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import gradio as gr
import torch
import spaces
from diffusers import FluxPipeline
from torchao.quantization import autoquant
# normal FluxPipeline
pipeline_normal = FluxPipeline.from_pretrained(
"sayakpaul/FLUX.1-merged",
torch_dtype=torch.bfloat16
).to("cuda")
# # optimized FluxPipeline
# pipeline_optimized = DiffusionPipeline.from_pretrained(
# "sayakpaul/FLUX.1-merged",
# torch_dtype=torch.bfloat16
# ).to("cuda")
# pipeline_optimized.transformer.to(memory_format=torch.channels_last)
# pipeline_optimized.transformer = torch.compile(
# pipeline_optimized.transformer,
# mode="max-autotune",
# fullgraph=True
# )
# pipeline_optimized.transformer = autoquant(
# pipeline_optimized.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 = pipeline_optimized(
# prompt=prompt,
# guidance_scale=guidance_scale,
# num_inference_steps=int(num_inference_steps)
# ).images[0]
# return image_normal, image_optimized
return image_normal
# 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="Normal FluxPipeline"),
# 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()
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