Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 82 tok/s
Gemini 2.5 Pro 52 tok/s Pro
GPT-5 Medium 19 tok/s Pro
GPT-5 High 17 tok/s Pro
GPT-4o 107 tok/s Pro
Kimi K2 174 tok/s Pro
GPT OSS 120B 468 tok/s Pro
Claude Sonnet 4 37 tok/s Pro
2000 character limit reached

X-Adapter: Adding Universal Compatibility of Plugins for Upgraded Diffusion Model (2312.02238v3)

Published 4 Dec 2023 in cs.CV, cs.AI, and cs.MM

Abstract: We introduce X-Adapter, a universal upgrader to enable the pretrained plug-and-play modules (e.g., ControlNet, LoRA) to work directly with the upgraded text-to-image diffusion model (e.g., SDXL) without further retraining. We achieve this goal by training an additional network to control the frozen upgraded model with the new text-image data pairs. In detail, X-Adapter keeps a frozen copy of the old model to preserve the connectors of different plugins. Additionally, X-Adapter adds trainable mapping layers that bridge the decoders from models of different versions for feature remapping. The remapped features will be used as guidance for the upgraded model. To enhance the guidance ability of X-Adapter, we employ a null-text training strategy for the upgraded model. After training, we also introduce a two-stage denoising strategy to align the initial latents of X-Adapter and the upgraded model. Thanks to our strategies, X-Adapter demonstrates universal compatibility with various plugins and also enables plugins of different versions to work together, thereby expanding the functionalities of diffusion community. To verify the effectiveness of the proposed method, we conduct extensive experiments and the results show that X-Adapter may facilitate wider application in the upgraded foundational diffusion model.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (44)
  1. Animecreative. https://civitai.com/models/146785.
  2. Kawaiitech. https://civitai.com/models/94663.
  3. Vangoghportraiture. https://civitai.com/models/157794.
  4. Animeoutline. https://civitai.com/models/16014.
  5. Moxin. https://civitai.com/models/12597.
  6. Toonyou. https://civitai.com/models/30240.
  7. Stability AI. https://huggingface.co/runwayml/stable-diffusion-v1-5, a.
  8. Stability AI. https://huggingface.co/stabilityai/stable-diffusion-2-1-base, b.
  9. Videocrafter1: Open diffusion models for high-quality video generation. arXiv preprint arxiv:2310.19512, 2023.
  10. Diffusion models beat gans on image synthesis. arXiv preprint arxiv:2105.05233, 2021.
  11. An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprint arxiv:2208.01618, 2022.
  12. Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. arXiv preprint arXiv:2307.04725, 2023.
  13. Deep residual learning for image recognition. pages 770–778, 2016.
  14. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020.
  15. Parameter-efficient transfer learning for NLP. pages 2790–2799, 2019.
  16. https://civitai.com/. civitai. https://civitai.com/, 2013.
  17. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021.
  18. Auto-encoding variational bayes. arXiv preprint arxiv:1312.6114, 2013.
  19. Variational diffusion models. arXiv preprint arxiv:2107.00630, 2021.
  20. Gligen: Open-set grounded text-to-image generation. arXiv preprint arxiv:2301.07093, 2023.
  21. Bridge diffusion model: bridge non-english language-native text-to-image diffusion model with english communities. arXiv preprint arXiv:2309.00952, 2023.
  22. Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arxiv:2108.01073, 2022.
  23. Midjourney. https://www.midjourney.com/.
  24. T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. arXiv preprint arXiv:2302.08453, 2023.
  25. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arxiv:2112.10741, 2022.
  26. OpenAI. Dall-e2. https://openai.com/dall-e-2, a.
  27. OpenAI. Dall-e3. https://openai.com/dall-e-3, b.
  28. Semantic image synthesis with spatially-adaptive normalization. arXiv preprint arxiv:1903.07291, 2019.
  29. Sdxl: improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023.
  30. Fatezero: Fusing attentions for zero-shot text-based video editing. arXiv preprint arxiv:2303.09535, 2023.
  31. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748–8763. PMLR, 2021.
  32. High-resolution image synthesis with latent diffusion models. 2021.
  33. U-net: Convolutional networks for biomedical image segmentation. arXiv preprint arxiv:1505.04597, 2015.
  34. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. arXiv preprint arxiv:2208.12242, 2023.
  35. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arxiv:2205.11487, 2022.
  36. Deep unsupervised learning using nonequilibrium thermodynamics. arXiv preprint arxiv:1503.03585, 2015.
  37. Attention is all you need. arXiv preprint arxiv:1706.03762, 2017.
  38. Styleadapter: A single-pass lora-free model for stylized image generation. arXiv preprint arxiv:2309.01770, 2023.
  39. Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models. arXiv preprint arxiv:2308.06721, 2023.
  40. Inserting anybody in diffusion models via celeb basis. arXiv preprint arXiv:2306.00926, 2023.
  41. Taca: Upgrading your visual foundation model with task-agnostic compatible adapter. arXiv preprint arxiv:2306.12642, 2023a.
  42. Show-1: Marrying pixel and latent diffusion models for text-to-video generation. arXiv preprint arxiv:2309.15818, 2023b.
  43. Adding conditional control to text-to-image diffusion models. 2023c.
  44. Revisit parameter-efficient transfer learning: A two-stage paradigm. arXiv preprint arxiv:2303.07910, 2023.
Citations (6)
View on Semantic Scholar
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

