# MIDMs: Matching Interleaved Diffusion Models for Exemplar-based Image Translation

arXiv: https://arxiv.org/abs/2209.11047 · HTML: https://mlsanity.com/paper/2209.11047/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 58 of its 73 references that are in the map.
- First posted on arXiv 22 Sep 2022.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 30 of 58)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)

## Built on by (most cited first, 22 of 22)

- [Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation](https://mlsanity.com/paper/2303.07937/) (2023)
- [DiffFace: Diffusion-based Face Swapping with Facial Guidance](https://mlsanity.com/paper/2212.13344/) (2022)
- [Prompt-Free Diffusion: Taking "Text" out of Text-to-Image Diffusion Models](https://mlsanity.com/paper/2305.16223/) (2023)
- [DreamMatcher: Appearance Matching Self-Attention for Semantically-Consistent Text-to-Image Personalization](https://mlsanity.com/paper/2402.09812/) (2024)
- [DirecT2V: Large Language Models are Frame-Level Directors for Zero-Shot Text-to-Video Generation](https://mlsanity.com/paper/2305.14330/) (2023)
- [Photoswap: Personalized Subject Swapping in Images](https://mlsanity.com/paper/2305.18286/) (2023)
- [High-Fidelity Guided Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2211.17084/) (2022)
- [Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives](https://mlsanity.com/paper/2404.08926/) (2024)
- [Diffusion Model for Dense Matching](https://mlsanity.com/paper/2305.19094/) (2023)
- [Diffusion Models for Contrast Harmonization of Magnetic Resonance Images](https://mlsanity.com/paper/2303.08189/) (2023)
- [MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model](https://mlsanity.com/paper/2311.07198/) (2023)
- [SwapAnything: Enabling Arbitrary Object Swapping in Personalized Visual Editing](https://mlsanity.com/paper/2404.05717/) (2024)
- [DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2212.08861/) (2022)
- [SketchDreamer: Interactive Text-Augmented Creative Sketch Ideation](https://mlsanity.com/paper/2308.14191/) (2023)
- [Masked and Adaptive Transformer for Exemplar Based Image Translation](https://mlsanity.com/paper/2303.17123/) (2023)
- [EBDM: Exemplar-guided Image Translation with Brownian-bridge Diffusion Models](https://mlsanity.com/paper/2410.09802/) (2024)
- [PIXELS: Progressive Image Xemplar-based Editing with Latent Surgery](https://mlsanity.com/paper/2501.09826/) (2025)
- [FIRST: A Million-Entry Dataset for Text-Driven Fashion Synthesis and Design](https://mlsanity.com/paper/2311.07414/) (2023)
- [Representative Feature Extraction During Diffusion Process for Sketch Extraction with One Example](https://mlsanity.com/paper/2401.04362/) (2024)
- [Stylized Face Sketch Extraction via Generative Prior with Limited Data](https://mlsanity.com/paper/2403.11263/) (2024)
- [Tuning-Free Visual Customization via View Iterative Self-Attention Control](https://mlsanity.com/paper/2406.06258/) (2024)
- [High-fidelity and Lip-synced Talking Face Synthesis via Landmark-based Diffusion Model](https://mlsanity.com/paper/2408.05416/) (2024)

## Abstract (arXiv)

We present a novel method for exemplar-based image translation, called matching interleaved diffusion models (MIDMs). Most existing methods for this task were formulated as GAN-based matching-then-generation framework. However, in this framework, matching errors induced by the difficulty of semantic matching across cross-domain, e.g., sketch and photo, can be easily propagated to the generation step, which in turn leads to degenerated results. Motivated by the recent success of diffusion models overcoming the shortcomings of GANs, we incorporate the diffusion models to overcome these limitations. Specifically, we formulate a diffusion-based matching-and-generation framework that interleaves cross-domain matching and diffusion steps in the latent space by iteratively feeding the intermediate warp into the noising process and denoising it to generate a translated image. In addition, to improve the reliability of the diffusion process, we design a confidence-aware process using cycle-consistency to consider only confident regions during translation. Experimental results show that our MIDMs generate more plausible images than state-of-the-art methods.