app.py

from datetime import datetime

import gradio as gr
import spaces
import torch
from diffusers import FluxPipeline

from optimization import optimize_pipeline_


pipeline = FluxPipeline.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
optimize_pipeline_(pipeline, "prompt")


@spaces.GPU
def generate_image(prompt: str, progress=gr.Progress(track_tqdm=True)):
    generator = torch.Generator(device='cuda').manual_seed(42)
    t0 = datetime.now()
    output = pipeline(
        prompt=prompt,
        num_inference_steps=28,
        generator=generator,
    )
    return (output.images[0], f'{(datetime.now() - t0).total_seconds():.2f}s')


gr.Interface(
    fn=generate_image,
    inputs=gr.Text(label="Prompt"),
    outputs=gr.Image(),
    examples=["A cat playing with a ball of yarn"],
    cache_examples=False,
).launch()