app.py

import gradio as gr
import torch
import spaces
from torchao.quantization import autoquant
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained(
    "sayakpaul/FLUX.1-merged",
    torch_dtype=torch.bfloat16
).to("cuda")
pipe.transformer.to(memory_format=torch.channels_last)
pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True)
pipe.transformer = autoquant(
    pipe.transformer,
    error_on_unseen=False
)

@spaces.GPU(duration=120)
def generate_images(prompt, guidance_scale, num_inference_steps):
    # # generate image with normal pipeline
    # image_normal = pipeline_normal(
    #     prompt=prompt,
    #     guidance_scale=guidance_scale,
    #     num_inference_steps=int(num_inference_steps)
    # ).images[0]
    
    # generate image with optimized pipeline
    image_optimized = pipe(
        prompt=prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=int(num_inference_steps)
    ).images[0]
    
    return image_optimized

# set up Gradio interface
demo = gr.Interface(
    fn=generate_images,
    inputs=[
        gr.Textbox(lines=2, placeholder="Enter your prompt here...", label="Prompt"),
        gr.Slider(1.0, 10.0, step=0.5, value=3.5, label="Guidance Scale"),
        gr.Slider(10, 100, step=1, value=50, label="Number of Inference Steps")
    ],
    outputs=[
        gr.Image(type="pil", label="Optimized FluxPipeline")
    ],
    title="FluxPipeline Comparison",
    description="Compare images generated by the normal FluxPipeline and the optimized one using torchao and torch.compile()."
)

demo.launch()