DiffBIR
The facts below are what the maker publishes about DiffBIR, checked and dated. Our editors have not written about it yet.
Plans and prices
From DiffBIR's own pricing page, checked 12 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| DiffBIR | Free | Open-source research code and pretrained weights; GPU memory can limit inference |
The facts
Each one read from DiffBIR's own pages, with the date it was checked.
What it does
- Additional components
- The project says it is based on ControlNet and BasicSR and uses Stable Diffusion v2.1 for training stage two.checked 12 Oct 2026
- Batch processing
- Yeschecked 22 Sep 2026
- Deployment
- The instructions clone the repository, create a Python 3.10 Conda environment, install requirements, and run inference locally.checked 12 Oct 2026
- License
- The project is released under the Apache 2.0 license.checked 12 Oct 2026
- Maximum scale
- 8xchecked 22 Sep 2026
- Model downloads
- Pretrained weights are linked from Hugging Face, Baidu Netdisk, and OpenXLab, and the README says inference weights download automatically.checked 12 Oct 2026
- Model integrations
- Inference uses BSRNet for super-resolution, SwinIR-Face for face restoration, and SCUNet-PSNR for denoising as stage-one models.checked 12 Oct 2026
- Online demo
- The README announces an online demo on OpenXLab for the general and face models.checked 12 Oct 2026
- Purpose
- DiffBIR is a blind image restoration pipeline that uses a generative diffusion prior.checked 12 Oct 2026
- Quality trade-off
- The README says using the condition as the sampling start point can improve stability but may decrease sample quality.checked 12 Oct 2026
- Restoration tasks
- The repository provides inference examples for blind image super-resolution, aligned and unaligned face restoration, and blind image denoising.checked 12 Oct 2026
- Support
- The README directs questions to [email protected].checked 12 Oct 2026
- Tiled sampling
- Tiled sampling supports super-resolution at large scale factors on low-VRAM graphics cards.checked 12 Oct 2026
- Training limitation
- The README states that the stage-two training script does not support a validation set.checked 12 Oct 2026
- Upscaling method
- aichecked 22 Sep 2026
- Versions
- The README lists DiffBIR v2 and v2.1, and says v2.1 includes a model trained on Unsplash captions generated with LLaVA, more samplers, and improved tiled-sampling support.checked 12 Oct 2026
Where it runs
- Apple Silicon
- The project announced Apple Silicon support and refers users to instructions for CPU, CUDA, and MPS devices.checked 12 Oct 2026
The company
- Founded
- 2023checked 28 Sep 2026
- Headquarters
- Chinachecked 28 Sep 2026
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