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StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Models

Zhizhong Wang, Lei Zhao, Wei Xing

arXiv:2308.07863v1cs.CV

TL;DR

Style transfer needs interpretable and controllable content-style disentanglement, which prior Gram-matrix and GAN-based approaches do not provide. StyleDiffusion explicitly extracts content, implicitly learns complementary style with diffusion models, and adds CLIP-based disentanglement training. The authors report encouraging stylizations and effectiveness against the state of the art, while noting style-specific fine-tuning and diffusion-related efficiency limits.

  • Problem

    Style transfer lacks fully disentangled, interpretable, and controllable content-style representations, despite prior Gram-matrix and GAN-based approaches.

  • Method

    StyleDiffusion explicitly extracts content with diffusion-based style removal and implicitly learns complementary style using diffusion-based transfer, CLIP disentanglement loss, and a style reconstruction prior.

  • Results

    StyleDiffusion yields encouraging stylizations, especially for challenging styles, and experiments verify effectiveness and superiority against the state of the art.

  • Takeaways & Limitations

    The framework provides more interpretable and controllable content-style disentanglement and style transfer within the paper’s evaluated setting.

  • Takeaways & Limitations

    The model must be fine-tuned for each style, arbitrary style transfer remains future work, and diffusion makes efficiency insufficiently fast.

Abstract

from arXiv · show

Content and style (C-S) disentanglement is a fundamental problem and critical challenge of style transfer. Existing approaches based on explicit definitions (e.g., Gram matrix) or implicit learning (e.g., GANs) are neither interpretable nor easy to control, resulting in entangled representations and less satisfying results. In this paper, we propose a new C-S disentangled framework for style transfer without using previous assumptions. The key insight is to explicitly extract the content information and implicitly learn the complementary style information, yielding interpretable and controllable C-S disentanglement and style transfer. A simple yet effective CLIP-based style disentanglement loss coordinated with a style reconstruction prior is introduced to disentangle C-S in the CLIP image space. By further leveraging the powerful style removal and generative ability of diffusion models, our framework achieves superior results than state of the art and flexible C-S disentanglement and trade-off control. Our work provides new insights into the C-S disentanglement in style transfer and demonstrates the potential of diffusion models for learning well-disentangled C-S characteristics.

1. Introduction

Style transfer requires separating content from style before transferring artistic characteristics to new content. StyleDiffusion addresses the interpretability and controllability problems of prior representations by explicitly extracting content and implicitly learning complementary style with diffusion models.

  • Motivation: Style transfer must disentangle content and style before transferring artistic characteristics to arbitrary content targets.The paper identifies content-style disentanglement and style transfer as two fundamental challenges.
  • Limitations of prior work: Gram-matrix-based methods intertwine content and style representations, use difficult-to-interpret CNN features, and separately optimize content and style losses.These limitations can produce unbalanced stylizations and disharmonious artifacts.
  • Proposed framework: StyleDiffusion avoids Gram-matrix and GAN assumptions by explicitly extracting content and implicitly learning its complementary style.Defining style as the complement of extracted content makes disentanglement controllable through content extraction and interpretable.
  • Proposed framework: A diffusion-based style removal module extracts domain-aligned content from content and style images, while diffusion generation supports controllable content extraction.The extracted contents should share a content domain and remain easy to control.
  • Contributions: The framework introduces diffusion models and a CLIP-based style disentanglement loss coordinated with a style reconstruction prior.These components are used to learn disentangled content-style characteristics and improve stylized results.

2. Related Work

Related work spans neural style transfer, disentangled representation learning, and diffusion models. StyleDiffusion builds on these areas while replacing explicit Gram-matrix representations and GAN-restricted disentanglement with diffusion-based representations.

  • Neural Style Transfer: Neural style transfer has advanced in efficiency, quality, generality, and diversity, but many methods still rely on explicitly defined content-style representations such as Gram matrices.The paper identifies these representations as having the limitations discussed in the introduction.
  • Disentangled Representation Learning: Disentangled representation learning models factors of data variation through supervised or unsupervised approaches, including methods separating style from content.Prior style-transfer methods use losses, metric learning, or peer regularization to separate representations.
  • Disentangled Representation Learning: GAN-based style-content disentanglement is often restricted to GAN-predefined domains and may require sufficient data for effective disentanglement.The paper contrasts these restrictions with its diffusion-based framework.
  • Diffusion Models: Diffusion models have demonstrated strong performance in image generation, manipulation, and text-conditional synthesis, motivating their use for style transfer.The paper positions diffusion models as higher-quality generative alternatives to VAEs, autoregressive models, flows, and GANs.

3. Background

The background describes DDPMs as latent-variable models with forward noising and learned reverse denoising processes. It also introduces StyleDiffusion’s diffusion-based pipeline, including deterministic DDIM fine-tuning and stochastic DDPM inference options.

