Update app_local.py

app_local.py

@@ -84,6 +84,21 @@ Please provide the rewritten instruction in a clean `json` format as:
 '''
 
 
+def add_noise_to_image(pil_image, noise_level=0.02):
+    """Add slight noise to image to create variation in outputs"""
+    try:
+        img_array = np.array(pil_image).astype(np.float32) / 255.0
+        noise = np.random.normal(0, noise_level, img # Clip values to valid range
+        noisy_array = np.clip(noisy_array, 0, 1)
+        
+        # Convert back to PIL
+        noisy_array = (noisy_array * 255).astype(np.uint8)
+        return Image.fromarray(noisy_array)
+    except Exception as e:
+        print(f"Warning: Could not add noise to image: {e}")
+        return pil_image  # Return original if noise addition fails
+
+
 def extract_json_response(model_output: str) -> str:
     """Extract rewritten instruction from potentially messy JSON output"""
     # Remove code block markers first
@@ -254,10 +269,8 @@ def infer(
     if device == "cuda":
         torch.cuda.empty_cache()
         gc.collect()
-    
     original_prompt = prompt
     prompt_info = ""
-    
     # Handle prompt rewriting
     if rewrite_prompt:
         try:
@@ -295,47 +308,88 @@ def infer(
             f"</div>"
         )
     
-    # Set seed for reproducibility
-    seed_val = seed if not randomize_seed else random.randint(0, MAX_SEED)
-    generator = torch.Generator(device=device).manual_seed(seed_val)
+    # Add noise function
+    def add_noise_to_image(pil_image, noise_level=0.02):
+        """Add slight noise to image to create variation in outputs"""
+        try:
+            img_array = np.array(pil_image).astype(np.float32) / 255.0
+            noise = np.random.normal(0, noise_level, img_array.shape)
+            noisy_array = img_array + noise
+            # Clip values to valid range
+            noisy_array = np.clip(noisy_array, 0, 1)
+            # Convert back to PIL
+            noisy_array = (noisy_array * 255).astype(np.uint8)
+            return Image.fromarray(noisy_array)
+        except Exception as e:
+            print(f"Warning: Could not add noise to image: {e}")
+            return pil_image  # Return original if noise addition fails
+    
+    # Set base seed for reproducibility
+    base_seed = seed if not randomize_seed else random.randint(0, MAX_SEED)
     
     try:
-        # Generate images
-        edited_images = pipe(
-            image=image,
-            prompt=prompt,
-            negative_prompt=" ",
-            num_inference_steps=num_inference_steps,
-            generator=generator,
-            true_cfg_scale=true_guidance_scale,
-            num_images_per_prompt=num_images_per_prompt
-        ).images
+        # Generate images with variation for batch mode
+        if num_images_per_prompt > 1:
+            edited_images = []
+            for i in range(num_images_per_prompt):
+                # Create unique seed for each image
+                generator = torch.Generator(device=device).manual_seed(base_seed + i*1000)
+                
+                # Add slight noise to the image for variation
+                noisy_image = add_noise_to_image(image, noise_level=0.01 + i*0.003)
+                
+                # Slightly vary guidance scale
+                varied_guidance = true_guidance_scale + random.uniform(-0.2, 0.2)
+                varied_guidance = max(1.0, min(10.0, varied_guidance))
+                
+                # Generate single image with variations
+                result = pipe(
+                    image=noisy_image,
+                    prompt=prompt,
+                    negative_prompt=" ",
+                    num_inference_steps=num_inference_steps,
+                    generator=generator,
+                    true_cfg_scale=varied_guidance,
+                    num_images_per_prompt=1
+                ).images
+                edited_images.extend(result)
+        else:
+            # Single image generation (unchanged)
+            generator = torch.Generator(device=device).manual_seed(base_seed)
+            edited_images = pipe(
+                image=image,
+                prompt=prompt,
+                negative_prompt=" ",
+                num_inference_steps=num_inference_steps,
+                generator=generator,
+                true_cfg_scale=true_guidance_scale,
+                num_images_per_prompt=num_images_per_prompt
+            ).images
         
         # Clear cache after generation
         if device == "cuda":
             torch.cuda.empty_cache()
             gc.collect()
-            
-        return edited_images, seed_val, prompt_info
+        return edited_images, base_seed, prompt_info
     except Exception as e:
         # Clear cache on error
         if device == "cuda":
             torch.cuda.empty_cache()
             gc.collect()
         gr.Error(f"Image generation failed: {str(e)}")
-        return [], seed_val, (
+        return [], base_seed, (
             f"<div style='margin:10px; padding:15px; border-radius:8px; border-left:4px solid #dd2c00; background: #fef5f5'>"
             f"<h4 style='margin-top: 0;'>⚠️ Processing Error</h4>"
             f"<p>{str(e)[:200]}</p>"
             f"</div>"
         )
-
-with gr.Blocks(title="Qwen Image Editor Fast") as demo:
+        
+with gr.Blocks(title="Qwen Image Edit - Fast Lightning Mode w/ Batch") as demo:
     gr.Markdown("""
     <div style="text-align: center; background: linear-gradient(to right, #3a7bd5, #00d2ff); color: white; padding: 20px; border-radius: 8px;">
         <h1 style="margin-bottom: 5px;">⚡️ Qwen-Image-Edit Lightning</h1>
-        <p>✨ 8-step inferencing with lightx2v's LoRA.")
-        <p>📝 Local Prompt Enhancement</p>
+        <p>✨ 8-step inferencing with lightx2v's LoRA.</p>
+        <p>📝 Local Prompt Enhancement, Batched Multi-image Generation</p>
     </div>
     """)
     
@@ -399,7 +453,7 @@ with gr.Blocks(title="Qwen Image Editor Fast") as demo:
                     minimum=1, 
                     maximum=4, 
                     step=1, 
-                    value=1
+                    value=2
                 )
             
         # Output Column