From c1e18f6722cabf87eeecdd8cd1d648bc952c38db Mon Sep 17 00:00:00 2001 From: Hao Zhang Date: Thu, 5 Feb 2026 16:28:00 -0800 Subject: [PATCH] Some minor fixes (#1068) --- README.md | 46 +++++++------------------ fastvideo/training/training_pipeline.py | 2 -- 2 files changed, 13 insertions(+), 35 deletions(-) diff --git a/README.md b/README.md index 9466b3fcc..f2e34ff2c 100644 --- a/README.md +++ b/README.md @@ -29,7 +29,7 @@ FastVideo has the following features: - Data preprocessing pipeline for video, image, and text data - Distribution Matching Distillation (DMD2) stepwise distillation. - Sparse attention with [Video Sparse Attention](https://arxiv.org/pdf/2505.13389) - - [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) to achineve >50x denoising speedup + - [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) to achieve >50x denoising speedup - Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing. - Causal distillation through Self-Forcing - See this [page](https://hao-ai-lab.github.io/FastVideo/training/overview/) for full list of supported models and recipes. @@ -108,55 +108,35 @@ python example.py For a more detailed guide, please see our [inference quick start](https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/). -### Other docs: +## More Guides - [Design Overview](https://hao-ai-lab.github.io/FastVideo/design/overview/) -- [Contribution Guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/) - -## Distillation and Finetuning - [Distillation Guide](https://hao-ai-lab.github.io/FastVideo/distillation/dmd/) +- [Contribution Guide](https://hao-ai-lab.github.io/FastVideo/contributing/overview/) ## Awesome work using FastVideo or our research projects -- [SGLang](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen): SGLang's diffusion inference functionality is based on a fork of FastVideo on Sept. 24, 2025. [![Star](https://img.shields.io/github/stars/sgl-project/sglang.svg?style=social&label=Star)](https://github.com/sgl-project/sglang) - -- [DanceGRPO](https://github.com/XueZeyue/DanceGRPO): A unified framework to adapt Group Relative Policy Optimization (GRPO) to visual generation paradigms. Code based on FastVideo. [![Star](https://img.shields.io/github/stars/XueZeyue/DanceGRPO.svg?style=social&label=Star)](https://github.com/XueZeyue/DanceGRPO) -- [SRPO](https://github.com/Tencent-Hunyuan/SRPO): A method to directly align the full diffusion trajectory with fine-grained human preference. Code based on FastVideo. [![Star](https://img.shields.io/github/stars/Tencent-Hunyuan/SRPO.svg?style=social&label=Star)](https://github.com/Tencent-Hunyuan/SRPO) -- [DCM](https://github.com/Vchitect/DCM): Dual-expert consistency model for efficient and high-quality video generation. Code based on FastVideo. [![Star](https://img.shields.io/github/stars/Vchitect/DCM.svg?style=social&label=Star)](https://github.com/Vchitect/DCM) -- [Hunyuan Video 1.5](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5): A leading lightweight video generation model, where they proposed SSTA based on Sliding Tile Attention. [![Star](https://img.shields.io/github/stars/Tencent-Hunyuan/HunyuanVideo-1.5.svg?style=social&label=Star)](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5) -- [Kandinsky-5.0](https://github.com/kandinskylab/kandinsky-5): A family of diffusion models for video & image generation, where their NABLA attention includes a Sliding Tile Attention branch. [![Star](https://img.shields.io/github/stars/kandinskylab/kandinsky-5.svg?style=social&label=Star)](https://github.com/kandinskylab/kandinsky-5) -- [LongCat Video](https://github.com/meituan-longcat/LongCat-Video): A foundational video generation model with 13.6B parameters with block-sparse attention similar to Video Sparse Attention. [![Star](https://img.shields.io/github/stars/meituan-longcat/LongCat-Video.svg?style=social&label=Star)](https://github.com/meituan-longcat/LongCat-Video) +- [SGLang](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen): SGLang's