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Improving In-Context Learning in Diffusion Models with Visual Context-Modulated Prompts (2312.01408v1)

Published 3 Dec 2023 in cs.CV
Improving In-Context Learning in Diffusion Models with Visual Context-Modulated Prompts

Abstract: In light of the remarkable success of in-context learning in LLMs, its potential extension to the vision domain, particularly with visual foundation models like Stable Diffusion, has sparked considerable interest. Existing approaches in visual in-context learning frequently face hurdles such as expensive pretraining, limiting frameworks, inadequate visual comprehension, and limited adaptability to new tasks. In response to these challenges, we introduce improved Prompt Diffusion (iPromptDiff) in this study. iPromptDiff integrates an end-to-end trained vision encoder that converts visual context into an embedding vector. This vector is subsequently used to modulate the token embeddings of text prompts. We show that a diffusion-based vision foundation model, when equipped with this visual context-modulated text guidance and a standard ControlNet structure, exhibits versatility and robustness across a variety of training tasks and excels in in-context learning for novel vision tasks, such as normal-to-image or image-to-line transformations. The effectiveness of these capabilities relies heavily on a deep visual understanding, which is achieved through relevant visual demonstrations processed by our proposed in-context learning architecture.

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Improving In-Context Learning in Diffusion Models with Visual Context-Modulated Prompts

This paper, authored by Tianqi Chen et al., presents a novel approach to enhancing in-context learning for diffusion models within the vision domain. The method, named Improved Prompt Diffusion (iPromptDiff), significantly advances the capabilities of visual foundation models like Stable Diffusion by addressing several key challenges inherent in visual in-context learning. These include high pretraining costs, restrictive problem formulations, limited visual comprehension, and difficulties in adapting to new tasks.

Key Contributions

The primary contributions of this research are as follows:

  1. Decoupled Processing for Visual Context: The authors address the inefficiency in previous methods that encode image queries and visual examples in the same manner. In contrast, iPromptDiff leverages a vision-transformer-based (ViT) encoder to extract high-level visual contexts from example images separately from the image query processing. This enables a more nuanced and sophisticated understanding of the tasks at hand.
  2. Visual Context-Modulated Text Prompts: By introducing a special visual context placeholder token, iPromptDiff dynamically incorporates visual context into text embeddings, effectively fusing linguistic and visual guidance. This approach mitigates information conflicts and enhances the contextual relevance of text prompts in guiding image generation.
  3. Enhanced Versatility and Robustness: The method demonstrates strong performance across a variety of vision tasks, including novel tasks that the model was not explicitly trained for. This is achieved through effective multitask training and strategic use of visual-context modulation.

Experimental Validation

The authors provide extensive qualitative and quantitative evaluations to demonstrate the efficacy of iPromptDiff.

In-Domain Map-to-Image Tasks

For in-domain tasks like depth-to-image, HED-to-image, and segmentation-to-image, iPromptDiff models—both trained on the Instruct Pixel-to-Pixel (iPromptDiff-IP2P) dataset and the larger MultiGen-20M dataset—outperform existing benchmarks. Table \ref{tab:fid} in the paper reports significantly lower Frechet Inception Distance (FID) scores for iPromptDiff variants compared to Prompt Diffusion and ControlNet, indicating superior image generation quality.

Out-of-Domain Map-to-Image Tasks

In novel tasks like normal-to-image and canny-to-image, iPromptDiff maintains robust performance even when text prompts are absent (denoted as 'n/a'), unlike Prompt Diffusion, which struggles under these conditions. This finding underscores the model's reliance on visual context, making it more adaptable to unseen tasks.

Image-to-Map Tasks

The reverse transformation tasks—image-to-depth, image-to-HED, and image-to-segmentation—pose greater challenges due to the less straightforward nature of the task and the inherent difficulties in visual foundation models that are not pretrained on condition map data. iPromptDiff manages to match or exceed the performance of specialized models like ControlNet, especially when text prompts are omitted during evaluation, further proving its versatility.

Implications and Future Directions

The iPromptDiff framework offers substantial improvements in both theoretical and practical aspects of visual in-context learning.

Theoretical Implications:

  • The decoupled processing and dynamic integration of visual context represent significant advancements in the methodology of visual understanding.
  • The robustness of vision transformers over CNN-based encoders in handling high-level semantic content showcases their potential for more sophisticated visual tasks.

Practical Implications:

  • The ability to perform well in novel tasks without extensive retraining suggests practical applications where flexibility and adaptability are vital.
  • The potential to enhance foundational vision models like Stable Diffusion means broader and more effective deployment in various image generation and transformation tasks.

Future Developments:

  • Extending the work to include multiple visual in-context examples could significantly improve model performance. Techniques like averaging embeddings or employing the perceiver resampler could be explored.
  • Investigating the impact of intelligent example selection mechanisms on in-context learning performance could offer substantial improvements.

