Update app.py

app.py

@@ -483,10 +483,19 @@ def run_lora(prompt, cfg_scale, steps, selected_info_1, selected_info_2, selecte
     prompt_mash = " ".join(prepends + [prompt] + appends)
     print("Prompt Mash: ", prompt_mash)  # Debugging statement
 
+    # Ensure valid width and height values
+    if width is None:
+        width = 1024  # Default value
+    if height is None:
+        height = 1024  # Default value
+
+    # Set seed value
+    if seed is None or randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
     # Unload previous LoRA weights
     with calculateDuration("Unloading LoRA"):
         pipe.unload_lora_weights()
-##        pipe_i2i.unload_lora_weights()        
     print("Active adapters before loading new LoRAs:", pipe.get_active_adapters())
     
     # Load LoRA weights with respective scales
@@ -503,62 +512,7 @@ def run_lora(prompt, cfg_scale, steps, selected_info_1, selected_info_2, selecte
             lora_path = lora['repo']
             weight_name = lora.get("weights")
             print(f"Lora Path: {lora_path}")
-            pipe_to_use = pipe_i2i if image_input is not None else pipe
-            pipe_to_use.load_lora_weights(
-                lora_path, 
-                weight_name=weight_name if weight_name else None,
-                low_cpu_mem_usage=True,
-                adapter_name=lora_name
-            )
-    print("Loaded LoRAs:", lora_names)
-    print("Adapter weights:", lora_weights)
-##        if image_input is not None:
-##            pipe_i2i.set_adapters(lora_names, adapter_weights=lora_weights)
-##        else:
-##        pipe.set_adapters(lora_names, adapter_weights=lora_weights)
-    
-##    print("Active adapters after loading new LoRAs:", pipe.get_active_adapters())
-    
-    # Set random seed for reproducibility
-    with calculateDuration("Randomizing seed"):
-        if randomize_seed:
-            seed = random.randint(0, MAX_SEED)
-        print("Image Seed:", seed)
-
-    # Generate image
-    try:
-        if image_input is not None:
-            final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, seed)
-            print("Debug: generate_image_to_image output type:", type(final_image))  # Debugging
-            print("Debug: generate_image_to_image output value:", final_image)  # Debugging
-            yield final_image, seed, gr.update(visible=False)
-        else:
-            image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
-            # Consume the generator to get the final image
-            final_image = None
-            step_counter = 0
-            for image in image_generator:
-                print("Debug: generate_image yielded type:", type(image))  # Debugging
-                print("Debug: generate_image yielded value:", image)  # Debugging
-                step_counter += 1
-                final_image = image
-                print(f"Yielding image {step_counter}/{steps}")  # Debugging statement
-                progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
-                yield image, seed, gr.update(value=progress_bar, visible=True)
-            if final_image is None:
-                print("No final image generated.")  # Debugging statement
-            else:
-                print("Debug: final_image type:", type(final_image))  # Debugging
-                print("Debug: final_image value:", final_image)  # Debugging
-                
-            yield final_image, seed, gr.update(value=progress_bar, visible=False)
-##            
-        yield None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None  # Placeholder for missing outputs
-    except Exception as e:
-        print(f"Error during image generation: {e}")  # Error handling
-        raise gr.Error("An error occurred during image generation.")
-
-run_lora.zerogpu = True
+            pipe_to_use = pipe_i2i if 
 
 def get_huggingface_safetensors(link):
     split_link = link.split("/")