DALL·E 3 Image prompt reverse-engineering
Pre-trained image-captioning model BLIP fine-tuned on a mixture of laion/dalle-3-dataset and semi-automatically gathered (image, prompt) data from DALLE·E 3.
It takes a generated image as an input and outputs a potential prompt to generate such an image, which can then be used as a base to generate similar images.
⚠️ Disclaimer: This model is not intended for commercial use as the data it was trained on includes images generated by DALLE·E 3. This is for educational purposes only.
Usage:
Loading the model and preprocessor:
from transformers import BlipForConditionalGeneration, AutoProcessor
model = BlipForConditionalGeneration.from_pretrained("dblasko/blip-dalle3-img2prompt").to(device)
processor = AutoProcessor.from_pretrained("dblasko/blip-dalle3-img2prompt")
Inference example on an image from laion/dalle-3-dataset:
from datasets import load_dataset
dataset = load_dataset("laion/dalle-3-dataset", split=f'train[0%:1%]') # for fast download time in the toy example
example = dataset[img_index][0]
image = example["image"]
caption = example["caption"]
inputs = processor(images=image, return_tensors="pt").to(device)
pixel_values = inputs.pixel_values
generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(f"Generated caption: {generated_caption}\nReal caption: {caption}")