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face3df
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1 Parent(s): c1da3e0

Update app.py

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Files changed (1) hide show
  1. app.py +4 -27
app.py CHANGED
@@ -330,7 +330,7 @@ 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 πŸ‘”
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  Transform your look with AI-powered virtual clothing try-on!
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  """
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  )
@@ -361,13 +361,7 @@ def app_gradio():
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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 πŸ–ŒοΈ
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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"],
@@ -376,13 +370,7 @@ def app_gradio():
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  )
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  submit = gr.Button("πŸš€ 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("βš™οΈ Advanced Settings", open=False):
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  num_inference_steps = gr.Slider(
@@ -502,18 +490,7 @@ def app_gradio():
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  result_image,
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  )
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- gr.Markdown(
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- """
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- ### πŸ’‘ Tips & Instructions
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- 1. Upload or select a person image
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- 2. Choose or upload a clothing item
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- 3. Select clothing type (upper/lower/overall)
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- 4. Adjust advanced settings if needed
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- 5. Click Generate and wait for results
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-
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- For best results, use clear, front-facing images with good lighting.
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- """
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- )
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  demo.queue().launch(share=True, show_error=True)
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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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+ # πŸ‘” Fashion Fit
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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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  elem_classes="image-container"
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  )
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  with gr.Column(scale=1, min_width=120):
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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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  )
371
 
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  submit = gr.Button("πŸš€ Generate Try-On", elem_classes="primary-button")
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+
 
 
 
 
 
 
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  with gr.Accordion("βš™οΈ Advanced Settings", open=False):
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  num_inference_steps = gr.Slider(
 
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  result_image,
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  )
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+
 
 
 
 
 
 
 
 
 
 
 
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  demo.queue().launch(share=True, show_error=True)
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