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.amlignore

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+## This file was auto generated by the Azure Machine Learning Studio. Please do not remove. 
+## Read more about the .amlignore file here: https://docs.microsoft.com/azure/machine-learning/how-to-save-write-experiment-files#storage-limits-of-experiment-snapshots
+        
+.ipynb_aml_checkpoints/ 
+*.amltmp 
+*.amltemp

.amlignore.amltmp

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+## This file was auto generated by the Azure Machine Learning Studio. Please do not remove. 
+## Read more about the .amlignore file here: https://docs.microsoft.com/azure/machine-learning/how-to-save-write-experiment-files#storage-limits-of-experiment-snapshots
+        
+.ipynb_aml_checkpoints/ 
+*.amltmp 
+*.amltemp

.gitattributes

@@ -1,38 +1,45 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
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-*tfevents* filter=lfs diff=lfs merge=lfs -text
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-resource/img/comfyui-1.png filter=lfs diff=lfs merge=lfs -text
-resource/img/teaser.jpg filter=lfs diff=lfs merge=lfs -text
+*.py    eol=lf
+*.js    eol=lf
+*.jsx   eol=lf
+*.json  eol=lf
+# .gitattributes snippet to force users to use same line endings for project.
+# Handle line endings automatically for files detected as text
+# and leave all files detected as binary untouched.
+* text=auto
+# The above will handle all files NOT found below
+# These files are text and should be normalized (Convert crlf => lf)
+*.php text
+*.css text
+*.js text
+*.json text
+*.htm text
+*.html text
+*.xml text
+*.txt text
+*.ini text
+*.inc text
+*.pl text
+*.rb text
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+*.scm text
+*.sql text
+.htaccess text
+*.sh text
+# These files are binary and should be left untouched
+# (binary is a macro for -text -diff)
+*.png binary
+*.jpg binary
+*.jpeg binary
+*.gif binary
+*.ico binary
+*.mov binary
+*.mp4 binary
+*.mp3 binary
+*.flv binary
+*.fla binary
+*.swf binary
+*.gz binary
+*.zip binary
+*.7z binary
+*.ttf binary
+*.pyc binary

.gitignore

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+.env
+.vscode
+.idea
+.venv
+venv
+vm
+*.pyc
+*.egg-info
+__pycache__
+.ebextensions
+.spyproject
+node_modules
+bak
+baks
+logs
+myTestes
+myHelpers
+conf
+.requirements.txt.bak
+USE.INFO
+templates/src
+dabolinux-clients-demo-32103b022bf6.json
+media/
+MANIFEST
+build
+dist
+docs/_build
+docs/_static
+npm-debug.log
+setup.cfg
+pyproject.toml

LICENSE

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README.md

@@ -1,3 +1,70 @@
----
-license: mit
----
+# Fashibles
+
+
+
+
+## Installation
+
+Create a conda environment & Install requirments
+```shell
+conda create -n catvton python==3.9.0
+conda activate catvton
+cd CatVTON-fashable  # or your path to CatVTON project dir
+pip install -r requirements.txt
+```
+
+## Run the Project First Init
+This will full the pretrained freeze models
+```shell
+python app.py \
+--output_dir="resource/demo/output" \
+--mixed_precision="bf16" \
+--allow_tf32 
+```
+
+## Run as an API Server
+```shell
+python app_api.py
+```
+
+## API Call Sample Payload
+```js
+import axios from "axios";
+
+const form = new FormData();
+form.append("person_image", "/Users/ahmadabdulnasirshuaib/wsp/ml-al/clothChanger/assets/istockphoto-521071031-612x612.jpg");
+form.append("cloth_image", "/Users/ahmadabdulnasirshuaib/wsp/ml-al/clothChanger/resource/demo/example/condition/upper/24083449_54173465_2048.jpg");
+form.append("cloth_type", "upper");
+
+const options = {
+  method: 'POST',
+  url: 'http://127.0.0.1:8000/process_images',
+  headers: {
+    'Content-Type': 'multipart/form-data; boundary=---011000010111000001101001',
+    'User-Agent': 'insomnia/9.3.3'
+  },
+  data: '[form]'
+};
+
+axios.request(options).then(function (response) {
+  console.log(response.data);
+}).catch(function (error) {
+  console.error(error);
+});
+
+```
+### Gradio App
+
+To deploy the Gradio App for CatVTON on your machine, run the following command, and checkpoints will be automatically downloaded from HuggingFace.
+
+```shell
+CUDA_VISIBLE_DEVICES=0 python app.py \
+--output_dir="resource/demo/output" \
+--mixed_precision="bf16" \
+--allow_tf32 
+```
+
+When using `bf16` precision, generating results with a resolution of `1024x768` only requires about `8G` VRAM.
+
+
+##

app.py

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+import argparse
+import os
+from datetime import datetime
+
+import gradio as gr
+import numpy as np
+import torch
+device = torch.device('cpu')  # Explicitly use CPU if desired
+
+from diffusers.image_processor import VaeImageProcessor
+from huggingface_hub import snapshot_download
+from PIL import Image
+
+from model.cloth_masker import AutoMasker, vis_mask
+from model.pipeline import CatVTONPipeline
+from utils import init_weight_dtype, resize_and_crop, resize_and_padding
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument(
+        "--base_model_path",
+        type=str,
+        default="Abhilashvj/stable-diffusion-inpainting-copy", #"runwayml/stable-diffusion-inpainting",
+        help=(
+            "The path to the base model to use for evaluation. This can be a local path or a model identifier from the Model Hub."
+        ),
+    )
+    parser.add_argument(
+        "--resume_path",
+        type=str,
+        default="zhengchong/CatVTON",
+        help=(
+            "The Path to the checkpoint of trained tryon model."
+        ),
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="resource/demo/output",
+        help="The output directory where the model predictions will be written.",
+    )
+
+    parser.add_argument(
+        "--width",
+        type=int,
+        default=768,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--height",
+        type=int,
+        default=1024,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--repaint", 
+        action="store_true", 
+        help="Whether to repaint the result image with the original background."
+    )
+    parser.add_argument(
+        "--allow_tf32",
+        action="store_true",
+        default=True,
+        help=(
+            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
+            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+        ),
+    )
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default="bf16",
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
+            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+        ),
+    )
+    
+    args = parser.parse_args()
+    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    if env_local_rank != -1 and env_local_rank != args.local_rank:
+        args.local_rank = env_local_rank
+
+    return args
+
+def image_grid(imgs, rows, cols):
+    assert len(imgs) == rows * cols
+
+    w, h = imgs[0].size
+    grid = Image.new("RGB", size=(cols * w, rows * h))
+
+    for i, img in enumerate(imgs):
+        grid.paste(img, box=(i % cols * w, i // cols * h))
+    return grid
+
+
+args = parse_args()
+repo_path = snapshot_download(repo_id=args.resume_path)
+# Pipeline
+pipeline = CatVTONPipeline(
+    base_ckpt=args.base_model_path,
+    attn_ckpt=repo_path,
+    attn_ckpt_version="mix",
+    weight_dtype=init_weight_dtype(args.mixed_precision),
+    use_tf32=args.allow_tf32,
+    # device='cuda'
+    device='cpu'
+)
+# AutoMasker
+mask_processor = VaeImageProcessor(vae_scale_factor=8, do_normalize=False, do_binarize=True, do_convert_grayscale=True)
+automasker = AutoMasker(
+    densepose_ckpt=os.path.join(repo_path, "DensePose"),
+    schp_ckpt=os.path.join(repo_path, "SCHP"),
+    # device='cuda', 
+    device='cpu'
+)
+
+def submit_function(
+    person_image,
+    cloth_image,
+    cloth_type,
+    num_inference_steps,
+    guidance_scale,
+    seed,
+    show_type
+):
+    person_image, mask = person_image["background"], person_image["layers"][0]
+    mask = Image.open(mask).convert("L")
+    if len(np.unique(np.array(mask))) == 1:
+        mask = None
+    else:
+        mask = np.array(mask)
+        mask[mask > 0] = 255
+        mask = Image.fromarray(mask)
+
+    tmp_folder = args.output_dir
+    date_str = datetime.now().strftime("%Y%m%d%H%M%S")
+    result_save_path = os.path.join(tmp_folder, date_str[:8], date_str[8:] + ".png")
+    if not os.path.exists(os.path.join(tmp_folder, date_str[:8])):
+        os.makedirs(os.path.join(tmp_folder, date_str[:8]))
+
+    generator = None
+    if seed != -1:
+        # generator = torch.Generator(device='cuda').manual_seed(seed)
+        generator = torch.Generator(device='cpu').manual_seed(seed)
+
+    person_image = Image.open(person_image).convert("RGB")
+    cloth_image = Image.open(cloth_image).convert("RGB")
+    person_image = resize_and_crop(person_image, (args.width, args.height))
+    cloth_image = resize_and_padding(cloth_image, (args.width, args.height))
+    
+    # Process mask
+    if mask is not None:
+        mask = resize_and_crop(mask, (args.width, args.height))
+    else:
+        mask = automasker(
+            person_image,
+            cloth_type
+        )['mask']
+    mask = mask_processor.blur(mask, blur_factor=9)
+
+    # Inference
+    # try:
+    result_image = pipeline(
+        image=person_image,
+        condition_image=cloth_image,
+        mask=mask,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        generator=generator
+    )[0]
+    # except Exception as e:
+    #     raise gr.Error(
+    #         "An error occurred. Please try again later: {}".format(e)
+    #     )
+    
+    # Post-process
+    masked_person = vis_mask(person_image, mask)
+    save_result_image = image_grid([person_image, masked_person, cloth_image, result_image], 1, 4)
+    save_result_image.save(result_save_path)
+    if show_type == "result only":
+        return result_image
+    else:
+        width, height = person_image.size
+        if show_type == "input & result":
+            condition_width = width // 2
+            conditions = image_grid([person_image, cloth_image], 2, 1)
+        else:
+            condition_width = width // 3
+            conditions = image_grid([person_image, masked_person , cloth_image], 3, 1)
+        conditions = conditions.resize((condition_width, height), Image.NEAREST)
+        new_result_image = Image.new("RGB", (width + condition_width + 5, height))
+        new_result_image.paste(conditions, (0, 0))
+        new_result_image.paste(result_image, (condition_width + 5, 0))
+    return new_result_image
+
+
+def person_example_fn(image_path):
+    return image_path
+
+HEADER = """
+<h1 style="text-align: center;"> 
+    Fashioble
+</h1>
+
+"""
+
+def app_gradio():
+    with gr.Blocks(title="CatVTON") as demo:
+        gr.Markdown(HEADER)
+        with gr.Row():
+            with gr.Column(scale=1, min_width=350):
+                with gr.Row():
+                    image_path = gr.Image(
+                        type="filepath",
+                        interactive=True,
+                        visible=False,
+                    )
+                    person_image = gr.ImageEditor(
+                        interactive=True, label="Person Image", type="filepath"
+                    )
+
+                with gr.Row():
+                    with gr.Column(scale=1, min_width=230):
+                        cloth_image = gr.Image(
+                            interactive=True, label="Condition Image", type="filepath"
+                        )
+                    with gr.Column(scale=1, min_width=120):
+                        gr.Markdown(
+                            '<span style="color: #808080; font-size: small;">Two ways to provide Mask:<br>1. Upload the person image and use the `🖌️` above to draw the Mask (higher priority)<br>2. Select the `Try-On Cloth Type` to generate automatically </span>'
+                        )
+                        cloth_type = gr.Radio(
+                            label="Try-On Cloth Type",
+                            choices=["upper", "lower", "overall"],
+                            value="upper",
+                        )
+
+
+                submit = gr.Button("Submit")
+                gr.Markdown(
+                    '<center><span style="color: #FF0000">!!! Click only Once, Wait for Delay !!!</span></center>'
+                )
+                
+                gr.Markdown(
+                    '<span style="color: #808080; font-size: small;">Advanced options can adjust details:<br>1. `Inference Step` may enhance details;<br>2. `CFG` is highly correlated with saturation;<br>3. `Random seed` may improve pseudo-shadow.</span>'
+                )
+                with gr.Accordion("Advanced Options", open=False):
+                    num_inference_steps = gr.Slider(
+                        label="Inference Step", minimum=10, maximum=100, step=5, value=50
+                    )
+                    # Guidence Scale
+                    guidance_scale = gr.Slider(
+                        label="CFG Strenth", minimum=0.0, maximum=7.5, step=0.5, value=2.5
+                    )
+                    # Random Seed
+                    seed = gr.Slider(
+                        label="Seed", minimum=-1, maximum=10000, step=1, value=42
+                    )
+                    show_type = gr.Radio(
+                        label="Show Type",
+                        choices=["result only", "input & result", "input & mask & result"],
+                        value="input & mask & result",
+                    )
+
+            with gr.Column(scale=2, min_width=500):
+                result_image = gr.Image(interactive=False, label="Result")
+                with gr.Row():
+                    # Photo Examples
+                    root_path = "resource/demo/example"
+                    with gr.Column():
+                        men_exm = gr.Examples(
+                            examples=[
+                                os.path.join(root_path, "person", "men", _)
+                                for _ in os.listdir(os.path.join(root_path, "person", "men"))
+                            ],
+                            examples_per_page=4,
+                            inputs=image_path,
+                            label="Person Examples ①",
+                        )
+                        women_exm = gr.Examples(
+                            examples=[
+                                os.path.join(root_path, "person", "women", _)
+                                for _ in os.listdir(os.path.join(root_path, "person", "women"))
+                            ],
+                            examples_per_page=4,
+                            inputs=image_path,
+                            label="Person Examples ②",
+                        )
+                        gr.Markdown(
+                            '<span style="color: #808080; font-size: small;">*Person examples come from the demos of <a href="https://huggingface.co/spaces/levihsu/OOTDiffusion">OOTDiffusion</a> and <a href="https://www.outfitanyone.org">OutfitAnyone</a>. </span>'
+                        )
+                    with gr.Column():
+                        condition_upper_exm = gr.Examples(
+                            examples=[
+                                os.path.join(root_path, "condition", "upper", _)
+                                for _ in os.listdir(os.path.join(root_path, "condition", "upper"))
+                            ],
+                            examples_per_page=4,
+                            inputs=cloth_image,
+                            label="Condition Upper Examples",
+                        )
+                        condition_overall_exm = gr.Examples(
+                            examples=[
+                                os.path.join(root_path, "condition", "overall", _)
+                                for _ in os.listdir(os.path.join(root_path, "condition", "overall"))
+                            ],
+                            examples_per_page=4,
+                            inputs=cloth_image,
+                            label="Condition Overall Examples",
+                        )
+                        condition_person_exm = gr.Examples(
+                            examples=[
+                                os.path.join(root_path, "condition", "person", _)
+                                for _ in os.listdir(os.path.join(root_path, "condition", "person"))
+                            ],
+                            examples_per_page=4,
+                            inputs=cloth_image,
+                            label="Condition Reference Person Examples",
+                        )
+                        gr.Markdown(
+                            '<span style="color: #808080; font-size: small;">*Condition examples come from the Internet. </span>'
+                        )
+
+            image_path.change(
+                person_example_fn, inputs=image_path, outputs=person_image
+            )
+
+            submit.click(
+                submit_function,
+                [
+                    person_image,
+                    cloth_image,
+                    cloth_type,
+                    num_inference_steps,
+                    guidance_scale,
+                    seed,
+                    show_type,
+                ],
+                result_image,
+            )
+    demo.queue().launch(share=True, show_error=True)
+
+
+if __name__ == "__main__":
+    app_gradio()

