W.A.L.T
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Stanford University,Google Research,Georgia Institute of Technology
- Organisation type
- Academia,Industry,Academia
- Country
- United States of America
- Published
- 11 December 2023
- Authors
- Agrim Gupta, Lijun Yu, Kihyuk Sohn, Xiuye Gu, Meera Hahn, Li Fei-Fei, Irfan Essa, Lu Jiang, José Lezama
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Video
- Task
- Video generation, Text-to-video
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 4.7B
- Training data
- tokens
"We train our base model at resolution 17 × 128 × 128 (3B parameters), and two 2× cascaded super-resolution models for 17 × 128 × 224 → 17 × 256 × 448 (L, 1.3B, p = 2) and 17 × 256 × 448 → 17 × 512 × 896 (L, 419M, p = 2) respectively" 3B + 1.3B + 419M = 4.719B
"We train W.A.L.T for text-to-video jointly on text-image and text-video pairs (Sec. 4.2). We used a dataset of ∼970M text-image pairs and ∼89M text-video pairs from the public internet and internal sources. "
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
"these design decisions enable us to achieve state-of-the-art performance on established video (UCF-101 and Kinetics-600) and image (ImageNet) generation benchmarks without using classifier free guidance"
Sources
Where this record came from and when it was last checked.
- Reference
- Photorealistic Video Generation with Diffusion Models
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
W.A.L.T was published by Stanford University,Google Research,Georgia Institute of Technology, in United States of America, in December 2023. academia,Industry,Academia is the category the publisher falls under.
It works in Video, and is recorded as doing video generation, Text-to-video.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
W.A.L.T — common questions
How many parameters does W.A.L.T have?
W.A.L.T has 4.7B parameters. "We train our base model at resolution 17 × 128 × 128 (3B parameters), and two 2× cascaded super-resolution models for 17 × 128 × 224 → 17 × 256 × 448 (L, 1.3B, p = 2) and 17 × 256 × 448 → 17 × 512 × 896 (L, 419M, p = 2) respectively" 3B + 1.3B + 419M = 4.719B. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created W.A.L.T?
W.A.L.T was published by Stanford University,Google Research,Georgia Institute of Technology, based in United States of America, categorised as academia,Industry,Academia.
When was W.A.L.T released?
W.A.L.T was published in December 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is W.A.L.T used for?
W.A.L.T works in Video, and is recorded as handling video generation, Text-to-video. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run W.A.L.T?
None. W.A.L.T is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is W.A.L.T open source?
No. W.A.L.T has not had its weights published, so it exists only as a service controlled by its owner.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.