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When ControlNet Meets Inexplicit Masks: A Case Study of ControlNet on its Contour-following Ability (2403.00467v3)

Published 1 Mar 2024 in cs.CV

Abstract: ControlNet excels at creating content that closely matches precise contours in user-provided masks. However, when these masks contain noise, as a frequent occurrence with non-expert users, the output would include unwanted artifacts. This paper first highlights the crucial role of controlling the impact of these inexplicit masks with diverse deterioration levels through in-depth analysis. Subsequently, to enhance controllability with inexplicit masks, an advanced Shape-aware ControlNet consisting of a deterioration estimator and a shape-prior modulation block is devised. The deterioration estimator assesses the deterioration factor of the provided masks. Then this factor is utilized in the modulation block to adaptively modulate the model's contour-following ability, which helps it dismiss the noise part in the inexplicit masks. Extensive experiments prove its effectiveness in encouraging ControlNet to interpret inaccurate spatial conditions robustly rather than blindly following the given contours, suitable for diverse kinds of conditions. We showcase application scenarios like modifying shape priors and composable shape-controllable generation. Codes are available at github.

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References (32)
  1. Spatext: Spatio-textual representation for controllable image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18370–18380, 2023.
  2. Loosecontrol: Lifting controlnet for generalized depth conditioning. arXiv preprint arXiv:2312.03079, 2023.
  3. Microsoft coco captions: Data collection and evaluation server. arXiv preprint arXiv:1504.00325, 2015.
  4. Make-a-scene: Scene-based text-to-image generation with human priors. In European Conference on Computer Vision, pages 89–106. Springer, 2022.
  5. Benchmarking spatial relationships in text-to-image generation. arXiv preprint arXiv:2212.10015, 2022.
  6. Lvis: A dataset for large vocabulary instance segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5356–5364, 2019.
  7. Hypernetworks, 2016.
  8. Clipscore: A reference-free evaluation metric for image captioning. arXiv preprint arXiv:2104.08718, 2021.
  9. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017.
  10. Semantic object accuracy for generative text-to-image synthesis. IEEE transactions on pattern analysis and machine intelligence, 44(3):1552–1565, 2020.
  11. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022.
  12. Cocktail: Mixing multi-modality controls for text-conditional image generation. arXiv preprint arXiv:2306.00964, 2023.
  13. Composer: Creative and controllable image synthesis with composable conditions. arXiv preprint arXiv:2302.09778, 2023.
  14. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4401–4410, 2019.
  15. Gligen: Open-set grounded text-to-image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22511–22521, 2023.
  16. 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.
  17. et.al. Matthias Minderer. Simple open-vocabulary object detection with vision transformers. ECCV, 2022.
  18. T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. arXiv preprint arXiv:2302.08453, 2023.
  19. Unicontrol: A unified diffusion model for controllable visual generation in the wild. arXiv preprint arXiv:2305.11147, 2023.
  20. Zero-shot text-to-image generation. In International Conference on Machine Learning, pages 8821–8831. PMLR, 2021.
  21. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 1(2):3, 2022.
  22. 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.
  23. Runway. Stable Diffusion v1-5, 2022.
  24. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 35:36479–36494, 2022.
  25. The sketchy database: learning to retrieve badly drawn bunnies. ACM Transactions on Graphics (TOG), 35(4):1–12, 2016.
  26. Smartmask: Context aware high-fidelity mask generation for fine-grained object insertion and layout control. arXiv preprint arXiv:2312.05039, 2023.
  27. Sketch-guided text-to-image diffusion models. In ACM SIGGRAPH 2023 Conference Proceedings, pages 1–11, 2023.
  28. Learning visual prior via generative pre-training. In Thirty-seventh Conference on Neural Information Processing Systems, 2023.
  29. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3836–3847, 2023.
  30. Controllable text-to-image generation with gpt-4. arXiv preprint arXiv:2305.18583, 2023.
  31. Uni-controlnet: All-in-one control to text-to-image diffusion models. arXiv preprint arXiv:2305.16322, 2023.
  32. Unipc: A unified predictor-corrector framework for fast sampling of diffusion models. NeurIPS, 2023.
Citations (1)
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Summary

  • The paper introduces Shape-aware ControlNet, which leverages a deterioration estimator and shape-prior modulation to improve contour-following with vague masks.
  • It demonstrates that while standard ControlNet tolerates noisy input, its precision suffers without the shape-aware enhancements, as evidenced by metrics like CLIP-Score and FID.
  • The study provides a practical framework for modulating contour guidance in T2I generation, paving the way for more intuitive and creative image synthesis.

Enhancing ControlNet's Interpretation of Inexplicit Masks with Shape-aware ControlNet

Introduction to ControlNet's Contour-following Ability

ControlNet, a prominent Text-to-Image (T2I) generation technique, excels at generating content that aligns with user-provided contours and shapes. While its ability to adhere to precise outlines is commendable, challenges arise when the model encounters inexplicit masks—commonly produced by non-expert users. Such scenarios often lead to the generation of images with unwanted artifacts. Addressing this, our paper extensively analyzes ControlNet’s performance across masks of varying precision and explores hyperparameters influencing its contour-following capability. Notably, our experiments unveil that while ControlNet demonstrates remarkable robustness to noise in input masks, this comes at the cost of reduced preciseness in contour-following.

The Shape-aware ControlNet Model

To mitigate these challenges, we introduce the Shape-aware ControlNet, an innovative enhancement that incorporates a deterioration estimator and a shape-prior modulation block. This advanced model evaluates the deterioration factor of provided masks and adapts the model's contour-following ability accordingly, enabling a robust interpretation of inaccuracies within spatial conditions. Our empirical evaluations demonstrate the effectiveness of this strategy, showcasing its ability to interpret inexplicit masks while maintaining high image fidelity and control. This advancement opens up new avenues for utilizing ControlNet, including scenarios involving scribbles or modifying object shapes in generated images.

Quantitative Evaluation and Practical Applications

Our experiments reveal striking findings. Specifically, ControlNet’s performance significantly deteriorates when utilizing inexplicit masks, underpinning the necessity for our proposed Shape-aware ControlNet. The Shape-aware ControlNet not only improves upon the baseline in handling inexplicit masks but also provides a flexible mechanism for controlling the influence of shape priors on the generation process. Our quantitative analysis, using metrics like CLIP-Score, FID, Layout Consistency (LC), and Semantic Retrieval (SR), validates these claims, demonstrating superior performance across a broad spectrum of conditions.

Pioneering a Path for Shape-prior Control in Generation

Through our exploration, we uncover the potential for explicitly controlling the shape prior during the generation process with ControlNet. This is facilitated by our shape-prior modulation block, which effectively adjusts the strength of contour guidance based on the explicitness of the provided mask. The ability to modulate this aspect empowers users with unprecedented control over the spatial aspects of generated images, enhancing the creativity and applicability of ControlNet in real-world scenarios.

Conclusions and Future Directions

This paper not only highlights the limitations of conventional ControlNet in dealing with inexplicit masks but also successfully introduces a shape-aware enhancement to address these challenges. The Shape-aware ControlNet represents a significant advancement in the domain of T2I generation, providing a robust framework for interpreting diverse spatial conditions without sacrificing image quality. Looking forward, we anticipate further exploration into optimizing the model's ability to discern and utilize shape priors, as well as expanding its applicability to more complex and creative content generation tasks.

The contributions of this paper not only address a critical gap in the current capabilities of ControlNet but also pave the way for future developments in AI-driven image synthesis, promising more intuitive and user-friendly interfaces for content creation across various fields.

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