Update app.py
Browse files
app.py
CHANGED
@@ -16,7 +16,6 @@ from model.cloth_masker import AutoMasker, vis_mask
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from model.pipeline import CatVTONPipeline
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from utils import init_weight_dtype, resize_and_crop, resize_and_padding
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-
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def parse_args():
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parser = argparse.ArgumentParser(description="Simple example of a training script.")
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parser.add_argument(
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@@ -41,7 +40,6 @@ def parse_args():
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default="resource/demo/output",
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help="The output directory where the model predictions will be written.",
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)
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-
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parser.add_argument(
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"--width",
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type=int,
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@@ -103,7 +101,6 @@ def image_grid(imgs, rows, cols):
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grid.paste(img, box=(i % cols * w, i // cols * h))
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return grid
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-
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args = parse_args()
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repo_path = snapshot_download(repo_id=args.resume_path)
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# Pipeline
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mask = mask_processor.blur(mask, blur_factor=9)
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# Inference
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# try:
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result_image = pipeline(
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image=person_image,
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condition_image=cloth_image,
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@@ -177,10 +173,6 @@ def submit_function(
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guidance_scale=guidance_scale,
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generator=generator
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)[0]
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# except Exception as e:
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# raise gr.Error(
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# "An error occurred. Please try again later: {}".format(e)
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# )
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# Post-process
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masked_person = vis_mask(person_image, mask)
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new_result_image.paste(result_image, (condition_width + 5, 0))
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return new_result_image
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-
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def person_example_fn(image_path):
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return image_path
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css = """
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footer {
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}
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"""
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with gr.Row():
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with gr.Column(scale=1, min_width=350):
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with gr.
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interactive=True, label="Person Image", type="filepath"
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)
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with gr.Row():
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with gr.Column(scale=1, min_width=230):
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cloth_image = gr.Image(
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interactive=True, label="Condition Image", type="filepath"
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)
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with gr.Column(scale=1, min_width=120):
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gr.Markdown(
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'<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>'
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)
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)
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submit = gr.Button("
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gr.Markdown(
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)
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gr.
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'<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>'
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)
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with gr.Accordion("Advanced Options", open=False):
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num_inference_steps = gr.Slider(
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label="
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)
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# Guidence Scale
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guidance_scale = gr.Slider(
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label="
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)
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# Random Seed
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seed = gr.Slider(
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label="Seed",
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)
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show_type = gr.Radio(
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label="
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choices=["result only", "input & result", "input & mask & result"],
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value="input & mask & result",
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)
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with gr.Column(scale=2, min_width=500):
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result_image = gr.Image(
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image_path.change(
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person_example_fn,
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)
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submit.click(
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@@ -347,8 +497,21 @@ def app_gradio():
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],
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result_image,
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)
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demo.queue().launch(share=True, show_error=True)
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if __name__ == "__main__":
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app_gradio()
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from model.pipeline import CatVTONPipeline
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from utils import init_weight_dtype, resize_and_crop, resize_and_padding
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def parse_args():
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parser = argparse.ArgumentParser(description="Simple example of a training script.")
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parser.add_argument(
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default="resource/demo/output",
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help="The output directory where the model predictions will be written.",
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)
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parser.add_argument(
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"--width",
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type=int,
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grid.paste(img, box=(i % cols * w, i // cols * h))
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return grid
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args = parse_args()
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repo_path = snapshot_download(repo_id=args.resume_path)
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# Pipeline
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mask = mask_processor.blur(mask, blur_factor=9)