  • DDPMs: DDPMs use a forward Markov chain that progressively adds Gaussian noise to clean data across timesteps.The forward process produces latent variables x_1 through x_T using a fixed variance schedule.
  • StyleDiffusion pipeline: StyleDiffusion first removes style from content and style images to extract domain-aligned content, then transfers the style to the content representation.Its overview uses diffusion-based style removal and style transfer modules.
  • DDPMs: The reverse process starts from Gaussian noise and learns parameterized Gaussian transitions because the true reverse posteriors are intractable.The learned mean depends on a neural noise approximator, with Gaussian variance and noise terms defining the transitions.
  • DDIM: DDIM sets the reverse-process noise scale to zero, yielding deterministic mappings that support near-perfect inversion and fewer sampling steps.Changing σ_t changes the reverse process; σ_t = 0 gives the DDIM case.

4. Method

StyleDiffusion extracts domain-aligned content explicitly and learns complementary style information implicitly, using diffusion modules and CLIP-space losses for controllable style transfer.

  • Framework overview: StyleDiffusion uses diffusion-based style removal, diffusion-based style transfer, and a CLIP-based style disentanglement loss with a style reconstruction prior.These are the framework’s three key ingredients.
  • Style removal module: The style removal module removes style information from content and style images to explicitly extract domain-aligned content information.It can use user-defined content extraction operations, such as structural outlines.
  • Style removal module: The module first removes color with an ITU-R 601-2 luma transform, then uses a pretrained diffusion model to remove brushstrokes and textures.Diffusion removal assumes differently styled images occupy different domains while their contents share a domain, using a surrogate photograph domain.
  • Style removal module: Adjusting the removal step count controls style-removal intensity while retaining main content structures; diffusion removal is optional for photographs.More removal steps eliminate more style characteristics, and photographs already lie within the pretrained domain apart from color.
  • Style transfer module: The style transfer module encodes extracted content into a latent, fine-tunes diffusion under CLIP-based disentanglement and reconstruction losses, and generates stylized results.Deterministic DDIM processes are used during fine-tuning, while stochastic DDPM inversion can produce diverse inference results.
  • CLIP-based disentanglement: In CLIP space, style is represented as the distance between content and stylized-result embeddings, with style defined implicitly as content’s complement.The style reconstruction prior helps recover style information and initializes disentanglement-loss optimization.
  • Loss functions and fine-tuning: The transfer objective combines L1 and direction losses because L1 alone may leave the style domain or collapse, whereas their combination yields accurate one-to-one mappings.The direction loss aligns transfer directions, while the combined objective addresses mapping accuracy.

5. Experimental Results

Experiments show that StyleDiffusion produces high-quality stylizations while offering controllable content–style disentanglement and trade-off adjustment. It outperforms prior methods on user preference and key similarity metrics, with diffusion introducing flexible control and diversity at some inference-speed cost.

  • Qualitative Comparisons: StyleDiffusion produces sufficient style details and well-preserved contents, yielding more natural and harmonious stylizations than previous methods.Qualitative comparisons particularly highlight challenging styles such as cubism.
  • Quantitative Comparisons: The method achieves the highest SSIM and CLIP Score, while its Style Loss remains comparable to and lower than GAN-based TPFR.Evaluation uses 384 stylized results synthesized from 32 content and 12 style images.
  • User Study: User studies show that StyleDiffusion is preferred over competing methods for both style consistency and overall stylization quality.Twenty users provided 1000 votes for each question.
  • Efficiency: The method requires less training time than competing approaches but is slower at inference than feed-forward methods because it uses diffusion models.It is faster at testing than optimization-based Gatys.
  • Control of C-S Disentanglement: Adjusting Tremov continuously controls style removal and disentanglement, preserving more content structure while transferring the removed style characteristics into stylized results.The default setting is marked with an asterisk in Figure 4.
  • Superiority of Diffusion-based Style Transfer: Adjusting Ttrans controls the content–style trade-off during training or testing, with Ttrans = 301 described as a sweet spot and larger test-time values enabling style extrapolation.Excessively large values such as Ttrans = 601 may degrade results.

6. Conclusion and Limitation

StyleDiffusion presents a framework for interpretable and controllable content–style disentanglement and style transfer, while identifying unresolved practical limitations.

  • StyleDiffusion leverages diffusion models to explicitly extract content and implicitly learn complementary style information.
  • A CLIP-based style disentanglement loss and style reconstruction prior encourage disentanglement and style transfer.
  • The method produces encouraging stylizations, particularly for challenging styles, and shows effectiveness against state-of-the-art methods.
  • Limitations: The framework requires fine-tuning for each style, leaving arbitrary style transfer for future work.
  • Limitations: The framework remains inefficient because it uses diffusion models, and some failure cases are reported in the supplementary material.

Supplementary Material

The supplementary material covers societal impact, assets, implementation details, timing, ablations, comparisons, additional results, and limitations.

