app.py

#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Power by Zongsheng Yue 2024-12-11 17:17:41

import spaces
import warnings
warnings.filterwarnings("ignore")

import argparse
import numpy as np
import gradio as gr
from pathlib import Path
from omegaconf import OmegaConf
from sampler_invsr import InvSamplerSR

from utils import util_common
from utils import util_image
from basicsr.utils.download_util import load_file_from_url

def get_configs(num_steps=1, chopping_size=128, seed=12345):
    configs = OmegaConf.load("./configs/sample-sd-turbo.yaml")

    if num_steps == 1:
        configs.timesteps = [200,]
    elif num_steps == 2:
        configs.timesteps = [200, 100]
    elif num_steps == 3:
        configs.timesteps = [200, 100, 50]
    elif num_steps == 4:
        configs.timesteps = [200, 150, 100, 50]
    elif num_steps == 5:
        configs.timesteps = [250, 200, 150, 100, 50]
    else:
        assert num_steps <= 250
        configs.timesteps = np.linspace(
            start=250, stop=0, num=num_steps, endpoint=False, dtype=np.int64()
        ).tolist()
    print(f'Setting timesteps for inference: {configs.timesteps}')

    # path to save noise predictor
    started_ckpt_name = "noise_predictor_sd_turbo_v5.pth"
    started_ckpt_dir = "./weights"
    util_common.mkdir(started_ckpt_dir, delete=False, parents=True)
    started_ckpt_path = Path(started_ckpt_dir) / started_ckpt_name
    if not started_ckpt_path.exists():
        load_file_from_url(
            url="https://huggingface.co./OAOA/InvSR/resolve/main/noise_predictor_sd_turbo_v5.pth",
            model_dir=started_ckpt_dir,
            progress=True,
            file_name=started_ckpt_name,
        )
    configs.model_start.ckpt_path = str(started_ckpt_path)

    configs.bs = 1
    configs.seed = seed 
    configs.basesr.chopping.pch_size = chopping_size
    if chopping_size == 128:
        configs.basesr.chopping.extra_bs = 8
    elif chopping_size == 256:
        configs.basesr.chopping.extra_bs = 4
    else:
        configs.basesr.chopping.extra_bs = 1

    return configs

@spaces.GPU
def predict(in_path, num_steps=1, chopping_size=128, seed=12345):
    configs = get_configs(num_steps=num_steps, chopping_size=chopping_size, seed=seed)

    sampler = InvSamplerSR(configs)

    out_dir = Path('invsr_output')
    if not out_dir.exists():
        out_dir.mkdir()
    sampler.inference(in_path, out_path=out_dir, bs=1)

    out_path = out_dir / f"{Path(in_path).stem}.png"
    assert out_path.exists(), 'Super-resolution failed!'
    im_sr = util_image.imread(out_path, chn="rgb", dtype="uint8")

    return im_sr, str(out_path)

title = "Arbitrary-steps Image Super-resolution via Diffusion Inversion"
description = r"""
<b>Official Gradio demo</b> for <a href='https://github.com/zsyOAOA/InvSR' target='_blank'><b>Arbitrary-steps Image Super-resolution via Diffuion Inversion</b></a>.<br>
🔥 InvSR is an image super-resolution method via Diffusion Inversion, supporting arbitrary sampling steps.<br>
"""
article = r"""
If you've found InvSR useful for your research or projects, please show your support by ⭐ the <a href='https://github.com/zsyOAOA/InvSR' target='_blank'>Github Repo</a>. Thanks!
[![GitHub Stars](https://img.shields.io/github/stars/zsyOAOA/InvSR?affiliations=OWNER&color=green&style=social)](https://github.com/zsyOAOA/InvSR)

---
If our work is useful for your research, please consider citing:
```bibtex
@article{yue2024InvSR,
  title={Arbitrary-steps Image Super-resolution via Diffusion Inversion},
  author={Yue, Zongsheng and Kang, Liao and Loy, Chen Change},
  journal = {arXiv preprint arXiv:2412.09013},
  year={2024},
}
```

📋 **License**

This project is licensed under <a rel="license" href="https://github.com/zsyOAOA/InvSR/blob/master/LICENSE">S-Lab License 1.0</a>.
Redistribution and use for non-commercial purposes should follow this license.

📧 **Contact**

If you have any questions, please feel free to contact me via <b>[email protected]</b>.
![visitors](https://visitor-badge.laobi.icu/badge?page_id=zsyOAOA/InvSR)
"""
demo = gr.Interface(
    fn=predict,
    inputs=[
        gr.Image(type="filepath", label="Input: Low Quality Image"),
        gr.Dropdown(
            choices=[1,2,3,4,5],
            value=1,
            label="Number of steps",
            ),
        gr.Dropdown(
            choices=[128, 256, 512],
            value=128,
            label="Chopping size",
            ),
        gr.Number(value=12345, precision=0, label="Ranom seed")
    ],
    outputs=[
        gr.Image(type="numpy", label="Output: High Quality Image"),
        gr.File(label="Download the output")
    ],
    title=title,
    description=description,
    article=article,
    examples=[
        ['./testdata/RealSet80/29.jpg',  3, 128, 12345],
        ['./testdata/RealSet80/32.jpg',  1, 128, 12345],
        ['./testdata/RealSet80/0030.jpg',  1, 128, 12345],
        ['./testdata/RealSet80/2684538-PH.jpg', 1, 128, 12345],
        ['./testdata/RealSet80/oldphoto6.png', 1, 128, 12345],
      ]
    )

demo.queue(max_size=5)
demo.launch(share=False)