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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Scalable and memory-optimized training of diffusion models
| Date | Stars |
|---|---|
| 2026-07-24 | 1355 |
| 2026-07-25 | 1355 |
| 2026-07-28 | 1355 |
| 2026-07-30 | 1355 |
| 2026-07-31 | 1355 |
| 2026-08-02 | 1356 |
| 2026-08-03 | 1357 |
| 2026-08-06 | 1358 |
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# finetrainers 🧪 Finetrainers is a work-in-progress library to support (accessible) training of diffusion models and various commonly used training algorithms. <table align="center"> <tr> <td align="center"><video src="https://github.com/user-attachments/assets/aad07161-87cb-4784-9e6b-16d06581e3e5">Your browser does not support the video tag.</video></td> <td align="center"><video src="https://github.com/user-attachments/assets/c23d53e2-b422-4084-9156-3fce9fd01dad">Your browser does not support the video tag.</video></td> </tr> <tr> <th align="center">CogVideoX LoRA training as the first iteration of this project</th> <th align="center">Replication of PikaEffects</th> </tr> </table> ## Table of Contents - [Quickstart](#quickstart) - [Features](#features) - [News](#news) - [Support Matrix](#support-matrix) - [Featured Projects](#featured-projects-) - [Acknowledgements](#acknowledgements) ## Quickstart Clone the repository and make sure the requirements are installed: `pip install -r requirements.txt` and install `diffusers` from source by `pip install git+https://github.com/huggingface/diffusers`. The requirements specify `diffusers>=0.32.1`, but it is always recommended to use the `main` branch of Diffusers for the latest features and bugfixes. Note that the `main` branch for `finetrainers` is also the development branch, and stable support should be expected from the release tags. Checkout to the latest stable release tag: ```bash git fetch --all --tags git checkout tags/v0.2.0 ``` Follow the instructions mentioned in the [README](https://github.com/a-r-r-o-w/finetrainers/tree/v0.2.0-release) for the latest stable release. #### Using the main branch To get started quickly with example training scripts on the main development branch, refer to the following: - [LTX-Video Pika Effects Crush](./examples/training/sft/ltx_video/crush_smol_lora/) - [CogVideoX Pika Effects Crush](./examples/training/sft/cogvideox/crush_smol_lora/) - [Wan T2V Pika Effects Crush](./examples/training/sft/wan/crush_smol_lora/) The following are some simple datasets/HF orgs with good datasets to test training with quickly: - [Disney Video Generation Dataset](https://huggingface.co/datasets/Wild-Heart/Disney-VideoGeneration-Dataset) - [bigdatapw Video Dataset Collection](https://huggingface.co/bigdata-pw) - [Finetrainers HF Dataset Collection](https://huggingface.co/finetrainers) Please checkout [`docs/models`](./docs/models/) and [`examples/training`](./examples/training/) to learn more about supported models for training & example reproducible training launch scripts. For a full list of arguments that can be set for training, refer to [`docs/args`](./docs/args.md). > [!IMPORTANT] > It is recommended to use Pytorch 2.5.1 or above for training. Previous versions can lead to completely black videos, OOM errors, or other issues and are not tested. For fully reproducible training, please use the same environment as mentioned in [environment.md](./docs/environment.md). ## Features - DDP, FSDP-2 & HSDP, CP support - LoRA and full-rank finetuning; Conditional Control training - Memory-efficient single-GPU training - Multiple attention backends supported - `flash`, `flex`, `sage`, `xformers` (see [attention](./docs/models/attention.md) docs) - Auto-detection of commonly used dataset formats - Combined image/video datasets, multiple chainable local/remote datasets, multi-resolution bucketing & more - Memory-efficient precomputation support with/without on-the-fly precomputation for large scale datasets - Standardized model specification format for training arbitrary models - Fake FP8 training (QAT upcoming!) ## News - 🔥 **2025-04-25**: Support for different attention providers added! - 🔥 **2025-04-21**: Wan I2V supported added! - 🔥 **2025-04-12**: Channel-concatenated control conditioning support added for CogView4 and Wan! - 🔥 **2025-04-08**: `torch.compile` support added! - 🔥 **2025-04-06**: Flux support added! - 🔥 **2025-03-07**: CogV
Excerpt of 8,996 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5df32bb132a18a14, topic:pytorch
matched fp:5df32bb132a18a14, topic:diffusion-models
