This is not intended to be a comprehensive all-in-one trainer, it's just the bare minimum framework to support simple one-file training scripts, in the spirit of diffusers example training scripts. It's meant to be easy to read and modify, without too much abstraction getting in the way, but with enough optimization to not require rental cloud compute. The default configuration uses < 16 GB of VRAM (although < 24 GB is the target), and runs natively on windows. To achieve this, the diffusion model is quantized to nf4 using bitsandbytes (similar to QLoRA), and text embeddings are pre-computed. Latents are encoded on the fly however, to reduce overfitting and make it easier to work with larger datasets.
Don't expect much support, as this is primarily for my own use, but I'm sharing it for others who want to tinker with training code, and because I was frustrated with diffusers' switch with recent video models from single file training scripts to finetrainers. Any code written by me is released under MIT license (aka do whatever you want), but the HunyuanVideo model is subject to the tencent community license.
git clone https://github.com/spacepxl/HunyuanVideo-Training
cd HunyuanVideo-Training
python -m venv .venv
.venv\Scripts\activate
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu124
(or follow whatever approach you prefer for environment management and pytorch/triton/etc)
pip install -r requirements.txt
Activate the venv (you can use launch_venv.bat if on windows)
Download models:
python train_hunyuan_lora.py --download_model
or if you want to train skyreels i2v:
python train_hunyuan_lora.py --download_model --skyreels_i2v
(if you already have the diffusers models saved elsewhere, you can skip downloading and train with --pretrained_model pointing to the correct folder)
Expected folder structure:
HunyuanVideo-Training
/models
/scheduler
/text_encoder
/text_encoder_2
/tokenizer
/tokenizer_2
/transformer
/transformer-skyreels-i2v
/vae
Your dataset should be structured something like this:
dataset
/train
/subfolder
sample1.mp4
sample1.txt
sample2.jpg
sample2.txt
...
/val
...
Training data goes in /train, validation data goes in /val, /validation, or /test. Subfolders within the train or val folder are scanned recursively, so organize them however you like.
If no validation set is provided, then validation will fall back to the training set. This is NOT RECOMMENDED, it will make it impossible to judge overfitting from the validation loss.
Image and video files are supported, and captions should be the same filename as the media file but with .txt extension. See here for the list of filetypes, although not all are tested, and others might work just by adding them to the list (anything supported by decord for videos or Pillow for images)
Once you have a dataset, cache the text embeddings:
python train_hunyuan_lora.py --dataset "example/dataset" --cache_embeddings
python train_hunyuan_lora.py --dataset "example/dataset"
All other arguments are optional, the defaults should be a reasonable starting point.
By default, resolutions are randomized in buckets, frame length is set based on a context length budget (so, semi-random but keeping similar memory and compute cost), and the start frame is randomly chosen from the range allowed by frame length. The default --token_limit of 10000 is good for < 16 GB, and about 30s/step on a 3090. Raising the token limit will use more memory and more time per step, and increase the number of frames at every resolution.
Optionally you can set --resolution to disable the resolution buckets and use a square crop of that exact size. If you set --num_frames it will use that as the upper limit (some samples may use fewer frames depending on resolution, or limited by short clips/images).
For image to video training, use --skyreels_i2v to load the skyreels model and use the first frame as image conditioning.
Warped noise from Go With The Flow can be enabled by --warped_noise, note that this will take longer to adapt to than normal random noise, so it's more for general adapter training than character/style loras, and you should use a large dataset, ideally larger than the number of training steps.
The saved lora checkpoints are in diffusers format, so if you want to use them with the original tencent model (or in comfyui), you'll need to convert them:
python convert_diffusers_lora_to_original.py --input_lora "./outputs/example/checkpoints/hyv-lora-00001000.safetensors"
You can optionally convert the lora dtype to bf16 or fp16 to save file size. If you set alpha to anything other than rank during training, you'll need to manually input the alpha during conversion with --alpha