- 2023-7-12: You can now train a LoRA that is compatibile with the webui extension! See instructions here.
- 2023-4-17: You can now convert your trained models from diffusers to
.ckptformat for A111 webui. Thanks @kabachuha! - 2023-4-8: LoRA Training released! Checkout
configs/v2/lora_training_config.yamlfor instructions. - 2023-4-8: Version 2 is released!
- 2023-3-29: Added gradient checkpointing support.
- 2023-3-27: Support for using Scaled Dot Product Attention for Torch 2.0 users.
git clone https://github.com/ExponentialML/Text-To-Video-Finetuning.git
cd Text-To-Video-Finetuning
git lfs install
git clone https://huggingface.co/damo-vilab/text-to-video-ms-1.7b ./models/model_scope_diffusers/Alternatively, you can train starting from other models made by the community.
| Contributer | Model Name | Link |
|---|---|---|
| cerspense | ZeroScope | https://huggingface.co/cerspense/zeroscope_v2_576w |
| cameduru | Potat1 | https://huggingface.co/camenduru/potat1 |
| strangeman3107 | animov-512x | https://huggingface.co/strangeman3107/animov-512x |
It is recommended to install Anaconda.
Windows Installation: https://docs.anaconda.com/anaconda/install/windows/
Linux Installation: https://docs.anaconda.com/anaconda/install/linux/
conda create -n text2video-finetune python=3.10
conda activate text2video-finetunepip install -r requirements.txtAll code was tested on Python 3.10.9 & Torch version 1.13.1 & 2.0.
It is highly recommended to install >= Torch 2.0. This way, you don't have to install Xformers or worry about memory performance.
If you don't have Xformers enabled, you can follow the instructions here: https://github.com/facebookresearch/xformers
Recommended to use a RTX 3090, but you should be able to train on GPUs with <= 16GB ram with:
- Validation turned off.
- Xformers or Torch 2.0 Scaled Dot-Product Attention
- Gradient checkpointing enabled.
- Resolution of 256.
- Hybrid LoRA training.
- Training only using LoRA with ranks between 4-16.
You can use caption files when training on images or video. Simply place them into a folder like so:
Images: /images/img.png /images/img.txt
Videos: /videos/vid.mp4 | /videos/vid.txt
Then in your config, make sure to have -folder enabled, along with the root directory containing the files.
You can automatically caption the videos using the Video-BLIP2-Preprocessor Script
The configuration uses a YAML config borrowed from Tune-A-Video reposotories.
All configuration details are placed in configs/v2/train_config.yaml. Each parameter has a definition for what it does.
I highly recommend (I did this myself) going to configs/v2/train_config.yaml. Then make a copy of it and name it whatever you wish my_train.yaml.
Then, follow each line and configure it for your specific use case.
The instructions should be clear enough to get you up and running with your dataset, but feel free to ask any questions in the discussion board.
Please read this section carefully if you are training a LoRA model
You can also train a LoRA that is compatible with the webui extension.
By default it's set to 'cloneofsimo', which was the first LoRA implementation for Stable Diffusion.
This ('cloneofsimo') version you can use in the inference.py file in this repository. It is not compatible with the webui.
To train and use a LoRA with the webui, change the lora_version to "stable_lora" in your config if you already have one made.
This will train an A1111 webui extension compatibile LoRA.
You can get started at configs/v2/stable_lora_config.yaml and everything is set by default in there. During and after training, LoRAs will be saved in your outputs directory with the prefix _webui.
If you do not choose this setting, you will not currently be able to use these in the webui. If you train a Stable LoRA file, you cannot currently use them in inference.py.
To continue training a LoRA, simply set your lora_path in your config to the directory that contains your LoRA file(s), not an individual file.
Each specific LoRA should have _unet or _text_encoder in the file name respectively, or else it will not work.
You should then be able to resume training from a LoRA model, regardless of which method you use (as long as the trained LoRA matches the version in the config).
- Use LoRA files that were made for SD image models in other trainers.
- Use 'cloneofsimo' LoRAs in another project (unless you build it or create a PR)
- Merge LoRA weights together (yet).
python train.py --config ./configs/v2/train_config.yamlWith a lot of data, you can expect training results to show at roughly 2500 steps at a constant learning rate of 5e-6.
When finetuning on a single video, you should see results in half as many steps.
After training, you should see your results in your output directory.
By default, it should be placed at the script root under ./outputs/train_<date>
From my testing, I recommend:
- Keep the number of sample frames between 4-16. Use long frame generation for inference, not training.
- If you have a low VRAM system, you can try single frame training or just use
n_sample_frames: 2. - Using a learning rate of about
5e-6seems to work well in all cases. - The best quality will always come from training the text encoder. If you're limited on VRAM, disabling it can help.