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

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

Summary

  • The paper introduces X-Adapter, a novel method that enables pre-trained plugins to work seamlessly with upgraded text-to-image diffusion models.
  • It employs trainable mapping layers to transform features from the base model, thus preserving functionality without requiring complete retraining.
  • Experimental results demonstrate improved image quality and versatile plugin remixing, offering practical benefits for AI-generated art.

Enhancing Text-to-Image Diffusion Models with Universal Plugin Compatibility: Introducing X-Adapter

In the evolving landscape of AI-generated art, diffusion models have captivated the imagination of both creators and researchers. One such model, which transforms text prompts into intricate images, is frequently augmented with plugins. These "add-ons" enhance the base functionality of the models, offering features like specific style replication or conditional image manipulation. However, these plugins often face compatibility issues when foundational models are upgraded—a situation the newly introduced X-Adapter promises to address.

The X-Adapter effectively serves as a bridge, enabling pre-trained plugins to operate seamlessly with updated diffusion models, without requiring additional retraining. This new adapter works by embedding an additional network within the diffusion model framework that guides it using newly paired text-image data.

To achieve this, during the training phase, X-Adapter maintains an untouched copy of the original model while adding trainable mapping layers to manage the transition to the new model version. These mapping layers transform features from the base model into a form that can be used to guide the upgraded one, essentially "teaching" the new model to understand and apply the existing plugins.

The method was tested extensively, showing that the X-Adapter allows for wide application across the community's foundational diffusion models. It not only retains the functionality of existing plugins but also takes advantage of the more powerful features of upgraded models, ultimately improving image quality.

One intriguing aspect of X-Adapter is its inference strategy, which enhances performance during the image generation phase. By initially processing images with the base model before passing them to the upgraded model, it becomes easier to align features and maintain the quality of the plugins' effects.

A particularly noteworthy capability of X-Adapter is the facilitation of "plugin remix," where plugins developed for different versions of the foundational model can be used interchangeably. For example, a plugin initially designed for an older version like Stable Diffusion v1.5 could be used in conjunction with one from an upgraded model like SDXL.

Despite its prowess, X-Adapter isn't without limitations. It might struggle to maintain the identity in plugins that generate personalized concepts because these work on the text-encoder rather than the feature space. These nuances however remind us of the tremendous complexity in teaching AI to comprehend and generate art.

In summary, X-Adapter represents a significant stride towards making text-to-image diffusion models more versatile and user-friendly. As the toolkit for AI-based art generation expands, solutions like X-Adapter ensure that creativity isn't hindered by technological transitions, but rather, is enhanced by them. It's an exciting time for AI art, with the promise of even more seamless and creative expression on the horizon.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
Dice Question Streamline Icon: https://streamlinehq.com

Follow-Up Questions

We haven't generated follow-up questions for this paper yet.

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

GitHub

  1. X-Adapter
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

Explore Trending Papers

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube