# Layered Rendering Diffusion Model for Controllable Zero-Shot Image Synthesis

arXiv: https://arxiv.org/abs/2311.18435 · HTML: https://mlsanity.com/paper/2311.18435/

## Key facts

- Cited by 9 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 27 of its 67 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## 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, 27 of 27)

- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [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)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Scaling Autoregressive Models for Content-Rich Text-to-Image Generation](https://mlsanity.com/paper/2206.10789/) (2022)
- [eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers](https://mlsanity.com/paper/2211.01324/) (2022)
- [Adversarial Diffusion Distillation](https://mlsanity.com/paper/2311.17042/) (2023)
- [DiffEdit: Diffusion-based semantic image editing with mask guidance](https://mlsanity.com/paper/2210.11427/) (2022)
- [MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation](https://mlsanity.com/paper/2302.08113/) (2023)
- [Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2305.16322/) (2023)
- [UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild](https://mlsanity.com/paper/2305.11147/) (2023)
- [Grounded Text-to-Image Synthesis with Attention Refocusing](https://mlsanity.com/paper/2306.05427/) (2023)
- [DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion model](https://mlsanity.com/paper/2310.07771/) (2023)
- [Directed Diffusion: Direct Control of Object Placement through Attention Guidance](https://mlsanity.com/paper/2302.13153/) (2023)
- [LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation](https://mlsanity.com/paper/2302.08908/) (2023)
- [Controllable Text-to-Image Generation with GPT-4](https://mlsanity.com/paper/2305.18583/) (2023)
- [GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generation](https://mlsanity.com/paper/2306.04607/) (2023)
- [Localized Text-to-Image Generation for Free via Cross Attention Control](https://mlsanity.com/paper/2306.14636/) (2023)
- [DiffBlender: Composable and Versatile Multimodal Text-to-Image Diffusion Models](https://mlsanity.com/paper/2305.15194/) (2023)

## Built on by (most cited first, 9 of 9)

- [Generating Compositional Scenes via Text-to-image RGBA Instance Generation](https://mlsanity.com/paper/2411.10913/) (2024)
- [SemanticDraw: Towards Real-Time Interactive Content Creation from Image Diffusion Models](https://mlsanity.com/paper/2403.09055/) (2024)
- [A Simple and Efficient Baseline for Zero-Shot Generative Classification](https://mlsanity.com/paper/2412.12594/) (2024)
- [TTOM: Test-Time Optimization and Memorization for Compositional Video Generation](https://mlsanity.com/paper/2510.07940/) (2025)
- [Identity-Consistent Video Generation under Large Facial-Angle Variations](https://mlsanity.com/paper/2603.21299/) (2026)
- [Efficient Semantic Splatting for Remote Sensing Multi-view Segmentation](https://mlsanity.com/paper/2412.05969/) (2024)
- [Guided Path Sampling: Steering Diffusion Models Back on Track with Principled Path Guidance](https://mlsanity.com/paper/2512.22881/) (2025)
- [MetaEarth-MM: Unified Multimodal Remote Sensing Image Generation with Scene-centered Joint Modeling](https://mlsanity.com/paper/2605.20090/) (2026)
- [ToLo: A Two-Stage, Training-Free Layout-To-Image Generation Framework For High-Overlap Layouts](https://mlsanity.com/paper/2503.01667/) (2025)

## Abstract (arXiv)

This paper introduces innovative solutions to enhance spatial controllability in diffusion models reliant on text queries. We first introduce vision guidance as a foundational spatial cue within the perturbed distribution. This significantly refines the search space in a zero-shot paradigm to focus on the image sampling process adhering to the spatial layout conditions. To precisely control the spatial layouts of multiple visual concepts with the employment of vision guidance, we propose a universal framework, Layered Rendering Diffusion (LRDiff), which constructs an image-rendering process with multiple layers, each of which applies the vision guidance to instructively estimate the denoising direction for a single object. Such a layered rendering strategy effectively prevents issues like unintended conceptual blending or mismatches while allowing for more coherent and contextually accurate image synthesis. The proposed method offers a more efficient and accurate means of synthesising images that align with specific layout and contextual requirements. Through experiments, we demonstrate that our method outperforms existing techniques, both quantitatively and qualitatively, in two specific layout-to-image tasks: bounding box-to-image and instance maskto-image. Furthermore, we extend the proposed framework to enable spatially controllable editing