Scaling Down Text Encoders of Text-to-Image Diffusion Models
This work makes high-quality text-to-image generation more accessible by reducing computational costs, though it is incremental as it optimizes an existing component.
The paper tackles the problem of oversized text encoders in text-to-image diffusion models by showing that a distilled T5-base model can generate images of comparable quality to the much larger T5-XXL encoder while being 50 times smaller, significantly reducing GPU requirements for models like FLUX and SD3.
Text encoders in diffusion models have rapidly evolved, transitioning from CLIP to T5-XXL. Although this evolution has significantly enhanced the models' ability to understand complex prompts and generate text, it also leads to a substantial increase in the number of parameters. Despite T5 series encoders being trained on the C4 natural language corpus, which includes a significant amount of non-visual data, diffusion models with T5 encoder do not respond to those non-visual prompts, indicating redundancy in representational power. Therefore, it raises an important question: "Do we really need such a large text encoder?" In pursuit of an answer, we employ vision-based knowledge distillation to train a series of T5 encoder models. To fully inherit its capabilities, we constructed our dataset based on three criteria: image quality, semantic understanding, and text-rendering. Our results demonstrate the scaling down pattern that the distilled T5-base model can generate images of comparable quality to those produced by T5-XXL, while being 50 times smaller in size. This reduction in model size significantly lowers the GPU requirements for running state-of-the-art models such as FLUX and SD3, making high-quality text-to-image generation more accessible.