A general fine-tuning kit geared toward image/video/audio diffusion models.
The main features of bghira/simpletuner are: Video Generation, Video Training Tools.
Open-source alternatives to bghira/simpletuner include: modelscope/diffsynth-studio — DiffSynth-Studio is a comprehensive platform for the lifecycle management of generative diffusion models, providing a… shengshu-ai/minwm. hao-ai-lab/fastvideo — FastVideo is a comprehensive system for accelerated video generation, serving as a video generation inference engine,… kohya-ss/musubi-tuner. huggingface/diffusers — Diffusers is a PyTorch-based library and generative AI framework used to build, train, and deploy diffusion pipelines… spacepxl/hunyuanvideo-training.
Diffusers is a PyTorch-based library and generative AI framework used to build, train, and deploy diffusion pipelines for producing multi-modal media. It provides a suite of tools for generating images, video, and audio from natural language descriptions, as well as specialized systems for text-to-image generation. The project differentiates itself through a modular architecture that separates noise schedulers, pretrained model blocks, and pipeline compositions. This structure allows for the construction of custom generation workflows and the ability to swap individual components of the diffu
FastVideo is a comprehensive system for accelerated video generation, serving as a video generation inference engine, a video diffusion training framework, and a modular pipeline orchestrator. It provides a distributed transformer optimizer and a distillation toolkit designed to reduce denoising steps and model complexity to increase frame rates. The project distinguishes itself through specialized acceleration techniques, including joint distillation and sparse attention training. It implements low-step video generation and weight quantization to FP8 or FP4 precision to increase throughput a
DiffSynth-Studio is a comprehensive platform for the lifecycle management of generative diffusion models, providing a unified environment for inference, fine-tuning, and training. It utilizes a modular pipeline architecture and a standardized abstraction layer to support consistent workflows across diverse model configurations for image and video generation. The platform distinguishes itself through a memory-optimized inference engine that dynamically manages resources to facilitate high-resolution generation on constrained hardware. It also integrates specialized training capabilities, inclu
