- The paper demonstrates that a minor correction to the DDIM scheduler enables single-step predictions to achieve over 200x speedup and competitive performance.
- It introduces a straightforward end-to-end fine-tuning method with task-specific losses that outperforms complex multi-step diffusion approaches.
- Experimental results on benchmarks like NYUv2 and KITTI confirm significant improvements in monocular depth and surface normal estimation for real-time applications.
Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think
In the domain of monocular geometry estimation, the paper "Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think" presents an insightful analysis and a significant enhancement to the efficacy of image-conditional diffusion models, particularly focusing on depth and surface normal estimation. The authors meticulously identify and rectify a critical issue in the DDIM scheduler, subsequently demonstrating that simple end-to-end fine-tuning can surpass more complex, conventional diffusion-based architectures in performance.
Critique of Existing Works
The paper begins by contextualizing its contributions within the broader landscape of monocular depth and normal estimation. Previous studies, such as Marigold and its derivative works (e.g., GeoWizard and DepthFM), relied heavily on multi-step inference processes, leading to high computational costs and suboptimal performance. A primary focus is the adaptability of large diffusion models, like Stable Diffusion, to depth estimation by formulating it as an image-conditional generation task. This process, although effective, was hampered by the necessity of multiple denoising iterations, a consequence not inherent to the model's design but rather to a flaw in implementation.
Central Findings
Fixing the DDIM Scheduler
A pivotal discovery of this paper is the identification of a flaw in the inference pipeline of these diffusion models. Existing implementations failed to align the noise level with the expected timestep during inference, particularly for single-step predictions. By implementing a minor correction to the DDIM scheduler—employing a trailing strategy instead of the conventional leading one—the authors underscore that Marigold-like models achieve performance comparable to their multi-step counterparts but at a dramatic speed increase, achieving over 200x enhancement.
Simplistic End-to-End Fine-Tuning
Capitalizing on the improved inference model, the authors conduct a comprehensive end-to-end fine-tuning using task-specific loss functions. Prior approaches often leveraged denoising objectives, which, although effective, were not directly aligned with the desired downstream task. By contrast, end-to-end fine-tuning with task-specific losses yielded a deterministic and highly performant model. The authors document that this simplicity—eschewing the complex diffusion fine-tuning stages in favor of direct optimization—results in superior performance metrics across multiple benchmarks.
Experimental Insights
The robust experimental setup utilized benchmarks such as NYUv2, KITTI, ETH3D, ScanNet, and DIODE to evaluate depth estimation models. The results were compelling: the fine-tuned models significantly outperformed previous state-of-the-art methods, including famously complex architectures like the multi-step, ensembled Marigold, on most key metrics—a significant achievement given the simplified nature of the approach.
Quantitative Results
The paper highlighted several key results:
- NYUv2 Depth Estimation: End-to-end fine-tuning delivered a 5.2 AbsRel and 96.6% δ1 accuracy, surpassing Marigold's 5.5 AbsRel and 96.4% δ1 accuracy.
- KITTI Depth Estimation: Notably, the fine-tuned models achieved a 9.6 AbsRel and 91.9% δ1 accuracy.
- Normal Estimation: Similar improvements were noted, with the fine-tuned models achieving top-tier performance on datasets like iBims-1 and Sintel.
The enhancements were not confined to Marigold alone; the paper describes analogous improvements for GeoWizard, further attesting to the versatility and robustness of the proposed fine-tuning approach.
Practical and Theoretical Implications
The implications of these findings are profoundly practical. The significant reduction in computational requirements, coupled with the improved performance, means these models are now more accessible for real-time applications, including autonomous systems and various computer vision tasks. Theoretically, the results underscore the potency of diffusion models as priors for geometric tasks, suggesting that simpler and more effective methodologies can be developed by focusing on task-specific optimizations rather than nested, iterative refinement procedures typical of diffusion model training regimes.
Future Prospects
Looking ahead, the implications of these findings provoke several intriguing avenues for future research. One promising direction is the integration of self-training procedures on a broad scale, leveraging the strengths of large diffusion model priors while incorporating efficiently-generated pseudo-labels. Additionally, ongoing improvements in diffusion model architectures and training methodologies are likely to further bolster the potential of these models for various computational geometric tasks.
Conclusion
The paper "Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think" offers a meticulously developed and experimentally validated approach that challenges and extends the current paradigms in monocular depth and normal estimation. By addressing foundational issues in the diffusion model inference pipeline and advocating for simplified yet effective end-to-end fine-tuning, it contributes significantly to both the theoretical understanding and practical deployment of these models in AI and computer vision.