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Update app.py
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app.py
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@@ -1,22 +1,20 @@
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import asyncio
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from generate_prompts import generate_prompt
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from diffusers import AutoPipelineForText2Image
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from io import BytesIO
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import gradio as gr
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#
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print("Loading the model...")
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model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
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print("Model loaded successfully.")
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asyncio.run(load_model())
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async def generate_image(prompt, prompt_name):
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try:
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print(f"Generating response for {prompt_name} with prompt: {prompt}")
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output = model(prompt=prompt, num_inference_steps=1, guidance_scale=0.0)
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@@ -53,7 +51,8 @@ async def queue_api_calls(sentence_mapping, character_dict, selected_style):
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print(f"Generated prompt for paragraph {paragraph_number}: {prompt}")
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# Generate images for each prompt in parallel
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print("Tasks created for image generation.")
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responses = await asyncio.gather(*tasks)
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print("Responses received from image generation tasks.")
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@@ -73,6 +72,9 @@ def process_prompt(sentence_mapping, character_dict, selected_style):
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asyncio.set_event_loop(loop)
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print("Event loop created.")
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# 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.
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cmpt_return = loop.run_until_complete(queue_api_calls(sentence_mapping, character_dict, selected_style))
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print(f"process_prompt completed with return value: {cmpt_return}")
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import os
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import asyncio
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from generate_prompts import generate_prompt
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from diffusers import AutoPipelineForText2Image
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from io import BytesIO
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import gradio as gr
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import threading
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# Load the model once outside of the function
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print("Loading the model...")
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model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
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print("Model loaded successfully.")
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# Create a thread-local storage object
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thread_local = threading.local()
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def generate_image(prompt, prompt_name):
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try:
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print(f"Generating response for {prompt_name} with prompt: {prompt}")
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output = model(prompt=prompt, num_inference_steps=1, guidance_scale=0.0)
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print(f"Generated prompt for paragraph {paragraph_number}: {prompt}")
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# Generate images for each prompt in parallel
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loop = asyncio.get_running_loop()
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tasks = [loop.run_in_executor(None, generate_image, prompt, f"Prompt {paragraph_number}") for paragraph_number, prompt in prompts]
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print("Tasks created for image generation.")
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responses = await asyncio.gather(*tasks)
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print("Responses received from image generation tasks.")
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asyncio.set_event_loop(loop)
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print("Event loop created.")
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# Initialize thread-local variables
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thread_local.step = 0
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# 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.
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cmpt_return = loop.run_until_complete(queue_api_calls(sentence_mapping, character_dict, selected_style))
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print(f"process_prompt completed with return value: {cmpt_return}")
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