Upload app.py

app.py

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+import torch
+from diffusers import StableDiffusionPipeline
+import gradio as gr
+from PIL import Image
+
+# Load the model and pipeline
+model_id = "ares1123/virtual-dress-try-on"
+pipeline = StableDiffusionPipeline.from_pretrained(model_id)
+pipeline.to("cuda" if torch.cuda.is_available() else "cpu")
+
+def virtual_try_on(image, clothing_image):
+    # Convert images to proper format and get dimensions
+    width, height = image.size
+    
+    # Ensure dimensions are multiples of 8
+    width = (width // 8) * 8
+    height = (height // 8) * 8
+
+    # Resize images to fit the model's expected input
+    image = image.resize((width, height))
+    clothing_image = clothing_image.resize((width, height))
+
+    # Define a prompt describing what you want the model to do
+    prompt = "A person wearing new clothes"
+
+    # Process the images using the model
+    result = pipeline(prompt=prompt, image=image, conditioning_image=clothing_image)
+    try_on_image = result.images[0]
+    
+    return try_on_image
+
+# Set up a simple Gradio interface for testing
+interface = gr.Interface(
+    fn=virtual_try_on,
+    inputs=[gr.Image(type="pil", label="User Image"), 
+            gr.Image(type="pil", label="Clothing Image")],
+    outputs=gr.Image(type="pil"),
+    title="Virtual Dress Try-On",
+    description="Upload an image of yourself and a clothing image to try it on virtually!"
+)
+
+# Launch the interface
+interface.launch(share=True)