gradio-app.py

import time
import gradio as gr
from PIL import Image
import torch
from diffusers import DiffusionPipeline, AutoencoderTiny
import os, sys

import subprocess


subprocess.check_call([sys.executable, "-m", "pip", "install", 'stable-fast'])

SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None)
TORCH_COMPILE = os.environ.get("TORCH_COMPILE", None)
# I have no idea where the env is config'ed.  Thus:
TORCH_COMPILE = True

if SAFETY_CHECKER:
    pipe = DiffusionPipeline.from_pretrained(
        "SimianLuo/LCM_Dreamshaper_v7",
        custom_pipeline="lcm_txt2img",
        scheduler=None,
    )
else:
    pipe = DiffusionPipeline.from_pretrained(
        "SimianLuo/LCM_Dreamshaper_v7",
        custom_pipeline="lcm_txt2img",
        scheduler=None,
        safety_checker=None,
    )
pipe.to(device="cuda", dtype=torch.float16)
pipe.vae = AutoencoderTiny.from_pretrained(
    "madebyollin/taesd", device="cuda", torch_dtype=torch.float16
)
pipe.vae = pipe.vae.cuda()
pipe.unet.to(memory_format=torch.channels_last)
pipe.set_progress_bar_config(disable=True)


def predict(prompt1, prompt2, merge_ratio, guidance, steps, sharpness, seed=1231231):
    torch.manual_seed(seed)
    tm0 = time.time()
    results = pipe(
        prompt1=prompt1,
        prompt2=prompt2,
        sv=merge_ratio,
        sharpness=sharpness,
        width=640,
        height=640,
        num_inference_steps=steps,
        guidance_scale=guidance,
        lcm_origin_steps=50,
        output_type="pil",
        # return_dict=False,
    )
    torch.cuda.synchronize()
    tmval = f"time = {time.time()-tm0}"
    print(f"time = {time.time()-tm0}")

    nsfw_content_detected = (
        results.nsfw_content_detected[0]
        if "nsfw_content_detected" in results
        else False
    )
    if nsfw_content_detected:
        raise gr.Error("NSFW content detected. Please try another prompt.")
    return results.images[0], tmval


css = """
#container{
    margin: 0 auto;
    max-width: 80rem;
}
#intro{
    max-width: 32rem;
    text-align: center;
    margin: 0 auto;
}
"""
with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="container"):
        gr.Markdown(
            """# SDZoom

    Welcome to sdzoom, a testbed application designed for optimizing and experimenting with various 
    configurations to achieve the fastest Stable Diffusion (SD) pipelines.
    RTSD leverages the expertise provided by Latent Consistency Models (LCM). For more information about LCM,
    visit their website at [Latent Consistency Models](https://latent-consistency-models.github.io/).

    """,
            elem_id="intro",
        )
        with gr.Row():
            with gr.Column():
                image = gr.Image(type="pil")
            with gr.Column():
                merge_ratio = gr.Slider(
                    value=50, minimum=1, maximum=100, step=1, label="Merge Ratio"
                )
                guidance = gr.Slider(
                    label="Guidance", minimum=1, maximum=50, value=10.0, step=0.01
                )
                steps = gr.Slider(label="Steps", value=4, minimum=2, maximum=20, step=1)
                sharpness = gr.Slider(
                    value=1.0, minimum=0, maximum=1, step=0.001, label="Sharpness"
                )
                seed = gr.Slider(
                    randomize=True, minimum=0, maximum=12013012031030, label="Seed"
                )
                prompt1 = gr.Textbox(label="Prompt 1")
                prompt2 = gr.Textbox(label="Prompt 2")
                msg = gr.Textbox(label="Message", interactive=False)
                generate_bt = gr.Button("Generate")

        inputs = [prompt1, prompt2, merge_ratio, guidance, steps, sharpness, seed]
        gr.Examples(
            examples=[
                ["Elon Musk", "Mark Zuckerberg", 50, 10.0, 4, 1.0, 1231231],
                ["Elon Musk", "Bill Gates", 50, 10.0, 4, 1.0, 53453],
                [
                    "Asian women, intricate jewlery in her hair, 8k",
                    "Tom Cruise, intricate jewlery in her hair, 8k",
                    50,
                    10.0,
                    4,
                    1.0,
                    542343,
                ],
            ],
            fn=predict,
            inputs=inputs,
            outputs=[image, msg],
        )
        generate_bt.click(fn=predict, inputs=inputs, outputs=[image, msg], show_progress=False)
        seed.change(fn=predict, inputs=inputs, outputs=[image, msg], show_progress=False)
        merge_ratio.change(
            fn=predict, inputs=inputs, outputs=[image, msg], show_progress=False
        )
        guidance.change(fn=predict, inputs=inputs, outputs=[image, msg], show_progress=False)
        steps.change(fn=predict, inputs=inputs, outputs=[image, msg], show_progress=False)
        sharpness.change(fn=predict, inputs=inputs, outputs=[image, msg], show_progress=False)
        prompt1.change(fn=predict, inputs=inputs, outputs=[image, msg], show_progress=False)
        prompt2.change(fn=predict, inputs=inputs, outputs=[image, msg], show_progress=False)

demo.queue()
if __name__ == "__main__":
    demo.launch()