Update app.py

app.py

@@ -11,32 +11,33 @@ print("Loading the model...")
 model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
 print("Model loaded successfully.")
 
-# Create a thread-local storage object
-thread_local = threading.local()
+# Create an asyncio lock
+lock = asyncio.Lock()
 
-def generate_image(prompt, prompt_name):
-    try:
-        print(f"Generating response for {prompt_name} with prompt: {prompt}")
-        output = model(prompt=prompt, num_inference_steps=1, guidance_scale=0.0)
-        print(f"Output for {prompt_name}: {output}")
+async def generate_image(prompt, prompt_name):
+    async with lock:
+        try:
+            print(f"Generating response for {prompt_name} with prompt: {prompt}")
+            output = model(prompt=prompt, num_inference_steps=1, guidance_scale=0.0)
+            print(f"Output for {prompt_name}: {output}")
 
-        # Check if the model returned images
-        if isinstance(output.images, list) and len(output.images) > 0:
-            image = output.images[0]
-            buffered = BytesIO()
-            try:
-                image.save(buffered, format="JPEG")
-                image_bytes = buffered.getvalue()
-                print(f"Image bytes length for {prompt_name}: {len(image_bytes)}")
-                return image_bytes
-            except Exception as e:
-                print(f"Error saving image for {prompt_name}: {e}")
-                return None
-        else:
-            raise Exception(f"No images returned by the model for {prompt_name}.")
-    except Exception as e:
-        print(f"Error generating image for {prompt_name}: {e}")
-        return None
+            # Check if the model returned images
+            if isinstance(output.images, list) and len(output.images) > 0:
+                image = output.images[0]
+                buffered = BytesIO()
+                try:
+                    image.save(buffered, format="JPEG")
+                    image_bytes = buffered.getvalue()
+                    print(f"Image bytes length for {prompt_name}: {len(image_bytes)}")
+                    return image_bytes
+                except Exception as e:
+                    print(f"Error saving image for {prompt_name}: {e}")
+                    return None
+            else:
+                raise Exception(f"No images returned by the model for {prompt_name}.")
+        except Exception as e:
+            print(f"Error generating image for {prompt_name}: {e}")
+            return None
 
 async def queue_api_calls(sentence_mapping, character_dict, selected_style):
     print(f"queue_api_calls invoked with sentence_mapping: {sentence_mapping}, character_dict: {character_dict}, selected_style: {selected_style}")
@@ -46,13 +47,12 @@ async def queue_api_calls(sentence_mapping, character_dict, selected_style):
     for paragraph_number, sentences in sentence_mapping.items():
         combined_sentence = " ".join(sentences)
         print(f"combined_sentence for paragraph {paragraph_number}: {combined_sentence}")
-        prompt = generate_prompt(combined_sentence, sentence_mapping, character_dict, selected_style)
+        prompt = generate_prompt(combined_sentence, character_dict, selected_style)
         prompts.append((paragraph_number, prompt))
         print(f"Generated prompt for paragraph {paragraph_number}: {prompt}")
 
     # Generate images for each prompt in parallel
-    loop = asyncio.get_running_loop()
-    tasks = [loop.run_in_executor(None, generate_image, prompt, f"Prompt {paragraph_number}") for paragraph_number, prompt in prompts]
+    tasks = [generate_image(prompt, f"Prompt {paragraph_number}") for paragraph_number, prompt in prompts]
     print("Tasks created for image generation.")
     responses = await asyncio.gather(*tasks)
     print("Responses received from image generation tasks.")
@@ -72,9 +72,6 @@ def process_prompt(sentence_mapping, character_dict, selected_style):
         asyncio.set_event_loop(loop)
     print("Event loop created.")
 
-    # Initialize thread-local variables
-    thread_local.step = 0
-
     # This sends the prompts to function that sets up the async calls. Once all the calls to the API complete, it returns a list of the gr.Textbox with value= set.
     cmpt_return = loop.run_until_complete(queue_api_calls(sentence_mapping, character_dict, selected_style))
     print(f"process_prompt completed with return value: {cmpt_return}")