return garment_result, tryon_result
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def main():
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parser = argparse.ArgumentParser(description='Run FLUX virtual try-on inference')
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parser.add_argument('--image', required=True, help='Path to the model image')
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parser.add_argument('--mask', required=True, help='Path to the agnostic mask')
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parser.add_argument('--output_garment', default='flux_inpaint_garment.png', help='Output path for garment result')
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parser.add_argument('--output_tryon', default='flux_inpaint_tryon.png', help='Output path for try-on result')
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parser.add_argument('--steps', type=int, default=50, help='Number of inference steps')
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parser.add_argument('--guidance_scale', type=float, default=30, help='Guidance scale')
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parser.add_argument('--seed', type=int, default=0, help='Random seed')
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parser.add_argument('--width', type=int, default=576, help='Width')
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parser.add_argument('--height', type=int, default=768, help='Height')
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args = parser.parse_args()
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check_min_version("0.30.2")
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garment_result, tryon_result = run_inference(
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image_path=args.image,
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mask_path=args.mask,
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num_steps=args.steps,
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guidance_scale=args.guidance_scale,
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seed=args.seed,
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size=(args.width, args.height)
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)
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output_tryon_path=args.output_tryon
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output_garment_path=args.output_garment
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tryon_result.save(output_tryon_path)
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garment_result.save(output_garment_path)
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print("Successfully saved garment and try-on images")
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if __name__ == "__main__":
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main()
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tryon.sh
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python tryon_inference.py \
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--image ./example/person/00008_00.jpg \
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--mask ./example/person/00008_00_mask.png \
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--garment ./example/garment/00034_00.jpg \
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--seed 42 \
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--output_tryon test.png \
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--steps 30
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