huggingface/finetrainers

Health v2 · maintenance only

Adoption not part of the score

1358 stars · 141 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

finetrainers is a Hugging Face library for scalable, memory-optimized training (fine-tuning) of diffusion models, including LoRA training of video generation models like CogVideoX, LTX-Video, and Wan. It builds on PyTorch and diffusers and provides reproducible example training scripts and dataset utilities.

Use cases

• train a lora for a video diffusion model
• fine-tune cogvideox on my own dataset
• memory-efficient training of diffusion transformers
• replicate pika effects style video generation
• train ltx-video on custom video-caption pairs
• run sft training for wan text-to-video model

When to choose

• you want to fine-tune or LoRA-train supported diffusion/video models with memory optimizations
• you want reproducible example training scripts built on diffusers
• you need scalable training on limited GPU memory

When to avoid

• you need training for models outside its support matrix
• you want a stable, fully polished tool - the project describes itself as work-in-progress
• you need image-only diffusion training with broad model coverage like other trainers

Facets

library · maturity active

llm-training machine-learning deep-learning machine-learning deep-learning artificial-intelligence image-processing python cross-platform diffusion-models lora fine-tuning pytorch diffusers video-generation text-to-video memory-optimization video gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="huggingface/finetrainers")