Model Card for TempoFunk
A community produced Text-To-Video model using Temporal Attention
Table of Contents
Model Details
Model Description
A community produced Text-To-Video model using Temporal Attention
- Developed by: Lopho, Chavez, Davut Emre, Julian Herrera
- Shared by [Optional]: More information needed
- Model type: Text-To-Video
- Language(s) (NLP): en
- License: creativeml-openrail-m
- Resources for more information: More information needed
Uses
The TempoFunk model is meant to be used as a Video Production Program.
Direct Use
Produce Generative Video
Downstream Use [Optional]
Meme production
Visualization
Personalized Text-To-Video
Out-of-Scope Use
Produce Disinformation
Produce Gore
Bias, Risks, and Limitations
During usage of TempoFunk, it may generate obscene or otherwise unpleasant to look imagery. This is because of both the VAE and the low amount of samples seen by the TempoFunk model. Video generated by TempoFunk may be uncanny.
Recommendations
Use superres or other methods to clean up visuals before publishing or using.
Training Details
Training Data
TempoFunk was trained on movement data from dancing videos. These dancing videos were scrapped and encoded into Stable Diffusion Vae Latents. More information forthcoming.
Results
Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
- Hardware Type: More information needed
- Hours used: More information needed
- Cloud Provider: More information needed
- Compute Region: More information needed
- Carbon Emitted: More information needed
Technical Specifications [optional]
Model Architecture and Objective
The temporal layers are a port of Make-A-Video PyTorch to FLAX.
The convolution is pseudo 3D and seperately convolves accross the spatial dimension in 2D and over the temporal dimension in 1D.
Temporal attention is purely self attention and also separately attends to time.
Only the new temporal layers have been fine tuned on a dataset of videos themed around dance.
The model has been trained for 80 epochs on a dataset of 18,000 Videos with 120 frames each, randomly selecting a 24 frame range from each sample.
Compute Infrastructure
TPU_V4
Hardware
TPU_V4
Software
Google JAX
Google FLAX
Model Card Authors [optional]
Lopho, Chavez, Davut Emre, Julian Herrera
How to Get Started with the Model
