画像
Insightface
State-of-the-art 2D and 3D Face Analysis Project
★ 29.9k スターCustom (check repo)
ガイドを見る →CodeFormerは、sczhou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: sczhou/CodeFormer）。GitHubスターは18,171、フォークは3,725で、Custom (check repo)ライセンスの下で公開されています。GitHubでの最終更新は2025年11月18日です。
[NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer
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.
The defining trait of this whole category: professional results without monthly fees.
Your images stay on your machine — important for client work and privacy.
Bring any compatible open checkpoint; you are never locked to one vendor's style.
Single-job tools (upscale, de-background, restore) that do one thing excellently.
Self-hosted managers with AI tagging, face recognition, and deduplication.
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/sczhou/CodeFormer.git
cd CodeFormerIsolate dependencies so nothing clashes with your other projects.
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activateInstall the requirements, then follow the README's launch command (it varies per project).
pip install -r requirements.txtBlind face restoration is a specialised corner of image-to-image translation, the field opened up by the original pix2pix and CycleGAN implementation — the codebase a generation of translation and restoration projects learned from.
For generation: a polished studio interface. For enhancement: a dedicated upscaler. For libraries: a photo manager. The guides above narrow it down.
Generation benefits hugely from one; upscalers and photo managers are far lighter.
It is an open-source project at sczhou/CodeFormer with 3,725 forks. Check the repo's contributors and recent commits to see how active maintenance is.
Mostly yes under MIT/Apache/GPL — but verify each license, especially for client deliverables.
With the right open checkpoints, absolutely — the interface matters less than the model you load.
Community hubs like CivitAI and Hugging Face host thousands of open checkpoints.
State-of-the-art 2D and 3D Face Analysis Project
Stable Diffusion web UI
Focus on prompting and generating
Real-ESRGAN — practical blind super-resolution for real-world photos and video: restores and upscales degraded images with models trained on pure synthetic data, BSD-3-Clause
MMagic — OpenMMLab's generative-AI toolbox: diffusion, text-to-image, restoration and a model zoo.
Visual diagnostics for LoRA training — spot overfitting with face biometrics
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 — CodeFormer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CodeFormer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。
SAM 2 — Meta's Segment Anything Model 2: promptable segmentation for images and video, Apache-2.0
Stable Diffusion with Core ML on Apple Silicon
How Python does AI. Agents, realtime voice, image generation, embeddings. Every model, every interface, typed end to end.
DeepSeek's Janus series - unified multimodal models that both understand and generate images
Rembg is a tool to remove images background
stable diffusion webui colab