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Trajectory Consistency Distillation: Improved Latent Consistency Distillation by Semi-Linear Consistency Function with Trajectory Mapping (2402.19159v2)

Published 29 Feb 2024 in cs.CV

Abstract: Latent Consistency Model (LCM) extends the Consistency Model to the latent space and leverages the guided consistency distillation technique to achieve impressive performance in accelerating text-to-image synthesis. However, we observed that LCM struggles to generate images with both clarity and detailed intricacy. Consequently, we introduce Trajectory Consistency Distillation (TCD), which encompasses trajectory consistency function and strategic stochastic sampling. The trajectory consistency function diminishes the parameterisation and distillation errors by broadening the scope of the self-consistency boundary condition with trajectory mapping and endowing the TCD with the ability to accurately trace the entire trajectory of the Probability Flow ODE in semi-linear form with an Exponential Integrator. Additionally, strategic stochastic sampling provides explicit control of stochastic and circumvents the accumulated errors inherent in multi-step consistency sampling. Experiments demonstrate that TCD not only significantly enhances image quality at low NFEs but also yields more detailed results compared to the teacher model at high NFEs.

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Trajectory Consistency Distillation: Advancing Latent Consistency Models for Efficient Text-to-Image Synthesis

The paper provides a detailed exploration of Trajectory Consistency Distillation (TCD), a novel approach designed to enhance the performance of Latent Consistency Models (LCMs) in text-to-image synthesis. TCD addresses the shortcomings of LCMs, particularly the challenge of generating images that balance clarity and intricate details. The authors identify three primary sources of errors that hinder the efficacy of these models: estimation errors in score matching, distillation errors, and discretization errors during sampling. Through the introduction of TCD, the paper proposes a method for overcoming these obstacles.

TCD operates by implementing a trajectory consistency function that extends the model's capacity to track Probability Flow Ordinary Differential Equation (PF ODE) trajectories accurately. Moreover, TCD integrates strategic stochastic sampling to mitigate accumulated errors that occur with multi-step consistency sampling. Experimental results indicate that TCD significantly enhances image quality at low numbers of function evaluations (NFEs) and surpasses the teacher model's performance in high NFEs, particularly demonstrating efficacy over the diffusion models trained without guided distillation techniques.

Core Contributions

The paper makes the following core contributions to the field of text-to-image generation models:

  1. Trajectory Consistency Function: This function expands the self-consistency boundary conditions, allowing the model to trace entire PF ODE trajectories. It effectively reduces distillation errors in consistency models by providing a more comprehensive framework for error correction.
  2. Strategic Stochastic Sampling (SSS): Designed to limit accumulated errors during multi-step sampling, SSS introduces a stochastic parameter to refine the sampling process further. By enabling controlled traversal along PF ODE trajectories, SSS minimizes discretization and estimation errors, leading to improved image quality.
  3. Experimental Validation: The paper conducts extensive experiments, demonstrating that TCD substantially enhances the performance of text-to-image generation models. Notably, TCD outperforms established models by improving image quality and detail precision, especially when considering higher NFE scenarios.

Theoretical and Practical Implications

Theoretically, the advancements made by TCD provide new insights into the error dynamics of consistency models. The authors rigorously analyze the consistency distillation error and introduce methodologies to address cumulative errors within multi-step sampling frameworks. This theoretical foundation not only aids in understanding but also in the development of more efficient generative frameworks.

Practically, the implications of TCD are profound in the context of accelerating text-to-image synthesis. The ability to generate high-quality images with fewer computational resources makes TCD an attractive option for deployment in real-world applications, where computational efficiency and output quality are essential. The versatility of TCD, exhibited by its compatibility with various models such as IP-Adapter and ControlNet, underscores its potential as a universal solution across different domains of generative modeling.

Future Directions

The paper opens several avenues for future exploration:

  • Single-Step Optimization: While TCD significantly enhances multi-step performance, further research could aim to refine single-step generation capabilities, potentially revolutionizing the efficiency of generative models.
  • Stability of High-Order Solutions: The instability observed in higher-order parameterizations suggests an area for further investigation. Developing a stable high-order model could unlock even greater performance improvements.
  • Application Expansion: TCD's adaptability suggests applications beyond image generation, such as video and audio synthesis. These fields could benefit from improved detail and quality offered by TCD's methodologies.

In summary, Trajectory Consistency Distillation introduces significant advancements in the field of text-to-image generative models by effectively addressing inherent model errors and efficiently refining multi-step image generation. The insights brought forth by this paper could shape the future of consistency models, providing researchers and practitioners with innovative tools to enhance both the efficiency and output quality of computational models in digital media synthesis.

