Update app.py

app.py

@@ -17,12 +17,21 @@ 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="runwayml/stable-diffusion-inpainting",
+        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."
         ),
@@ -105,7 +114,10 @@ def image_grid(imgs, rows, cols):
 
 
 args = parse_args()
-repo_path = snapshot_download(repo_id=args.resume_path)
+
+# Mask-based CatVTON
+catvton_repo = "zhengchong/CatVTON"
+repo_path = snapshot_download(repo_id=catvton_repo)
 # Pipeline
 pipeline = CatVTONPipeline(
     base_ckpt=args.base_model_path,
@@ -123,98 +135,6 @@ automasker = AutoMasker(
     device='cuda', 
 )
 
-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(person_image["layers"]) > 0:
-        mask = Image.open(person_image["layers"][0]).convert("L")
-    else:
-        raise ValueError("No layers found in person image.")
-
-
-    
-    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")
-    if person_image.get("background"):
-        person_image = Image.open(person_image["background"]).convert("RGB")
-    else:
-        raise ValueError("Background image not found.")
-
-    
-    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(
@@ -419,143 +339,146 @@ def submit_function_flux(
 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.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>'
+        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,
                         )
-                        cloth_type = gr.Radio(
-                            label="Try-On Cloth Type",
-                            choices=["upper", "lower", "overall"],
-                            value="upper",
+                        person_image = gr.ImageEditor(
+                            interactive=True, label="Person Image", type="filepath"
                         )
 
-
-                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
+                    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>'
                     )
-                    # Random Seed
-                    seed = gr.Slider(
-                        label="Seed", minimum=-1, maximum=10000, step=1, value=42
+                    
+                    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>'
                     )
-                    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",
+                    with gr.Accordion("Advanced Options", open=False):
+                        num_inference_steps = gr.Slider(
+                            label="Inference Step", minimum=10, maximum=100, step=5, value=50
                         )
-                        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>'
+                        # Guidence Scale
+                        guidance_scale = gr.Slider(
+                            label="CFG Strenth", minimum=0.0, maximum=7.5, step=0.5, value=2.5
                         )
-                    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",
+                        # Random Seed
+                        seed = gr.Slider(
+                            label="Seed", minimum=-1, maximum=10000, step=1, value=42
                         )
-                        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>'
+                        show_type = gr.Radio(
+                            label="Show Type",
+                            choices=["result only", "input & result", "input & mask & result"],
+                            value="input & mask & result",
                         )
 
-            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,
-            )
+                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_gradio()