app.py.amltmp

@@ -0,0 +1,353 @@
+import argparse
+import os
+from datetime import datetime
+
+import gradio as gr
+import numpy as np
+import torch
+device = torch.device('cpu')  # Explicitly use CPU if desired
+
+from diffusers.image_processor import VaeImageProcessor
+from huggingface_hub import snapshot_download
+from PIL import Image
+
+from model.cloth_masker import AutoMasker, vis_mask
+from model.pipeline import CatVTONPipeline
+from utils import init_weight_dtype, resize_and_crop, resize_and_padding
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument(
+        "--base_model_path",
+        type=str,
+        default="Abhilashvj/stable-diffusion-inpainting-copy", #"runwayml/stable-diffusion-inpainting",
+        help=(
+            "The path to the base model to use for evaluation. This can be a local path or a model identifier from the Model Hub."
+        ),
+    )
+    parser.add_argument(
+        "--resume_path",
+        type=str,
+        default="zhengchong/CatVTON",
+        help=(
+            "The Path to the checkpoint of trained tryon model."
+        ),
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="resource/demo/output",
+        help="The output directory where the model predictions will be written.",
+    )
+
+    parser.add_argument(
+        "--width",
+        type=int,
+        default=768,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--height",
+        type=int,
+        default=1024,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--repaint", 
+        action="store_true", 
+        help="Whether to repaint the result image with the original background."
+    )
+    parser.add_argument(
+        "--allow_tf32",
+        action="store_true",
+        default=True,
+        help=(
+            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
+            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+        ),
+    )
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default="bf16",
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
+            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+        ),
+    )
+    
+    args = parser.parse_args()
+    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    if env_local_rank != -1 and env_local_rank != args.local_rank:
+        args.local_rank = env_local_rank
+
+    return args
+
+def image_grid(imgs, rows, cols):
+    assert len(imgs) == rows * cols
+
+    w, h = imgs[0].size
+    grid = Image.new("RGB", size=(cols * w, rows * h))
+
+    for i, img in enumerate(imgs):
+        grid.paste(img, box=(i % cols * w, i // cols * h))
+    return grid
+
+
+args = parse_args()
+repo_path = snapshot_download(repo_id=args.resume_path)
+# Pipeline
+pipeline = CatVTONPipeline(
+    base_ckpt=args.base_model_path,
+    attn_ckpt=repo_path,
+    attn_ckpt_version="mix",
+    weight_dtype=init_weight_dtype(args.mixed_precision),
+    use_tf32=args.allow_tf32,
+    # device='cuda'
+    device='cpu'
+)
+# AutoMasker
+mask_processor = VaeImageProcessor(vae_scale_factor=8, do_normalize=False, do_binarize=True, do_convert_grayscale=True)
+automasker = AutoMasker(
+    densepose_ckpt=os.path.join(repo_path, "DensePose"),
+    schp_ckpt=os.path.join(repo_path, "SCHP"),
+    # device='cuda', 
+    device='cpu'
+)
+
+def submit_function(
+    person_image,
+    cloth_image,
+    cloth_type,
+    num_inference_steps,
+    guidance_scale,
+    seed,
+    show_type
+):
+    person_image, mask = person_image["background"], person_image["layers"][0]
+    mask = Image.open(mask).convert("L")
+    if len(np.unique(np.array(mask))) == 1:
+        mask = None
+    else:
+        mask = np.array(mask)
+        mask[mask > 0] = 255
+        mask = Image.fromarray(mask)
+
+    tmp_folder = args.output_dir
+    date_str = datetime.now().strftime("%Y%m%d%H%M%S")
+    result_save_path = os.path.join(tmp_folder, date_str[:8], date_str[8:] + ".png")
+    if not os.path.exists(os.path.join(tmp_folder, date_str[:8])):
+        os.makedirs(os.path.join(tmp_folder, date_str[:8]))
+
+    generator = None
+    if seed != -1:
+        # generator = torch.Generator(device='cuda').manual_seed(seed)
+        generator = torch.Generator(device='cpu').manual_seed(seed)
+
+    person_image = Image.open(person_image).convert("RGB")
+    cloth_image = Image.open(cloth_image).convert("RGB")
+    person_image = resize_and_crop(person_image, (args.width, args.height))
+    cloth_image = resize_and_padding(cloth_image, (args.width, args.height))
+    
+    # Process mask
+    if mask is not None:
+        mask = resize_and_crop(mask, (args.width, args.height))
+    else:
+        mask = automasker(
+            person_image,
+            cloth_type
+        )['mask']
+    mask = mask_processor.blur(mask, blur_factor=9)
+
+    # Inference
+    # try:
+    result_image = pipeline(
+        image=person_image,
+        condition_image=cloth_image,
+        mask=mask,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        generator=generator
+    )[0]
+    # except Exception as e:
+    #     raise gr.Error(
+    #         "An error occurred. Please try again later: {}".format(e)
+    #     )
+    
+    # Post-process
+    masked_person = vis_mask(person_image, mask)
+    save_result_image = image_grid([person_image, masked_person, cloth_image, result_image], 1, 4)
+    save_result_image.save(result_save_path)
+    if show_type == "result only":
+        return result_image
+    else:
+        width, height = person_image.size
+        if show_type == "input & result":
+            condition_width = width // 2
+            conditions = image_grid([person_image, cloth_image], 2, 1)
+        else:
+            condition_width = width // 3
+            conditions = image_grid([person_image, masked_person , cloth_image], 3, 1)
+        conditions = conditions.resize((condition_width, height), Image.NEAREST)
+        new_result_image = Image.new("RGB", (width + condition_width + 5, height))
+        new_result_image.paste(conditions, (0, 0))
+        new_result_image.paste(result_image, (condition_width + 5, 0))
+    return new_result_image
+
+
+def person_example_fn(image_path):
+    return image_path
+
+HEADER = """
+<h1 style="text-align: center;"> 
+    Fashioble
+</h1>
+
+"""
+
+def app_gradio():
+    with gr.Blocks(title="CatVTON") as demo:
+        gr.Markdown(HEADER)
+        with gr.Row():
+            with gr.Column(scale=1, min_width=350):
+                with gr.Row():
+                    image_path = gr.Image(
+                        type="filepath",
+                        interactive=True,
+                        visible=False,
+                    )
+                    person_image = gr.ImageEditor(
+                        interactive=True, label="Person Image", type="filepath"
+                    )
+
+                with gr.Row():
+                    with gr.Column(scale=1, min_width=230):
+                        cloth_image = gr.Image(
+                            interactive=True, label="Condition Image", type="filepath"
+                        )
+                    with gr.Column(scale=1, min_width=120):
+                        gr.Markdown(
+                            '<span style="color: #808080; font-size: small;">Two ways to provide Mask:<br>1. Upload the person image and use the `🖌️` above to draw the Mask (higher priority)<br>2. Select the `Try-On Cloth Type` to generate automatically </span>'
+                        )
+                        cloth_type = gr.Radio(
+                            label="Try-On Cloth Type",
+                            choices=["upper", "lower", "overall"],
+                            value="upper",
+                        )
+
+
+                submit = gr.Button("Submit")
+                gr.Markdown(
+                    '<center><span style="color: #FF0000">!!! Click only Once, Wait for Delay !!!</span></center>'
+                )
+                
+                gr.Markdown(
+                    '<span style="color: #808080; font-size: small;">Advanced options can adjust details:<br>1. `Inference Step` may enhance details;<br>2. `CFG` is highly correlated with saturation;<br>3. `Random seed` may improve pseudo-shadow.</span>'
+                )
+                with gr.Accordion("Advanced Options", open=False):
+                    num_inference_steps = gr.Slider(
+                        label="Inference Step", minimum=10, maximum=100, step=5, value=50
+                    )
+                    # Guidence Scale
+                    guidance_scale = gr.Slider(
+                        label="CFG Strenth", minimum=0.0, maximum=7.5, step=0.5, value=2.5
+                    )
+                    # Random Seed
+                    seed = gr.Slider(
+                        label="Seed", minimum=-1, maximum=10000, step=1, value=42
+                    )
+                    show_type = gr.Radio(
+                        label="Show Type",
+                        choices=["result only", "input & result", "input & mask & result"],
+                        value="input & mask & result",
+                    )
+
+            with gr.Column(scale=2, min_width=500):
+                result_image = gr.Image(interactive=False, label="Result")
+                with gr.Row():
+                    # Photo Examples
+                    root_path = "resource/demo/example"
+                    with gr.Column():
+                        men_exm = gr.Examples(
+                            examples=[
+                                os.path.join(root_path, "person", "men", _)
+                                for _ in os.listdir(os.path.join(root_path, "person", "men"))
+                            ],
+                            examples_per_page=4,
+                            inputs=image_path,
+                            label="Person Examples ①",
+                        )
+                        women_exm = gr.Examples(
+                            examples=[
+                                os.path.join(root_path, "person", "women", _)
+                                for _ in os.listdir(os.path.join(root_path, "person", "women"))
+                            ],
+                            examples_per_page=4,
+                            inputs=image_path,
+                            label="Person Examples ②",
+                        )
+                        gr.Markdown(
+                            '<span style="color: #808080; font-size: small;">*Person examples come from the demos of <a href="https://huggingface.co/spaces/levihsu/OOTDiffusion">OOTDiffusion</a> and <a href="https://www.outfitanyone.org">OutfitAnyone</a>. </span>'
+                        )
+                    with gr.Column():
+                        condition_upper_exm = gr.Examples(
+                            examples=[
+                                os.path.join(root_path, "condition", "upper", _)
+                                for _ in os.listdir(os.path.join(root_path, "condition", "upper"))
+                            ],
+                            examples_per_page=4,
+                            inputs=cloth_image,
+                            label="Condition Upper Examples",
+                        )
+                        condition_overall_exm = gr.Examples(
+                            examples=[
+                                os.path.join(root_path, "condition", "overall", _)
+                                for _ in os.listdir(os.path.join(root_path, "condition", "overall"))
+                            ],
+                            examples_per_page=4,
+                            inputs=cloth_image,
+                            label="Condition Overall Examples",
+                        )
+                        condition_person_exm = gr.Examples(
+                            examples=[
+                                os.path.join(root_path, "condition", "person", _)
+                                for _ in os.listdir(os.path.join(root_path, "condition", "person"))
+                            ],
+                            examples_per_page=4,
+                            inputs=cloth_image,
+                            label="Condition Reference Person Examples",
+                        )
+                        gr.Markdown(
+                            '<span style="color: #808080; font-size: small;">*Condition examples come from the Internet. </span>'
+                        )
+
+            image_path.change(
+                person_example_fn, inputs=image_path, outputs=person_image
+            )
+
+            submit.click(
+                submit_function,
+                [
+                    person_image,
+                    cloth_image,
+                    cloth_type,
+                    num_inference_steps,
+                    guidance_scale,
+                    seed,
+                    show_type,
+                ],
+                result_image,
+            )
+    demo.queue().launch(share=True, show_error=True)
+
+
+if __name__ == "__main__":
+    app_gradio()

