# Taming Diffusion Transformer for Efficient Mobile Video Generation in Seconds

## Paper ID #1249

## Supplementary Material

- Comparisons
- More Results
- Mobile Demo

## Comparisons

We provide video comparison results of our method with other methods in the paper. For LTX-Video, we follow the diffuser example to generation samples using the same prompt.
LTX-Video[1] | CogVideoX-2B[2] | Wan2.1-1.3B[3] | Ours
--- | --- | --- | ---
Prompt: 3D animation of a small, round, fluffy creature with big, expressive eyes explores a vibrant, enchanted forest. The creature, a whimsical blend of a rabbit and a squirrel, has soft blue fur and a bush.
Prompt: A cat sitting at a grand piano, elegantly playing a classical piece with its paws.
Prompt: A corgi vlogging itself in tropical Maui.
Prompt: A movie trailer featuring the adventures of the 30-year-old spaceman wearing a red wool knitted motorcycle helmet, blue sky, salt desert, cinematic style, shot on 35mm film, vivid colors.
Prompt: A pair of lovebirds preening each other's feathers.
Prompt: A skeleton wearing a flower hat and sunglasses dances in the wild at sunset.

## More Results

In this section, we provide more results of our model.

Below, we provide more results of our mobile model.

## Mobile Demo on iPhone 16 Pro Max

## References

[1] Yoav HaCohen et al. "LTX-Video: Realtime Video Latent Diffusion." https://arxiv.org/abs/2501.00103 (2024).

[2] Yang, Zhuoyi, et al. "CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer." ICLR (2025).

[3] Team Wan, et al. "Wan: Open and Advanced Large-Scale Video Generative Models." https://arxiv.org/abs/2503.20314 (2025)