- Leave some memory to avoid OOM when saving models during training.
The inference.py script can be used to render videos with trained checkpoints.
Example usage:
python inference.py \
--model camenduru/potat1 \
--prompt "a fast moving fancy sports car" \
--num-frames 60 \
--window-size 12 \
--width 1024 \
--height 576 \
--sdp
> python inference.py --help
usage: inference.py [-h] -m MODEL -p PROMPT [-n NEGATIVE_PROMPT] [-o OUTPUT_DIR]
[-B BATCH_SIZE] [-W WIDTH] [-H HEIGHT] [-T NUM_FRAMES]
[-WS WINDOW_SIZE] [-VB VAE_BATCH_SIZE] [-s NUM_STEPS]
[-g GUIDANCE_SCALE] [-i INIT_VIDEO] [-iw INIT_WEIGHT] [-f FPS]
[-d DEVICE] [-x] [-S] [-lP LORA_PATH] [-lR LORA_RANK] [-rw]
options:
-h, --help show this help message and exit
-m MODEL, --model MODEL
HuggingFace repository or path to model checkpoint directory
-p PROMPT, --prompt PROMPT
Text prompt to condition on
-n NEGATIVE_PROMPT, --negative-prompt NEGATIVE_PROMPT
Text prompt to condition against
-o OUTPUT_DIR, --output-dir OUTPUT_DIR
Directory to save output video to
-B BATCH_SIZE, --batch-size BATCH_SIZE
Batch size for inference
-W WIDTH, --width WIDTH
Width of output video
-H HEIGHT, --height HEIGHT
Height of output video
-T NUM_FRAMES, --num-frames NUM_FRAMES
Total number of frames to generate
-WS WINDOW_SIZE, --window-size WINDOW_SIZE
Number of frames to process at once (defaults to full
sequence). When less than num_frames, a round robin diffusion
process is used to denoise the full sequence iteratively one
window at a time. Must be divide num_frames exactly!
-VB VAE_BATCH_SIZE, --vae-batch-size VAE_BATCH_SIZE
Batch size for VAE encoding/decoding to/from latents (higher
values = faster inference, but more memory usage).
-s NUM_STEPS, --num-steps NUM_STEPS
Number of diffusion steps to run per frame.
-g GUIDANCE_SCALE, --guidance-scale GUIDANCE_SCALE
Scale for guidance loss (higher values = more guidance, but
possibly more artifacts).
-i INIT_VIDEO, --init-video INIT_VIDEO
Path to video to initialize diffusion from (will be resized to
the specified num_frames, height, and width).
-iw INIT_WEIGHT, --init-weight INIT_WEIGHT
Strength of visual effect of init_video on the output (lower
values adhere more closely to the text prompt, but have a less
recognizable init_video).
-f FPS, --fps FPS FPS of output video
-d DEVICE, --device DEVICE
Device to run inference on (defaults to cuda).
-x, --xformers Use XFormers attnetion, a memory-efficient attention
implementation (requires `pip install xformers`).
-S, --sdp Use SDP attention, PyTorch's built-in memory-efficient
attention implementation.
-lP LORA_PATH, --lora_path LORA_PATH
Path to Low Rank Adaptation checkpoint file (defaults to empty
string, which uses no LoRA).
-lR LORA_RANK, --lora_rank LORA_RANK
Size of the LoRA checkpoint's projection matrix (defaults to
64).
-rw, --remove-watermark
Post-process the videos with LAMA to inpaint ModelScope's
common watermarks.
Please feel free to open a pull request if you have a feature implementation or suggesstion! I welcome all contributions.
I've tried to make the code fairly modular so you can hack away, see how the code works, and what the implementations do.
If you want to use the V1 repository, you can use the branch here.
- Showlab and bryandlee[https://github.com/bryandlee/Tune-A-Video] for their Tune-A-Video contribution that made this much easier.
- lucidrains for their implementations around video diffusion.
- cloneofsimo for their diffusers implementation of LoRA.
- kabachuha for their conversion scripts, training ideas, and webui works.
- JCBrouwer Inference implementations.
- sergiobr Helpful ideas and bug fixes.
If you find this work interesting, consider citing the original ModelScope Text-to-Video Technical Report:
@article{ModelScopeT2V,
title={ModelScope Text-to-Video Technical Report},
author={Wang, Jiuniu and Yuan, Hangjie and Chen, Dayou and Zhang, Yingya and Wang, Xiang and Zhang, Shiwei},
journal={arXiv preprint arXiv:2308.06571},
year={2023}
}