app_api.py

@@ -0,0 +1,111 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+__author__ = 'Ahmad Abdulnasir Shuaib <[email protected]>'
+__homepage__ = https://ahmadabdulnasir.com.ng
+__copyright__ = 'Copyright (c) 2024, salafi'
+__version__ = "0.01t"
+"""
+from fastapi import FastAPI, File, UploadFile, Form
+from fastapi.responses import FileResponse
+# from pydantic import BaseSettings
+from pydantic_settings import BaseSettings
+
+
+from PIL import Image
+import io
+import os
+from datetime import datetime
+import torch
+import numpy as np
+from diffusers.image_processor import VaeImageProcessor
+from huggingface_hub import snapshot_download
+from model.cloth_masker import AutoMasker
+from model.pipeline import CatVTONPipeline
+from utils import init_weight_dtype, resize_and_crop, resize_and_padding
+
+class Settings(BaseSettings):
+    base_model_path: str = "Abhilashvj/stable-diffusion-inpainting-copy" #"runwayml/stable-diffusion-inpainting"  #
+    resume_path: str = "abubakar123456/CatVTON" #"zhengchong/CatVTON"
+    output_dir: str = "resource/demo/output" 
+    width: int = 768
+    height: int = 1024
+    allow_tf32: bool = True
+    mixed_precision: str = "bf16"
+
+    class Config:
+        env_file = ".env"
+
+settings = Settings()
+
+app = FastAPI()
+
+# Initialize your models and processors here
+repo_path = snapshot_download(repo_id=settings.resume_path)
+
+pipeline = CatVTONPipeline(
+    base_ckpt=settings.base_model_path,
+    attn_ckpt=repo_path,
+    attn_ckpt_version="mix",
+    weight_dtype=init_weight_dtype(settings.mixed_precision),
+    use_tf32=settings.allow_tf32,
+    device='cpu'
+)
+
+mask_processor = VaeImageProcessor(vae_scale_factor=8, do_normalize=False, do_binarize=True, do_convert_grayscale=True)
+automasker = AutoMasker(
+    densepose_ckpt=os.path.join(repo_path, "DensePose"),
+    schp_ckpt=os.path.join(repo_path, "SCHP"),
+    device='cpu'
+)
+
+@app.post("/process_images/")
+async def process_images(
+    person_image: UploadFile = File(...),
+    cloth_image: UploadFile = File(...),
+    cloth_type: str = Form(...),
+    num_inference_steps: int = Form(50),
+    guidance_scale: float = Form(2.5),
+    seed: int = Form(42)
+):
+    # Read and process the uploaded images
+    person_img = Image.open(io.BytesIO(await person_image.read())).convert("RGB")
+    cloth_img = Image.open(io.BytesIO(await cloth_image.read())).convert("RGB")
+
+    person_img = resize_and_crop(person_img, (settings.width, settings.height))
+    cloth_img = resize_and_padding(cloth_img, (settings.width, settings.height))
+
+    # Generate mask
+    mask = automasker(person_img, cloth_type)['mask']
+    mask = mask_processor.blur(mask, blur_factor=9)
+
+    # Set up generator for reproducibility
+    generator = torch.Generator(device='cpu').manual_seed(seed) if seed != -1 else None
+
+    # Run inference
+    result_image = pipeline(
+        image=person_img,
+        condition_image=cloth_img,
+        mask=mask,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        generator=generator
+    )[0]
+
+    # Save the result
+    tmp_folder = settings.output_dir
+    date_str = datetime.now().strftime("%Y%m%d%H%M%S")
+    result_save_path = os.path.join(tmp_folder, date_str[:8], date_str[8:] + ".png")
+    os.makedirs(os.path.dirname(result_save_path), exist_ok=True)
+    result_image.save(result_save_path)
+
+    # Return the result image
+    return FileResponse(result_save_path, media_type="image/png")
+
+    
+def boot():
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)
+
+if __name__ == "__main__":
+    boot()

app_api.py.amltmp

@@ -0,0 +1,111 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+__author__ = 'Ahmad Abdulnasir Shuaib <[email protected]>'
+__homepage__ = https://ahmadabdulnasir.com.ng
+__copyright__ = 'Copyright (c) 2024, salafi'
+__version__ = "0.01t"
+"""
+from fastapi import FastAPI, File, UploadFile, Form
+from fastapi.responses import FileResponse
+# from pydantic import BaseSettings
+from pydantic_settings import BaseSettings
+
+
+from PIL import Image
+import io
+import os
+from datetime import datetime
+import torch
+import numpy as np
+from diffusers.image_processor import VaeImageProcessor
+from huggingface_hub import snapshot_download
+from model.cloth_masker import AutoMasker
+from model.pipeline import CatVTONPipeline
+from utils import init_weight_dtype, resize_and_crop, resize_and_padding
+
+class Settings(BaseSettings):
+    base_model_path: str = "Abhilashvj/stable-diffusion-inpainting-copy" #"runwayml/stable-diffusion-inpainting"  #
+    resume_path: str = "abubakar123456/CatVTON" #"zhengchong/CatVTON"
+    output_dir: str = "resource/demo/output" 
+    width: int = 768
+    height: int = 1024
+    allow_tf32: bool = True
+    mixed_precision: str = "bf16"
+
+    class Config:
+        env_file = ".env"
+
+settings = Settings()
+
+app = FastAPI()
+
+# Initialize your models and processors here
+repo_path = snapshot_download(repo_id=settings.resume_path)
+
+pipeline = CatVTONPipeline(
+    base_ckpt=settings.base_model_path,
+    attn_ckpt=repo_path,
+    attn_ckpt_version="mix",
+    weight_dtype=init_weight_dtype(settings.mixed_precision),
+    use_tf32=settings.allow_tf32,
+    device='cpu'
+)
+
+mask_processor = VaeImageProcessor(vae_scale_factor=8, do_normalize=False, do_binarize=True, do_convert_grayscale=True)
+automasker = AutoMasker(
+    densepose_ckpt=os.path.join(repo_path, "DensePose"),
+    schp_ckpt=os.path.join(repo_path, "SCHP"),
+    device='cpu'
+)
+
+@app.post("/process_images/")
+async def process_images(
+    person_image: UploadFile = File(...),
+    cloth_image: UploadFile = File(...),
+    cloth_type: str = Form(...),
+    num_inference_steps: int = Form(50),
+    guidance_scale: float = Form(2.5),
+    seed: int = Form(42)
+):
+    # Read and process the uploaded images
+    person_img = Image.open(io.BytesIO(await person_image.read())).convert("RGB")
+    cloth_img = Image.open(io.BytesIO(await cloth_image.read())).convert("RGB")
+
+    person_img = resize_and_crop(person_img, (settings.width, settings.height))
+    cloth_img = resize_and_padding(cloth_img, (settings.width, settings.height))
+
+    # Generate mask
+    mask = automasker(person_img, cloth_type)['mask']
+    mask = mask_processor.blur(mask, blur_factor=9)
+
+    # Set up generator for reproducibility
+    generator = torch.Generator(device='cpu').manual_seed(seed) if seed != -1 else None
+
+    # Run inference
+    result_image = pipeline(
+        image=person_img,
+        condition_image=cloth_img,
+        mask=mask,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        generator=generator
+    )[0]
+
+    # Save the result
+    tmp_folder = settings.output_dir
+    date_str = datetime.now().strftime("%Y%m%d%H%M%S")
+    result_save_path = os.path.join(tmp_folder, date_str[:8], date_str[8:] + ".png")
+    os.makedirs(os.path.dirname(result_save_path), exist_ok=True)
+    result_image.save(result_save_path)
+
+    # Return the result image
+    return FileResponse(result_save_path, media_type="image/png")
+
+    
+def boot():
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)
+
+if __name__ == "__main__":
+    boot()

