# Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think

**Authors:** Gonzalo Martin Garcia, Karim Abou Zeid, Christian Schmidt, Daan de Geus, Alexander Hermans, Bastian Leibe  
**arXiv:** [2409.11355](https://arxiv.org/abs/2409.11355)  
**Date:** 2024-09-17  
**Summary page:** https://www.summarizepaper.com/en/paper/2409.11355v1/

## Key points

- Authors: Gonzalo Martin Garcia, Karim Abou Zeid, Christian Schmidt, Daan de Geus, Alexander Hermans, Bastian Leibe
- Explored use of large diffusion models for monocular depth estimation
- Identified critical flaw in inference pipeline and developed fixed model for faster speed
- Conducted end-to-end fine-tuning with task-specific losses to enhance model's effectiveness
- Deterministic model surpassed other diffusion-based depth and normal estimation models on benchmarks
- Fine-tuning protocol successful with Stable Diffusion models as well
- Achieved comparable performance to existing state-of-the-art diffusion-based models
- Highlights efficiency gains through optimization and fine-tuning processes

## Summary

In their paper titled "Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think," authors Gonzalo Martin Garcia, Karim Abou Zeid, Christian Schmidt, Daan de Geus, Alexander Hermans, and Bastian Leibe explore the use of large diffusion models for monocular depth estimation. Previous research has shown that these models can be repurposed for accurate depth estimation by framing it as an image-conditional image generation task. However, the high computational demands of multi-step inference hindered its practical application. The authors identified a critical flaw in the inference pipeline and developed a fixed model that operated at more than 200 times faster speed while matching the best reported configuration's performance. They also conducted end-to-end fine-tuning with task-specific losses to further enhance the model's effectiveness for downstream tasks. This resulted in a deterministic model that surpassed all other diffusion-based depth and normal estimation models on widely used zero-shot benchmarks. Surprisingly, this fine-tuning protocol was successful when applied directly to Stable Diffusion models as well. The refined approach achieved comparable performance to existing state-of-the-art diffusion-based depth and normal estimation models, prompting a reevaluation of some conclusions drawn from prior works in this field. Overall, this study highlights the efficiency gains achievable through meticulous optimization and fine-tuning processes in complex diffusion models for image-related tasks.