  In subject-driven text-to-image generation, recent works have achieved
superior performance by training the model on synthetic datasets containing
numerous image pairs. Trained on these datasets, generative models can produce
text-aligned images for specific subject from arbitrary testing image in a
zero-shot manner. They even outperform methods which require additional
fine-tuning on testing images. However, the cost of creating such datasets is
prohibitive for most researchers. To generate a single training pair, current
methods fine-tune a pre-trained text-to-image model on the subject image to
capture fine-grained details, then use the fine-tuned model to create images
for the same subject based on creative text prompts. Consequently, constructing
a large-scale dataset with millions of subjects can require hundreds of
thousands of GPU hours. To tackle this problem, we propose Toffee, an efficient
method to construct datasets for subject-driven editing and generation.
Specifically, our dataset construction does not need any subject-level
fine-tuning. After pre-training two generative models, we are able to generate
infinite number of high-quality samples. We construct the first large-scale
dataset for subject-driven image editing and generation, which contains 5
million image pairs, text prompts, and masks. Our dataset is 5 times the size
of previous largest dataset, yet our cost is tens of thousands of GPU hours
lower. To test the proposed dataset, we also propose a model which is capable
of both subject-driven image editing and generation. By simply training the
model on our proposed dataset, it obtains competitive results, illustrating the
effectiveness of the proposed dataset construction framework.