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# Inference
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result_image = pipeline(
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image=person_image,
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condition_image=cloth_image,
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guidance_scale=guidance_scale,
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generator=generator
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)[0]
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# Post-process
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masked_person = vis_mask(person_image, mask)
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new_result_image.paste(result_image, (condition_width + 5, 0))
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return new_result_image
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def person_example_fn(image_path):
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return image_path
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+
# Custom CSS for enhanced visual appeal
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css = """
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footer {visibility: hidden}
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/* Main container styling */
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.gradio-container {
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background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
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border-radius: 20px;
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box-shadow: 0 8px 32px rgba(31, 38, 135, 0.15);
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}
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/* Header styling */
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h1, h2, h3 {
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color: #2c3e50;
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font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
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text-shadow: 1px 1px 2px rgba(0,0,0,0.1);
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}
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/* Button styling */
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button.primary-button {
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background: linear-gradient(45deg, #4CAF50, #45a049);
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border: none;
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border-radius: 10px;
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color: white;
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padding: 12px 24px;
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font-weight: bold;
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transition: all 0.3s ease;
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box-shadow: 0 4px 15px rgba(76, 175, 80, 0.3);
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}
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button.primary-button:hover {
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transform: translateY(-2px);
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box-shadow: 0 6px 20px rgba(76, 175, 80, 0.4);
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}
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/* Image container styling */
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.image-container {
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border-radius: 15px;
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overflow: hidden;
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box-shadow: 0 4px 15px rgba(0,0,0,0.1);
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transition: transform 0.3s ease;
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}
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.image-container:hover {
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transform: scale(1.02);
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}
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/* Radio button styling */
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.radio-group label {
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background-color: #ffffff;
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border-radius: 8px;
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padding: 10px 15px;
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margin: 5px;
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cursor: pointer;
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transition: all 0.3s ease;
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}
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.radio-group input:checked + label {
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background-color: #4CAF50;
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color: white;
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}
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/* Slider styling */
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.slider-container {
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background: white;
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padding: 15px;
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border-radius: 10px;
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box-shadow: 0 2px 10px rgba(0,0,0,0.05);
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}
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.slider {
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height: 8px;
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border-radius: 4px;
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background: #e0e0e0;
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}
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.slider .thumb {
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width: 20px;
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height: 20px;
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background: #4CAF50;
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border-radius: 50%;
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box-shadow: 0 2px 5px rgba(0,0,0,0.2);
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}
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/* Alert/warning text styling */
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.warning-text {
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color: #ff5252;
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font-weight: bold;
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text-align: center;
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padding: 10px;
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background: rgba(255,82,82,0.1);
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border-radius: 8px;
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margin: 10px 0;
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}
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/* Example gallery styling */
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.example-gallery {
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display: grid;
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grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
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gap: 15px;
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padding: 15px;
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background: white;
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border-radius: 10px;
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box-shadow: 0 2px 10px rgba(0,0,0,0.05);
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}
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.example-item {
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border-radius: 8px;
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overflow: hidden;
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transition: transform 0.3s ease;
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}
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.example-item:hover {
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transform: scale(1.05);
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}
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"""
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def app_gradio():
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with gr.Blocks(theme=gr.themes.Soft(primary_hue="green", secondary_hue="blue"), css=css) as demo:
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gr.Markdown(
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"""
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# Virtual Try-On App 👔
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Transform your look with AI-powered virtual clothing try-on!