eval.py

@@ -0,0 +1,163 @@
+import os
+import torch
+from cleanfid import fid as FID
+from PIL import Image
+from torch.utils.data import Dataset
+from torchmetrics.image import StructuralSimilarityIndexMeasure
+from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
+from torchvision import transforms
+from tqdm import tqdm
+
+from utils import scan_files_in_dir
+from prettytable import PrettyTable
+
+class EvalDataset(Dataset):
+    def __init__(self, gt_folder, pred_folder, height=1024):
+        self.gt_folder = gt_folder
+        self.pred_folder = pred_folder
+        self.height = height
+        self.data = self.prepare_data()
+        self.to_tensor = transforms.ToTensor()
+    
+    def extract_id_from_filename(self, filename):
+        # find first number in filename
+        start_i = None
+        for i, c in enumerate(filename):
+            if c.isdigit():
+                start_i = i
+                break
+        if start_i is None:
+            assert False, f"Cannot find number in filename {filename}"
+        return filename[start_i:start_i+8]
+    
+    def prepare_data(self):
+        gt_files = scan_files_in_dir(self.gt_folder, postfix={'.jpg', '.png'})
+        gt_dict = {self.extract_id_from_filename(file.name): file for file in gt_files}
+        pred_files = scan_files_in_dir(self.pred_folder, postfix={'.jpg', '.png'})
+        
+        tuples = []
+        for pred_file in pred_files:
+            pred_id = self.extract_id_from_filename(pred_file.name)
+            if pred_id not in gt_dict:
+                print(f"Cannot find gt file for {pred_file}")
+            else:
+                tuples.append((gt_dict[pred_id].path, pred_file.path))
+        return tuples
+        
+    def resize(self, img):
+        w, h = img.size
+        new_w = int(w * self.height / h)
+        return img.resize((new_w, self.height), Image.LANCZOS)
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        gt_path, pred_path = self.data[idx]
+        gt, pred = self.resize(Image.open(gt_path)), self.resize(Image.open(pred_path))
+        if gt.height != self.height:
+            gt = self.resize(gt)
+        if pred.height != self.height:
+            pred = self.resize(pred)
+        gt = self.to_tensor(gt)
+        pred = self.to_tensor(pred)
+        return gt, pred
+
+
+def copy_resize_gt(gt_folder, height):
+    new_folder = f"{gt_folder}_{height}"
+    if not os.path.exists(new_folder):
+        os.makedirs(new_folder, exist_ok=True)
+    for file in tqdm(os.listdir(gt_folder)):
+        if os.path.exists(os.path.join(new_folder, file)):
+            continue
+        img = Image.open(os.path.join(gt_folder, file))
+        w, h = img.size
+        new_w = int(w * height / h)
+        img = img.resize((new_w, height), Image.LANCZOS)
+        img.save(os.path.join(new_folder, file))
+    return new_folder
+
+
+@torch.no_grad()
+def ssim(dataloader):
+    ssim_score = 0
+    # ssim = StructuralSimilarityIndexMeasure(data_range=1.0).to("cuda")
+    ssim = StructuralSimilarityIndexMeasure(data_range=1.0).to("cpu")
+    for gt, pred in tqdm(dataloader, desc="Calculating SSIM"):
+        batch_size = gt.size(0)
+        # gt, pred = gt.to("cuda"), pred.to("cuda")
+        gt, pred = gt.to("cpu"), pred.to("cpu")
+        ssim_score += ssim(pred, gt) * batch_size
+    return ssim_score / len(dataloader.dataset)
+
+
+@torch.no_grad()
+def lpips(dataloader):
+    # lpips_score = LearnedPerceptualImagePatchSimilarity(net_type='squeeze').to("cuda")
+    lpips_score = LearnedPerceptualImagePatchSimilarity(net_type='squeeze').to("cpu")
+    score = 0
+    for gt, pred in tqdm(dataloader, desc="Calculating LPIPS"):
+        batch_size = gt.size(0)
+        # pred = pred.to("cuda")
+        pred = pred.to("cpu")
+        # gt = gt.to("cuda")
+        gt = gt.to("cpu")
+        # LPIPS needs the images to be in the [-1, 1] range.
+        gt = (gt * 2) - 1
+        pred = (pred * 2) - 1
+        score += lpips_score(gt, pred) * batch_size
+    return score / len(dataloader.dataset)
+
+
+def eval(args):
+    # Check gt_folder has images with target height, resize if not
+    pred_sample = os.listdir(args.pred_folder)[0]
+    gt_sample = os.listdir(args.gt_folder)[0]
+    img = Image.open(os.path.join(args.pred_folder, pred_sample))
+    gt_img = Image.open(os.path.join(args.gt_folder, gt_sample))
+    if img.height != gt_img.height:
+        title = "--"*30 + "Resizing GT Images to height {img.height}" + "--"*30
+        print(title)
+        args.gt_folder = copy_resize_gt(args.gt_folder, img.height)
+        print("-"*len(title))
+    
+    # Form dataset
+    dataset = EvalDataset(args.gt_folder, args.pred_folder, img.height)
+    dataloader = torch.utils.data.DataLoader(
+        dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False, drop_last=False
+    )
+    
+    # Calculate Metrics
+    header = []
+    row = []
+    header = ["FID", "KID"]
+    fid_ = FID.compute_fid(args.gt_folder, args.pred_folder)
+    kid_ = FID.compute_kid(args.gt_folder, args.pred_folder) * 1000
+    row = [fid_, kid_]
+    if args.paired:
+        header += ["SSIM", "LPIPS"]
+        ssim_ = ssim(dataloader).item()
+        lpips_ = lpips(dataloader).item()
+        row += [ssim_, lpips_]
+    
+    # Print Results
+    print("GT Folder  : ", args.gt_folder)
+    print("Pred Folder: ", args.pred_folder)
+    table = PrettyTable()
+    table.field_names = header
+    table.add_row(row)
+    print(table)
+    
+         
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--gt_folder", type=str, required=True)
+    parser.add_argument("--pred_folder", type=str, required=True)
+    parser.add_argument("--paired", action="store_true")
+    parser.add_argument("--batch_size", type=int, default=16)
+    parser.add_argument("--num_workers", type=int, default=4)
+    args = parser.parse_args()
+    
+    eval(args)

