sdnext
SD.Next: All-in-one WebUI for AI generative image and video creation, captioning and processing
SD.Next is a feature-forward web UI for image generation that supports a wide range of model architectures and backends with frequent updates.
From the README
git clone https://github.com/vladmandic/sdnext cd sdnext ./webui.sh # Linux/Mac
- Balanced Offload: Dynamically balance CPU and GPU memory to run larger models on limited hardware
- Caption and Enhance with 25+ built-in LLM and VLM models, OpenCLiP models, Tagger with WaifuDiffusion and DeepDanbooru models
- Image Processing with full image correction color-grading suite of tools
- Multi-platform!
Excerpts from the project README on GitHub. Copyright and licensing remain with the respective authors.
Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.
Get an email alert on its next release or when it starts trending — never miss the moment.
Free · no card · unsubscribe anytimeSD.Next: All-in-one WebUI for AI generative image and video creation, captioning and processing
sdnext has 7.2k stars on GitHub. It has been forked 564 times. sdnext is written mainly in Python. It has been in active development since 2022. sdnext is available under the Apache-2.0 license. Its main topics are ai-art, caption, diffusers, generative-art.
Read the full guideWhat is sdnext?
SD.Next: All-in-one WebUI for AI generative image and video creation, captioning and processing
Is sdnext open source?
sdnext is an open-source project. It is released under the Apache-2.0 license.
Is sdnext free?
Yes. sdnext is free and open source — you can use, modify and self-host it.
What license does sdnext use?
sdnext is available under the Apache-2.0 license.
What language is sdnext written in?
sdnext is written mainly in Python.
Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.
[](https://olud.ai/project/vladmandic-sdnext.html)
Measured from GitHub topics shared by both projects, weighted by how rare each topic is.