BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing

Dongxu Li, Junnan Li, Steven Hoi

Keywords: Diffusion-based Models, Text-to-Image Generation, Image Editing, Vision-and-Language, Multimodal

TL;DR: A new diffusion model achieves zero-shot subject-driven generation/editing and efficient, high-fidelity few-step finetuning for generic subjects.

Abstract:

Subject-driven text-to-image generation models create novel renditions of an input subject based on text prompts. Existing models suffer from lengthy fine-tuning and difficulties preserving the subject fidelity. To overcome these limitations, we introduce BLIP-Diffusion, a new subject-driven image generation model that supports multimodal control which consumes inputs of subject images and text prompts.
Unlike other subject-driven generation models, BLIP-Diffusion introduces a new multimodal encoder which is pre-trained to provide subject representation. We first pre-train the multimodal encoder following BLIP-2 to produce visual representation aligned with the text.
Then we design a subject representation learning task which enables a diffusion model to leverage such visual representation and generates new subject renditions. Compared with previous methods such as DreamBooth, our model enables zero-shot subject-driven generation, and efficient fine-tuning for customized subject with up to 20x speedup. We also demonstrate that BLIP-Diffusion can be flexibly combined with existing techniques such as ControlNet and prompt-to-prompt to enable novel subject-driven generation and editing applications. Implementations are available at: https://github.com/salesforce/LAVIS/tree/main/projects/blip-diffusion.

Submission Number: 3074