What is FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space

FSVideo is a research project that turns a single image into a short video, very fast. It keeps video quality high while working in a small, compressed space to save time and memory.

FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space

It is built by the FSVideo Team at Intelligent Creation, ByteDance. The team shares a paper, a gallery, and clear speed numbers on the project page.

Read More: Text To Video

FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space Overview

Here is a quick view of what the project is about.

Item	Details
Type	Image-to-Video diffusion model (I2V)
Purpose	Make short, high-quality videos from a single image, fast
Creator	FSVideo Team, Intelligent Creation, ByteDance
Core Ideas	1) New video autoencoder with highly-compressed latent space (64 × 64 × 4). 2) Diffusion Transformer (DiT) with a new layer memory design. 3) Few-step, multi-resolution upsampler for sharper videos.
Model Size	14B DiT base + 14B DiT upsampler
Speed (from the team)	720×1280, 5-second video on 2×H100 GPUs: Base DiT 14.2s + SR DiT 4.6s = 18.8s total; 60 NFE at low res + 8 NFE at high res
Input	A single image (I2V)
Output	Short video (example: 5 seconds at 720×1280)
Project Page	https://kingofprank.github.io/fsvideo/
Paper	https://arxiv.org/abs/2602.02092

FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space Key Features

Fast video creation: The DiT part makes a 5-second 720×1280 video in about 18.8 seconds on 2 H100 GPUs.
Strong compression: A new video autoencoder shrinks data by 64 × 64 × 4 while keeping good detail.
Sharper finish: A few-step upsampler boosts resolution and adds detail at the end.
Better memory flow: The “layer memory” design helps reuse context inside the model.
Scales well: The team reports a 14B base and a 14B upsampler for high quality and speed.

Read More: Bytedance

FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space Use Cases

Creative previews: Turn a still image into a moving scene for storyboards or mood films.
Product teasers: Make quick motion clips from product photos for ads or social posts.
Concept videos: Show camera motion or subtle actions on art and design mockups.
Education and media: Add motion to stills for explainers and news clips.
Social content: Make short, fun videos from selfies or photos while keeping quality high.

How FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space Works (Plain English)

FSVideo first compresses video data into a small space using a special autoencoder. This small space keeps key motion and look, but is much lighter to process.

Then a Diffusion Transformer (DiT) builds the motion over a few steps. A “layer memory” helps pass useful info across layers, so the model does not waste effort.

Last, a few-step upsampler boosts the video to a higher resolution. This adds fine detail without needing many extra steps.

The Technology Behind It (Kept Simple)

Highly-compressed latent space: The data is shrunk by 64 × 64 × 4 across space and time. The goal is speed, while keeping the look close to the input.
Diffusion with DiT: Diffusion builds frames step by step. The DiT model guides these steps.
Layer memory: This helps the model remember and reuse what it learned in earlier layers.
Multi-resolution steps: The model first works at low size (more steps), then finishes at high size (fewer steps).

If you want a friendly intro to how text or images can become video, check our short guide here: basic text-to-video concepts.

Meet the Team Behind FSVideo

The project credits the FSVideo Team at Intelligent Creation, ByteDance. You can find names and roles on the project page.

FSVideo Team

For more background, see our quick intro page here: FSVideo overview.

Performance & Showcases

Below are demo clips shared by the team. Each item is labeled exactly as shown on the project site.

Showcase 1 — FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space This clip is labeled: "FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space FSVideo Team Intelligent Creation, ByteDance Paper". It presents the project’s image-to-video results in action.

Showcase 2 — FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space This clip is labeled: "FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space FSVideo Team Intelligent Creation, ByteDance Paper". It highlights sample motion and detail created from still images.

Showcase 3 — FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space This clip is labeled: "FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space FSVideo Team Intelligent Creation, ByteDance Paper". It shows how the model handles different scenes and styles.

Showcase 4 — FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space This clip is labeled: "FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space FSVideo Team Intelligent Creation, ByteDance Paper". It offers another view of quality and motion smoothness from the same setup.

Showcase 5 — FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space This clip is labeled: "FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space FSVideo Team Intelligent Creation, ByteDance Paper". It displays more examples that match the paper and gallery.

Showcase 6 — FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space This clip is labeled: "FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space FSVideo Team Intelligent Creation, ByteDance Paper". It rounds out the set with further cases from the authors.

Getting Started: What You Can Do Today

Visit the project page: https://kingofprank.github.io/fsvideo/. You can watch the gallery and read the paper.
Check the speed notes on the page. The team lists how long it takes to make a 5-second 720×1280 video on 2 H100 GPUs.
Watch all demos above to see quality and motion.

If you want a simple path from text to moving clips, see our short guide: how text becomes video.

Practical Tips for Better Results

Start with a clear, well-lit image. Sharper inputs tend to yield sharper videos.
Use images with a simple subject for your first tests. This makes motion easier to judge.
Compare low-res and high-res outputs side by side. You can spot where the upsampler adds fine detail.

Speed Notes (From the Authors)

720×1280, 5-second video on 2 H100 GPUs: 60 steps at low size, then 8 steps at high size.
Base DiT time: 14.2 seconds.
Super-resolution DiT time: 4.6 seconds.
Total DiT inference time: 18.8 seconds.

Ethical Notes

The team states the images and videos on the page are from public sources or made by AI. They are used only to show research work. If there is a concern, they will remove the content in time.

FAQs

What kind of input does FSVideo need?

It needs a single image to start. The model turns that image into a short video clip.

How fast is FSVideo?

The team reports about 18.8 seconds to make a 5-second 720×1280 video on 2 H100 GPUs. This includes both the base model and the upsampler.

Is the project open for use right now?

The page shares the paper, gallery, and speed notes. Check the project site for any updates on code or model access.

What makes FSVideo special?

It works in a very small compressed space to save time. It also uses a memory design inside the model to keep helpful context across layers.

Where can I learn more?

Visit the official page and the paper. For a friendly intro to video AI, see our simple guide here: Text to video basics.

Image source: FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space