Update app.py

app.py

@@ -1,76 +1,55 @@
-import gradio as gr
-from diffusers import AutoPipelineForText2Image
-from io import BytesIO
 import asyncio
-from generate_propmts import generate_prompt
+import json
+import gradio as gr
+from diffusers import StableDiffusionPipeline
+from generate_prompts import generate_prompt
 
-# Load the model once outside of the function
-model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
+# Load the model pipeline
+pipeline = StableDiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo").to("cuda")
 
 async def generate_image(prompt, prompt_name):
     try:
         print(f"Generating image for {prompt_name}")
-        output = await asyncio.to_thread(model, prompt=prompt, num_inference_steps=1, guidance_scale=0.0)
-
-        # 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}.")
+        image = await pipeline(prompt).images[0]
+        img_bytes = image.tobytes()
+        print(f"Image bytes length for {prompt_name}: {len(img_bytes)}")
+        return img_bytes
     except Exception as e:
         print(f"Error generating image for {prompt_name}: {e}")
         return None
 
-async def process_prompt(sentence_mapping, character_dict, selected_style):
-    images = {}
-    print(f'sentence_mapping: {sentence_mapping}, character_dict: {character_dict}, selected_style: {selected_style}')
-    prompts = []
-
-    # Generate prompts for each paragraph
-    for paragraph_number, sentences in sentence_mapping.items():
-        combined_sentence = " ".join(sentences)
-        prompt = generate_prompt(combined_sentence, character_dict, selected_style)
-        prompts.append((paragraph_number, prompt))
-        print(f"Generated prompt for paragraph {paragraph_number}: {prompt}")
-
-    # Create tasks for all prompts and run them concurrently
-    tasks = [generate_image(prompt, f"Prompt {paragraph_number}") for paragraph_number, prompt in prompts]
-    results = await asyncio.gather(*tasks)
+async def queue_image_calls(prompts):
+    tasks = [generate_image(prompts[i], f"Prompt {i}") for i in range(len(prompts))]
+    responses = await asyncio.gather(*tasks)
+    return responses
 
-    # Map results back to paragraphs
-    for i, (paragraph_number, _) in enumerate(prompts):
-        if i < len(results):
-            images[paragraph_number] = results[i]
-        else:
-            print(f"Error: No result for paragraph {paragraph_number}")
-
-    return images
-
-# Helper function to generate a prompt based on the input
-def generate_prompt(combined_sentence, character_dict, selected_style):
-    characters = " ".join([" ".join(character) if isinstance(character, list) else character for character in character_dict.values()])
-    return f"Make an illustration in {selected_style} style from: {characters}. {combined_sentence}"
-
-# Gradio interface with high concurrency limit
-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",
-    concurrency_limit=20  # Set a high concurrency limit
-).queue(default_concurrency_limit=20)
+def async_image_generation(prompts):
+    try:
+        loop = asyncio.get_running_loop()
+    except RuntimeError:
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+    results = loop.run_until_complete(queue_image_calls(prompts))
+    return results
+
+def gradio_interface(sentence_mapping, character_dict, selected_style):
+    prompts = generate_prompt(sentence_mapping, character_dict, selected_style)
+    image_bytes_list = async_image_generation(prompts)
+    outputs = [gr.Image.update(value=img_bytes) if img_bytes else gr.Image.update(value=None) for img_bytes in image_bytes_list]
+    return outputs
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            sentence_mapping_input = gr.Textbox(label="Sentence Mapping")
+            character_dict_input = gr.Textbox(label="Character Dictionary")
+            selected_style_input = gr.Textbox(label="Selected Style")
+            submit_btn = gr.Button(value='Submit')
+            prompt_responses = [gr.Image(label=f"Prompt {i} Response") for i in range(4)]
+            submit_btn.click(fn=gradio_interface,
+                             inputs=[sentence_mapping_input, character_dict_input, selected_style_input],
+                             outputs=prompt_responses)
 
 if __name__ == "__main__":
-    gradio_interface.launch()
+    demo.launch()