chore: Update roi_scale in inpainting.yaml and add gradio UI for SDXL LORA Inpainting

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api/endpoints.py

@@ -50,6 +50,11 @@ async def root():
 def check_health():
     return {"status": "ok"}
 
+<<<<<<< HEAD:api/endpoints.py
 
 
-uvicorn.run(app, host='127.0.0.1', port=7860)
+uvicorn.run(app, host='127.0.0.1', port=7860)
+=======
+uvicorn.run(app, host="0.0.0.0", port=8000)
+
+>>>>>>> cceaa9e (chore: Update roi_scale in inpainting.yaml and add gradio UI for SDXL LORA Inpainting):product_diffusion_api/endpoints.py

api/yolov8s.pt.REMOVED.git-id

@@ -0,0 +1 @@
+5f7efb1ee991ebccb1ee9a360066829e6435a168

configs/inpainting.yaml

@@ -7,10 +7,14 @@ target_height : 1472
 prompt : 'Product on the table 4k ultrarealistic'
 negative_prompt : 'low resolution , bad resolution , Deformation , Weird Artifacts, bad quality,blown up image, high brightness , high saturation '
 <<<<<<< HEAD
+<<<<<<< HEAD
 roi_scale : 0.9
 =======
 roi_scale : 0.5
 >>>>>>> bb1cf42 (chore: Update inpainting pipeline configuration and parameters)
+=======
+roi_scale : 0.9
+>>>>>>> cceaa9e (chore: Update roi_scale in inpainting.yaml and add gradio UI for SDXL LORA Inpainting)
 strength : 0.6
 guidance_scale : 7
 num_inference_steps : 150

gradio-ui/ui.py

@@ -0,0 +1,52 @@
+import gradio as gr
+import requests
+from pydantic import BaseModel
+
+# Define your API endpoint
+SDXL_LORA_API_URL = 'http://127.0.0.1:8000/api/v1/product-diffusion/sdxl_v0_lora_inference'
+
+# Define the InpaintingRequest model
+class InpaintingRequest(BaseModel):
+    prompt: str
+    num_inference_steps: int
+    guidance_scale: float
+    negative_prompt: str
+    num_images: int
+    mode: str
+
+def generate_sdxl_lora_image(prompt, negative_prompt, num_inference_steps, guidance_scale, num_images, mode):
+    # Prepare the payload for SDXL LORA API
+    payload = InpaintingRequest(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        num_images=num_images,
+        mode=mode
+    ).dict()
+    
+    response = requests.post(SDXL_LORA_API_URL, json=payload)
+    if response.status_code == 200:
+        return response.json().get('image')
+    else:
+        return f"Error: {response.json().get('detail', 'Unknown error')}"
+
+with gr.Blocks() as demo:
+    with gr.Tab("SDXL LORA Inpainting"):
+        gr.Markdown("## SDXL LORA Inpainting")
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("### Input Parameters")
+                prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
+                negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt here")
+                num_inference_steps = gr.Slider(minimum=1, maximum=100, step=1, value=20, label="Inference Steps")
+                guidance_scale = gr.Slider(minimum=1.0, maximum=20.0, step=0.1, value=7.5, label="Guidance Scale")
+                num_images = gr.Slider(minimum=1, maximum=10, step=1, value=1, label="Number of Images")
+                mode = gr.Dropdown(choices=["s3_json", "default"], value="s3_json", label="Mode")
+                generate_button = gr.Button(value="Generate Image")
+            with gr.Column():
+                gr.Markdown("### Output")
+                output_image = gr.Image(label="Generated Image")
+        generate_button.click(fn=generate_sdxl_lora_image, inputs=[prompt, negative_prompt, num_inference_steps, guidance_scale, num_images, mode], outputs=output_image)
+
+demo.launch()

scripts/inpainting_pipeline.py

@@ -19,7 +19,7 @@ def load_pipeline(model_name: str, device, enable_compile: bool = True):
     pipeline.to(device)
     return pipeline
 
-@lru_cache(maxsize=1)
+
 class AutoPaintingPipeline:
     def __init__(self, pipeline, image: Image, mask_image: Image, target_width: int, target_height: int):
         self.pipeline = pipeline