chore: Update SDXL LORA TEXT-TO-IMAGE gradio UI theme to 'gradio/soft'

Former-commit-id: 731cce08b3feac70284eaa11f48f8de3855baaec [formerly fb82dce3a4067c5c2b3b03e7dfab059ee9372a80]
Former-commit-id: e9d63ad1c7d652c7928f28c89c2513c4f8b4d289

api/routers/sdxl_text_to_image.py

@@ -60,7 +60,7 @@ loaded_pipeline = load_pipeline(config.MODEL_NAME, config.ADAPTER_NAME, config.E
 
 
 # SDXLLoraInference class for running inference
-class SDXLLoraInference(AsyncBatcher):
+class SDXLLoraInference:
     """
     Class for performing SDXL Lora inference.
 
@@ -182,9 +182,9 @@ async def sdxl_v0_lora_inference(data: InputFormat):
 
 @router.post("/sdxl_v0_lora_inference/batch")
 async def sdxl_v0_lora_inference_batch(data: List[InputFormat]):
-    batcher = SDXLLoraBatcher(max_batch_size=64, max_queue_time=0.001)
+    batcher = SDXLLoraBatcher(max_batch_size=64)
     try:
-        predictions = await batcher.process(batch=data)
+        predictions = batcher.process_batch(data)
         return predictions
     except Exception as e:
         print(f"Error in /sdxl_v0_lora_inference/batch: {e}")

gradio-ui/ui.py

@@ -0,0 +1,59 @@
+import gradio as gr
+import requests
+from pydantic import BaseModel
+from diffusers.utils import load_image
+
+
+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
+    ).model_dump()
+    
+    response = requests.post(SDXL_LORA_API_URL, json=payload)
+    response_json = response.json()
+    url = response_json['url']
+    
+    image = load_image(url)
+    return image
+
+with gr.Blocks(theme='gradio/soft') as demo:
+    with gr.Tab("SDXL LORA TEXT-TO-IMAGE"):
+        with gr.Row():
+            with gr.Column(scale=1):
+              
+                    prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
+                    negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt here")
+                   
+
+               
+                    with gr.Column(scale=1):
+                        num_inference_steps = gr.Slider(minimum=1, maximum=1000, step=1, value=20, label="Inference Steps")
+                        guidance_scale = gr.Slider(minimum=1.0, maximum=10.0, 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", "b64_json"], value="s3_json", label="Mode")
+                        generate_button = gr.Button("Generate Image")
+            
+            
+            image_preview = gr.Image(label="Generated Image", height=512, width=512,scale=1,show_download_button=True,show_share_button=True,container=True)
+        
+        generate_button.click(generate_sdxl_lora_image, inputs=[prompt, negative_prompt, num_inference_steps, guidance_scale, num_images, mode], outputs=[image_preview])
+    
+
+demo.launch()