# HYPIR **Repository Path**: huangyaoyu/HYPIR ## Basic Information - **Project Name**: HYPIR - **Description**: 图像修复，超分辨率 - **Primary Language**: Python - **License**: Not specified - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 2 - **Created**: 2025-07-31 - **Last Updated**: 2025-07-31 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

## HYPIR: Harnessing Diffusion-Yielded Score Priors for Image Restoration [Paper](https://arxiv.org/abs/2507.20590) | [Project Page](https://hypir.xpixel.group/) | [Project Page (CN)](https://hypirzh.xpixel.group/) ![visitors](https://visitor-badge.laobi.icu/badge?page_id=XPixelGroup/HYPIR) [![Try a demo on Replicate](https://replicate.com/0x3f3f3f3fun/hypir-sd2/badge)](https://replicate.com/0x3f3f3f3fun/hypir-sd2) [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](https://openxlab.org.cn/apps/detail/linxinqi/HYPIR-SD2) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1MbpICKc22S6ysD32uj3ORkqg6AgZrNpx?usp=sharing) Xinqi Lin1,2, [Fanghua Yu](https://github.com/Fanghua-Yu)1, Jinfan Hu1,2, [Zhiyuan You](https://zhiyuanyou.github.io/)1,3, Wu Shi1, [Jimmy S. Ren](https://www.jimmyren.com/)4,5, [Jinjin Gu](https://www.jasongt.com/)6,\*, [Chao Dong](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ)1,7,\* \*: Corresponding author 1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
2University of Chinese Academy of Sciences
3The Chinese University of Hong Kong
4SenseTime Research
5Hong Kong Metropolitan University
6INSAIT, Sofia University
7Shenzhen University of Advanced Technology

:star:If HYPIR is helpful for you, please help star this repo. Thanks!:hugs: ## :book:Table Of Contents - [What's Next](#next) - [Update](#update) - [Installation](#installation) - [Pretrained Models](#pretrained_models) - [Quick Start](#quick_start) - [Train](#train) ## :soon:What's Next **Our current open-source version** is based on the Stable Diffusion 2.1. While the number of parameters is small, this model was trained on our best-quality data and with significant computational resources (batch size 1024). Therefore, its performance is also quite good. ~~We'll provide access to more advanced models based on **FLUX** and **Stable Diffusion 3.5** through web interfaces and APIs in the future. Stay tuned!✨~~ **Our most advanced model** has been launched on [suppixel.ai](https://supir.suppixel.ai/home) and [suppixel.cn](https://www.suppixel.cn/home)! We welcome you to experience it.🔥🔥🔥 This state-of-the-art model offers **more stable results**, **more flexible capabilities**, while still maintaining **incredibly fast speeds**🔥🔥🔥. Also, be aware that **pirated HYPIR websites**⚠️⚠️⚠️ are now active. Please verify the legitimacy of any site to prevent financial loss. The following website is illegally using our project: - https://www.hypir.org/ ## :new:Update - **2025.07.28**: :white_check_mark: Provide [colab](https://colab.research.google.com/drive/1MbpICKc22S6ysD32uj3ORkqg6AgZrNpx?usp=sharing) example. Free T4 GPU is good enough for running this model! - **2025.07.28**: :white_check_mark: Integrated to [openxlab](https://openxlab.org.cn/apps/detail/linxinqi/HYPIR-SD2). - **2025.07.19**: :white_check_mark: Integrated to [replicate](https://replicate.com/0x3f3f3f3fun/hypir-sd2). - **2025.07.19**: This repo is created. ## :gear:Installation ```shell git clone https://github.com/XPixelGroup/HYPIR.git cd HYPIR conda create -n hypir python=3.10 conda activate hypir pip install -r requirements.txt ``` ## :dna:Pretrained Models | Model Name | Description | HuggingFace | OpenXLab | | :---------: | :----------: | :----------: | :----------: | | HYPIR_sd2.pth | Lora weights of HYPIR-SD2 | [download](https://huggingface.co/lxq007/HYPIR/tree/main) | [download](https://openxlab.org.cn/models/detail/linxinqi/HYPIR/tree/main) | ## :flight_departure:Quick Start 1. Download model weight `HYPIR_sd2.pth`. 2. Fill `weight_path` in [configs/sd2_gradio.yaml](configs/sd2_gradio.yaml). 3. Run the following command to launch gradio. ```shell python app.py --config configs/sd2_gradio.yaml --local --device cuda ``` 4. (Optional) Tired of manually typing out prompts for your images? Let GPT do the work for you! First, create a file named `.env` in the project directory. ```conf GPT_API_KEY=your-awesome-api-key GPT_BASE_URL=openai-gpt-base-url GPT_MODEL=gpt-4o-mini ``` Second, add your API base URL and API key in the `.env` file. For the model, 4o-mini is usually sufficient. Finally, pass `--gpt_caption` argument to the program, and type "auto" in the prompt box to use GPT-generated prompt.
## :stars:Train 1. Generate a parquet file to save both image paths and prompts. For example: ```python import os import polars as pl # Recursively collect image files. For example, you can crop # the LSDIR dataset into 512x512 patches and place all patches # in one folder. image_dir = "/opt/data/common/data260t/LSDIR_512" image_exts = (".jpg", ".jpeg", ".png") image_paths = [] for root, dirs, files in os.walk(image_dir): for file in files: if file.lower().endswith(image_exts): image_paths.append(os.path.join(root, file)) # Create dataframe object with prompts. Here we use empty # prompt for simplicity. df = pl.from_dict({ "image_path": image_paths, "prompt": [""] * len(image_paths) }) # Save as parquet file, which will be used in the next step. df.write_parquet("path/to/save/LSDIR_512_nulltxt.parquet") ``` 2. Fill in the values marked as TODO in [configs/sd2_train.yaml](configs/sd2_train.yaml). For example: ```yaml output_dir: /path/to/save/experiment data_config: train: ... dataset: target: HYPIR.dataset.realesrgan.RealESRGANDataset params: file_meta: file_list: path/to/LSDIR_512_nulltxt.parquet image_path_prefix: "" image_path_key: image_path prompt_key: prompt ... ``` 3. Start training. ```shell accelerate launch train.py --config configs/sd2_train.yaml ``` ## :email: Contact For questions about code or paper, please email `xqlin0613@gmail.com`. For authorization and collaboration inquiries, please email `jinjin.gu@suppixel.ai`. ## Non-Commercial Use Only Declaration The HYPIR ("Software") is made available for use, reproduction, and distribution strictly for non-commercial purposes. For the purposes of this declaration, "non-commercial" is defined as not primarily intended for or directed towards commercial advantage or monetary compensation. By using, reproducing, or distributing the Software, you agree to abide by this restriction and not to use the Software for any commercial purposes without obtaining prior written permission from Dr. Jinjin Gu. This declaration does not in any way limit the rights under any open source license that may apply to the Software; it solely adds a condition that the Software shall not be used for commercial purposes. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. For inquiries or to obtain permission for commercial use, please contact Dr. Jinjin Gu (jinjin.gu@suppixel.ai).