Papers
Topics
Authors
Recent
Search
2000 character limit reached

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

Published 24 May 2023 in cs.CV and cs.AI | (2305.14720v2)

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/.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (39)
  1. Adding conditional control to text-to-image diffusion models. arXiv preprint arXiv:2302.05543, 2023.
  2. Prompt-to-prompt image editing with cross attention control. arXiv preprint arXiv:2208.01626, 2022.
  3. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. In International Conference on Machine Learning, pages 16784–16804. PMLR, 2022.
  4. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022.
  5. Zero-shot text-to-image generation. In International Conference on Machine Learning, pages 8821–8831. PMLR, 2021.
  6. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684–10695, 2022.
  7. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022.
  8. An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprint arXiv:2208.01618, 2022.
  9. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. arXiv preprint arXiv:2208.12242, 2022.
  10. Multi-concept customization of text-to-image diffusion. arXiv preprint arXiv:2212.04488, 2022.
  11. Designing an encoder for fast personalization of text-to-image models. arXiv preprint arXiv:2302.12228, 2023.
  12. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597, 2023.
  13. Denoising diffusion implicit models. In International Conference on Learning Representations, 2021.
  14. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840–6851, 2020.
  15. Diffusion models beat gans on image synthesis. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, volume 34, pages 8780–8794. Curran Associates, Inc., 2021.
  16. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
  17. Taming encoder for zero fine-tuning image customization with text-to-image diffusion models. arXiv preprint arXiv:2304.02642, 2023.
  18. Instantbooth: Personalized text-to-image generation without test-time finetuning. arXiv preprint arXiv:2304.03411, 2023.
  19. Subject-driven text-to-image generation via apprenticeship learning. arXiv preprint arXiv:2304.00186, 2023.
  20. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748–8763. PMLR, 2021.
  21. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415, 2016.
  22. The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale. International Journal of Computer Vision, 128(7):1956–1981, 2020.
  23. Image segmentation using text and image prompts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7086–7096, 2022.
  24. A closed-form solution to natural image matting. IEEE transactions on pattern analysis and machine intelligence, 30(2):228–242, 2007.
  25. Pymatting: A python library for alpha matting. Journal of Open Source Software, 5(54):2481, 2020.
  26. Decoupled weight decay regularization. In International Conference on Learning Representations, 2017.
  27. Edict: Exact diffusion inversion via coupled transformations. arXiv preprint arXiv:2211.12446, 2022.
  28. Laion-400m: Open dataset of clip-filtered 400 million image-text pairs. arXiv preprint arXiv:2111.02114, 2021.
  29. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning, pages 12888–12900. PMLR, 2022.
  30. Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740–755. Springer, 2014.
  31. Visual genome: Connecting language and vision using crowdsourced dense image annotations. International journal of computer vision, 123:32–73, 2017.
  32. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2556–2565, 2018.
  33. Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3558–3568, 2021.
  34. Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT, pages 4171–4186, 2019.
  35. Instructpix2pix: Learning to follow image editing instructions. arXiv preprint arXiv:2211.09800, 2022.
  36. Re-imagen: Retrieval-augmented text-to-image generator. arXiv preprint arXiv:2209.14491, 2022.
  37. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9650–9660, 2021.
  38. Im2text: Describing images using 1 million captioned photographs. Advances in neural information processing systems, 24, 2011.
  39. Pseudo numerical methods for diffusion models on manifolds. In International Conference on Learning Representations, 2022.
Citations (219)
View on Semantic Scholar

Summary

  • The paper presents BLIP-Diffusion, which leverages a pre-trained multimodal encoder for efficient, subject-specific text-to-image generation and editing.
  • It employs a two-stage pre-training strategy that aligns image features with textual prompts, reducing fine-tuning steps up to 20x compared to earlier methods.
  • The method integrates with frameworks like ControlNet to improve subject fidelity and controllability in generated images for advanced editing applications.

