{
  "id": "2305.14720",
  "version": "v1",
  "published": "2023-05-24T04:51:04.000Z",
  "updated": "2023-05-24T04:51:04.000Z",
  "title": "BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing",
  "authors": [
    "Dongxu Li",
    "Junnan Li",
    "Steven C. H. Hoi"
  ],
  "categories": [
    "cs.CV",
    "cs.AI"
  ],
  "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. Code and models will be released at https://github.com/salesforce/LAVIS/tree/main/projects/blip-diffusion. Project page at https://dxli94.github.io/BLIP-Diffusion-website/.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-05-24T04:51:04.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "pre-trained subject representation",
      "controllable text-to-image generation",
      "models create novel renditions",
      "enables zero-shot subject-driven generation",
      "generation models create novel"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}