Conclusion

The introduction of Improved Prompt Diffusion (iPromptDiff) marks a significant step forward in the quest to improve visual in-context learning with diffusion models. By innovatively addressing existing limitations through advanced visual context processing and dynamic multimodal fusion, this research offers a robust framework for future developments in the field. The evidence from comprehensive experiments suggests broad applicability, potentially transforming how visual in-context tasks are approached and solved.

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References (46)
  1. Flamingo: a visual language model for few-shot learning. Advances in Neural Information Processing Systems, 35:23716–23736, 2022.
  2. Structured denoising diffusion models in discrete state-spaces. In Advances in Neural Information Processing Systems, 2021.
  3. ediffi: Text-to-image diffusion models with an ensemble of expert denoisers. arXiv preprint arXiv:2211.01324, 2022.
  4. Visual prompting via image inpainting. Advances in Neural Information Processing Systems, 35:25005–25017, 2022.
  5. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020.
  6. Learning to generate line drawings that convey geometry and semantics. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7915–7925, 2022.
  7. Learning to jump: Thinning and thickening latent counts for generative modeling. In Proceedings of the 40th International Conference on Machine Learning, pages 5367–5382. PMLR, 2023.
  8. Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8789–8797, 2018.
  9. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311, 2022.
  10. Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34:8780–8794, 2021.
  11. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2021.
  12. An image is worth one word: Personalizing text-to-image generation using textual inversion. In The Eleventh International Conference on Learning Representations, 2022.
  13. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020.
  14. Argmax flows and multinomial diffusion: Learning categorical distributions. Advances in Neural Information Processing Systems, 34:12454–12465, 2021.
  15. Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1125–1134, 2017.
  16. Elucidating the design space of diffusion-based generative models. Advances in Neural Information Processing Systems, 35:26565–26577, 2022.
  17. Diffusionclip: Text-guided diffusion models for robust image manipulation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2426–2435, 2022.
  18. Conditional image-to-image translation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5524–5532, 2018.
  19. What makes good in-context examples for gpt-3? In Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pages 100–114, 2022.
  20. Unsupervised image-to-image translation networks. Advances in neural information processing systems, 30, 2017.
  21. MetaICL: Learning to learn in context. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2791–2809, Seattle, United States, 2022. Association for Computational Linguistics.
  22. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35:27730–27744, 2022.
  23. Image-to-image translation: Methods and applications. IEEE Transactions on Multimedia, 24:3859–3881, 2021.
  24. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4195–4205, 2023.
  25. Diffusion-based image translation with label guidance for domain adaptive semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 808–820, 2023.
  26. Unicontrol: A unified diffusion model for controllable visual generation in the wild. In Thirty-seventh Conference on Neural Information Processing Systems, 2023.
  27. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748–8763. PMLR, 2021.
  28. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 1(2):3, 2022.
  29. 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.
  30. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 35:36479–36494, 2022.
  31. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning, pages 2256–2265. PMLR, 2015.
  32. Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems, 32, 2019.
  33. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020.
  34. Pixel difference networks for efficient edge detection. In Proceedings of the IEEE/CVF international conference on computer vision, pages 5117–5127, 2021.
  35. Plug-and-play diffusion features for text-driven image-to-image translation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1921–1930, 2023.
  36. Images speak in images: A generalist painter for in-context visual learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6830–6839, 2023a.
  37. Seggpt: Towards segmenting everything in context. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 1130–1140, 2023b.
  38. In-context learning unlocked for diffusion models. In Thirty-seventh Conference on Neural Information Processing Systems, 2023c.
  39. Finetuned language models are zero-shot learners. In International Conference on Learning Representations, 2022a.
  40. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824–24837, 2022b.
  41. Compositional exemplars for in-context learning. In Proceedings of the 40th International Conference on Machine Learning, pages 39818–39833. PMLR, 2023.
  42. Dualgan: Unsupervised dual learning for image-to-image translation. In Proceedings of the IEEE international conference on computer vision, pages 2849–2857, 2017.
  43. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 3836–3847, 2023a.
  44. Active example selection for in-context learning. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9134–9148, Abu Dhabi, United Arab Emirates, 2022. Association for Computational Linguistics.
  45. What makes good examples for visual in-context learning? In Thirty-seventh Conference on Neural Information Processing Systems, 2023b.
  46. Beta diffusion. In Thirty-seventh Conference on Neural Information Processing Systems, 2023.
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Authors (7)
  1. Tianqi Chen (77 papers)
  2. Yongfei Liu (25 papers)
  3. Zhendong Wang (60 papers)
  4. Jianbo Yuan (33 papers)
  5. Quanzeng You (41 papers)
  6. Hongxia Yang (130 papers)
  7. Mingyuan Zhou (161 papers)
Citations (4)
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