Create app.py

app.py

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+import gradio as gr
+from diffusers import AutoPipelineForInpainting, AutoencoderKL
+from diffusers.utils import load_image
+import torch
+from PIL import Image
+
+from SegBody import segment_body
+
+
+# Load models
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+pipeline = AutoPipelineForInpainting.from_pretrained(
+    "diffusers/stable-diffusion-xl-1.0-inpainting-0.1", 
+    vae=vae, 
+    torch_dtype=torch.float16, 
+    variant="fp16", 
+    use_safetensors=True
+).to("cuda")
+pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin", low_cpu_mem_usage=True)
+
+# Function to process images
+def virtual_try_on(img, clothing, prompt, negative_prompt, ip_scale=1.0, strength=0.99, guidance_scale=7.5, steps=100):
+    _, mask_img = segment_body(img, face=False)
+    pipeline.set_ip_adapter_scale(ip_scale)
+    images = pipeline(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        image=img,
+        mask_image=mask_img,
+        ip_adapter_image=clothing,
+        strength=strength,
+        guidance_scale=guidance_scale,
+        num_inference_steps=steps,
+    ).images
+    return images[0]
+
+def process_images(image, ip_image):
+    image = image.convert("RGB").resize((512, 512))
+    ip_image = ip_image.convert("RGB").resize((512, 512))
+    
+    seg_image, mask_image = segment_body(image, face=False)
+    mask_image.resize((512, 512)) 
+    
+
+    return virtual_try_on(img=image,
+               clothing=ip_image,
+               prompt="photorealistic, perfect body, beautiful skin, realistic skin, natural skin",
+               negative_prompt="ugly, bad quality, bad anatomy, deformed body, deformed hands, deformed feet, deformed face, deformed clothing, deformed skin, bad skin, leggings, tights, stockings")
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=process_images, 
+    inputs=[gr.Image(type="pil"), gr.Image(type="pil")], 
+    outputs=gr.Image(type="pil"),
+    title="Image Inpainting Demo",
+    description="Upload two images for inpainting using Stable Diffusion XL."
+)
+
+# Launch the Gradio interface
+interface.launch()