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

Former-commit-id: b7b92f4bad53cf4a209420d4c71d1c87cdf69869 [formerly fbf9c06519715f33d9c88503d3d72e426703db29]
Former-commit-id: d9b48f2c7818aa2732a3388320ed23afd13b49cd

configs/inpainting.yaml

@@ -5,7 +5,7 @@ target_width : 2560
 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 '
-roi_scale : 0.5
+roi_scale : 0.9
 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()

product_diffusion_api/endpoints.py

@@ -3,7 +3,7 @@ from fastapi.middleware.cors import CORSMiddleware
 from routers import sdxl_text_to_image
 from routers import painting
 import logfire
-
+import uvicorn 
 
 
 
@@ -51,3 +51,5 @@ async def root():
 def check_health():
     return {"status": "ok"}
 
+uvicorn.run(app, host="0.0.0.0", port=8000)
+

product_diffusion_api/routers/painting.py

@@ -127,7 +127,7 @@ def run_inference(cfg: dict, image_path: str, prompt: str, negative_prompt: str,
                                     guidance_scale=guidance_scale)
     return pil_to_s3_json(output, file_name="output.png")
 
-@router.post("kandinskyv2.2_inpainting")
+@router.post("/kandinskyv2.2_inpainting")
 async def inpainting_inference(image: UploadFile = File(...), 
                                prompt: str = "", 
                                negative_prompt: str = "", 

product_diffusion_api/yolov8s.pt.REMOVED.git-id

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

scripts/inpainting_pipeline.py

@@ -8,7 +8,7 @@ from PIL import Image
 from functools import lru_cache
 
 
-@lru_cache(maxsize=1)
+
 class AutoPaintingPipeline:
     """
     AutoPaintingPipeline class represents a pipeline for auto painting using an inpainting model from diffusers.