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license: apache-2.0
tags:
- lora
- video-generation
- wan
- wan-2.1
- wan-2.2
- training
- text-to-video
- image-to-video
- diffusion-transformer
---
# Flimmer
Video LoRA training toolkit for diffusion transformer models. Built by [Alvdansen Labs](https://github.com/alvdansen).
Full pipeline from raw footage to trained LoRA checkpoint — scene detection, captioning,
dataset validation, latent pre-encoding, and training. Currently supports WAN 2.1 and
WAN 2.2 (T2V and I2V).
Early release. Building in the open.
## What it covers
- **Video ingestion** — scene detection, clip splitting, fps/resolution normalization
- **Captioning** — Gemini and Replicate backends
- **CLIP-based triage** — find clips matching a reference person or concept in large footage sets
- **Dataset validation** — catch missing captions, resolution mismatches, and format issues before spending GPU time
- **Latent pre-encoding** — VAE + T5 cached to disk so training doesn't repeat encoding every epoch
- **Training** — LoRA training with checkpoint resume, W&B logging, and in-training video sampling
## Phased training
The standout feature. Break a training run into sequential stages — each with its own
learning rate, epoch budget, and dataset — while the LoRA checkpoint carries forward
automatically between phases.
Use it for curriculum training (simple compositions before complex motion) or for
WAN 2.2's dual-expert MoE architecture, where the high-noise and low-noise experts
can be trained with specialized hyperparameters after a shared base phase.
MoE expert specialization is experimental — hyperparameters are still being validated.
## Standalone data tools
The data preparation tools output standard formats compatible with any trainer —
kohya, ai-toolkit, or anything else. You don't need to use Flimmer's training loop
to benefit from the captioning, triage, and validation tooling.
## Model support
| Model | T2V | I2V |
|---|---|---|
| WAN 2.1 | ✅ | ✅ |
| WAN 2.2 | ✅ | ✅ |
| LTX | 🔜 | 🔜 |
Image training is out of scope — ai-toolkit handles it thoroughly and there's no point
duplicating it. Flimmer is video-native.
## Installation & docs
Full installation instructions, config reference, and guides are on GitHub:
**[github.com/alvdansen/flimmer-trainer](https://github.com/alvdansen/flimmer-trainer)**
Supports RunPod and local GPU (tested on A6000/48GB). |