diffusion inference functionality is based on a fork of FastVideo on Sept. 24, 2025. +- [DanceGRPO](https://github.com/XueZeyue/DanceGRPO): A unified framework to adapt Group Relative Policy Optimization (GRPO) to visual generation paradigms. Code based on FastVideo. +- [SRPO](https://github.com/Tencent-Hunyuan/SRPO): A method to directly align the full diffusion trajectory with fine-grained human preference. Code based on FastVideo. +- [DCM](https://github.com/Vchitect/DCM): Dual-expert consistency model for efficient and high-quality video generation. Code based on FastVideo. +- [Hunyuan Video 1.5](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5): A leading lightweight video generation model, where they proposed SSTA based on Sliding Tile Attention. +- [Kandinsky-5.0](https://github.com/kandinskylab/kandinsky-5): A family of diffusion models for video & image generation, where their NABLA attention includes a Sliding Tile Attention branch. +- [LongCat Video](https://github.com/meituan-longcat/LongCat-Video): A foundational video generation model with 13.6B parameters with block-sparse attention similar to Video Sparse Attention. ## 🤝 Contributing We welcome all contributions. Please check out our guide [here](https://hao-ai-lab.github.io/FastVideo/contributing/overview/). See details in [development roadmap](https://github.com/hao-ai-lab/FastVideo/issues/899). -## Acknowledgement -We learned and reused code from the following projects: -- [Wan-Video](https://github.com/Wan-Video) -- [ThunderKittens](https://github.com/HazyResearch/ThunderKittens) -- [Triton](https://github.com/triton-lang/triton) -- [DMD2](https://github.com/tianweiy/DMD2) -- [diffusers](https://github.com/huggingface/diffusers) -- [xDiT](https://github.com/xdit-project/xDiT) -- [vLLM](https://github.com/vllm-project/vllm) -- [SGLang](https://github.com/sgl-project/sglang) -We thank [MBZUAI](https://ifm.mbzuai.ac.ae/), [Anyscale](https://www.anyscale.com/), and [GMI Cloud](https://www.gmicloud.ai/) for their support throughout this project. +## Acknowledgement +We learned the design and reused code from the following projects: [Wan-Video](https://github.com/Wan-Video), [ThunderKittens](https://github.com/HazyResearch/ThunderKittens), [DMD2](https://github.com/tianweiy/DMD2), [diffusers](https://github.com/huggingface/diffusers), [xDiT](https://github.com/xdit-project/xDiT), [vLLM](https://github.com/vllm-project/vllm), [SGLang](https://github.com/sgl-project/sglang). We thank [MBZUAI](https://ifm.mbzuai.ac.ae/), [Anyscale](https://www.anyscale.com/), and [GMI Cloud](https://www.gmicloud.ai/) for their support throughout this project. ## Citation -If you find FastVideo useful, please considering citing our work: +If you find FastVideo useful, please consider citing our research work: ```bibtex -@software{fastvideo2024, - title = {FastVideo: A Unified Framework for Accelerated Video Generation}, - author = {The FastVideo Team}, - url = {https://github.com/hao-ai-lab/FastVideo}, - month = apr, - year = {2024}, -} - @article{zhang2025vsa, title={Vsa: Faster video diffusion with trainable sparse attention}, author={Zhang, Peiyuan and Chen, Yongqi and Huang, Haofeng and Lin, Will and Liu, Zhengzhong and Stoica, Ion and Xing, Eric and Zhang, Hao}, diff --git a/fastvideo/training/training_pipeline.py b/fastvideo/training/training_pipeline.py index bc23a3b07..07c09b005 100644 --- a/fastvideo/training/training_pipeline.py +++ b/fastvideo/training/training_pipeline.py @@ -550,8 +550,6 @@ class TrainingPipeline(LoRAPipeline, ABC): self.optimizer.step() self.lr_scheduler.step() - training_batch.total_loss = training_batch.total_loss - training_batch.grad_norm = training_batch.grad_norm return training_batch def _resume_from_checkpoint(self) -> None: