SUPIR
The facts below are what the maker publishes about SUPIR, checked and dated. Our editors have not written about it yet.
The facts
Each one read from SUPIR's own pages, with the date it was checked.
What it does
- Batch processing
- Yeschecked 22 Sep 2026
- Commercial use
- The repository declares SUPIR software available strictly for non-commercial purposes unless prior written permission is obtained from Dr. Jinjin Gu.checked 12 Oct 2026
- Commercial-use restriction
- The repository declares SUPIR software use, reproduction, and distribution strictly non-commercial unless prior written permission is obtained from Dr. Jinjin Gu.checked 12 Oct 2026
- Compute requirements
- The repository notes a lower-VRAM configuration that uses 12G for diffusion and 16G for LLaVA, with slower performance.checked 12 Oct 2026
- Dependent models
- The documented dependencies include SDXL CLIP encoders, SDXL base, LLaVA CLIP, and LLaVA v1.5 13B.checked 12 Oct 2026
- Hardware requirements
- The repository describes a lower-VRAM demo mode as using 12 GB for diffusion and 16 GB for LLaVA, with slower processing.checked 12 Oct 2026
- Image controls
- Inference options include an upsampling ratio, model selection, random seed, minimum output size, sampling steps, prompts, and color-fixing choices.checked 12 Oct 2026
- Image enhancement
- The project presents SUPIR for image processing and upscaling, including compressed, noisy, or blurry images.checked 12 Oct 2026
- Installation
- The repository instructions create a Python 3.8 Conda environment and install dependencies from requirements.txt.checked 12 Oct 2026
- Local installation
- The repository instructions use Python 3.8, Conda, pip, and the project's requirements file to install SUPIR.checked 12 Oct 2026
- Maximum scale
- customchecked 22 Sep 2026
- Model
- SUPIR is described as a high-fidelity general image restoration model based on a large-scale diffusion generative prior.checked 12 Oct 2026
- Model choices
- The repository provides SUPIR-v0Q with default training settings and SUPIR-v0F trained with light degradation settings.checked 12 Oct 2026
- Model dependencies
- The installation instructions list SDXL CLIP encoders, an SDXL base checkpoint, and LLaVA CLIP and v1.5 13B among dependent models.checked 12 Oct 2026
- Model variants
- The repository provides SUPIR-v0Q with default training settings and SUPIR-v0F trained with light degradation settings.checked 12 Oct 2026
- Online demo
- The repository links to SupPixel AI, an online image processing and upscaling tool built on SUPIR.checked 12 Oct 2026
- Purpose
- SUPIR aims to develop practical algorithms for photo-realistic image restoration in the wild.checked 12 Oct 2026
- Quality and fidelity
- The repository says its empirical hyperparameters affect image quality and appearance, and describes a tradeoff between visual quality and fidelity.checked 12 Oct 2026
- Run options
- The repository supports command-line inference with input and output folders, an upsampling ratio, model selection, and configurable sampling and prompt parameters.checked 12 Oct 2026
- Security and compliance
- The project pages opened do not state security certifications or compliance claims.checked 12 Oct 2026
- Support
- The repository directs questions to [email protected] or [email protected].checked 12 Oct 2026
The company
- Founded
- 2024checked 28 Sep 2026
Where it runs
- Online app
- The repository links to an online app and describes SupPixel AI as an easy-to-use image processing and upscaling tool built on SUPIR.checked 12 Oct 2026
- Restoration approach
- The project describes SUPIR as a high-fidelity general image restoration model based on a large-scale diffusion generative prior.checked 12 Oct 2026
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