README
OneTrainer
OneTrainer is a one-stop solution for all your Diffusion training needs.
Features
- Supported models: Z-Image, Qwen Image, FLUX.1, Flux.2 Dev and Klein, Chroma, Stable Diffusion 1.5, 2.0, 2.1, 3.0, 3.5, SDXL, Würstchen-v2, Stable Cascade, PixArt-Alpha, PixArt-Sigma, Sana, Hunyuan Video and inpainting models
- Model formats: diffusers and ckpt models
- Training methods: Full fine-tuning, LoRA, embeddings
- Masked Training: Let the training focus on just certain parts of the samples
- Automatic backups: Fully back up your training progress regularly during training. This includes all information to seamlessly continue training
- Image augmentation: Apply random transforms such as rotation, brightness, contrast or saturation to each image sample to quickly create a more diverse dataset
- TensorBoard: A simple TensorBoard integration to track the training progress
- Multiple prompts per image: Train the model on multiple different prompts per image sample
- Noise Scheduler Rescaling: From the paper Common Diffusion Noise Schedules and Sample Steps are Flawed
- EMA: Train your own EMA model. Optionally keep EMA weights in CPU memory to reduce VRAM usage
- Aspect Ratio Bucketing: Automatically train on multiple aspect ratios at a time. Just select the target resolutions, buckets are created automatically
- Multi-Resolution Training: Train multiple resolutions at the same time
- Dataset Tooling: Automatically caption your dataset using BLIP, BLIP2 and WD-1.4, or create masks for masked training using ClipSeg or Rembg
- Model Tooling: Convert between different model formats from a simple UI
- Sampling UI: Sample the model during training without switching to a different application
[!NOTE] Explore our 📚 wiki for essential tips and tutorials after installing. Start here!.