metadata
language:
- en
pipeline_tag: text2text-generation
inference: false
ViPE-M-CTX7
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
- Developed by: Computer Graphics Group, University of Tuebingen
- Model type: Auto-Regressive
- Language: English
- License: MIT License for Non-Commercial Use
Model Sources
Music Video Genrations
- Repository: Github
- Demo:[ViPE Videos] (youtube link)
Direct Use
You can directly use the model to generate detailed prompts for any arbitrary text.
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
[More Information Needed]
Training Procedure
Evaluation
Citation
If you find ViPE useful, please cite our paper.