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@ -57,7 +57,8 @@ HunyuanVideo-1.5 is a video generation model that delivers top-tier quality with
</p>
## 🔥🔥🔥 News
* 🚀 Dec 05, 2025: **New Release**: We now release the [480p I2V step-distilled model](https://huggingface.co/tencent/HunyuanVideo-1.5/tree/main/transformer/480p_i2v_step_distilled), which generates videos in 8 or 12 steps (recommended)! On RTX 4090, end-to-end generation time is reduced by 75%, and a single RTX 4090 can generate videos within 75 seconds. The step-distilled model maintains comparable quality to the original model while achieving significant speedup. See [Step Distillation Comparison](./assets/step_distillation_comparison.md) for detailed quality comparisons. For even faster generation, you can also try 4 steps (faster speed with slightly reduced quality). **To enable the step-distilled model, run `generate.py` with the `--enable_step_distill` parameter.** See [Usage](#-usage) for detailed usage instructions. 🔥🔥🔥🆕
* 🚀 Dec 09, 2025: LoRA tuning script is released, enjoy it! 🔥🔥🔥🆕
* 🚀 Dec 05, 2025: **New Release**: We now release the [480p I2V step-distilled model](https://huggingface.co/tencent/HunyuanVideo-1.5/tree/main/transformer/480p_i2v_step_distilled), which generates videos in 8 or 12 steps (recommended)! On RTX 4090, end-to-end generation time is reduced by 75%, and a single RTX 4090 can generate videos within **75 seconds**. The step-distilled model maintains comparable quality to the original model while achieving significant speedup. See [Step Distillation Comparison](./assets/step_distillation_comparison.md) for detailed quality comparisons. For even faster generation, you can also try 4 steps (faster speed with slightly reduced quality). **To enable the step-distilled model, run `generate.py` with the `--enable_step_distill` parameter.** See [Usage](#-usage) for detailed usage instructions. 🔥🔥🔥🆕
* 📚 Dec 05, 2025: **Training Code Released**: We now open-source the training code for HunyuanVideo-1.5! The training script (`train.py`) provides a full training pipeline with support for distributed training, FSDP, context parallel, gradient checkpointing, and more. HunyuanVideo-1.5 is trained using the Muon optimizer, which we have open-sourced in the [Training](#-training) section. **If you would like to continue training our model or fine-tune it with LoRA, please use the Muon optimizer.** See [Training](#-training) section for detailed usage instructions. 🔥🔥🔥🆕
* 🎉 **Diffusers Support**: HunyuanVideo-1.5 is now available on Hugging Face Diffusers! Check out [Diffusers collection](https://huggingface.co/collections/hunyuanvideo-community/hunyuanvideo-15) for easy integration. 🔥🔥🔥🆕
* 🚀 Nov 27, 2025: We now support cache inference (deepcache, teacache, taylorcache), achieving significant speedup! Pull the latest code to try it. 🔥🔥🔥🆕
@ -498,6 +499,11 @@ torchrun --nproc_per_node=8 train.py \
| `--i2v_prob` | Probability of i2v task for video data | 0.3 |
| `--use_muon` | Use Muon optimizer | true |
| `--resume_from_checkpoint` | Resume from checkpoint directory | None |
| `--use_lora` | Enable LoRA fine-tuning | false |
| `--lora_r` | LoRA rank | 8 |
| `--lora_alpha` | LoRA alpha scaling parameter | 16 |
| `--lora_dropout` | LoRA dropout rate | 0.0 |
| `--pretrained_lora_path` | Path to pretrained LoRA adapter | None |
#### 4. Monitor Training
@ -514,6 +520,28 @@ python train.py \
--resume_from_checkpoint ./outputs/checkpoint-1000
```
#### 6. LoRA Fine-tuning
To enable LoRA fine-tuning, add `--use_lora` to your training command. LoRA adapters will be saved in the checkpoint directory under `lora/`:
```bash
torchrun --nproc_per_node=8 train.py \
--pretrained_model_root ./ckpts \
--use_lora \
--lora_r 8 \
--lora_alpha 16 \
--learning_rate 1e-4 \
--output_dir ./outputs
```
To load a pretrained LoRA adapter, use `--pretrained_lora_path`:
```bash
torchrun --nproc_per_node=8 train.py \
--pretrained_model_root ./ckpts \
--use_lora \
--pretrained_lora_path ./outputs/checkpoint-1000/lora/default
```
## 📊 Evaluation