index.html

@@ -0,0 +1,339 @@
+<!DOCTYPE html>
+<html>
+<head>
+  <meta charset="utf-8">
+  <meta name="description"
+        content="🐈 CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models">
+  <meta name="keywords" content="">
+  <meta name="viewport" content="width=device-width, initial-scale=1">
+
+  <title>🐈 CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models</title>
+  <script async src="https://www.googletagmanager.com/gtag/js?id=G-PYVRSFMDRL"></script>
+  <script>
+    window.dataLayer = window.dataLayer || [];
+    function gtag() {
+      dataLayer.push(arguments);
+    }
+    gtag('js', new Date());
+    gtag('config', 'G-PYVRSFMDRL');
+  </script>
+
+
+  <link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
+        rel="stylesheet">
+  <link rel="stylesheet" href="resource/css/bulma.min.css">
+  <link rel="stylesheet" href="resource/css/bulma-carousel.min.css">
+  <link rel="stylesheet" href="resource/css/bulma-slider.min.css">
+  <link rel="stylesheet" href="resource/css/fontawesome.all.min.css">
+  <link rel="stylesheet"
+        href="https://cdn.jsdelivr.net/gh/jpswalsh/academicons@1/css/academicons.min.css">
+  <link rel="stylesheet" href="resource/css/index.css">
+  <link rel="icon" href="resource/images/favicon.svg">
+
+  <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
+  <script defer src="resource/js/fontawesome.all.min.js"></script>
+  <script src="resource/js/bulma-carousel.min.js"></script>
+  <script src="resource/js/bulma-slider.min.js"></script>
+  <script src="resource/js/index.js"></script>
+</head>
+<body>
+
+
+<section class="hero">
+  <div class="hero-body">
+    <div class="container is-max-desktop">
+      <div class="columns is-centered">
+        <div class="column has-text-centered">
+          <h1 class="title is-1 publication-title">🐈 CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models</h1>
+          <div class="is-size-5 publication-authors">
+            <span class="author-block">
+              <a href="">Zheng Chong</a><sup>1,3</sup>,</span>
+            <span class="author-block">
+              <a href="">Xiao Dong</a><sup>1</sup>,</span>
+            <span class="author-block">
+              <a href="">Haoxiang Li</a><sup>2</sup>,</span>
+            <span class="author-block">
+              <a href="">Shiyue Zhang</a><sup>1</sup>,
+            </span>
+            <span class="author-block">
+              <a href="">Wenqing Zhang</a><sup>1</sup>,
+            </span>
+            <span class="author-block">
+                <a href="">Xujie Zhang</a><sup>1</sup>,
+            </span>
+            <span class="author-block">
+                <a href="">Hanqing Zhao</a><sup>3,4</sup>,
+            </span>
+            <span class="author-block">
+              <a href="">Xiaodan Liang</a><sup>*1,3</sup>,
+            </span>
+          </div>
+          <div class="is-size-5 publication-authors">
+            <span class="author-block"><sup>1</sup>Sun Yat-Sen University,</span>
+            <span class="author-block"><sup>2</sup>Pixocial Technology,</span>
+            <span class="author-block"><sup>3</sup>Peng Cheng Laboratory,</span>
+            <span class="author-block"><sup>4</sup>SIAT</span>
+
+          </div>
+
+          <div class="column has-text-centered">
+            <div class="publication-links">
+              <!-- PDF Link. -->
+              <span class="link-block">
+                <a href="https://arxiv.org/pdf/2407.15886"
+                    class="external-link button is-normal is-rounded is-dark">
+                  <span class="icon">
+                      <i class="fas fa-file-pdf"></i>
+                  </span>
+                  <span>Paper</span>
+                </a>
+              </span>
+              <!--  Arxiv Link. -->
+              <span class="link-block">
+                <a href="http://arxiv.org/abs/2407.15886"
+                   class="external-link button is-normal is-rounded is-dark">
+                  <span class="icon">
+                      <i class="ai ai-arxiv"></i>
+                  </span>
+                  <span>arXiv</span>
+                </a>
+              </span>
+              <!--  Demo Link. -->
+              <span class="link-block">
+                <a href="http://120.76.142.206:8888"
+                   class="external-link button is-normal is-rounded is-dark">
+                  <span class="icon">
+                      <i class="fas fa-gamepad"></i>
+                  </span>
+                  <span>Demo</span>
+                  </a>
+              </span>
+              <!--  Demo Link. -->
+              <span class="link-block">
+                <a href="https://huggingface.co/spaces/zhengchong/CatVTON"
+                    class="external-link button is-normal is-rounded is-dark">
+                  <span class="icon">
+                      <i class="fas fa-gamepad"></i>
+                  </span>
+                  <span>Space</span>
+                  </a>
+              </span>
+              <!-- Models Link. -->
+              <span class="link-block">
+                <a href="https://huggingface.co/zhengchong/CatVTON"
+                   class="external-link button is-normal is-rounded is-dark">
+                  <span class="icon">
+                    <i class="fas fa-cube"></i>
+                  </span>
+                  <span>Models</span>
+                </a>
+              </span>
+              <!-- Code Link. -->
+              <span class="link-block">
+                <a href="https://github.com/Zheng-Chong/CatVTON"
+                   class="external-link button is-normal is-rounded is-dark">
+                  <span class="icon">
+                      <i class="fab fa-github"></i>
+                  </span>
+                  <span>Code</span>
+                  </a>
+              </span>
+            </div>
+          </div>
+        </div>
+      </div>
+    </div>
+  </div>
+</section>
+
+<section class="hero teaser">
+  <div class="container is-max-desktop">
+    <div class="hero-body">
+      <img src="resource/img/teaser.jpg" alt="teaser">
+      <p>
+        CatVTON is a simple and efficient virtual try-on diffusion model with 1) Lightweight Network (899.06M parameters totally),
+         2) Parameter-Efficient Training (49.57M parameters trainable) and 3) Simplified Inference (< 8G VRAM for 1024X768 
+         resolution).
+      </p>
+    </div>
+  </div>
+</section>
+
+<!-- Abstract -->
+<section class="section">
+  <div class="container is-max-desktop">
+    <!-- Abstract. -->
+    <div class="columns is-centered has-text-centered">
+      <div class="column is-four-fifths">
+        <h2 class="title is-3">Abstract</h2>
+        <div class="content has-text-justified">
+          <p>
+            Virtual try-on methods based on diffusion models achieve realistic try-on effects but replicate the backbone network 
+            as a ReferenceNet or leverage additional image encoders to process condition inputs, resulting in high training and 
+            inference costs. 
+            In this work, we rethink the necessity of ReferenceNet and image encoders and innovate the interaction between garment 
+            and person, proposing CatVTON, a simple and efficient virtual try-on diffusion model. It facilitates the seamless 
+            transfer of in-shop or worn garments of arbitrary categories to target persons by simply concatenating them in spatial 
+            dimensions as inputs. The efficiency of our model is demonstrated in three aspects: 
+
+            (1) Lightweight network. Only the original diffusion modules are used, without additional network modules. The text 
+            encoder and cross attentions for text injection in the backbone are removed, further reducing the parameters by 167.02M.
+
+            (2) Parameter-efficient training. We identified the try-on relevant modules through experiments and achieved 
+            high-quality try-on effects by training only 49.57M parameters (~5.51% of the backbone network’s parameters). 
+
+            (3) Simplified inference. CatVTON eliminates all unnecessary conditions and preprocessing steps, including 
+            pose estimation, human parsing, and text input, requiring only garment reference, target person image, and mask for 
+            the virtual try-on process. 
+
+            Extensive experiments demonstrate that CatVTON achieves superior qualitative and 
+            quantitative results with fewer prerequisites and trainable parameters than baseline methods. Furthermore, 
+            CatVTON shows good generalization in in-the-wild scenarios despite using open-source datasets with only 73K samples.
+          </p>
+        </div>
+      </div>
+    </div>
+    <!--/ Abstract. -->
+  </div>
+</section>
+
+
+<section class="section">
+  <div class="container is-max-desktop">
+    <!-- Architecture. -->
+    <div class="columns is-centered">
+      <div class="column is-full-width">
+        <h2 class="title is-3">Architecture</h2>
+        <div class="content has-text-justified">
+          <img src="resource/img/architecture.jpg">
+          <p>
+            Our method achieves the high-quality try-on by simply concatenating the conditional image (garment or reference person) 
+            with the target person image in the spatial dimension, ensuring they remain in the same feature space throughout the 
+            diffusion process. Only the self-attention parameters, which provide global interaction, are learnable during training.
+             Unnecessary cross-attention for text interaction is omitted, and no additional conditions, such as pose and parsing, 
+             are required. These factors result in a lightweight network with minimal trainable parameters and simplified inference.
+          </p>
+          
+        </div>
+      </div>
+    </div>
+    <!-- Two Columns -->
+    <div class="columns is-centered">
+      <!-- Visual Effects. -->
+      <div class="column">
+        <div class="content">
+          <h2 class="title is-3">Structure Comparison</h2>
+          <p>
+            We illustrate simple structure comparison of different kinds of try-on methods below. Our approach neither relies on warped garments nor 
+            requires the heavy ReferenceNet for additional garment encoding; it only needs simple concatenation of the garment 
+            and person images as input to obtain high-quality try-on results.
+          </p>
+          <img src="resource/img/structure.jpg">
+        </div>
+      </div>
+
+      <!-- Efficiency Comparison -->
+      <div class="column">
+        <h2 class="title is-3">Efficiency Comparison</h2>
+        <div class="columns is-centered">
+          <div class="column content">
+            <p>
+              We represent each method by two concentric circles, 
+              where the outer circle denotes the total parameters and the inner circle denotes the trainable parameters, with the 
+              area proportional to the parameter number. CatVTON achieves lower FID on the VITONHD dataset with fewer total 
+              parameters, trainable parameters, and memory usage.
+            </p>
+            <img src="resource/img/efficency.jpg">
+          </div>
+
+        </div>
+      </div>
+    </div>
+
+    <!-- Demo -->
+    <div class="columns is-centered">
+      <div class="column is-full-width">
+        <h2 class="title is-3">Online Demo</h2>
+        <div class="content has-text-justified">
+          <!-- <iframe src="http://120.76.142.206:8888" width="100%" height="700px"  frameborder="1/0"  name="demo"  scrolling="yes/no/auto">   
+          </iframe>           -->
+          <p>
+            Since GitHub Pages does not support embedded web pages, please jump to our <a href="http://120.76.142.206:8888">Demo </a>.
+          </p>
+        </div>
+      </div>
+    </div>
+
+    <!-- Acknowledgement -->
+    <div class="columns is-centered">
+      <div class="column is-full-width">
+        <h2 class="title is-3">Acknowledgement</h2>
+        <div class="content has-text-justified">
+          <p>
+            Our code is modified based on <a href="https://github.com/huggingface/diffusers">Diffusers</a>. 
+            We adopt <a href="https://huggingface.co/runwayml/stable-diffusion-inpainting">Stable Diffusion v1.5 inpainitng</a> as base model.
+            We use <a href="https://github.com/GoGoDuck912/Self-Correction-Human-Parsing/tree/master">SCHP</a> 
+            and <a href="https://github.com/facebookresearch/DensePose">DensePose</a> to automatically generate masks in our 
+            <a href="https://github.com/gradio-app/gradio">Gradio</a> App. 
+            Thanks to all the contributors!
+          </p>
+        </div>
+      </div>
+    </div>
+    <!-- "BibTeX -->
+
+    <div class="container is-max-desktop content">
+      <h2 class="title">BibTeX</h2>
+      <pre><code>
+        @misc{chong2024catvtonconcatenationneedvirtual,
+          title={CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models}, 
+          author={Zheng Chong and Xiao Dong and Haoxiang Li and Shiyue Zhang and Wenqing Zhang and Xujie Zhang and Hanqing Zhao and Xiaodan Liang},
+          year={2024},
+          eprint={2407.15886},
+          archivePrefix={arXiv},
+          primaryClass={cs.CV},
+          url={https://arxiv.org/abs/2407.15886}, 
+        }
+      </code></pre>
+    </div>
+  </div>
+</section>
+
+
+
+<footer class="footer">
+  <div class="container">
+    <div class="content has-text-centered">
+      <a class="icon-link" href="http://arxiv.org/abs/2407.15886" class="external-link" disabled>
+        <i class="ai ai-arxiv"></i>
+      </a>
+      <a class="icon-link" href="https://arxiv.org/pdf/2407.15886">
+        <i class="fas fa-file-pdf"></i>
+      </a>
+      <a class="icon-link" href="http://120.76.142.206:8888" class="external-link" disabled>
+        <i class="fas fa-gamepad"></i>
+      </a>
+      <a class="icon-link" href="https://github.com/Zheng-Chong/CatVTON" class="external-link" disabled>
+        <i class="fab fa-github"></i>
+      </a>
+
+      <a class="icon-link" href="https://huggingface.co/zhengchong/CatVTON" class="external-link" disabled>
+        <i class="fas fa-cube"></i>
+      </a>
+      
+    </div>
+    <div class="columns is-centered">
+      <div class="column is-8">
+        <div class="content">
+          <p>
+            This website is modified from <a href="https://nerfies.github.io/">Nerfies</a>. Thanks for the great work!
+            Their source code is available on <a href="https://github.com/nerfies/nerfies.github.io">GitHub</a>.
+         </p>
+        </div>
+      </div>
+    </div>
+  </div>
+</footer>
+
+</body>
+</html>

