Diffusion-Based Texture Editing with Natural Language Prompts

Grunnleggende konsepter

A novel diffusion-based approach for texture editing that leverages CLIP image embeddings to define intuitive editing directions from natural language prompts, while preserving the identity of the input texture.

Sammendrag

The authors propose TexSliders, a diffusion-based texture editing method that allows users to define editing directions using simple text prompts, such as "aged wood" to "new wood". The key idea is to leverage CLIP image embeddings to find a suitable direction in the embedding space that captures the desired edit, while preserving the identity of the input texture.
The method works as follows:

The authors use a diffusion prior model to convert the text prompts into CLIP image embeddings, which are then used to condition a pre-trained diffusion model for texture generation.
To define an editing direction, the authors compute multiple CLIP image embeddings for the original and target prompts, and take the difference between their centroids as the initial direction.
To improve identity preservation, the authors select a subset of dimensions from the initial direction based on the relative intra- and inter-cluster variability. This helps to focus the edit on the desired attribute while minimizing changes to the texture identity.
The final editing direction is then used to condition the diffusion model and generate the edited texture, allowing the user to control the intensity of the edit by adjusting the step size along the direction.

The authors evaluate their method on a diverse set of generated and real-world textures, demonstrating its ability to perform a wide range of edits, such as weathering, scale, and roughness, while preserving the identity of the input texture. They also compare their approach to state-of-the-art diffusion-based image editing methods, showing superior performance in terms of both edit adherence and identity preservation.

Statistikk

"Diffusion models have enabled intuitive image creation and manipulation using natural language."
"Textures are ubiquitous in image manipulation, graphic design, illustrations, rendering, and 3D modeling."
"Recent work has proposed to leverage attention maps for general diffusion-based image editing, but these are not as informative in the context of texture."

Sitater

"We explore a different solution: texture manipulations in the CLIP embedding space, akin to latent manipulations in GANs."
"Our approach allows to define new sliders for custom concepts with simple text prompts in a matter of minutes."

Viktige innsikter hentet fra

TexSliders: Diffusion-Based Texture Editing in CLIP Space

by Julia Guerre... klokken arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00672.pdf

Dypere Spørsmål

How could this approach be extended to handle more complex texture attributes, such as structural changes or the introduction of new elements

To handle more complex texture attributes like structural changes or the introduction of new elements, the approach could be extended by incorporating more sophisticated editing mechanisms. One way to achieve this is by refining the dimension selection process in the editing direction computation. By analyzing the relevance of each dimension in the CLIP embedding space more comprehensively, the method can better capture intricate texture attributes and structural changes. Additionally, introducing a mechanism for hierarchical editing could allow for the manipulation of different levels of texture details, enabling the editing of specific elements within the texture while preserving overall coherence. This hierarchical approach could involve segmenting the texture into different regions or components and applying targeted edits to each segment independently. By incorporating these enhancements, the method can effectively handle more complex texture attributes and structural modifications.

What are the potential limitations of using CLIP embeddings for texture editing, and how could they be addressed

Using CLIP embeddings for texture editing may have certain limitations that need to be addressed for optimal performance. One potential limitation is the sensitivity of CLIP embeddings to specific concepts or attributes, which can lead to biases in the generated textures. To mitigate this, a more extensive analysis of the CLIP embedding space and its representation of texture attributes could be conducted. By identifying and addressing these biases through data augmentation or bias correction techniques, the method can produce more diverse and unbiased texture edits. Another limitation is the potential loss of texture identity when extrapolating too far from the input texture during editing. This issue could be mitigated by implementing constraints or regularization techniques that limit the extent of extrapolation, ensuring that the edited textures remain within a realistic range of variations. Additionally, exploring alternative embedding spaces or incorporating feedback mechanisms from users to guide the editing process could help overcome limitations associated with CLIP embeddings and enhance the overall performance of the method.

Could this method be adapted to work with other types of generative models beyond diffusion, such as GANs or autoregressive models

Adapting this method to work with other types of generative models beyond diffusion, such as GANs or autoregressive models, is feasible with certain modifications. For GANs, the approach could involve conditioning the generator on CLIP embeddings or utilizing CLIP-guided training to ensure that the generated textures align with the desired attributes specified in the prompts. By integrating CLIP embeddings into the training process of GANs, the model can learn to generate textures that are consistent with the input text descriptions. Similarly, for autoregressive models, incorporating CLIP embeddings as additional input features or leveraging CLIP-based guidance during the generation process can enable the model to produce textured images that align with the specified editing directions. By adapting the method to suit the architecture and training dynamics of GANs or autoregressive models, it can be extended to work seamlessly with a broader range of generative models, enhancing its versatility and applicability in various contexts.