app.py

import gradio as gr
from diffusers import AutoPipelineForText2Image
from transformers import AutoTokenizer
from PIL import Image
import asyncio

class SchedulerWrapper:
    def __init__(self, scheduler):
        self.scheduler = scheduler
        
    def __getattr__(self, name):
        return getattr(self.scheduler, name)
        
    @property
    def timesteps(self):
        return self.scheduler.timesteps

    def set_timesteps(self, timesteps):
        self.scheduler.set_timesteps(timesteps)

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/sdxl-turbo")
model = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo")

# Wrap the scheduler
scheduler = model.scheduler
wrapped_scheduler = SchedulerWrapper(scheduler)
model.scheduler = wrapped_scheduler

async def generate_image(prompt):
    try:
        num_inference_steps = 5
        output = await asyncio.to_thread(
            model,
            prompt=prompt,
            num_inference_steps=num_inference_steps,
            guidance_scale=0.0,
            output_type="pil"
        )
        if output.images:
            return output.images[0]
        else:
            raise Exception("No images returned by the model.")
    except Exception as e:
        print(f"Error generating image: {e}")
        return None

async def inference(sentence_mapping, character_dict, selected_style):
    images = []
    prompts = []
    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(prompt)
    tasks = [generate_image(prompt) for prompt in prompts]
    images = await asyncio.gather(*tasks)
    images = [image for image in images if image is not None]
    return images

gradio_interface = gr.Interface(
    fn=inference,
    inputs=[
        gr.JSON(label="Sentence Mapping"),
        gr.JSON(label="Character Dict"),
        gr.Dropdown(["oil painting", "sketch", "watercolor"], label="Selected Style")
    ],
    outputs=gr.Gallery(label="Generated Images")
)

if __name__ == "__main__":
    gradio_interface.launch()