superprompt v1 EU AI Act Compliance Profile

SuperPrompt

A T5 model fine-tuned on the SuperPrompt dataset for upsampling text prompts to more detailed descriptions. Meant to be used as a pre-generation step for text-to-image models that benefit from more detailed prompts.

Usage

pip install transformers

from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("roborovski/superprompt-v1")
model = T5ForConditionalGeneration.from_pretrained("roborovski/superprompt-v1", device_map="auto")

input_text = "Expand the following prompt to add more detail: A storefront with 'Text to Image' written on it."
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(input_ids, max_new_tokens=77)
print(tokenizer.decode(outputs[0]))

# The neon sign above the storefront reads "NeurIPS" in bold, white letters. The storefront is surrounded by a bustling cityscape, with skyscrapers and neon signs lining the walls. The sign is surrounded by a variety of colorful goods, including a variety of fruits, vegetables, and fruits, all arranged in a neat and organized manner. The storefront is surrounded by a bustling crowd of people, all chatting and laughing as they go about their daily routines.

It's important that you use the exact task prefix: Expand the following prompt to add more detail: and limit the max tokens to 77 - which is the max prompt length for Stable Diffusion models.

Examples

Sample prompts taken from the DrawBench dataset.

| Prompt | Upsampled | | --- | --- | | A storefront with 'Hello World' written on it. | The storefront of "Hello World" is painted in vibrant shades of orange and yellow, with the words "Hello World" written in bold, cursive letters across the front. The storefront is surrounded by a bustling cityscape, with skyscrapers and neon signs lining the walls. The storefront is surrounded by a sea of people, all gazing up at the sky wit

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