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OneTrainer
OneTrainer is a one-stop solution for all your Diffusion training needs.
★ 3.2k スターAGPL-3.0
ガイドを見る →ml-stable-diffusionは、apple-aiml-researchが公開しているオープンソースの画像プロジェクトです（GitHub: apple-aiml-research/ml-stable-diffusion）。GitHubスターは17,968、フォークは1,081で、MITライセンスの下で公開されています。GitHubでの最終更新は2026年9月11日です。
Stable Diffusion with Core ML on Apple Silicon
Generation studio, upscaler, background remover, or photo library — match the tool to the task.
Desktop apps, Docker images, and portable builds cover most platforms; pick the simplest for your OS.
Generation tools need checkpoint files; utilities like upscalers usually bundle everything.
Generate, enhance, or import — then export, share, or keep building your library.
Bring any compatible open checkpoint; you are never locked to one vendor's style.
Your images stay on your machine — important for client work and privacy.
The defining trait of this whole category: professional results without monthly fees.
Edits stay reversible; originals are never touched.
Run whole folders through upscalers and converters unattended.
Extensions, scripts, and community presets extend the popular studios far beyond stock.
Download the source and move into the project folder.
git clone https://github.com/apple-aiml-research/ml-stable-diffusion.git
cd ml-stable-diffusionEvery project documents its own setup; start with install requirements.
cat README.md | head -60Follow the project's launch command — Docker, pip, npm, or a binary release.
# see README for the exact launch commandThis is the Apple-silicon route to on-device generation. Its Android counterpart is local-dream, which runs Stable Diffusion on Snapdragon phones — the same no-cloud idea, carried by the phone’s NPU, CPU or GPU.
It is an open-source project at apple-aiml-research/ml-stable-diffusion with 1,081 forks. Check the repo's contributors and recent commits to see how active maintenance is.
MIT licensed — use it commercially, no strings attached.
Generation benefits hugely from one; upscalers and photo managers are far lighter.
For generation: a polished studio interface. For enhancement: a dedicated upscaler. For libraries: a photo manager.
With the right open checkpoints, absolutely — the interface matters less than the model you load.
OneTrainer is a one-stop solution for all your Diffusion training needs.
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.
kohya_ss — open-source image project on GitHub.
ControlNet — the original conditional control for diffusion models: steer Stable Diffusion with edges, depth, pose and scribbles instead of prompts alone, Apache-2.0
Easily modify and tune a Stable Diffusion VAE
Scriptable command-line image and animation generator for Stable Diffusion
LibreAIカタログに掲載された画像プロジェクトの中で最もスターの多いもの — ml-stable-diffusionが合わなかった場合に検討したい、現実的な候補リストです。
Stable Diffusion web UI
The node-graph powerhouse for precise, professional AI image and video generation.
High performance self-hosted photo and video management solution
PhotoCraft — a clean-room Photoshop reimplementation in pure Rust: layers, masks, adjustment layers, type, vectors, brushes and real PSD files, offline, MIT / Apache-2.0
Focus on prompting and generating
カタログの中でml-stable-diffusionとスター数が近い画像プロジェクト — 前後の棚もそのまま見て回れます。
[NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer
DeepSeek's Janus series - unified multimodal models that both understand and generate images
SAM 2 — Meta's Segment Anything Model 2: promptable segmentation for images and video, Apache-2.0
stable diffusion webui colab
How Python does AI. Agents, realtime voice, image generation, embeddings. Every model, every interface, typed end to end.
🍌 World's largest Nano Banana Pro prompt library — 10,000+ curated prompts with preview images, 16 languages. Google Gemini AI image generation. Free & open …