Helios/_DEV2/scripts/training/README.md

# <u>Training Details</u> by *Helios*


## 🎉 Overview

We use a three-stage progressive pipeline, all the setting can be found [here](./configs). Stage-1 (Base) performs architectural adaptation: we apply Unified History Injection, Easy Anti-Drifting, and Multi-Term Memory Patchification to convert the bidirectional pretrained model into an autoregressive generator. Stage-2 (Mid) targets token compression by introducing Pyramid Unified Predictor Corrector, which aggressively reduces the number of noisy tokens and thus the overall computation. Stage-3 (Distilled) applies Adversarial Hierarchical Distillation, reducing the sampling steps from 50 to 3 and eliminating the need for classifier-free guidance (CFG). Throughout training, we apply dynamic shifting to all timestep-dependent operations to match the noise schedule to the latent size. For Stages 1 and 2, training is further divided into two phases: a high learning-rate phase for rapid convergence, followed by a low learning-rate phase for refinement.

<div align=center>
  <img src="https://raw.githubusercontent.com/PKU-YuanGroup/Helios-Page/main/figures/training_configs.png">
</div>

### Data Preparation

Please refer to [this guide](../..//tools/offload_data/README.md) for how to obtain the training data required by Helios. And we prepare a toy training data [here](https://huggingface.co/BestWishYsh/HeliosBench-Weights/tree/main/demo_data).

### Run the model

```bash
# Use DDP
bash scripts/training/train_ddp.sh

# or

# Use DeepSpeed
bash scripts/training/train_deepspeed.sh
```

Training configuration can be adjusted in `./configs`. You can use `./compare_yaml.py` to check for configuration completeness or differences between stages.

### Model Merging

After training, you can use this [script](../..//tools/merge_lora_for_helios.py) to merge all the checkpoints and obtain the final safetensors file, similar to [this](https://huggingface.co/BestWishYsh/Helios-Distilled/tree/main/transformer).


## 💡 Important

Based on the findings in [issue #38](https://github.com/PKU-YuanGroup/Helios/issues/38), we have identified several areas with potential for further improving Helios's performance. These include fixing the train-inference inconsistency in i2v to address the issue where i2v tends to produce very slow motion at the beginning, as well as fully enabling Easy Anti-Drifting to enhance Helios's resistance to quality degradation over time. For the relevant configuration details, please refer to [correct.yaml](./configs/correct.yaml).