Add Gradio interface and pipeline for inpainting

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+from PIL import Image
+from diffusers import AutoPipelineForInpainting, AutoencoderKL
+import torch
+
+# 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")
+
+# Define the inference function
+def inpaint(prompt, image, mask_image, ip_image):
+    image = image.convert("RGB").resize((512, 512))
+    mask_image = mask_image.convert("RGB").resize((512, 512))
+    ip_image = ip_image.convert("RGB").resize((512, 512))
+
+    results = pipeline(
+        prompt=prompt,
+        negative_prompt="ugly, bad quality, bad anatomy",
+        image=image,
+        mask_image=mask_image,
+        ip_adapter_image=ip_image,
+        strength=0.99,
+        guidance_scale=8.0,
+        num_inference_steps=100
+    )
+    return results.images[0]
+
+# Set up the Gradio interface
+demo = gr.Interface(
+    fn=inpaint,
+    inputs=[
+        gr.Textbox(label="Prompt", placeholder="Enter the prompt for the model"),
+        gr.Image(type="pil", label="Input Image"),
+        gr.Image(type="pil", label="Mask Image"),
+        gr.Image(type="pil", label="IP Adapter Image")
+    ],
+    outputs=gr.Image(type="pil"),
+    title="Stable Diffusion Inpainting",
+    description="A model for inpainting and image editing using Stable Diffusion XL."
+)
+
+demo.launch()

requirements.txt

@@ -0,0 +1,3 @@
+gradio
+diffusers
+torch