README.md

---
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)


### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

You can directly use the model to generate detailed prompts for any arbitrary text.


```python
from transformers import GPT2LMHeadModel, GPT2Tokenizer


def generate(text, model, tokenizer,device,do_sample,top_k=100, epsilon_cutoff=.00005, temperature=1):
    #mark the text with special tokens
    text=[tokenizer.eos_token +  i + tokenizer.eos_token for i in text]
    batch=tokenizer(text, padding=True, return_tensors="pt")

    input_ids = batch["input_ids"].to(device)
    attention_mask = batch["attention_mask"].to(device)

    #how many new tokens to generate at max
    max_prompt_length=50

    generated_ids = model.generate(input_ids=input_ids,attention_mask=attention_mask, max_new_tokens=max_prompt_length, do_sample=do_sample,top_k=top_k, epsilon_cutoff=epsilon_cutoff, temperature=temperature)
    #return only the generated prompts
    pred_caps = tokenizer.batch_decode(generated_ids[:, -(generated_ids.shape[1] - input_ids.shape[1]):], skip_special_tokens=True)

    return pred_caps

device='cpu'
model = GPT2LMHeadModel.from_pretrained('fittar/ViPE-M-CTX7')
model.to(device)

#ViPE-M's tokenizer is identical to that of GPT2-Medium
tokenizer = GPT2Tokenizer.from_pretrained('gpt2-medium')
tokenizer.pad_token = tokenizer.eos_token

# A list of abstract/figurative or any arbitrary combinations of keywords
texts=['lalala', 'I wanna start learning', 'free your mind; you will see the other side of life', 'brave; fantasy']

prompts=generate(texts,model,tokenizer,do_sample=True,device=device)
for t,p in zip(texts,prompts):
    print('{} --> {}'.format(t,p))

lalala -->  A group of people chanting "la la la" around a bonfire on a beach at night
I wanna start learning -->  A child sitting in a library surrounded by books, excitedly flipping through pages of a book
free your mind; you will see the other side of life -->  An astronaut floating in space with a sense of floating weightlessness, looking down towards the earth
brave; fantasy -->  A brave knight with shining armor fighting a fierce dragon in a misty forest

```


### Recommendations

You can use either a comma or a semicolon to combine multiple keywords. for example ['dark, fantasy, brave'] or  ['This is gonna be the best day of my life; do you agree?'].
However, a semicolon draws a stronger boundary between the keywords and encourages the model to transfer the last keyword in a given context (previous keywords).


## Training Details

### Training Data

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[More Information Needed]

### 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

<!-- This section describes the evaluation protocols and provides the results. -->

### 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]

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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]

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**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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