Core Concepts
Combining the diversity of diffusion models, the efficiency of flow matching, and the effectiveness of convolutional decoders enables state-of-the-art high-resolution image synthesis at minimal computational cost.
Abstract
The content presents a novel approach to high-resolution image synthesis that integrates the strengths of diffusion models, flow matching, and convolutional decoders.
Key highlights:
- Diffusion models excel at generating diverse samples but face challenges in high-resolution synthesis, slow sampling speed, and large memory footprint.
- Flow matching models offer faster training and inference but less diverse synthesis compared to diffusion models.
- The authors propose combining a compact diffusion model for low-resolution content generation and a flow matching model for efficient high-resolution upsampling.
- The flow matching model is trained with data-dependent couplings to establish optimal transport paths from the low-resolution latent to the high-resolution latent, enabling fast and detailed image generation.
- Experiments show that the proposed approach achieves state-of-the-art performance in high-resolution image synthesis, outperforming existing diffusion and flow matching methods in terms of speed and quality.
- The method is orthogonal to recent advancements in diffusion models and can be easily integrated into various diffusion model frameworks.
Stats
The content does not provide specific numerical data or metrics to support the key claims. However, it presents several quantitative comparisons in tables, including:
Comparison of the proposed approach (CFM) with a state-of-the-art diffusion speed-up method (LCM-LoRA SDXL) on 10242 image synthesis, showing superior performance in terms of FID, Patch-FID, and inference time.
Comparison of CFM with diffusion-based upsampling, regression baselines, and naive flow matching on the FacesHQ and LHQ datasets, demonstrating the effectiveness of the proposed approach.
Comparison of CFM with other state-of-the-art models on the COCO 1024x1024 dataset, showing competitive FID at faster inference speed.
Quotes
The content does not include any direct quotes that support the key claims.