  • Supplementary sections discuss societal impact and list the assets used for experiments and training.
  • The material provides details of the style removal process and StyleDiffusion fine-tuning procedure.
  • Additional sections report timing and resource information, more ablations, extensions, state-of-the-art comparisons, and synthesized results.
  • The supplementary material includes a dedicated limitation and discussion section.
  • The paper identifies possible positive impacts for artistic creation and creative design, alongside a risk of counterfeit artwork generation.
  • Implementation details: Style removal removes image color, applies deterministic DDIM forward and reverse processes, and uses repeated iterations to dispel style characteristics.
  • Implementation details: Fine-tuning uses precomputed content latents that can be reused for other styles, with style reconstruction and disentanglement losses guiding model updates.

E. Timing and Resource Information

The supplementary timing analysis quantifies StyleDiffusion’s style-removal, fine-tuning, and inference costs, while noting unoptimized model size and memory consumption.

  • Timing and resources: Style removal takes around 49.21 seconds and requires about 11GB of GPU memory for 512 × 512 images.
  • Timing and resources: Precomputing latents from 50 images takes around 246.05 seconds and requires about 11GB of GPU memory.
  • Timing and resources: Inference takes 5.612 seconds with default settings (Sfor, Srev) = (40, 6) and requires about 13GB of GPU memory.
  • Limitations: Model size and GPU memory consumption have not been optimized, leaving substantial room for improvement.
  • Style removal: The style-removal analysis varies the return step Tremov to examine how style removal affects style transfer.

F. More Ablation Study and Analyses

The supplementary analyses examine disentanglement, content–style trade-offs, representation spaces, CLIP encoders, and diffusion-based versus auto-encoder style removal.

  • C-S disentanglement: Adjusting Tremov shows that removing more style information from a style image transfers it to the corresponding stylized result.
  • C-S trade-off: Adjusting Ttrans enables flexible content–style trade-off control at both training and testing stages.
  • Disentanglement space: The CLIP image space is compared with VGG-19 for formulating style disentanglement, using CLIP’s open-domain image knowledge to measure style distance.
  • CLIP image encoders: Vision Transformers generally produce better visual results than ResNets, and ViT-B/16 captures finer-grained styles than ViT-B/32.
  • Style removal ablation: Auto-encoder-based style removal often introduces color noise or artifacts and damages content structures, whereas diffusion-based removal preserves main content structures while removing style details.

G. Extensions

StyleDiffusion extends controllable style transfer to photo-realistic, multimodal, and diversified settings. Diffusion-based removal preserves content structures while removing detailed style more effectively than AE-based removal.

  • Style Removal: Diffusion-based style removal better removes detailed style while preserving main content structures than AE-based removal.AE-based removal often introduces color noise or artifacts and destroys content structures.
  • Photo-realistic Style Transfer: Photo-realistic style transfer is achieved by reducing style-removal and style-transfer steps to preserve photographic content structures.The reported settings are Tremov = 401 and Ttrans = 101.
  • Multi-modal Style Manipulation: The framework supports image- and text-guided style manipulation through its open-domain CLIP style-distance space.A directional CLIP loss is added to the total loss for multimodal manipulation.
  • Diversified Style Transfer: Replacing deterministic DDIM forward diffusion with stochastic DDPM forward diffusion enables diversified style transfer during inference.Users can trade off diversity and quality by adjusting the return step.
  • Failure Cases: The method exhibits failure cases involving vanishing salient content and biased style-image color distributions.These cases are illustrated in Figures 17 and 18.

H. More Comparison Results

The paper provides additional qualitative comparisons with state-of-the-art methods and further stylized results synthesized by StyleDiffusion.

  • More Qualitative Comparisons: Figures 20 and 21 present additional qualitative comparison results with state-of-the-art style transfer methods.Both figure sets recommend zooming in for comparison.
  • Additional Stylized Results: Figure 22 presents an additional set of stylized results synthesized by StyleDiffusion.The first row contains content images and the first column contains style images.
  • Additional Stylized Results: Figure 23 presents a second additional set of stylized results synthesized by StyleDiffusion.The first row contains content images and the first column contains style images.

J. Limitation and Discussion

The discussion identifies scope, efficiency, and quality limitations, including failure cases where salient content vanishes, colors diverge, or content and style cannot be separated.

  • Scope and Efficiency: The framework requires fine-tuning for each style, while arbitrary style transfer remains future work.The authors also identify diffusion-model efficiency as a limitation.
  • Vanishing Salient Content: The method may remove salient content because the ITU-R 601-2 luma transform can poorly preserve RGB color contrast and importance.The authors use this transform for simplicity and speed.
  • Biased Color Distribution: Increasing style reconstruction iterations can alleviate biased color distributions but significantly increases training time.The issue is illustrated for pointillism-style transfer.
  • Inseparable Content and Style: Style transfer is difficult for images with inseparable content and style, such as simple line art.The authors suggest treating line art as style and increasing the style-removal return step.
  • Additional Results: Figures 20 and 21 add qualitative comparisons, while Figures 22 and 23 add further StyleDiffusion stylizations.The comparison figures are presented as additional results rather than new quantitative analyses.
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