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import gradio as gr
import torch
import spaces
from diffusers import FluxPipeline
try:
from torchao.quantization import autoquant
except ImportError:
raise ImportError("torchao is not installed. Please install it to use the optimized pipeline.")
# normal FluxPipeline
pipeline_normal = FluxPipeline.from_pretrained(
"sayakpaul/FLUX.1-merged",
torch_dtype=torch.bfloat16
).to("cuda")
# optimized FluxPipeline
pipeline_optimized = FluxPipeline.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
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
# 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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