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

BLIP-Diffusion introduces an innovative approach in the domain of text-to-image generation by embedding subject-driven generation capabilities through a pre-trained multimodal encoder. Building upon the architecture of BLIP-2 and a latent diffusion model, it facilitates efficient zero-shot or few-step fine-tuning processes. This document dissects the methodology and implications of the BLIP-Diffusion model, focusing on its application in controllable generation and editing.

Introduction to BLIP-Diffusion

The primary distinction of BLIP-Diffusion lies in its subject-driven approach enabled through a robust multimodal encoder. Traditional methods like DreamBooth and Textual Inversion depend heavily on extensive fine-tuning to adapt to new subjects, which restricts scalability and efficiency. The BLIP-Diffusion model addresses these constraints by employing a pre-trained subject representation that can align visual and textual inputs seamlessly. Figure 1

Figure 1: Leveraging the pre-trained subject representation, BLIP-Diffusion enables subject-driven generation under efficient fine-tuning or zero-shot setups.

Unlike standard text-to-image models, it integrates image and text as control inputs, enhancing the model's ability to maintain subject fidelity during novel renditions. This integration is achieved through a two-stage pre-training strategy that first aligns image features with text and then enables the diffusion model to generate new subject-specific images.

Methodology

Pre-Training Strategy

BLIP-Diffusion's pre-training involves two critical stages: multimodal representation learning and subject representation learning.

  1. Multimodal Representation Learning: This stage helps align image features with textual prompts by leveraging BLIP-2's vision-language encoder. By outputting text-aligned image features, the model can embed subject details into a diffusion framework.
  2. Subject Representation Learning: Here, the model generates novel renditions by processing input images with random backgrounds and target images. This setup encourages the model to separate subject and context, allowing flexible subject representation in generation processes. Figure 2

    Figure 2: Illustration of the two-staged pre-training for BLIP-Diffusion.

Multimodal Encoder Integration

BLIP-Diffusion employs BLIP-2's encoder to derive visual features aligned with text prompts, supplemented by a subject representation learning task. This task synthesizes subject input images, leading to a robust framework for capturing subject-specific appearance while maintaining the diffusion model's capabilities.

Fine-Tuning and Inference

The model supports both zero-shot generation and efficient fine-tuning, significantly reducing the computational overhead required by earlier methods. The fine-tuning process leverages cached subject embeddings, focusing computational resources on refining image outputs rather than recalibrating the model from scratch. Figure 3

Figure 3: \smallLeft: example training image pairs, target images (top) and input images (bottom) with random background.

Applications

BLIP-Diffusion extends beyond basic image synthesis, facilitating advanced applications such as controllable generation and intelligent image editing. It incorporates established frameworks like ControlNet and prompt-to-prompt, enhancing image structure control and subject-specific editing without necessitating retraining. Figure 4

Figure 4: \smallLeft: BLIP-Diffusion cooperates with ControlNet for structure and subject controllable generation.

Experimental Results

In comparative evaluations, BLIP-Diffusion demonstrates superior subject fidelity and prompt adherence with up to 20x efficiency in fine-tuning compared to existing benchmarks like DreamBooth. Its architecture can handle diverse subjects more efficiently, proving particularly effective in handling generalized subject categories. Figure 5

Figure 5: Qualitative results categorized by generative capabilities.

Limitations and Future Directions

Despite its strengths, BLIP-Diffusion inherits some drawbacks from its underlying diffusion mechanisms, including occasional misinterpretations of text prompts and compositional nuances. Ongoing developments in diffusion model architectures can potentially address these issues.

Conclusion

BLIP-Diffusion emerges as a versatile model in the landscape of text-to-image generation, combining high-fidelity subject representation with controlled generative capabilities. As foundational diffusion models evolve, BLIP-Diffusion offers a scalable path forward for precision-driven image generation tasks across varied domains.

Paper to Video (Beta)

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up