app.py

import gradio as gr
import requests
import shutil
import spaces
import torch
from diffusers import AutoencoderKL, StableDiffusionXLImg2ImgPipeline
from loguru import logger
from pathlib import Path
from PIL import Image
from tqdm import tqdm


def download(file: str, url: str):
    file_path = Path(file)
    if file_path.exists():
        return

    r = requests.get(url, stream=True)
    r.raise_for_status()

    temp_path = f"/tmp/{file_path.name}"
    with tqdm(
        desc=file, total=int(r.headers["content-length"]), unit="B", unit_scale=True
    ) as pbar, open(temp_path, "wb") as f:
        for chunk in r.iter_content(chunk_size=1024 * 1024):
            f.write(chunk)
            pbar.update(len(chunk))

    shutil.move(temp_path, file_path)


model_path = "pony-diffusion-v6-xl.safetensors"
download(
    model_path,
    "https://civitai.com/api/download/models/290640?type=Model&format=SafeTensor&size=pruned&fp=fp16",
)

vae_path = "pony-diffusion-v6-xl.vae.safetensors"
download(
    vae_path,
    "https://civitai.com/api/download/models/290640?type=VAE&format=SafeTensor",
)

vae = AutoencoderKL.from_single_file(vae_path)

pipe = StableDiffusionXLImg2ImgPipeline.from_single_file(
    model_path, torch_dtype=torch.float16, use_safetensors=True, variant="fp16", vae=vae
)
pipe = pipe.to("cuda")


@logger.catch(reraise=True)
@spaces.GPU
def generate(
    prompt: str,
    init_image: Image.Image,
    strength: float,
    progress=gr.Progress(),
):
    logger.info(
        f"Starting image generation: {dict(prompt=prompt, image=init_image, strength=strength)}"
    )

    # Downscale the image
    init_image.thumbnail((1024, 1024))

    def progress_callback(pipe, step_index, timestep, callback_kwargs):
        logger.trace(
            f"Callback: {dict(num_timesteps=pipe.num_timesteps, step_index=step_index, timestep=timestep)}"
        )
        progress((step_index + 1, pipe.num_timesteps))
        return callback_kwargs

    images = pipe(
        prompt=prompt,
        image=init_image,
        callback_on_step_end=progress_callback,
        strength=strength,
    ).images

    return images[0]


demo = gr.Interface(
    fn=generate,
    inputs=[
        gr.Text(label="Prompt"),
        gr.Image(label="Init image", type="pil"),
        gr.Slider(label="Strength", minimum=0, maximum=1, value=0.3),
    ],
    outputs=[gr.Image(label="Output")],
)

demo.launch()