# Zippo: Zipping Color and Transparency Distributions into a Single Diffusion Model

arXiv: https://arxiv.org/abs/2403.11077 · HTML: https://mlsanity.com/paper/2403.11077/

## Key facts

- Cited by 2 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 33 of its 44 references that are in the map.
- First posted on arXiv 17 Mar 2024.

## 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 33)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Wonder3D: Single Image to 3D using Cross-Domain Diffusion](https://mlsanity.com/paper/2310.15008/) (2023)
- [Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2303.04803/) (2023)
- [VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization](https://mlsanity.com/paper/2103.16874/) (2021)
- [DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Models](https://mlsanity.com/paper/2303.11681/) (2023)
- [Deep Image Matting](https://mlsanity.com/paper/1703.03872/) (2017)
- [DDP: Diffusion Model for Dense Visual Prediction](https://mlsanity.com/paper/2303.17559/) (2023)
- [MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition](https://mlsanity.com/paper/2011.11961/) (2020)
- [Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding](https://mlsanity.com/paper/2401.12954/) (2024)
- [Background Matting: The World is Your Green Screen](https://mlsanity.com/paper/2004.00626/) (2020)
- [Indices Matter: Learning to Index for Deep Image Matting](https://mlsanity.com/paper/1908.00672/) (2019)
- [Deconstructing Denoising Diffusion Models for Self-Supervised Learning](https://mlsanity.com/paper/2401.14404/) (2024)
- [Bridging Composite and Real: Towards End-to-end Deep Image Matting](https://mlsanity.com/paper/2010.16188/) (2020)
- [Information-Flow Matting](https://mlsanity.com/paper/1707.05055/) (2017)
- [ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers](https://mlsanity.com/paper/2305.15272/) (2023)
- [Generative Models: What Do They Know? Do They Know Things? Let's Find Out!](https://mlsanity.com/paper/2311.17137/) (2023)
- [MatteFormer: Transformer-Based Image Matting via Prior-Tokens](https://mlsanity.com/paper/2203.15662/) (2022)
- [StyleGAN knows Normal, Depth, Albedo, and More](https://mlsanity.com/paper/2306.00987/) (2023)
- [AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis](https://mlsanity.com/paper/2204.03105/) (2022)
- [UniGS: Unified Representation for Image Generation and Segmentation](https://mlsanity.com/paper/2312.01985/) (2023)
- [Rethinking Portrait Matting with Privacy Preserving](https://mlsanity.com/paper/2203.16828/) (2022)
- [A Simple Latent Diffusion Approach for Panoptic Segmentation and Mask Inpainting](https://mlsanity.com/paper/2401.10227/) (2024)
- [Harnessing Diffusion Models for Visual Perception with Meta Prompts](https://mlsanity.com/paper/2312.14733/) (2023)
- [LayerBuilder: Layer Decomposition for Interactive Image and Video Color Editing](https://mlsanity.com/paper/1701.03754/) (2017)

## Built on by (most cited first, 2 of 2)

- [Unified Dense Prediction of Video Diffusion](https://mlsanity.com/paper/2503.09344/) (2025)
- [Trans-Adapter: A Plug-and-Play Framework for Transparent Image Inpainting](https://mlsanity.com/paper/2508.01098/) (2025)

## Abstract (arXiv)

Beyond the superiority of the text-to-image diffusion model in generating high-quality images, recent studies have attempted to uncover its potential for adapting the learned semantic knowledge to visual perception tasks. In this work, instead of translating a generative diffusion model into a visual perception model, we explore to retain the generative ability with the perceptive adaptation. To accomplish this, we present Zippo, a unified framework for zipping the color and transparency distributions into a single diffusion model by expanding the diffusion latent into a joint representation of RGB images and alpha mattes. By alternatively selecting one modality as the condition and then applying the diffusion process to the counterpart modality, Zippo is capable of generating RGB images from alpha mattes and predicting transparency from input images. In addition to single-modality prediction, we propose a modality-aware noise reassignment strategy to further empower Zippo with jointly generating RGB images and its corresponding alpha mattes under the text guidance. Our experiments showcase Zippo's ability of efficient text-conditioned transparent image generation and present plausible results of Matte-to-RGB and RGB-to-Matte translation.