inference.py

@@ -0,0 +1,327 @@
+import os
+import numpy as np
+import torch
+import argparse
+from torch.utils.data import Dataset, DataLoader
+from diffusers.image_processor import VaeImageProcessor
+from tqdm import tqdm
+from PIL import Image, ImageFilter
+
+from model.pipeline import CatVTONPipeline
+
+class InferenceDataset(Dataset):
+    def __init__(self, args):
+        self.args = args
+    
+        self.vae_processor = VaeImageProcessor(vae_scale_factor=8) 
+        self.mask_processor = VaeImageProcessor(vae_scale_factor=8, do_normalize=False, do_binarize=True, do_convert_grayscale=True) 
+        self.data = self.load_data()
+    
+    def load_data(self):
+        return []
+    
+    def __len__(self):
+        return len(self.data)
+    
+    def __getitem__(self, idx):
+        data = self.data[idx]
+        person, cloth, mask = [Image.open(data[key]) for key in ['person', 'cloth', 'mask']]
+        return {
+            'index': idx,
+            'person_name': data['person_name'],
+            'person': self.vae_processor.preprocess(person, self.args.height, self.args.width)[0],
+            'cloth': self.vae_processor.preprocess(cloth, self.args.height, self.args.width)[0],
+            'mask': self.mask_processor.preprocess(mask, self.args.height, self.args.width)[0]
+        }
+
+class VITONHDTestDataset(InferenceDataset):
+    def load_data(self):
+        assert os.path.exists(pair_txt:=os.path.join(self.args.data_root_path, 'test_pairs_unpaired.txt')), f"File {pair_txt} does not exist."
+        with open(pair_txt, 'r') as f:
+            lines = f.readlines()
+        self.args.data_root_path = os.path.join(self.args.data_root_path, "test")
+        output_dir = os.path.join(self.args.output_dir, "vitonhd", 'unpaired' if not self.args.eval_pair else 'paired')
+        data = []
+        for line in lines:
+            person_img, cloth_img = line.strip().split(" ")
+            if os.path.exists(os.path.join(output_dir, person_img)):
+                continue
+            if self.args.eval_pair:
+                cloth_img = person_img
+            data.append({
+                'person_name': person_img,
+                'person': os.path.join(self.args.data_root_path, 'image', person_img),
+                'cloth': os.path.join(self.args.data_root_path, 'cloth', cloth_img),
+                'mask': os.path.join(self.args.data_root_path, 'agnostic-mask', person_img.replace('.jpg', '_mask.png')),
+            })
+        return data
+
+class DressCodeTestDataset(InferenceDataset):
+    def load_data(self):
+        data = []
+        for sub_folder in ['upper_body', 'lower_body', 'dresses']:
+            assert os.path.exists(os.path.join(self.args.data_root_path, sub_folder)), f"Folder {sub_folder} does not exist."
+            pair_txt = os.path.join(self.args.data_root_path, sub_folder, 'test_pairs_paired.txt' if self.args.eval_pair else 'test_pairs_unpaired.txt')
+            assert os.path.exists(pair_txt), f"File {pair_txt} does not exist."
+            with open(pair_txt, 'r') as f:
+                lines = f.readlines()
+
+            output_dir = os.path.join(self.args.output_dir, f"dresscode-{self.args.height}", 
+                                      'unpaired' if not self.args.eval_pair else 'paired', sub_folder)
+            for line in lines:
+                person_img, cloth_img = line.strip().split(" ")
+                if os.path.exists(os.path.join(output_dir, person_img)):
+                    continue
+                data.append({
+                    'person_name': os.path.join(sub_folder, person_img),
+                    'person': os.path.join(self.args.data_root_path, sub_folder, 'images', person_img),
+                    'cloth': os.path.join(self.args.data_root_path, sub_folder, 'images', cloth_img),
+                    'mask': os.path.join(self.args.data_root_path, sub_folder, 'agnostic_masks', person_img.replace('.jpg', '.png'))
+                })
+        return data
+                    
+       
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument(
+        "--base_model_path",
+        type=str,
+        default="runwayml/stable-diffusion-inpainting",
+        help=(
+            "The path to the base model to use for evaluation. This can be a local path or a model identifier from the Model Hub."
+        ),
+    )
+    parser.add_argument(
+        "--resume_path",
+        type=str,
+        default="zhengchong/CatVTON",
+        help=(
+            "The Path to the checkpoint of trained tryon model."
+        ),
+    )
+    parser.add_argument(
+        "--dataset_name",
+        type=str,
+        required=True,
+        help="The datasets to use for evaluation.",
+    )
+    parser.add_argument(
+        "--data_root_path", 
+        type=str, 
+        required=True,
+        help="Path to the dataset to evaluate."
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="output",
+        help="The output directory where the model predictions will be written.",
+    )
+
+    parser.add_argument(
+        "--seed", type=int, default=555, help="A seed for reproducible evaluation."
+    )
+    parser.add_argument(
+        "--batch_size", type=int, default=8, help="The batch size for evaluation."
+    )
+    
+    parser.add_argument(
+        "--num_inference_steps",
+        type=int,
+        default=50,
+        help="Number of inference steps to perform.",
+    )
+    parser.add_argument(
+        "--guidance_scale",
+        type=float,
+        default=2.5,
+        help="The scale of classifier-free guidance for inference.",
+    )
+
+    parser.add_argument(
+        "--width",
+        type=int,
+        default=384,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--height",
+        type=int,
+        default=512,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--repaint", 
+        action="store_true", 
+        help="Whether to repaint the result image with the original background."
+    )
+    parser.add_argument(
+        "--eval_pair",
+        action="store_true",
+        help="Whether or not to evaluate the pair.",
+    )
+    parser.add_argument(
+        "--concat_eval_results",
+        action="store_true",
+        help="Whether or not to  concatenate the all conditions into one image.",
+    )
+    parser.add_argument(
+        "--allow_tf32",
+        action="store_true",
+        default=True,
+        help=(
+            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
+            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+        ),
+    )
+    parser.add_argument(
+        "--dataloader_num_workers",
+        type=int,
+        default=8,
+        help=(
+            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
+        ),
+    )
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default="bf16",
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
+            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+        ),
+    )
+
+    parser.add_argument(
+        "--concat_axis",
+        type=str,
+        choices=["x", "y", 'random'],
+        default="y",
+        help="The axis to concat the cloth feature, select from ['x', 'y', 'random'].",
+    )
+    parser.add_argument(
+        "--enable_condition_noise",
+        action="store_true",
+        default=True,
+        help="Whether or not to enable condition noise.",
+    )
+    
+    args = parser.parse_args()
+    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    if env_local_rank != -1 and env_local_rank != args.local_rank:
+        args.local_rank = env_local_rank
+
+    return args
+
+
+def repaint(person, mask, result):
+    _, h = result.size
+    kernal_size = h // 50
+    if kernal_size % 2 == 0:
+        kernal_size += 1
+    mask = mask.filter(ImageFilter.GaussianBlur(kernal_size))
+    person_np = np.array(person)
+    result_np = np.array(result)
+    mask_np = np.array(mask) / 255
+    repaint_result = person_np * (1 - mask_np) + result_np * mask_np
+    repaint_result = Image.fromarray(repaint_result.astype(np.uint8))
+    return repaint_result
+
+def to_pil_image(images):
+    images = (images / 2 + 0.5).clamp(0, 1)
+    images = images.cpu().permute(0, 2, 3, 1).float().numpy()
+    if images.ndim == 3:
+        images = images[None, ...]
+    images = (images * 255).round().astype("uint8")
+    if images.shape[-1] == 1:
+        # special case for grayscale (single channel) images
+        pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
+    else:
+        pil_images = [Image.fromarray(image) for image in images]
+    return pil_images
+
+@torch.no_grad()
+def main():
+    args = parse_args()
+    # Pipeline
+    pipeline = CatVTONPipeline(
+        attn_ckpt_version=args.dataset_name,
+        attn_ckpt=args.resume_path,
+        base_ckpt=args.base_model_path,
+        weight_dtype={
+            "no": torch.float32,
+            "fp16": torch.float16,
+            "bf16": torch.bfloat16,
+        }[args.mixed_precision],
+        # device="cuda",
+        device='cpu',
+        skip_safety_check=True
+    )
+    # Dataset
+    if args.dataset_name == "vitonhd":
+        dataset = VITONHDTestDataset(args)
+    elif args.dataset_name == "dresscode":
+        dataset = DressCodeTestDataset(args)
+    else:
+        raise ValueError(f"Invalid dataset name {args.dataset}.")
+    print(f"Dataset {args.dataset_name} loaded, total {len(dataset)} pairs.")
+    dataloader = DataLoader(
+        dataset,
+        batch_size=args.batch_size,
+        shuffle=False,
+        num_workers=args.dataloader_num_workers
+    )
+    # Inference
+    # generator = torch.Generator(device='cuda').manual_seed(args.seed)
+    generator = torch.Generator(device='cpu').manual_seed(args.seed)
+    args.output_dir = os.path.join(args.output_dir, f"{args.dataset_name}-{args.height}", "paired" if args.eval_pair else "unpaired")
+    if not os.path.exists(args.output_dir):
+        os.makedirs(args.output_dir)
+    for batch in tqdm(dataloader):
+        person_images = batch['person']
+        cloth_images = batch['cloth']
+        masks = batch['mask']
+        results = pipeline(
+            person_images,
+            cloth_images,
+            masks,
+            num_inference_steps=args.num_inference_steps,
+            guidance_scale=args.guidance_scale,
+            height=args.height,
+            width=args.width,
+            generator=generator,
+        )
+        
+        if args.concat_eval_results or args.repaint:
+            person_images = to_pil_image(person_images)
+            cloth_images = to_pil_image(cloth_images)
+            masks = to_pil_image(masks)
+        for i, result in enumerate(results):
+            person_name = batch['person_name'][i]
+            output_path = os.path.join(args.output_dir, person_name)
+            if not os.path.exists(os.path.dirname(output_path)):
+                os.makedirs(os.path.dirname(output_path))
+            if args.repaint:
+                person_path, mask_path = dataset.data[batch['index'][i]]['person'], dataset.data[batch['index'][i]]['mask']
+                person_image= Image.open(person_path).resize(result.size, Image.LANCZOS)
+                mask = Image.open(mask_path).resize(result.size, Image.NEAREST)
+                result = repaint(person_image, mask, result)
+            if args.concat_eval_results:
+                w, h = result.size
+                concated_result = Image.new('RGB', (w*3, h))
+                concated_result.paste(person_images[i], (0, 0))
+                concated_result.paste(cloth_images[i], (w, 0))  
+                concated_result.paste(result, (w*2, 0))
+                result = concated_result
+            result.save(output_path)
+
+if __name__ == "__main__":
+    main()

preprocess_agnostic_mask.py

@@ -0,0 +1,65 @@
+import argparse
+import os
+
+from huggingface_hub import snapshot_download
+from tqdm import tqdm
+
+from model.cloth_masker import AutoMasker
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of Preprocess Agnostic Mask")
+    parser.add_argument(
+        "--data_root_path", 
+        type=str, 
+        required=True,
+        help="Path to the dataset to evaluate."
+    )
+    parser.add_argument(
+        "--repo_path",
+        type=str,
+        default="zhengchong/CatVTON",
+        help=(
+            "The Path or repo name of CatVTON. "
+        ),
+    )
+    args = parser.parse_args()
+    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    if env_local_rank != -1 and env_local_rank != args.local_rank:
+        args.local_rank = env_local_rank
+
+    return args
+
+def main(args):
+    args.repo_path = snapshot_download(repo_id=args.repo_path)
+
+    automasker = AutoMasker(
+        densepose_ckpt=os.path.join(args.repo_path, "DensePose"),
+        schp_ckpt=os.path.join(args.repo_path, "SCHP"),
+        # device='cuda', 
+        device='cpu',
+    )
+    for sub_folder in ['upper_body', 'lower_body', 'dresses']:
+        assert os.path.exists(os.path.join(args.data_root_path, sub_folder)), f"Folder {sub_folder} does not exist."
+        pair_txt = os.path.join(args.data_root_path, sub_folder, 'test_pairs_paired.txt')
+        assert os.path.exists(pair_txt), f"File {pair_txt} does not exist."
+        cloth_type = {'upper_body': 'upper', 'lower_body': 'lower', 'dresses': 'overall'}[sub_folder]
+        with open(pair_txt, 'r') as f:
+            lines = f.readlines()
+        output_dir = os.path.join(args.data_root_path, sub_folder, 'agnostic_masks')
+        if not os.path.exists(output_dir):
+            os.makedirs(output_dir)
+        for line in tqdm(lines, desc=f"Processing {sub_folder}"):
+            person_img, _ = line.strip().split(" ")
+            if os.path.exists(os.path.join(output_dir, person_img.replace('.jpg', '.png'))):
+                continue
+            mask = automasker(
+                os.path.join(args.data_root_path, sub_folder, 'images', person_img),
+                cloth_type
+            )['mask']
+            mask.save(os.path.join(output_dir, person_img.replace('.jpg', '.png')))
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)
+            

requirements.txt

@@ -0,0 +1,91 @@
+accelerate==0.31.0
+aiofiles==23.2.1
+annotated-types==0.7.0
+antlr4-python3-runtime==4.9.3
+anyio==4.4.0
+av==12.3.0
+certifi==2024.7.4
+charset-normalizer==3.3.2
+click==8.1.7
+cloudpickle==3.0.0
+contourpy==1.3.0
+cycler==0.12.1
+diffusers==0.29.2
+exceptiongroup==1.2.2
+fastapi==0.112.2
+ffmpy==0.4.0
+filelock==3.15.4
+fonttools==4.53.1
+fsspec==2024.6.1
+fvcore==0.1.5.post20221221
+gradio==4.41.0
+gradio_client==1.3.0
+h11==0.14.0
+httpcore==1.0.5
+httpx==0.27.2
+huggingface-hub==0.23.4
+idna==3.8
+imageio==2.35.1
+importlib_metadata==8.4.0
+importlib_resources==6.4.4
+iopath==0.1.10
+Jinja2==3.1.4
+kiwisolver==1.4.5
+lazy_loader==0.4
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.9.1
+mdurl==0.1.2
+mpmath==1.3.0
+networkx==3.2.1
+numpy==1.26.4
+omegaconf==2.3.0
+opencv-python==4.10.0.84
+orjson==3.10.7
+packaging==24.1
+pandas==2.2.2
+pillow==10.3.0
+portalocker==2.10.1
+psutil==6.0.0
+pycocotools==2.0.8
+pydantic==2.8.2
+pydantic-settings==2.4.0
+pydantic_core==2.20.1
+pydub==0.25.1
+Pygments==2.18.0
+pyparsing==3.1.4
+python-dateutil==2.9.0.post0
+python-dotenv==1.0.1
+python-multipart==0.0.9
+pytz==2024.1
+PyYAML==6.0.1
+regex==2024.7.24
+requests==2.32.3
+rich==13.8.0
+ruff==0.6.2
+safetensors==0.4.4
+scikit-image==0.24.0
+scipy==1.13.1
+semantic-version==2.10.0
+shellingham==1.5.4
+six==1.16.0
+sniffio==1.3.1
+starlette==0.38.2
+sympy==1.13.2
+tabulate==0.9.0
+termcolor==2.4.0
+tifffile==2024.8.28
+tokenizers==0.13.3
+tomlkit==0.12.0
+torch==2.1.2
+torchvision==0.16.2
+tqdm==4.66.4
+transformers==4.27.3
+typer==0.12.5
+typing_extensions==4.12.2
+tzdata==2024.1
+urllib3==2.2.2
+uvicorn==0.30.6
+websockets==12.0
+yacs==0.1.8
+zipp==3.20.1

