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.

应用扩散技术编辑纹理，通过操作 CLIP 图像嵌入来调节扩散生成，无需标注数据即可用自然语言提示进行编辑。