Finetrainers

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finetrainers is an AI Frameworks & Infra tool that supports scalable and memory-optimized training of diffusion models. It offers various training algorithms and features like DDP, FSDP-2, and LoRA finetuning.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is finetrainers?

finetrainers is a work-in-progress library from Hugging Face designed for scalable and memory-optimized training of diffusion models. It provides support for various commonly used training algorithms, including DDP, FSDP-2, HSDP, and CP. Key features include LoRA and full-rank finetuning, conditional control training, and memory-efficient single-GPU training. The library also supports multiple attention backends like flash, flex, sage, and xformers, along with auto-detection of common dataset formats. It's built to handle combined image/video datasets, multi-resolution bucketing, and offers memory-efficient precomputation. finetrainers is recommended for use with PyTorch 2.5.1 or above for optimal performance and reproducibility.

Best used for

Ideal for AI researchers and machine learning engineers who need to efficiently train and finetune diffusion models, replicate advanced video generation effects, and optimize model training for various tasks. Especially valuable for those working with large datasets and requiring memory-efficient solutions.

Common actions

train diffusion models

finetune AI models

optimize model training

scale AI experiments

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Capabilities

Key features

Scalable diffusion model training
Memory-optimized training
LoRA finetuning
Full-rank finetuning
Conditional control training
Multiple attention backends
Distributed training support

Target Audience

ml engineersai researchersdata scientistsdeep learning practitioners

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the recommended system requirements for finetrainers?

It is strongly recommended to use PyTorch 2.5.1 or above for training with finetrainers to avoid issues like black videos or out-of-memory errors. For fully reproducible training, users should refer to the environment.md file for specific environment configurations.

Does finetrainers support distributed training?

Yes, finetrainers supports various distributed training paradigms including DDP (Distributed Data Parallel), FSDP-2 (Fully Sharded Data Parallel), and HSDP. This allows for scalable training across multiple GPUs and machines.

What types of finetuning does finetrainers offer?

finetrainers supports both LoRA (Low-Rank Adaptation) and full-rank finetuning for diffusion models. It also includes capabilities for conditional control training, enabling fine-grained adjustments to model behavior and output.

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Also listed in

This tool also appears in

Content & Design › Image Generation Coding & Development › Open Source & Models Research & Education › Scientific Computing

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