Content-style disentangled representation for controllable artistic image stylization and generation
Keywords: disentangled representation, image stylization, generation
Abstract: Controllable artistic image stylization and generation aims to render the content provided by text or image with the learned artistic style, where content and style decoupling is the key to achieve satisfactory results. However, current methods for content and style disentanglement primarily rely on image information for supervision, which leads to two problems: 1) models can only support one modality for style or content input;2) incomplete disentanglement resulting in semantic interference from the reference image. To address the above issues, this paper proposes a content-style representation disentangling method for controllable artistic image stylization and generation. We construct a WikiStyle+ dataset consists of artworks with corresponding textual descriptions for style and content. Based on the multimodal dataset, we propose a disentangled content and style representations guided diffusion model. The disentangled representations are first learned by Q-Formers and then injected into a pre-trained diffusion model using learnable multi-step cross-attention layers for better controllable stylization. This approach allows model to accommodate inputs from different modalities. Experimental results show that our method achieves a thorough disentanglement of content and style in reference images under multimodal supervision, thereby enabling a harmonious integration of content and style in the generated outputs, successfully producing style-consistent and expressive stylized images.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Submission Number: 2477
Loading