CogVideo/sat/README.md

SAT CogVideoX-2B

中文阅读

日本語で読む

This folder contains the inference code using SAT weights and the fine-tuning code for SAT weights.

This code is the framework used by the team to train the model. It has few comments and requires careful study.

Inference Model

1. Ensure that you have correctly installed the dependencies required by this folder.

pip install -r requirements.txt

2. Download the model weights

First, go to the SAT mirror to download the dependencies.

mkdir CogVideoX-2b-sat
cd CogVideoX-2b-sat
wget https://cloud.tsinghua.edu.cn/f/fdba7608a49c463ba754/?dl=1
mv 'index.html?dl=1' vae.zip
unzip vae.zip
wget https://cloud.tsinghua.edu.cn/f/556a3e1329e74f1bac45/?dl=1
mv 'index.html?dl=1' transformer.zip
unzip transformer.zip

Then unzip, the model structure should look like this:

.
├── transformer
│   ├── 1000
│   │   └── mp_rank_00_model_states.pt
│   └── latest
└── vae
    └── 3d-vae.pt

Next, clone the T5 model, which is not used for training and fine-tuning, but must be used.

git clone https://huggingface.co/THUDM/CogVideoX-2b.git
mkdir t5-v1_1-xxl
mv CogVideoX-2b/text_encoder/* CogVideoX-2b/tokenizer/* t5-v1_1-xxl

By following the above approach, you will obtain a safetensor format T5 file. Ensure that there are no errors when loading it into Deepspeed in Finetune.

├── added_tokens.json
├── config.json
├── model-00001-of-00002.safetensors
├── model-00002-of-00002.safetensors
├── model.safetensors.index.json
├── special_tokens_map.json
├── spiece.model
└── tokenizer_config.json

0 directories, 8 files

3. Modify the file configs/cogvideox_2b_infer.yaml.

load: "{your_CogVideoX-2b-sat_path}/transformer" ## Transformer model path

conditioner_config:
  target: sgm.modules.GeneralConditioner
  params:
    emb_models:
      - is_trainable: false
        input_key: txt
        ucg_rate: 0.1
        target: sgm.modules.encoders.modules.FrozenT5Embedder
        params:
          model_dir: "google/t5-v1_1-xxl" ## T5 model path
          max_length: 226

first_stage_config:
  target: sgm.models.autoencoder.VideoAutoencoderInferenceWrapper
  params:
    cp_size: 1
    ckpt_path: "{your_CogVideoX-2b-sat_path}/vae/3d-vae.pt" ## VAE model path

• If using txt to save multiple prompts, please refer to configs/test.txt for modification. One prompt per line. If you don't know how to write prompts, you can first use this code to call LLM for refinement.
• If using the command line as input, modify

input_type: cli

so that prompts can be entered from the command line.

If you want to change the output video directory, you can modify:

output_dir: outputs/

The default is saved in the .outputs/ folder.

4. Run the inference code to start inference

bash inference.sh

Fine-Tuning the Model

Preparing the Environment

Please note that currently, SAT needs to be installed from the source code for proper fine-tuning.

You need to get the code from the source to support the fine-tuning functionality, as these features have not yet been released in the Pip package.

We will address this issue in future stable releases.

git clone https://github.com/THUDM/SwissArmyTransformer.git
cd SwissArmyTransformer
pip install -e .

Preparing the Dataset

The dataset format should be as follows:

.
├── labels
│   ├── 1.txt
│   ├── 2.txt
│   ├── ...
└── videos
    ├── 1.mp4
    ├── 2.mp4
    ├── ...

Each txt file should have the same name as its corresponding video file and contain the labels for that video. Each video should have a one-to-one correspondence with a label. Typically, a video should not have multiple labels.

For style fine-tuning, please prepare at least 50 videos and labels with similar styles to facilitate fitting.

Modifying the Configuration File

We support both Lora and full-parameter fine-tuning methods. Please note that both fine-tuning methods only apply to the transformer part. The VAE part is not modified. T5 is only used as an Encoder.

the configs/cogvideox_2b_sft.yaml (for full fine-tuning) as follows.

  # checkpoint_activations: True ## using gradient checkpointing (both checkpoint_activations in the configuration file need to be set to True)
  model_parallel_size: 1 # Model parallel size
  experiment_name: lora-disney  # Experiment name (do not change)
  mode: finetune # Mode (do not change)
  load: "{your_CogVideoX-2b-sat_path}/transformer" # Transformer model path
  no_load_rng: True # Whether to load the random seed
  train_iters: 1000 # Number of training iterations
  eval_iters: 1 # Number of evaluation iterations
  eval_interval: 100 # Evaluation interval
  eval_batch_size: 1 # Batch size for evaluation
  save: ckpts # Model save path
  save_interval: 100 # Model save interval
  log_interval: 20 # Log output interval
  train_data: [ "your train data path" ]
  valid_data: [ "your val data path" ] # Training and validation sets can be the same
  split: 1,0,0 # Ratio of training, validation, and test sets
  num_workers: 8 # Number of worker threads for data loading
  force_train: True # Allow missing keys when loading ckpt (refer to T5 and VAE which are loaded independently)
  only_log_video_latents: True # Avoid using VAE decoder when eval to save memory

If you wish to use Lora fine-tuning, you also need to modify:

model:
  scale_factor: 1.15258426
  disable_first_stage_autocast: true
  not_trainable_prefixes: [ 'all' ] ## Uncomment
  log_keys:
    - txt'

  lora_config: ## Uncomment
    target: sat.model.finetune.lora2.LoraMixin
    params:
      r: 256

Fine-Tuning and Validation

1. Run the inference code to start fine-tuning.

bash finetune_single_gpu.sh # Single GPU
bash finetune_multi_gpus.sh # Multi GPUs

Converting to Huggingface Diffusers Supported Weights

The SAT weight format is different from Huggingface's weight format and needs to be converted. Please run:

python ../tools/convert_weight_sat2hf.py

Note: This content has not yet been tested with LORA fine-tuning models.