Update app.py

app.py

@@ -1,508 +1,484 @@
-import argparse
-import os
-os.environ['CUDA_HOME'] = '/usr/local/cuda'
-os.environ['PATH'] = os.environ['PATH'] + ':/usr/local/cuda/bin'
-from datetime import datetime
-
-import gradio as gr
-import spaces
-import numpy as np
-import torch
-from diffusers.image_processor import VaeImageProcessor
-from huggingface_hub import snapshot_download
-from PIL import Image
-torch.jit.script = lambda f: f
-from model.cloth_masker import AutoMasker, vis_mask
-from model.pipeline import CatVTONPipeline, CatVTONPix2PixPipeline
-from model.flux.pipeline_flux_tryon import FluxTryOnPipeline
-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="booksforcharlie/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(
-        "--p2p_base_model_path",
-        type=str,
-        default="timbrooks/instruct-pix2pix", 
-        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()
-
-# Mask-based CatVTON
-catvton_repo = "zhengchong/CatVTON"
-repo_path = snapshot_download(repo_id=catvton_repo)
-# 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'
-)
-# 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', 
-)
-
-
-# Flux-based CatVTON
-access_token = os.getenv("HUGGING_FACE_HUB_TOKEN")
-flux_repo = "black-forest-labs/FLUX.1-Fill-dev"
-pipeline_flux = FluxTryOnPipeline.from_pretrained(flux_repo, use_auth_token=access_token)
-pipeline_flux.load_lora_weights(
-    os.path.join(repo_path, "flux-lora"), 
-    weight_name='pytorch_lora_weights.safetensors'
-)
-pipeline_flux.to("cuda", init_weight_dtype(args.mixed_precision))
-
-
-# Mask-free CatVTON
-catvton_mf_repo = "zhengchong/CatVTON-MaskFree"
-repo_path_mf = snapshot_download(repo_id=catvton_mf_repo, use_auth_token=access_token)
-pipeline_p2p = CatVTONPix2PixPipeline(
-    base_ckpt=args.p2p_base_model_path,
-    attn_ckpt=repo_path_mf,
-    attn_ckpt_version="mix-48k-1024",
-    weight_dtype=init_weight_dtype(args.mixed_precision),
-    use_tf32=args.allow_tf32,
-    device='cuda'
-)
-
-
-@spaces.GPU(duration=120)
-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)
-
-    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
-
-@spaces.GPU(duration=120)
-def submit_function_p2p(
-    person_image,
-    cloth_image,
-    num_inference_steps,
-    guidance_scale,
-    seed):
-    person_image= person_image["background"]
-
-    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)
-
-    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))
-
-    # Inference
-    try:
-        result_image = pipeline_p2p(
-            image=person_image,
-            condition_image=cloth_image,
-            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
-    save_result_image = image_grid([person_image, cloth_image, result_image], 1, 3)
-    save_result_image.save(result_save_path)
-    return result_image
-
-@spaces.GPU(duration=120)
-def submit_function_flux(
-    person_image,
-    cloth_image,
-    cloth_type,
-    num_inference_steps,
-    guidance_scale,
-    seed,
-    show_type
-):
-
-    # Process image editor input
-    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)
-
-    # Set random seed
-    generator = None
-    if seed != -1:
-        generator = torch.Generator(device='cuda').manual_seed(seed)
-
-    # Process input images
-    person_image = Image.open(person_image).convert("RGB")
-    cloth_image = Image.open(cloth_image).convert("RGB")
-    
-    # Adjust image sizes
-    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
-    result_image = pipeline_flux(
-        image=person_image,
-        condition_image=cloth_image,
-        mask_image=mask,
-        width=args.width,
-        height=args.height,
-        num_inference_steps=num_inference_steps,
-        guidance_scale=guidance_scale,
-        generator=generator
-    ).images[0]
-
-    # Post-processing
-    masked_person = vis_mask(person_image, mask)
-
-    # Return result based on show type
-    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 = ""
-
-def app_gradio():
-    with gr.Blocks(title="CatVTON") as demo:
-        gr.Markdown(HEADER)
-        with gr.Tab("Mask-based & SD1.5"):
-            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()
+import argparse
+import os
+os.environ['CUDA_HOME'] = '/usr/local/cuda'
+os.environ['PATH'] = os.environ['PATH'] + ':/usr/local/cuda/bin'
+from datetime import datetime
+
+import gradio as gr
+import spaces
+import numpy as np
+import torch
+from diffusers.image_processor import VaeImageProcessor
+from huggingface_hub import snapshot_download
+from PIL import Image
+torch.jit.script = lambda f: f
+from model.cloth_masker import AutoMasker, vis_mask
+from model.pipeline import CatVTONPipeline, CatVTONPix2PixPipeline
+from model.flux.pipeline_flux_tryon import FluxTryOnPipeline
+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="booksforcharlie/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(
+        "--p2p_base_model_path",
+        type=str,
+        default="timbrooks/instruct-pix2pix", 
+        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()
+
+# Mask-based CatVTON
+catvton_repo = "zhengchong/CatVTON"
+repo_path = snapshot_download(repo_id=catvton_repo)
+# 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'
+)
+# 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', 
+)
+
+
+@spaces.GPU(duration=120)
+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)
+
+    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
+
+@spaces.GPU(duration=120)
+def submit_function_p2p(
+    person_image,
+    cloth_image,
+    num_inference_steps,
+    guidance_scale,
+    seed):
+    person_image= person_image["background"]
+
+    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)
+
+    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))
+
+    # Inference
+    try:
+        result_image = pipeline_p2p(
+            image=person_image,
+            condition_image=cloth_image,
+            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
+    save_result_image = image_grid([person_image, cloth_image, result_image], 1, 3)
+    save_result_image.save(result_save_path)
+    return result_image
+
+@spaces.GPU(duration=120)
+def submit_function_flux(
+    person_image,
+    cloth_image,
+    cloth_type,
+    num_inference_steps,
+    guidance_scale,
+    seed,
+    show_type
+):
+
+    # Process image editor input
+    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)
+
+    # Set random seed
+    generator = None
+    if seed != -1:
+        generator = torch.Generator(device='cuda').manual_seed(seed)
+
+    # Process input images
+    person_image = Image.open(person_image).convert("RGB")
+    cloth_image = Image.open(cloth_image).convert("RGB")
+    
+    # Adjust image sizes
+    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
+    result_image = pipeline_flux(
+        image=person_image,
+        condition_image=cloth_image,
+        mask_image=mask,
+        width=args.width,
+        height=args.height,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        generator=generator
+    ).images[0]
+
+    # Post-processing
+    masked_person = vis_mask(person_image, mask)
+
+    # Return result based on show type
+    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 = ""
+
+def app_gradio():
+    with gr.Blocks(title="CatVTON") as demo:
+        gr.Markdown(HEADER)
+        with gr.Tab("Mask-based & SD1.5"):
+            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()