Residual Diffusion Implicit Models

arXiv:2609.33020 · cs.CV · Submitted 2026-09-26 · Read on arXiv

cs.CV

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/joaolcguerreiro/RDIM

Terminology

• Diffusion Model — 22× in this paper · explained in 8 episodes
• Diffusion Models — 19× in this paper · explained in 6 episodes
• Consistency — 14× in this paper · explained in 3 episodes
• Inverse Problem — 14× in this paper · explained in 2 episodes
• Diffusion Process — 13× in this paper · explained in 2 episodes
• Fidelity — 13× in this paper · explained in 2 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition — 8× in this paper
• In International Conference on Learning Representations — 7× in this paper
• Hallucination — 6× in this paper · explained in 3 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Iterative Refinement — 5× in this paper · explained in 3 episodes
• Adam — 4× in this paper · explained in 2 episodes
• Distillation — 4× in this paper · explained in 2 episodes
• Generative Modeling — 4× in this paper · explained in 3 episodes
• Objective Function — 4× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Core — 2× in this paper · explained in 2 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Adversarial Model — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Diffusion Processes — 1× in this paper · explained in 2 episodes
• Fluorescence Microscopy Denoising — 1× in this paper
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Initial Data — 1× in this paper · explained in 2 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Ordinary Differential Equations (ODEs) — 1× in this paper · explained in 2 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Smartphone Image Denoising Dataset — 1× in this paper
• Stochastic Optimal Control (SOC) — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Variational Flow Models: Flowing in Your Style
• Adam: A Method for Stochastic Optimization
• Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed
• Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference
• A Unified and Fast-Sampling Diffusion Bridge Framework via Stochastic Optimal Control
• RB-Modulation: Training-Free Personalization of Diffusion Models using Stochastic Optimal Control
• UNIT-DDPM: UNpaired Image Translation with Denoising Diffusion Probabilistic Models

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