# TexSliders: Diffusion-Based Texture Editing in CLIP Space

arXiv: https://arxiv.org/abs/2405.00672 · HTML: https://mlsanity.com/paper/2405.00672/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 39 of its 49 references that are in the map.
- First posted on arXiv 1 May 2024.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 30 of 39)

- [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)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery](https://mlsanity.com/paper/2103.17249/) (2021)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [GANSpace: Discovering Interpretable GAN Controls](https://mlsanity.com/paper/2004.02546/) (2020)
- [Zero-shot Image-to-Image Translation](https://mlsanity.com/paper/2302.03027/) (2023)
- [Blended Latent Diffusion](https://mlsanity.com/paper/2206.02779/) (2022)
- [Universal Style Transfer via Feature Transforms](https://mlsanity.com/paper/1705.08086/) (2017)
- [SinGAN: Learning a Generative Model from a Single Natural Image](https://mlsanity.com/paper/1905.01164/) (2019)
- [StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation](https://mlsanity.com/paper/2011.12799/) (2020)
- [Diffusion Models already have a Semantic Latent Space](https://mlsanity.com/paper/2210.10960/) (2022)
- [Diffusion Self-Guidance for Controllable Image Generation](https://mlsanity.com/paper/2306.00986/) (2023)
- [TEXTure: Text-Guided Texturing of 3D Shapes](https://mlsanity.com/paper/2302.01721/) (2023)
- [Text2Tex: Text-driven Texture Synthesis via Diffusion Models](https://mlsanity.com/paper/2303.11396/) (2023)
- [Texture Networks: Feed-forward Synthesis of Textures and Stylized Images](https://mlsanity.com/paper/1603.03417/) (2016)
- [StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators](https://mlsanity.com/paper/2108.00946/) (2021)
- [Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models](https://mlsanity.com/paper/2311.12092/) (2023)
- [Non-Stationary Texture Synthesis by Adversarial Expansion](https://mlsanity.com/paper/1805.04487/) (2018)

## Built on by (most cited first, 23 of 23)

- [Magnet: We Never Know How Text-to-Image Diffusion Models Work, Until We Learn How Vision-Language Models Function](https://mlsanity.com/paper/2409.19967/) (2024)
- [IP-Composer: Semantic Composition of Visual Concepts](https://mlsanity.com/paper/2502.13951/) (2025)
- [PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with Auto-Regressive Transformer](https://mlsanity.com/paper/2505.04622/) (2025)
- [MaterialPicker: Multi-Modal DiT-Based Material Generation](https://mlsanity.com/paper/2412.03225/) (2024)
- [Can CLIP Count Stars? An Empirical Study on Quantity Bias in CLIP](https://mlsanity.com/paper/2409.15035/) (2024)
- [MARBLE: Material Recomposition and Blending in CLIP-Space](https://mlsanity.com/paper/2506.05313/) (2025)
- [SAEdit: Token-level control for continuous image editing via Sparse AutoEncoder](https://mlsanity.com/paper/2510.05081/) (2025)
- [ParSEL: Parameterized Shape Editing with Language](https://mlsanity.com/paper/2405.20319/) (2024)
- [MatSwap: Light-aware material transfers in images](https://mlsanity.com/paper/2502.07784/) (2025)
- [From Cradle to Cane: A Two-Pass Framework for High-Fidelity Lifespan Face Aging](https://mlsanity.com/paper/2506.20977/) (2025)
- [Generative Detail Enhancement for Physically Based Materials](https://mlsanity.com/paper/2502.13994/) (2025)
- [A Controllable Appearance Representation for Flexible Transfer and Editing](https://mlsanity.com/paper/2504.15028/) (2025)
- [Example-Based Feature Painting on Textures](https://mlsanity.com/paper/2511.01513/) (2025)
- [Learning to Refocus with Video Diffusion Models](https://mlsanity.com/paper/2512.19823/) (2025)
- [ScribbleSense: Generative Scribble-Based Texture Editing with Intent Prediction](https://mlsanity.com/paper/2601.22455/) (2026)
- [TexEditor: Structure-Preserving Text-Driven Texture Editing](https://mlsanity.com/paper/2603.18488/) (2026)
- [PixelSmile: Toward Fine-Grained Facial Expression Editing](https://mlsanity.com/paper/2603.25728/) (2026)
- [Self-Supervised Vision Transformer for Enhanced Virtual Clothes Try-On](https://mlsanity.com/paper/2406.10539/) (2024)
- [Pattern Analogies: Learning to Perform Programmatic Image Edits by Analogy](https://mlsanity.com/paper/2412.12463/) (2024)
- [Beyond Sliders: Mastering the Art of Diffusion-based Image Manipulation](https://mlsanity.com/paper/2509.11213/) (2025)
- [Style-Aware Gloss Control for Generative Non-Photorealistic Rendering](https://mlsanity.com/paper/2602.16611/) (2026)
- [SemanticSlider3D: Training-Free Continuous Semantic Editing for 3D Objects](https://mlsanity.com/paper/2608.18560/) (2026)
- [Attribute Token Arithmetic: Disentangled and Continuous Semantic Control for Visual Autoregressive Models](https://mlsanity.com/paper/2608.28082/) (2026)

## Abstract (arXiv)

Generative models have enabled intuitive image creation and manipulation using natural language. In particular, diffusion models have recently shown remarkable results for natural image editing. In this work, we propose to apply diffusion techniques to edit textures, a specific class of images that are an essential part of 3D content creation pipelines. We analyze existing editing methods and show that they are not directly applicable to textures, since their common underlying approach, manipulating attention maps, is unsuitable for the texture domain. To address this, we propose a novel approach that instead manipulates CLIP image embeddings to condition the diffusion generation. We define editing directions using simple text prompts (e.g., "aged wood" to "new wood") and map these to CLIP image embedding space using a texture prior, with a sampling-based approach that gives us identity-preserving directions in CLIP space. To further improve identity preservation, we project these directions to a CLIP subspace that minimizes identity variations resulting from entangled texture attributes. Our editing pipeline facilitates the creation of arbitrary sliders using natural language prompts only, with no ground-truth annotated data necessary.