ResShift
Efficient diffusion model for image super-resolution using residual shifting.
High-performance diffusion-based image upscaling, deblurring, and inpainting with minimal steps.
ResShift is an open-source diffusion model that accelerates image super-resolution, deblurring, and inpainting by shifting residuals between high and low-resolution images. It is built for graphic designers and creative professionals who need high-fidelity image restoration without hundreds of sampling steps.
What it is
The tool constructs a Markov chain transferring between high-res and low-res images by shifting residuals, using an elaborate noise schedule to control speed and noise. It performs real-world and bicubic super-resolution, image deblurring, natural and face inpainting, and blind face restoration in as few as 4 to 15 sampling steps.
Who it's for
It suits digital artists, retouchers, and developers working with generative AI who need advanced image upscaling and restoration pipelines. It does not suit casual users looking for a one-click desktop app without command-line experience.
Key features
Residual Shifting Diffusion
Transfers between high and low-resolution images by shifting residuals to improve transition efficiency.
Multi-Task Capabilities
Handles real-world super-resolution, bicubic resize SR, deblurring, natural and face inpainting, and blind face restoration.
Flexible Noise Scheduling
Develops a custom noise schedule to control shifting speed and noise strength during the diffusion process.
Configurable Sampling Steps
Achieves state-of-the-art results using only 15 sampling steps for conference versions and 4 steps for journal versions.
Up and running in 3 steps
Our honest verdict
What's great
- Significantly reduces inference steps compared to traditional slow diffusion models
- Prevents over-blurry super-resolution results during acceleration
- Provides multiple task configurations including deblurring and face restoration
- Includes pretrained models and reproduction scripts for research verification
Where it falls short
- Requires multi-GPU hardware setups with torchrun for full model training
- Command-line interface requires familiarity with Python and Conda environments
- Dependencies include specific versions of PyTorch, Python, and xformers
Pricing & license
Free under its license, plus an optional online Replicate demo. The only other cost is running it yourself on your own hardware.
Open remote AI-training roles
767 open remote AI-training roles, updated November 14, 2026; most pay $20–$175 an hour. Zekai lists them free; the application goes to the employer’s platform.
Alternatives
These reviewed AI tools do the same job instantly — from our scored directory.
Use it, or have us set it up
FAQ
How many sampling steps does ResShift require?
It can produce superior results with as few as 15 sampling steps for the conference version and 4 steps for the journal version.
Can I use ResShift for tasks other than super-resolution?
Yes, it also supports image deblurring, natural and face inpainting, and blind face restoration.
