





# Model Card for TempoFunk

<!-- Provide a quick summary of what the model is/does. [Optional] -->
A community produced Text-To-Video model using Temporal Attention




#  Table of Contents

- [Model Card for TempoFunk](#model-card-for--model_id-)
- [Table of Contents](#table-of-contents)
- [Table of Contents](#table-of-contents-1)
- [Model Details](#model-details)
  - [Model Description](#model-description)
- [Uses](#uses)
  - [Direct Use](#direct-use)
  - [Downstream Use [Optional]](#downstream-use-optional)
  - [Out-of-Scope Use](#out-of-scope-use)
- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
  - [Recommendations](#recommendations)
- [Training Details](#training-details)
  - [Training Data](#training-data)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications [optional]](#technical-specifications-optional)
  - [Model Architecture and Objective](#model-architecture-and-objective)
  - [Compute Infrastructure](#compute-infrastructure)
    - [Hardware](#hardware)
    - [Software](#software)
- [Model Card Authors [optional]](#model-card-authors-optional)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)


# Model Details

## Model Description

<!-- Provide a longer summary of what this model is/does. -->
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
    - [GitHub Repo](https://github.com/lopho/makeavid-sd-tpu)


# Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

The TempoFunk model is meant to be used as a Video Production Program.

## Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->

Produce Generative Video


## Downstream Use [Optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
 
Meme production
Visualization
Personalized Text-To-Video



## Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->

Produce Disinformation
Produce Gore



# Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical 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

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->


Use superres or other methods to clean up visuals before publishing or using.


# Training Details

## Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

TempoFunk was trained on movement data from dancing videos. These dancing videos were scrapped and encoded into Stable Diffusion Vae Latents. More information forthcoming.


<!-- ## Training Procedure -->

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

## Results 

[https://huggingface.co/spaces/TempoFunk/makeavid-sd-jax]

# Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **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]

<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->
Lopho, Chavez, Davut Emre, Julian Herrera

# How to Get Started with the Model

Use the space below to get started!
[https://huggingface.co/spaces/TempoFunk/makeavid-sd-jax]
{"language": ["en"], "license": "creativeml-openrail-m", "datasets": ["TempoFunk/tempofunk-sdance"], "pipeline_tag": "text-to-video"}
text-to-video en dataset:TempoFunk/tempofunk-sdance arxiv:1910.09700 license:creativeml-openrail-m region:us
TAGS
#text-to-video #en #dataset-TempoFunk/tempofunk-sdance #arxiv-1910.09700 #license-creativeml-openrail-m #region-us 







# Model Card for TempoFunk


A community produced Text-To-Video model using Temporal Attention




#  Table of Contents

- Model Card for TempoFunk
- Table of Contents
- Table of Contents
- Model Details
  - Model Description
- Uses
  - Direct Use
  - [Downstream Use [Optional]](#downstream-use-optional)
  - Out-of-Scope Use
- Bias, Risks, and Limitations
  - Recommendations
- Training Details
  - Training Data
- Environmental Impact
- [Technical Specifications [optional]](#technical-specifications-optional)
  - Model Architecture and Objective
  - Compute Infrastructure
    - Hardware
    - Software
- [Model Card Authors [optional]](#model-card-authors-optional)
- How to Get Started with the Model


# 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
    - GitHub Repo


# 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 

[URL

# 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

Use the space below to get started!
[URL
# Model Card for TempoFunk


A community produced Text-To-Video model using Temporal Attention #  Table of Contents

- Model Card for TempoFunk
- Table of Contents
- Table of Contents
- Model Details
  - Model Description
- Uses
  - Direct Use
  - [Downstream Use [Optional]](#downstream-use-optional)
  - Out-of-Scope Use
- Bias, Risks, and Limitations
  - Recommendations
- Training Details
  - Training Data
- Environmental Impact
- [Technical Specifications [optional]](#technical-specifications-optional)
  - Model Architecture and Objective
  - Compute Infrastructure
    - Hardware
    - Software
- [Model Card Authors [optional]](#model-card-authors-optional)
- How to Get Started with the Model # 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
    - GitHub Repo # 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 

[URL # 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

Use the space below to get started!
[URL
TAGS
#text-to-video #en #dataset-TempoFunk/tempofunk-sdance #arxiv-1910.09700 #license-creativeml-openrail-m #region-us 
 # Model Card for TempoFunk


A community produced Text-To-Video model using Temporal Attention #  Table of Contents

- Model Card for TempoFunk
- Table of Contents
- Table of Contents
- Model Details
  - Model Description
- Uses
  - Direct Use
  - [Downstream Use [Optional]](#downstream-use-optional)
  - Out-of-Scope Use
- Bias, Risks, and Limitations
  - Recommendations
- Training Details
  - Training Data
- Environmental Impact
- [Technical Specifications [optional]](#technical-specifications-optional)
  - Model Architecture and Objective
  - Compute Infrastructure
    - Hardware
    - Software
- [Model Card Authors [optional]](#model-card-authors-optional)
- How to Get Started with the Model # 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
    - GitHub Repo # 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 

[URL # 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

Use the space below to get started!
[URL