simplified.py

@@ -0,0 +1,42 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+__author__ = 'Ahmad Abdulnasir Shuaib <[email protected]>'
+__homepage__ = https://ahmadabdulnasir.com.ng
+__copyright__ = 'Copyright (c) 2024, salafi'
+__version__ = "0.01t"
+"""
+import gradio as gr
+
+from PIL import Image
+
+def change_clothes(person_img, shirt_img=None, trouser_img=None):
+    person = Image.open(person_img).convert("RGBA")  # Ensure person image has an alpha channel
+    if shirt_img:
+        shirt = Image.open(shirt_img).convert("RGBA").resize((person.width, int(person.height * 0.5)))
+        person.paste(shirt, (0, 0), shirt)  # Paste shirt with transparency
+    if trouser_img:
+        trouser = Image.open(trouser_img).convert("RGBA").resize((person.width, int(person.height * 0.5)))
+        person.paste(trouser, (0, int(person.height * 0.5)), trouser)  # Paste trouser with transparency
+    return person
+
+def run():
+    iface = gr.Interface(
+    fn=change_clothes,
+    inputs=[
+        gr.Image(type="filepath", label="Upload Person Image"),
+        gr.Image(type="filepath", label="Upload Shirt Image", ),
+        gr.Image(type="filepath", label="Upload Trouser Image", ),
+    ],
+        outputs="image",
+        title="Clothes Change Interface"
+    )
+
+    iface.launch(show_error=True )
+
+
+def boot():
+    run()
+
+if __name__ == "__main__":
+    boot()

