#1010th of 30 in Image and videoFor image, video and design, image and design

sdnext

Web UI for generating and editing images and video with diffusion models

Stars
7.4k
License
Apache-2.0
Last commit
Oct 2026
Language
Python
Stars, last 29 weeks+428
1 Apr 2026: 6,930 stars8 Apr 2026: 6,948 stars15 Apr 2026: 6,964 stars22 Apr 2026: 6,976 stars29 Apr 2026: 6,988 stars6 May 2026: 6,996 stars13 May 2026: 7,007 stars20 May 2026: 7,015 stars27 May 2026: 7,026 stars3 Jun 2026: 7,050 stars10 Jun 2026: 7,060 stars17 Jun 2026: 7,069 stars24 Jun 2026: 7,080 stars1 Jul 2026: 7,091 stars8 Jul 2026: 7,102 stars15 Jul 2026: 7,119 stars22 Jul 2026: 7,131 stars29 Jul 2026: 7,149 stars5 Aug 2026: 7,161 stars12 Aug 2026: 7,269 stars19 Aug 2026: 7,279 stars26 Aug 2026: 7,290 stars2 Sept 2026: 7,304 stars9 Sept 2026: 7,315 stars16 Sept 2026: 7,319 stars23 Sept 2026: 7,325 stars30 Sept 2026: 7,332 stars7 Oct 2026: 7,338 stars14 Oct 2026: 7,347 stars21 Oct 2026: 7,358 stars

6,930 on 1 Apr 20267,358 on 21 Oct 2026

From GitHub's daily star counts.

Checked on GitHub.

Overview

SD.Next is a Python web interface, forked from AUTOMATIC1111's Stable Diffusion WebUI, for text-to-image, image-to-image, text-to-video and image-to-video generation. It adds editing, inpainting, outpainting, LoRA, ControlNet, IP-Adapters, captioning and upscaling. It runs on NVIDIA, AMD, Intel Arc, Apple Silicon, OpenVINO devices or CPU, and a built-in installer handles dependencies and updates.

Who it is for: Self-hosters generating images and video locally on varied GPU vendors

Ranked among open-source alternatives to Midjourney (#10 of 10).

Strengths

  • Covers NVIDIA CUDA, AMD ROCm/ZLUDA, Intel XPU, OpenVINO, Apple MPS and CPU-only
  • Handles image and video generation, plus captioning, tagging, upscaling and interpolation
  • SDNQ quantization and CPU/GPU offloading help fit models into limited memory
  • Built-in installer and updater; Windows launcher exe and Docker recipes available

Weaknesses

  • README gives no RAM or VRAM minimums
  • No tagged release; the quick start installs by cloning the repo
  • First launch installs dependencies, which can take a while
  • Supported model list lives in external docs, not the README

What it needs

  • GPU optional
  • Needs Python, Git
  • Models: Diffusion models, LoRA, ControlNet, IP-Adapter

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