Roborovski

Models by this creator

superprompt-v1

roborovski

Total Score

68

The superprompt-v1 model is a T5 model fine-tuned on the SuperPrompt dataset to upsampled text prompts into more detailed descriptions. This can be used as a pre-generation step for text-to-image models that benefit from more detailed prompts. The model was developed by the maintainer roborovski. Similar models include cosmo-1b, a 1.8B model trained on synthetic data, t5-base-finetuned-question-generation-ap, a T5-base model fine-tuned on SQuAD for question generation, and t5-large, the 770M parameter checkpoint of Google's T5 model. Model inputs and outputs The superprompt-v1 model takes in a text prompt as input and generates a more detailed version of that prompt as output. For example, given the prompt "A storefront with 'Text to Image' written on it", the model might generate: Inputs A text prompt to be expanded Outputs A more detailed version of the input prompt, with additional descriptive details added Capabilities The superprompt-v1 model can take a simple text prompt and expand it into a more detailed description. This can be useful for text-to-image models that benefit from more specific and nuanced prompts. The model was able to add details about the storefront's surroundings, the neon sign, and the bustling crowd in the example prompt. What can I use it for? You can use the superprompt-v1 model as a pre-processing step for generating images from text. By feeding your initial text prompt into the superprompt-v1 model, you can obtain a more detailed prompt that can then be used as input for a text-to-image model like Stable Diffusion. This may result in higher quality and more detailed generated images. Things to try One interesting thing to try with the superprompt-v1 model is to experiment with prompts of varying complexity and length. See how the model handles simple, one-sentence prompts versus more elaborate, multi-sentence ones. You could also try providing the model with prompts that have specific requirements or constraints, such as a limit on the maximum number of tokens, and observe how it adapts the output to meet those guidelines.

Read more

Updated 5/17/2024