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References (51)
  1. Intrinsic dimensionality explains the effectiveness of language model fine-tuning. arXiv preprint arXiv:2012.13255, 2020.
  2. ediffi: Text-to-image diffusion models with an ensemble of expert denoisers. arXiv preprint arXiv:2211.01324, 2022.
  3. Why are conditional generative models better than unconditional ones? arXiv preprint arXiv:2212.00362, 2022.
  4. Wavegrad: Estimating gradients for waveform generation. arXiv preprint arXiv:2009.00713, 2020.
  5. Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions. arXiv preprint arXiv:2209.11215, 2022.
  6. Diffusion schrödinger bridge with applications to score-based generative modeling. Advances in Neural Information Processing Systems, 34:17695–17709, 2021.
  7. Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34:8780–8794, 2021.
  8. Dieleman, S. Guidance: a cheat code for diffusion models, 2022. URL https://benanne.github.io/2022/05/26/guidance.html.
  9. Ic9600: A benchmark dataset for automatic image complexity assessment. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
  10. Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. arXiv preprint arXiv:2307.04725, 2023.
  11. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022.
  12. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020.
  13. Parameter-efficient transfer learning for nlp. In International Conference on Machine Learning, pp.  2790–2799. PMLR, 2019.
  14. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021.
  15. Estimation of non-normalized statistical models. Natural Image Statistics: A Probabilistic Approach to Early Computational Vision, pp.  419–426, 2009.
  16. Gotta go fast when generating data with score-based models. arXiv preprint arXiv:2105.14080, 2021.
  17. Elucidating the design space of diffusion-based generative models. Advances in Neural Information Processing Systems, 35:26565–26577, 2022.
  18. Consistency trajectory models: Learning probability flow ode trajectory of diffusion. arXiv preprint arXiv:2310.02279, 2023.
  19. Pick-a-pic: An open dataset of user preferences for text-to-image generation. arXiv preprint arXiv:2305.01569, 2023.
  20. On fast sampling of diffusion probabilistic models. arXiv preprint arXiv:2106.00132, 2021.
  21. Diffwave: A versatile diffusion model for audio synthesis. arXiv preprint arXiv:2009.09761, 2020.
  22. Convergence of score-based generative modeling for general data distributions. In International Conference on Algorithmic Learning Theory, pp.  946–985. PMLR, 2023.
  23. Do diffusion models suffer error propagation? theoretical analysis and consistency regularization. arXiv preprint arXiv:2308.05021, 2023.
  24. Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. Advances in Neural Information Processing Systems, 35:5775–5787, 2022a.
  25. Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models. arXiv preprint arXiv:2211.01095, 2022b.
  26. Knowledge distillation in iterative generative models for improved sampling speed. arXiv preprint arXiv:2101.02388, 2021.
  27. Latent consistency models: Synthesizing high-resolution images with few-step inference. arXiv preprint arXiv:2310.04378, 2023a.
  28. Lcm-lora: A universal stable-diffusion acceleration module. arXiv preprint arXiv:2311.05556, 2023b.
  29. Convergence guarantee for consistency models. arXiv preprint arXiv:2308.11449, 2023.
  30. On distillation of guided diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  14297–14306, 2023.
  31. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. In International Conference on Machine Learning, pp.  16784–16804. PMLR, 2022.
  32. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023.
  33. Grad-tts: A diffusion probabilistic model for text-to-speech. In International Conference on Machine Learning, pp.  8599–8608. PMLR, 2021.
  34. Prasad, D. An introduction to numerical analysis. Mathematics and Computers in Simulation, pp.  319, May 1990. doi: 10.1016/0378-4754(90)90206-x. URL http://dx.doi.org/10.1016/0378-4754(90)90206-x.
  35. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 1(2):3, 2022.
  36. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.  10684–10695, 2022.
  37. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 35:36479–36494, 2022.
  38. Progressive distillation for fast sampling of diffusion models. arXiv preprint arXiv:2202.00512, 2022.
  39. Adversarial diffusion distillation. arXiv preprint arXiv:2311.17042, 2023.
  40. Schuhmann, C. Clip+mlp aesthetic score predictor. https://github.com/christophschuhmann/improved-aesthetic-predictor, 2022.
  41. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35:25278–25294, 2022.
  42. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning, pp.  2256–2265. PMLR, 2015.
  43. Denoising diffusion implicit models. In International Conference on Learning Representations, 2020a.
  44. Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems, 32, 2019.
  45. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020b.
  46. Consistency models. arXiv preprint arXiv:2303.01469, 2023.
  47. Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.  7623–7633, 2023.
  48. Imagereward: Learning and evaluating human preferences for text-to-image generation. arXiv preprint arXiv:2304.05977, 2023a.
  49. Restart sampling for improving generative processes. arXiv preprint arXiv:2306.14878, 2023b.
  50. One-step diffusion with distribution matching distillation. arXiv preprint arXiv:2311.18828, 2023.
  51. Fast sampling of diffusion models with exponential integrator. arXiv preprint arXiv:2204.13902, 2022.
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Authors (7)
  1. Jianbin Zheng (8 papers)
  2. Minghui Hu (15 papers)
  3. Zhongyi Fan (3 papers)
  4. Chaoyue Wang (51 papers)
  5. Changxing Ding (52 papers)
  6. Dacheng Tao (826 papers)
  7. Tat-Jen Cham (35 papers)
Citations (17)
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