utils.py

@@ -0,0 +1,508 @@
+import os
+
+import math
+import PIL
+import numpy as np
+import torch
+from PIL import Image
+from accelerate.state import AcceleratorState
+from packaging import version
+import accelerate
+from typing import List, Optional, Tuple, Set
+from diffusers import UNet2DConditionModel, SchedulerMixin
+from tqdm import tqdm
+
+
+# Compute DREAM and update latents for diffusion sampling
+def compute_dream_and_update_latents_for_inpaint(
+    unet: UNet2DConditionModel,
+    noise_scheduler: SchedulerMixin,
+    timesteps: torch.Tensor,
+    noise: torch.Tensor,
+    noisy_latents: torch.Tensor,
+    target: torch.Tensor,
+    encoder_hidden_states: torch.Tensor,
+    dream_detail_preservation: float = 1.0,
+) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]:
+    """
+    Implements "DREAM (Diffusion Rectification and Estimation-Adaptive Models)" from http://arxiv.org/abs/2312.00210.
+    DREAM helps align training with sampling to help training be more efficient and accurate at the cost of an extra
+    forward step without gradients.
+
+    Args:
+        `unet`: The state unet to use to make a prediction.
+        `noise_scheduler`: The noise scheduler used to add noise for the given timestep.
+        `timesteps`: The timesteps for the noise_scheduler to user.
+        `noise`: A tensor of noise in the shape of noisy_latents.
+        `noisy_latents`: Previously noise latents from the training loop.
+        `target`: The ground-truth tensor to predict after eps is removed.
+        `encoder_hidden_states`: Text embeddings from the text model.
+        `dream_detail_preservation`: A float value that indicates detail preservation level.
+          See reference.
+
+    Returns:
+        `tuple[torch.Tensor, torch.Tensor]`: Adjusted noisy_latents and target.
+    """
+    alphas_cumprod = noise_scheduler.alphas_cumprod.to(timesteps.device)[timesteps, None, None, None]
+    sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
+
+    # The paper uses lambda = sqrt(1 - alpha) ** p, with p = 1 in their experiments.
+    dream_lambda = sqrt_one_minus_alphas_cumprod**dream_detail_preservation
+
+    pred = None  # b, 4, h, w
+    with torch.no_grad():
+        pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
+
+    noisy_latents_no_condition = noisy_latents[:, :4]
+    _noisy_latents, _target = (None, None)
+    if noise_scheduler.config.prediction_type == "epsilon":
+        predicted_noise = pred
+        delta_noise = (noise - predicted_noise).detach()
+        delta_noise.mul_(dream_lambda)
+        _noisy_latents = noisy_latents_no_condition.add(sqrt_one_minus_alphas_cumprod * delta_noise)
+        _target = target.add(delta_noise)
+    elif noise_scheduler.config.prediction_type == "v_prediction":
+        raise NotImplementedError("DREAM has not been implemented for v-prediction")
+    else:
+        raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
+    
+    _noisy_latents = torch.cat([_noisy_latents, noisy_latents[:, 4:]], dim=1)
+    return _noisy_latents, _target
+
+# Prepare the input for inpainting model.
+def prepare_inpainting_input(
+    noisy_latents: torch.Tensor, 
+    mask_latents: torch.Tensor,
+    condition_latents: torch.Tensor,
+    enable_condition_noise: bool = True,
+    condition_concat_dim: int = -1,
+) -> torch.Tensor:
+    """
+    Prepare the input for inpainting model.
+    
+    Args:
+        noisy_latents (torch.Tensor): Noisy latents.
+        mask_latents (torch.Tensor): Mask latents.
+        condition_latents (torch.Tensor): Condition latents.
+        enable_condition_noise (bool): Enable condition noise.
+    
+    Returns:
+        torch.Tensor: Inpainting input.
+    """
+    if not enable_condition_noise:
+        condition_latents_ = condition_latents.chunk(2, dim=condition_concat_dim)[-1]
+        noisy_latents = torch.cat([noisy_latents, condition_latents_], dim=condition_concat_dim)
+    noisy_latents = torch.cat([noisy_latents, mask_latents, condition_latents], dim=1)
+    return noisy_latents
+
+# Compute VAE encodings
+def compute_vae_encodings(image: torch.Tensor, vae: torch.nn.Module) -> torch.Tensor:
+    """
+    Args:
+        images (torch.Tensor): image to be encoded
+        vae (torch.nn.Module): vae model
+
+    Returns:
+        torch.Tensor: latent encoding of the image
+    """
+    pixel_values = image.to(memory_format=torch.contiguous_format).float()
+    pixel_values = pixel_values.to(vae.device, dtype=vae.dtype)
+    with torch.no_grad():
+        model_input = vae.encode(pixel_values).latent_dist.sample()
+    model_input = model_input * vae.config.scaling_factor
+    return model_input
+
+
+# Init Accelerator
+from accelerate import Accelerator, DistributedDataParallelKwargs
+from accelerate.utils import ProjectConfiguration
+
+def init_accelerator(config):
+    accelerator_project_config = ProjectConfiguration(
+        project_dir=config.project_name,
+        logging_dir=os.path.join(config.project_name, "logs"),
+    )
+    accelerator_ddp_config = DistributedDataParallelKwargs(find_unused_parameters=True)
+    accelerator = Accelerator(
+        mixed_precision=config.mixed_precision,
+        log_with=config.report_to,
+        project_config=accelerator_project_config,
+        kwargs_handlers=[accelerator_ddp_config],
+        gradient_accumulation_steps=config.gradient_accumulation_steps,
+    )
+    # Disable AMP for MPS.
+    if torch.backends.mps.is_available():
+        accelerator.native_amp = False
+        
+    if accelerator.is_main_process:
+        accelerator.init_trackers(
+            project_name=config.project_name,
+            config={
+                "learning_rate": config.learning_rate,
+                "train_batch_size": config.train_batch_size,
+                "image_size": f"{config.width}x{config.height}",
+            },
+        )
+        
+    return accelerator
+
+
+def init_weight_dtype(wight_dtype):
+    return {
+        "no": torch.float32,
+        "fp16": torch.float16,
+        "bf16": torch.bfloat16,
+    }[wight_dtype]
+
+
+def init_add_item_id(config):
+    return torch.tensor(
+        [
+            config.height,
+            config.width * 2,
+            0,
+            0,
+            config.height,
+            config.width * 2,
+        ]
+    ).repeat(config.train_batch_size, 1)
+
+
+def prepare_eval_data(dataset_root, dataset_name, is_pair=True):
+    assert dataset_name in ["vitonhd", "dresscode", "farfetch"], "Unknown dataset name {}.".format(dataset_name)
+    if dataset_name == "vitonhd":
+        data_root = os.path.join(dataset_root, "VITONHD-1024", "test")
+        if is_pair:
+            keys = os.listdir(os.path.join(data_root, "Images"))
+            cloth_image_paths = [
+                os.path.join(data_root, "Images", key, key + "-0.jpg") for key in keys
+            ]
+            person_image_paths = [
+                os.path.join(data_root, "Images", key, key + "-1.jpg") for key in keys
+            ]
+        else:
+            # read ../test_pairs.txt
+            cloth_image_paths = []
+            person_image_paths = []
+            with open(
+                os.path.join(dataset_root, "VITONHD-1024", "test_pairs.txt"), "r"
+            ) as f:
+                lines = f.readlines()
+                for line in lines:
+                    cloth_image, person_image = (
+                        line.replace(".jpg", "").strip().split(" ")
+                    )
+                    cloth_image_paths.append(
+                        os.path.join(
+                            data_root, "Images", cloth_image, cloth_image + "-0.jpg"
+                        )
+                    )
+                    person_image_paths.append(
+                        os.path.join(
+                            data_root, "Images", person_image, person_image + "-1.jpg"
+                        )
+                    )
+    elif dataset_name == "dresscode":
+        data_root = os.path.join(dataset_root, "DressCode-1024")
+        if is_pair:
+            part = ["lower", "lower", "upper", "upper", "dresses", "dresses"]
+            ids = ["013581", "051685", "000190", "050072", "020829", "053742"]
+            cloth_image_paths = [
+                os.path.join(data_root, "Images", part[i], ids[i], ids[i] + "_1.jpg")
+                for i in range(len(part))
+            ]
+            person_image_paths = [
+                os.path.join(data_root, "Images", part[i], ids[i], ids[i] + "_0.jpg")
+                for i in range(len(part))
+            ]
+        else:
+            raise ValueError("DressCode dataset does not support non-pair evaluation.")
+    elif dataset_name == "farfetch":
+        data_root = os.path.join(dataset_root, "FARFETCH-1024")
+        cloth_image_paths = [
+            # TryOn
+            "/home/chongzheng/Projects/hivton/Datasets/FARFETCH-1024/Images/women/Tops/Blouses/13732751/13732751-2.jpg",
+            "/home/chongzheng/Projects/hivton/Datasets/FARFETCH-1024/Images/women/Tops/Hoodies/14661627/14661627-4.jpg",
+            "/home/chongzheng/Projects/hivton/Datasets/FARFETCH-1024/Images/women/Tops/Vests & Tank Tops/16532697/16532697-4.jpg",
+            "Images/men/Pants/Loose Fit Pants/14750720/14750720-6.jpg",
+            # Garment Transfer
+            "/home/chongzheng/Projects/hivton/Datasets/FARFETCH-1024/Images/women/Tops/Shirts/10889688/10889688-3.jpg",
+            "/home/chongzheng/Projects/hivton/Datasets/FARFETCH-1024/Images/women/Shorts/Leather & Faux Leather Shorts/20143338/20143338-1.jpg",
+            "/home/chongzheng/Projects/hivton/Datasets/FARFETCH-1024/Images/women/Jackets/Blazers/15541224/15541224-2.jpg",
+            "/home/chongzheng/Projects/hivton/Datasets/FARFETCH-1024/Images/men/Polo Shirts/Polo Shirts/17652415/17652415-0.jpg"
+            
+            # "Images/men/Jackets/Hooded Jackets/12550261/12550261-1.jpg",
+            # "Images/men/Shirts/Shirts/15614589/15614589-4.jpg",
+            # "Images/women/Dresses/Day Dresses/10372515/10372515-3.jpg",
+            # "Images/women/Dresses/Sundresses/18520992/18520992-4.jpg",
+            # "Images/women/Skirts/Asymmetric & Draped Skirts/12404908/12404908-2.jpg",
+        ]
+        person_image_paths = [
+            # TryOn
+            "/home/chongzheng/Projects/hivton/Datasets/FARFETCH-1024/Images/women/Tops/Blouses/13732751/13732751-0.jpg",
+            "/home/chongzheng/Projects/hivton/Datasets/FARFETCH-1024/Images/women/Tops/Hoodies/14661627/14661627-2.jpg",
+            "/home/chongzheng/Projects/hivton/Datasets/FARFETCH-1024/Images/women/Tops/Vests & Tank Tops/16532697/16532697-1.jpg",
+            "Images/men/Pants/Loose Fit Pants/14750720/14750720-5.jpg",
+            # Garment Transfer
+            "/home/chongzheng/Projects/hivton/Datasets/FARFETCH-1024/Images/women/Tops/Shirts/10889688/10889688-1.jpg",
+            "/home/chongzheng/Projects/hivton/Datasets/FARFETCH-1024/Images/women/Shorts/Leather & Faux Leather Shorts/20143338/20143338-2.jpg",
+            "/home/chongzheng/Projects/hivton/Datasets/FARFETCH-1024/Images/women/Jackets/Blazers/15541224/15541224-0.jpg",
+            "/home/chongzheng/Projects/hivton/Datasets/FARFETCH-1024/Images/men/Polo Shirts/Polo Shirts/17652415/17652415-4.jpg",
+            
+            # "Images/men/Jackets/Hooded Jackets/12550261/12550261-3.jpg",
+            # "Images/men/Shirts/Shirts/15614589/15614589-3.jpg",
+            # "Images/women/Dresses/Day Dresses/10372515/10372515-0.jpg",
+            # "Images/women/Dresses/Sundresses/18520992/18520992-1.jpg",
+            # "Images/women/Skirts/Asymmetric & Draped Skirts/12404908/12404908-1.jpg",
+        ]
+        cloth_image_paths = [
+            os.path.join(data_root, path) for path in cloth_image_paths
+        ]
+        person_image_paths = [
+            os.path.join(data_root, path) for path in person_image_paths
+        ]
+    else:
+        raise ValueError(f"Unknown dataset name: {dataset_name}")
+
+    samples = [
+        {
+            "folder": os.path.basename(os.path.dirname(cloth_image)),
+            "cloth": cloth_image,
+            "person": person_image,
+        }
+        for cloth_image, person_image in zip(
+            cloth_image_paths, person_image_paths
+        )
+    ]
+    return samples
+
+
+def repaint_result(result, person_image, mask_image):
+    result, person, mask = np.array(result), np.array(person_image), np.array(mask_image)
+    # expand the mask to 3 channels & to 0~1
+    mask = np.expand_dims(mask, axis=2)
+    mask = mask / 255.0
+    # mask for result, ~mask for person
+    result_ = result * mask + person * (1 - mask)
+    return Image.fromarray(result_.astype(np.uint8))
+
+
+def prepare_image(image):
+    if isinstance(image, torch.Tensor):
+        # Batch single image
+        if image.ndim == 3:
+            image = image.unsqueeze(0)
+        image = image.to(dtype=torch.float32)
+    else:
+        # preprocess image
+        if isinstance(image, (PIL.Image.Image, np.ndarray)):
+            image = [image]
+        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
+            image = [np.array(i.convert("RGB"))[None, :] for i in image]
+            image = np.concatenate(image, axis=0)
+        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
+            image = np.concatenate([i[None, :] for i in image], axis=0)
+        image = image.transpose(0, 3, 1, 2)
+        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
+    return image
+
+
+def prepare_mask_image(mask_image):
+    if isinstance(mask_image, torch.Tensor):
+        if mask_image.ndim == 2:
+            # Batch and add channel dim for single mask
+            mask_image = mask_image.unsqueeze(0).unsqueeze(0)
+        elif mask_image.ndim == 3 and mask_image.shape[0] == 1:
+            # Single mask, the 0'th dimension is considered to be
+            # the existing batch size of 1
+            mask_image = mask_image.unsqueeze(0)
+        elif mask_image.ndim == 3 and mask_image.shape[0] != 1:
+            # Batch of mask, the 0'th dimension is considered to be
+            # the batching dimension
+            mask_image = mask_image.unsqueeze(1)
+
+        # Binarize mask
+        mask_image[mask_image < 0.5] = 0
+        mask_image[mask_image >= 0.5] = 1
+    else:
+        # preprocess mask
+        if isinstance(mask_image, (PIL.Image.Image, np.ndarray)):
+            mask_image = [mask_image]
+
+        if isinstance(mask_image, list) and isinstance(mask_image[0], PIL.Image.Image):
+            mask_image = np.concatenate(
+                [np.array(m.convert("L"))[None, None, :] for m in mask_image], axis=0
+            )
+            mask_image = mask_image.astype(np.float32) / 255.0
+        elif isinstance(mask_image, list) and isinstance(mask_image[0], np.ndarray):
+            mask_image = np.concatenate([m[None, None, :] for m in mask_image], axis=0)
+
+        mask_image[mask_image < 0.5] = 0
+        mask_image[mask_image >= 0.5] = 1
+        mask_image = torch.from_numpy(mask_image)
+
+    return mask_image
+
+
+def numpy_to_pil(images):
+    """
+    Convert a numpy image or a batch of images to a PIL image.
+    """
+    if images.ndim == 3:
+        images = images[None, ...]
+    images = (images * 255).round().astype("uint8")
+    if images.shape[-1] == 1:
+        # special case for grayscale (single channel) images
+        pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
+    else:
+        pil_images = [Image.fromarray(image) for image in images]
+
+    return pil_images
+
+
+def tensor_to_image(tensor: torch.Tensor):
+    """
+    Converts a torch tensor to PIL Image.
+    """
+    assert tensor.dim() == 3, "Input tensor should be 3-dimensional."
+    assert tensor.dtype == torch.float32, "Input tensor should be float32."
+    assert (
+        tensor.min() >= 0 and tensor.max() <= 1
+    ), "Input tensor should be in range [0, 1]."
+    tensor = tensor.cpu()
+    tensor = tensor * 255
+    tensor = tensor.permute(1, 2, 0)
+    tensor = tensor.numpy().astype(np.uint8)
+    image = Image.fromarray(tensor)
+    return image
+
+
+def concat_images(images: List[Image.Image], divider: int = 4, cols: int = 4):
+    """
+    Concatenates images horizontally and with
+    """
+    widths = [image.size[0] for image in images]
+    heights = [image.size[1] for image in images]
+    total_width = cols * max(widths)
+    total_width += divider * (cols - 1)
+    # `col` images each row
+    rows = math.ceil(len(images) / cols)
+    total_height = max(heights) * rows
+    # add divider between rows
+    total_height += divider * (len(heights) // cols - 1)
+
+    # all black image
+    concat_image = Image.new("RGB", (total_width, total_height), (0, 0, 0))
+
+    x_offset = 0
+    y_offset = 0
+    for i, image in enumerate(images):
+        concat_image.paste(image, (x_offset, y_offset))
+        x_offset += image.size[0] + divider
+        if (i + 1) % cols == 0:
+            x_offset = 0
+            y_offset += image.size[1] + divider
+
+    return concat_image
+
+
+def read_prompt_file(prompt_file: str):
+    if prompt_file is not None and os.path.isfile(prompt_file):
+        with open(prompt_file, "r") as sample_prompt_file:
+            sample_prompts = sample_prompt_file.readlines()
+            sample_prompts = [sample_prompt.strip() for sample_prompt in sample_prompts]
+    else:
+        sample_prompts = []
+    return sample_prompts
+
+
+def save_tensors_to_npz(tensors: torch.Tensor, paths: List[str]):
+    assert len(tensors) == len(paths), "Length of tensors and paths should be the same!"
+    for tensor, path in zip(tensors, paths):
+        np.savez_compressed(path, latent=tensor.cpu().numpy())
+
+
+def deepspeed_zero_init_disabled_context_manager():
+    """
+    returns either a context list that includes one that will disable zero.Init or an empty context list
+    """
+    deepspeed_plugin = (
+        AcceleratorState().deepspeed_plugin
+        if accelerate.state.is_initialized()
+        else None
+    )
+    if deepspeed_plugin is None:
+        return []
+
+    return [deepspeed_plugin.zero3_init_context_manager(enable=False)]
+
+
+def is_xformers_available():
+    try:
+        import xformers
+
+        xformers_version = version.parse(xformers.__version__)
+        if xformers_version == version.parse("0.0.16"):
+            print(
+                "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, "
+                "please update xFormers to at least 0.0.17. "
+                "See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
+            )
+        return True
+    except ImportError:
+        raise ValueError(
+            "xformers is not available. Make sure it is installed correctly"
+        )
+
+
+
+def resize_and_crop(image, size):
+    # Crop to size ratio
+    w, h = image.size
+    target_w, target_h = size
+    if w / h < target_w / target_h:
+        new_w = w
+        new_h = w * target_h // target_w
+    else:
+        new_h = h
+        new_w = h * target_w // target_h
+    image = image.crop(
+        ((w - new_w) // 2, (h - new_h) // 2, (w + new_w) // 2, (h + new_h) // 2)
+    )
+    # resize
+    image = image.resize(size, Image.LANCZOS)
+    return image
+
+
+def resize_and_padding(image, size):
+    # Padding to size ratio
+    w, h = image.size
+    target_w, target_h = size
+    if w / h < target_w / target_h:
+        new_h = target_h
+        new_w = w * target_h // h
+    else:
+        new_w = target_w
+        new_h = h * target_w // w
+    image = image.resize((new_w, new_h), Image.LANCZOS)
+    # padding
+    padding = Image.new("RGB", size, (255, 255, 255))
+    padding.paste(image, ((target_w - new_w) // 2, (target_h - new_h) // 2))
+    return padding
+
+
+def scan_files_in_dir(directory, postfix: Set[str] = None, progress_bar: tqdm = None) -> list:
+    file_list = []
+    progress_bar = tqdm(total=0, desc=f"Scanning", ncols=100) if progress_bar is None else progress_bar
+    for entry in os.scandir(directory):
+        if entry.is_file():
+            if postfix is None or os.path.splitext(entry.path)[1] in postfix:
+                file_list.append(entry)
+                progress_bar.total += 1
+                progress_bar.update(1)
+        elif entry.is_dir():
+            file_list += scan_files_in_dir(entry.path, postfix=postfix, progress_bar=progress_bar)
+    return file_list
+
+if __name__ == "__main__":
+    ...