OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Models

Finetrainers

Finetrainers is a work-in-progress, Apache-2.0 Python library for scalable and memory-optimized diffusion-model training, including LoRA, full-rank fine-tuning and distributed workflows.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Finetrainers — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Finetrainers when a project needs code-controlled, self-hosted training of diffusion models and benefits from LoRA, memory optimization, configurable attention backends or distributed execution.

Who it is for

  • Developers building self-hosted diffusion-model training pipelines
  • Researchers and engineers fine-tuning image or video diffusion models
  • Teams working with PyTorch, Diffusers and Transformers

Documented uses

  • Fine-tune diffusion models with LoRA
  • Run full-rank diffusion-model fine-tuning
  • Build memory-constrained single-GPU training workflows
  • Train video diffusion models with documented example scripts
  • Use distributed training and dataset precomputation workflows
Before you choose

Requirements & limits

The repository is public, active and licensed under Apache-2.0. It is intended for Linux, command-line and self-hosted use; no account, pricing or hosted-service requirement is documented here.

  • The library is work in progress, and the main branch is identified as unstable; release tags are the documented stable path.
  • PyTorch 2.5.1 or newer is recommended, while earlier versions may cause black videos, out-of-memory errors or other issues.
  • Available memory depends on the model, fine-tuning method and branch; published VRAM figures refer to an earlier release branch.
  • Supported workflows include SFT and Control trainers, with model-specific documentation and example launch scripts.
  • Attention choices include flash, flex, sage and xformers, so the environment must match the selected backend.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up Finetrainers from a stable release

Prepare the library and Diffusers dependency for a documented training workflow.

Before you start

  • A Linux command-line environment
  • Python requirements from the repository
  • PyTorch 2.5.1 or newer recommended
  • Git access to the Finetrainers repository

Steps

  1. Clone the Finetrainers repository and install its requirements with the repository-provided pip command.

  2. Install Diffusers from source as specified in the quickstart.

  3. Fetch repository tags and check out the v0.2.0 stable release tag.

  4. Use the release README, model documentation and training examples to continue configuration.

Expected outcome

A Finetrainers environment is prepared from the documented stable release path, with Diffusers installed from source.

Guide limitations

  • The procedure documents setup and release selection, not a completed training run.
  • The main branch is described as a development branch; the README associates stable support with release tags.
  • The README recommends using the same environment described in its environment documentation for reproducibility.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
3 October 2026
Original website title
huggingface/finetrainers · Official homepage title · retained in its source language
How we review sourcesSuggest a correction