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language:
- en
---
# ViPE-M-CTX7

<!-- Provide a quick summary of what the model is/does. -->

ViPE: Visualize Pretty-much Everything, is the first automated model for translating any arbitraty piece of text into a visualizable prompt. It helps any text-to-image model in figurative or non-lexical language visualizations.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** [Computer Graphics Group, University of Tuebingen](https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/computergrafik/lehrstuhl/)
- **Model type:** Auto-Regressive
- **Language:** English
- **License:** [MIT License for Non-Commercial Use](https://github.com/Hazel1994/ViPE/blob/main/LICENSE)


### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** [Github](https://github.com/Hazel1994/ViPE)
- **Paper:** [EMNLP2023](https://2023.emnlp.org/program/)
- **Demo:**[ViPE Videos] (youtube link)

## Uses

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### Direct Use

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[More Information Needed]

### Downstream Use [optional]

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[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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[More Information Needed]

## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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## 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

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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