Update app.py

app.py

@@ -1,50 +1,40 @@
 import os
 import asyncio
-import time
-from generate_prompts import generate_prompt
-from diffusers import AutoPipelineForText2Image
 from io import BytesIO
+from diffusers import AutoPipelineForText2Image
 import gradio as gr
-import ray
-
-ray.init()
+from generate_prompts import generate_prompt
 
-@ray.remote
-class ModelActor:
-    def __init__(self):
-        """
-        Initializes the ModelActor class and loads the text-to-image model.
-        """
-        self.model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
+# Initialize model
+model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
 
-    async def generate_image(self, prompt, prompt_name):
-        """
-        Generates an image based on the provided prompt.
-        Parameters:
-            - prompt (str): The input text for image generation.
-            - prompt_name (str): A name for the prompt, used for logging.
-        Returns:
-            bytes: The generated image data in bytes format, or None if generation fails.
-        """
-        start_time = time.time()
-        process_id = os.getpid()
-        try:
-            output = await self.model(prompt=prompt, num_inference_steps=1, guidance_scale=0.0)
-            if isinstance(output.images, list) and len(output.images) > 0:
-                image = output.images[0]
-                buffered = BytesIO()
-                image.save(buffered, format="JPEG")
-                image_bytes = buffered.getvalue()
-                end_time = time.time()
-                return image_bytes
-            else:
-                return None
-        except Exception as e:
+async def generate_image(prompt, prompt_name):
+    """
+    Generates an image based on the provided prompt.
+    Parameters:
+        - prompt (str): The input text for image generation.
+        - prompt_name (str): A name for the prompt, used for logging.
+    Returns:
+        bytes: The generated image data in bytes format, or None if generation fails.
+    """
+    try:
+        print(f"Generating image for {prompt_name}")
+        output = await model(prompt=prompt, num_inference_steps=50, guidance_scale=7.5)
+        if isinstance(output.images, list) and len(output.images) > 0:
+            image = output.images[0]
+            buffered = BytesIO()
+            image.save(buffered, format="JPEG")
+            image_bytes = buffered.getvalue()
+            return image_bytes
+        else:
             return None
+    except Exception as e:
+        print(f"An error occurred while generating image for {prompt_name}: {e}")
+        return None
 
 async def queue_api_calls(sentence_mapping, character_dict, selected_style):
     """
-    Generates images for all provided prompts in parallel using Ray actors.
+    Generates images for all provided prompts in parallel using asyncio.
     Parameters:
         - sentence_mapping (dict): Mapping between paragraph numbers and sentences.
         - character_dict (dict): Dictionary mapping characters to their descriptions.
@@ -58,14 +48,8 @@ async def queue_api_calls(sentence_mapping, character_dict, selected_style):
         prompt = generate_prompt(combined_sentence, sentence_mapping, character_dict, selected_style)
         prompts.append((paragraph_number, prompt))
 
-    num_prompts = len(prompts)
-    num_actors = min(num_prompts, 20)
-    model_actors = [ModelActor.remote() for _ in range(num_actors)]
-    tasks = [model_actors[i % num_actors].generate_image.remote(prompt, f"Prompt {paragraph_number}") for i, (paragraph_number, prompt) in enumerate(prompts)]
-
-    # Convert ray.get(task) to awaitable coroutines
-    async_tasks = [asyncio.wrap_future(ray.get(task)) for task in tasks]
-    responses = await asyncio.gather(*async_tasks)
+    tasks = [generate_image(prompt, f"Prompt {paragraph_number}") for paragraph_number, prompt in prompts]
+    responses = await asyncio.gather(*tasks)
     
     images = {paragraph_number: response for (paragraph_number, _), response in zip(prompts, responses)}
     return images
@@ -93,7 +77,7 @@ gradio_interface = gr.Interface(
     fn=process_prompt,
     inputs=[gr.JSON(label="Sentence Mapping"), gr.JSON(label="Character Dict"), gr.Dropdown(["oil painting", "sketch", "watercolor"], label="Selected Style")],
     outputs="json"
-).queue(default_concurrency_limit=20)  # Set concurrency limit to match the number of model actors
+).queue(default_concurrency_limit=20)  # Set concurrency limit if needed
 
 if __name__ == "__main__":
     gradio_interface.launch()