GuanjieChen/Skip-DiT · Hugging Face

Towards Stabilized and Efficient Diffusion Transformers through Long-Skip-Connections with Spectral Constraints

(a) Feature similarities between standard and cache-accelerated outputs in vanilla DiT caching with FORA and Faster-Diff and Skip-DiT. Skip-DiT presents consistently higher feature similarity, demonstrating superior stability after caching. (b) Illustration of Skip-DiT that modifies vanilla DiT models using long-skip-connection to connect shallow and deep DiT blocks. Dashed arrows indicate paths where computation can be skipped in cached inference. (c) Comparison of video generation quality (PNSR) and inference speedup of different DiT caching methods. Skip-DiT maintains higher generation quality even at greater speedup factors.

🎉🎉🎉 About

This repository contains the official PyTorch implementation of the paper: Towards Stabilized and Efficient Diffusion Transformers through Long-Skip-Connections with Spectral Constraints.

Diffusion Transformers (DiT) have emerged as a powerful architecture for image and video generation, offering superior quality and scalability. However, their practical application suffers from inherent dynamic feature instability, leading to error amplification during cached inference. Through systematic analysis, we identify the absence of long-range feature preservation mechanisms as the root cause of unstable feature propagation and perturbation sensitivity. To this end, we propose Skip-DiT, an image and video generative DiT variant enhanced with Long-Skip-Connections (LSCs) - the key efficiency component in U-Nets. Theoretical spectral norm and visualization analysis demonstrate how LSCs stabilize feature dynamics. Skip-DiT architecture and its stabilized dynamic feature enable an efficient statical caching mechanism that reuses deep features across timesteps while updating shallow components. Extensive experiments across the image and video generation tasks demonstrate that Skip-DiT achieves: (1) 4.4x training acceleration and faster convergence, (2) 1.5-2x inference acceleration with negligible quality loss and high fidelity to the original output, outperforming existing DiT caching methods across various quantitative metrics.Our findings establish Long-Skip-Connections as critical architectural components for stable and efficient diffusion transformers. More visualizations can be found here.

🌟 Feature Stability of Skip-DiT

Visualization of the feature stability of Skip-DiT compared with vanilla DiT. Skip-DiT also shows superior training efficiency.

🛒 Released Models

Model	Task	Training Data	Backbone	Size(G)	Skip-Cache
Latte-skip	text-to-video	Vimeo	Latte	8.76	✅
DiT-XL/2-skip	class-to-image	ImageNet	DiT-XL/2	11.40	✅
ucf101-skip	class-to-video	UCF101	Latte	2.77	✅
taichi-skip	class-to-video	Taichi-HD	Latte	2.77	✅
skytimelapse-skip	class-to-video	SkyTimelapse	Latte	2.77	✅
ffs-skip	class-to-video	FaceForensics	Latte	2.77	✅

Pretrained text-to-image Model of HunYuan-DiT can be found in Huggingface and Tencent-cloud.

(Visualizations of Latte-Skip. You can replicate them here)

🚀 Quick Start

Text-to-video Inference

To generate videos with Latte-skip, you just need 3 steps

# 1. Prepare your conda environments
cd text-to-video ; conda env create -f environment.yaml ; conda activate latte
# 2. Download checkpoints of Latte and Latte-skip
python download.py
# 3. Generate videos with only one command line!
python sample/sample_t2v.py --config ./configs/t2v/t2v_sample_skip.yaml
# 4. (Optional) To accelerate generation with skip-cache, run following command
python sample/sample_t2v.py --config ./configs/t2v/t2v_sample_skip_cache.yaml --cache N2-700-50

Text-to-image Inference

In the same way, to generate images with Hunyuan-DiT, you only need 3 steps

# 1. Prepare your conda environments
cd text-to-image ; conda env create -f environment.yaml ; conda activate HunyuanDiT
# 2. Download checkpoints of Hunyuan-DiT
mkdir ckpts ; huggingface-cli download Tencent-Hunyuan/HunyuanDiT-v1.2 --local-dir ./ckpts
# 3. Generate images with only one command line!
python sample_t2i.py --prompt "渔舟唱晚"  --no-enhance --infer-steps 100 --image-size 1024 1024
# 4. (Optional) To accelerate generation with skip-cache, run the following command
python sample_t2i.py --prompt "渔舟唱晚"  --no-enhance --infer-steps 100 --image-size 1024 1024 --cache --cache-step 2

About the class-to-video and class-to-image task, you can found detailed instructions in class-to-video/README.md and class-to-image/README.md

🏃 Training

We have already released the training code of Latte-skip! It takes only a few days on 8 H100 GPUs. To train the text-to-video model:

Prepare your text-video datasets and implement the text-to-video/datasets/t2v_joint_dataset.py
Run the two-stage training strategy:
1. Freeze all the parameters except skip-branches. Set freeze=True in text-to-video/configs/train_t2v.yaml. And then run the training scripts at text-to-video/train_scripts/t2v_joint_train_skip.sh.
2. Overall training. Set freeze=False in text-to-video/configs/train_t2v.yaml. And then run the training scripts. The text-to-video model we released is trained with only 300k text-video pairs of Vimeo for around 1 week on 8 H100 GPUs.

The training instructions of class-to-video and text-to-video tasks can be found in class-to-video/README.md and class-to-image/README.md

🌺 Acknowledgement

Skip-DiT has been greatly inspired by the following amazing works and teams: DeepCache, Latte, DiT, and HunYuan-DiT, we thank all the contributors for open-sourcing.

License

The code and model weights are licensed under LICENSE.

Visualization

1. Teasers

(Results of Latte with skip-branches on text-to-video and class-to-video tasks with Latte. Left: text-to-video with 1.7x and 2.0x speedup. Right: class-to-video with 2.2x and 2.4x speedup. Latency is measured on one A100.)