# IntrinsicEdit: Precise generative image manipulation in intrinsic space

arXiv: https://arxiv.org/abs/2505.08889 · HTML: https://mlsanity.com/paper/2505.08889/

## Key facts

- Cited by 17 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 50 of its 83 references that are in the map.
- First posted on arXiv 13 May 2025.

## 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 50)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [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)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Interpreting the Latent Space of GANs for Semantic Face Editing](https://mlsanity.com/paper/1907.10786/) (2019)
- [Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?](https://mlsanity.com/paper/1904.03189/) (2019)
- [Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding](https://mlsanity.com/paper/2011.02523/) (2020)
- [AnyDoor: Zero-shot Object-level Image Customization](https://mlsanity.com/paper/2307.09481/) (2023)
- [Generative Visual Manipulation on the Natural Image Manifold](https://mlsanity.com/paper/1609.03552/) (2016)
- [Emu Edit: Precise Image Editing via Recognition and Generation Tasks](https://mlsanity.com/paper/2311.10089/) (2023)
- [DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing](https://mlsanity.com/paper/2306.14435/) (2023)
- [Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance](https://mlsanity.com/paper/2403.14781/) (2024)
- [An Edit Friendly DDPM Noise Space: Inversion and Manipulations](https://mlsanity.com/paper/2304.06140/) (2023)
- [Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold](https://mlsanity.com/paper/2305.10973/) (2023)
- [Editing in Style: Uncovering the Local Semantics of GANs](https://mlsanity.com/paper/2004.14367/) (2020)
- [Negative-prompt Inversion: Fast Image Inversion for Editing with Text-guided Diffusion Models](https://mlsanity.com/paper/2305.16807/) (2023)
- [RGBleftrightarrowX: Image decomposition and synthesis using material- and lighting-aware diffusion models](https://mlsanity.com/paper/2405.00666/) (2024)
- [Single Image Portrait Relighting](https://mlsanity.com/paper/1905.00824/) (2019)
- [ReNoise: Real Image Inversion Through Iterative Noising](https://mlsanity.com/paper/2403.14602/) (2024)
- [Deep Image Harmonization](https://mlsanity.com/paper/1703.00069/) (2017)
- [DiLightNet: Fine-grained Lighting Control for Diffusion-based Image Generation](https://mlsanity.com/paper/2402.11929/) (2024)
- [Neural Gaffer: Relighting Any Object via Diffusion](https://mlsanity.com/paper/2406.07520/) (2024)
- [Learning Physics-guided Face Relighting under Directional Light](https://mlsanity.com/paper/1906.03355/) (2019)
- [Effective Real Image Editing with Accelerated Iterative Diffusion Inversion](https://mlsanity.com/paper/2309.04907/) (2023)
- [Learning-based Inverse Rendering of Complex Indoor Scenes with Differentiable Monte Carlo Raytracing](https://mlsanity.com/paper/2211.03017/) (2022)

## Built on by (most cited first, 17 of 17)

- [UniLight: A Unified Representation for Lighting](https://mlsanity.com/paper/2512.04267/) (2025)
- [V-RGBX: Video Editing with Accurate Controls over Intrinsic Properties](https://mlsanity.com/paper/2512.11799/) (2025)
- [IntrinsicWeather: Controllable Weather Editing in Intrinsic Space](https://mlsanity.com/paper/2508.06982/) (2025)
- [TokenLight: Precise Lighting Control in Images using Attribute Tokens](https://mlsanity.com/paper/2604.15310/) (2026)
- [GOR-IS: 3D Gaussian Object Removal in the Intrinsic Space](https://mlsanity.com/paper/2605.00498/) (2026)
- [Decoupled Illumination Priors for Spatially Controllable Multi-View Indoor Scene Relighting](https://mlsanity.com/paper/2607.08879/) (2026)
- [FROMAT: Multiview Material Appearance Transfer via Few-Shot Self-Attention Adaptation](https://mlsanity.com/paper/2512.09617/) (2025)
- [SyncLight: Single-Edit Multi-View Relighting](https://mlsanity.com/paper/2601.16981/) (2026)
- [Relighting as a Probe of Visual Priors via Augmented Latent Intrinsics](https://mlsanity.com/paper/2602.01391/) (2026)
- [GeRM: A Generative Rendering Model From Physically Realistic to Photorealistic](https://mlsanity.com/paper/2604.09304/) (2026)
- [AlbedoEdit: Unified Instance-Level Video Editing with Albedo Guidance](https://mlsanity.com/paper/2606.01362/) (2026)
- [TRON: Tracing Rays to Orchestrate a Neural Renderer for 3D Gaussian Reconstructions](https://mlsanity.com/paper/2606.11314/) (2026)
- [ResEdit: Residual embeddings for precise generative image editing](https://mlsanity.com/paper/2606.16457/) (2026)
- [Lighting-Consistent Object Transfer Across Radiance Fields](https://mlsanity.com/paper/2606.22481/) (2026)
- [Controllable Texture Tiling with Transformed RoPE-Enhanced Diffusion Models](https://mlsanity.com/paper/2606.22945/) (2026)
- [Consistent Feature Transport for Image Relighting](https://mlsanity.com/paper/2607.17833/) (2026)
- [Physically Based Rendering in the Latent Space](https://mlsanity.com/paper/2609.21054/) (2026)

## Abstract (arXiv)

Generative diffusion models have advanced image editing with high-quality results and intuitive interfaces such as prompts and semantic drawing. However, these interfaces lack precise control, and the associated methods typically specialize on a single editing task. We introduce a versatile, generative workflow that operates in an intrinsic-image latent space, enabling semantic, local manipulation with pixel precision for a range of editing operations. Building atop the RGB-X diffusion framework, we address key challenges of identity preservation and intrinsic-channel entanglement. By incorporating exact diffusion inversion and disentangled channel manipulation, we enable precise, efficient editing with automatic resolution of global illumination effects -- all without additional data collection or model fine-tuning. We demonstrate state-of-the-art performance across a variety of tasks on complex images, including color and texture adjustments, object insertion and removal, global relighting, and their combinations.