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import gradio as gr
from diffusers import AutoPipelineForText2Image
from io import BytesIO
from generate_propmts import generate_prompt
import asyncio
# Load the model once outside of the function
model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")
async def generate_image(prompt):
try:
# Truncate prompt if necessary
output = model(prompt=prompt, num_inference_steps=1, guidance_scale=0.0)
print(f"Model output: {output}")
# Check if the model returned images
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:
raise Exception("No images returned by the model.")
except Exception as e:
print(f"Error generating image: {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, sentence_mapping, character_dict, selected_style)
prompts.append((paragraph_number, prompt))
print(f"Generated prompt for paragraph {paragraph_number}: {prompt}")
tasks = [generate_image(prompt) for _, prompt in prompts]
for (paragraph_number, _), image_bytes in zip(prompts, await asyncio.gather(*tasks)):
if image_bytes:
images[paragraph_number] = image_bytes
return images
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=10 # Allow up to 10 concurrent executions
)
if __name__ == "__main__":
gradio_interface.launch()
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