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"""
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)
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with gr.Row():
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with gr.Column(scale=1, min_width=350):
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with gr.Box():
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gr.Markdown("### 📸 Upload Images")
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with gr.Row():
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image_path = gr.Image(
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type="filepath",
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interactive=True,
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visible=False,
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)
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person_image = gr.ImageEditor(
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interactive=True,
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label="Person Image",
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type="filepath",
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elem_classes="image-container"
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)
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with gr.Row():
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with gr.Column(scale=1, min_width=230):
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cloth_image = gr.Image(
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interactive=True,
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label="Clothing Item",
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type="filepath",
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elem_classes="image-container"
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)
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with gr.Column(scale=1, min_width=120):
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gr.Markdown(
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"""
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### 🎯 Masking Options
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1. Draw mask manually with 🖌️
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2. Auto-generate based on clothing type
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"""
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)
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cloth_type = gr.Radio(
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label="Clothing Type",
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choices=["upper", "lower", "overall"],
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value="upper",
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elem_classes="radio-group"
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)
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submit = gr.Button("🚀 Generate Try-On", elem_classes="primary-button")
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gr.Markdown(
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"""
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<div class="warning-text">
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⚠️ Please click only once and wait patiently for processing
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</div>
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"""
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)
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with gr.Accordion("⚙️ Advanced Settings", open=False):
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num_inference_steps = gr.Slider(
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label="Quality Level",
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minimum=10,
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maximum=100,
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step=5,
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value=50,
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elem_classes="slider-container"
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)
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guidance_scale = gr.Slider(
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label="Style Strength",
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minimum=0.0,
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maximum=7.5,
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step=0.5,
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value=2.5,
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elem_classes="slider-container"
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)
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seed = gr.Slider(
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label="Random Seed",
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minimum=-1,
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maximum=10000,
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step=1,
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value=42,
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elem_classes="slider-container"
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)
|
400 |
show_type = gr.Radio(
|
401 |
+
label="Display Mode",
|
402 |
choices=["result only", "input & result", "input & mask & result"],
|
403 |
value="input & mask & result",
|
404 |
+
elem_classes="radio-group"
|
405 |
)
|
|
|
406 |
with gr.Column(scale=2, min_width=500):
|
407 |
+
result_image = gr.Image(
|
408 |
+
interactive=False,
|
409 |
+
label="Final Result",
|
410 |
+
elem_classes="image result_image = gr.Image(
|
411 |
+
interactive=False,
|
412 |
+
label="Final Result",
|
413 |
+
elem_classes="image-container"
|
414 |
+
)
|
415 |
+
|
416 |
+
with gr.Row():
|
417 |
+
# Photo Examples
|
418 |
+
root_path = "resource/demo/example"
|
419 |
+
with gr.Column():
|
420 |
+
gr.Markdown("#### 👤 Model Examples")
|
421 |
+
men_exm = gr.Examples(
|
422 |
+
examples=[
|
423 |
+
os.path.join(root_path, "person", "men", _)
|
424 |
+
for _ in os.listdir(os.path.join(root_path, "person", "men"))
|
425 |
+
],
|
426 |
+
examples_per_page=4,
|
427 |
+
inputs=image_path,
|
428 |
+
label="Men's Examples",
|
429 |
+
elem_classes="example-item"
|
430 |
+
)
|
431 |
+
women_exm = gr.Examples(
|
432 |
+
examples=[
|
433 |
+
os.path.join(root_path, "person", "women", _)
|
434 |
+
for _ in os.listdir(os.path.join(root_path, "person", "women"))
|
435 |
+
],
|
436 |
+
examples_per_page=4,
|
437 |
+
inputs=image_path,
|
438 |
+
label="Women's Examples",
|
439 |
+
elem_classes="example-item"
|
440 |
+
)
|
441 |
+
gr.Markdown(
|
442 |
+
'<div class="info-text">Model examples courtesy of <a href="https://huggingface.co/spaces/levihsu/OOTDiffusion">OOTDiffusion</a> and <a href="https://www.outfitanyone.org">OutfitAnyone</a></div>'
|
443 |
+
)
|
444 |
+
|
445 |
+
with gr.Column():
|
446 |
+
gr.Markdown("#### 👕 Clothing Examples")
|
447 |
+
condition_upper_exm = gr.Examples(
|
448 |
+
examples=[
|
449 |
+
os.path.join(root_path, "condition", "upper", _)
|
450 |
+
for _ in os.listdir(os.path.join(root_path, "condition", "upper"))
|
451 |
+
],
|
452 |
+
examples_per_page=4,
|
453 |
+
inputs=cloth_image,
|
454 |
+
label="Upper Garments",
|
455 |
+
elem_classes="example-item"
|
456 |
+
)
|
457 |
+
condition_overall_exm = gr.Examples(
|
458 |
+
examples=[
|
459 |
+
os.path.join(root_path, "condition", "overall", _)
|
460 |
+
for _ in os.listdir(os.path.join(root_path, "condition", "overall"))
|
461 |
+
],
|
462 |
+
examples_per_page=4,
|
463 |
+
inputs=cloth_image,
|
464 |
+
label="Full Outfits",
|
465 |
+
elem_classes="example-item"
|
466 |
+
)
|
467 |
+
condition_person_exm = gr.Examples(
|
468 |
+
examples=[
|
469 |
+
os.path.join(root_path, "condition", "person", _)
|
470 |
+
for _ in os.listdir(os.path.join(root_path, "condition", "person"))
|
471 |
+
],
|
472 |
+
examples_per_page=4,
|
473 |
+
inputs=cloth_image,
|
474 |
+
label="Reference Styles",
|
475 |
+
elem_classes="example-item"
|
476 |
+
)
|
477 |
+
gr.Markdown(
|
478 |
+
'<div class="info-text">Clothing examples sourced from various online retailers</div>'
|
479 |
+
)
|
480 |
|
481 |
image_path.change(
|
482 |
+
person_example_fn,
|
483 |
+
inputs=image_path,
|
484 |
+
outputs=person_image
|
485 |
)
|
486 |
|
487 |
submit.click(
|
|
|
497 |
],
|
498 |
result_image,
|
499 |
)
|
500 |
+
|
501 |
+
gr.Markdown(
|
502 |
+
"""
|
503 |
+
### 💡 Tips & Instructions
|
504 |
+
1. Upload or select a person image
|
505 |
+
2. Choose or upload a clothing item
|
506 |
+
3. Select clothing type (upper/lower/overall)
|
507 |
+
4. Adjust advanced settings if needed
|
508 |
+
5. Click Generate and wait for results
|
509 |
+
|
510 |
+
For best results, use clear, front-facing images with good lighting.
|
511 |
+
"""
|
512 |
+
)
|
513 |
+
|
514 |
demo.queue().launch(share=True, show_error=True)
|
515 |
|
|
|
516 |
if __name__ == "__main__":
|
517 |
+
app_gradio()
|