Facial attribute editing and style manipulation are crucial for applications like virtual avatars and photo editing. However, achieving precise control over facial attributes without altering unrelated features is challenging due to the complexity of facial structures and the strong correlations between attributes. While conditional GANs have shown progress, they are limited by accuracy issues and training instability. Diffusion models, though promising, face challenges in style manipulation due to the limited expressiveness of semantic directions. In this paper, we propose LatRef-Diff, a novel diffusion-based framework that addresses these limitations. We replace the traditional semantic directions in diffusion models with style codes and propose two methods for generating them: latent and reference guidance. Based on these style codes, we design a style modulation module that integrates them into the target image, enabling both random and customized style manipulation. This module incorporates learnable vectors, cross-attention mechanisms, and a hierarchical design to improve accuracy and image quality. Additionally, to enhance training stability while eliminating the need for paired images (e.g., before and after editing), we propose a forward-backward consistency training strategy. This strategy first removes the target attribute approximately using image-specific semantic directions and then restores it via style modulation, guided by perceptual and classification losses. Extensive experiments on CelebA-HQ demonstrate that LatRef-Diff achieves state-of-the-art performance in both qualitative and quantitative evaluations. Ablation studies validate the effectiveness of our model's design choices.
Facial attribute editing aims to modify target attributes while preserving attribute-irrelevant content and overall image fidelity. Existing GAN-based methods provide favorable controllability, but often suffer from weak alignment between style codes and attribute semantics. Diffusion-based methods can synthesize highly realistic images; however, their editing precision is limited by the entanglement of semantic directions among different attributes. In this paper, we propose AttDiff-GAN, a hybrid framework that combines GAN-based attribute manipulation with diffusion-based image generation. A key challenge in such integration lies in the inconsistency between one-step adversarial learning and multi-step diffusion denoising, which makes effective optimization difficult. To address this issue, we decouple attribute editing from image synthesis by introducing a feature-level adversarial learning scheme to learn explicit attribute manipulation, and then using the manipulated features to guide the diffusion process for image generation, while also removing the reliance on semantic direction-based editing. Moreover, we enhance style-attribute alignment by introducing PriorMapper, which incorporates facial priors into style generation, and RefineExtractor, which captures global semantic relationships through a Transformer for more precise style extraction. Experimental results on CelebA-HQ show that the proposed method achieves more accurate facial attribute editing and better preservation of non-target attributes than state-of-the-art methods in both qualitative and quantitative evaluations.
Text-to-image diffusion models have revolutionized image synthesis and editing, but precise control over stylistic attributes remains a challenge, often causing unintended content modifications. We propose an approach for fine-grained parametric control of stylistic attributes in latent diffusion models by learning disentangled editing directions from synthetic datasets. We use guidance composition to close the domain gap between stylistically finetuned and foundation models, preserving the original image semantics while applying stylistic adjustments. To ensure consistent edits, we introduce a training regularization loss and enhance DDIM inversion with optimized null-conditional embeddings for real image editing. We validate our approach by learning from stylistically filtered synthetic datasets varying a range of stylistic attributes, including outlines, local contrast, watercolorization effects, and geometric patterns. Our evaluations demonstrate that compared to current text-based editing techniques, our method offers well-integrated, more precise and continuously adjustable stylistic modifications.
The rapid advancement of diffusion models has enabled the generation of high-fidelity images from textual prompts, yet achieving precise, disentangled control over specific attributes remains a significant challenge. A fundamental limitation arises because visual differences between images are often far more descriptive and nuanced than what can be captured through human-crafted text descriptions, which frequently fail to convey fine-grained semantic details. To address this, we introduce ViDiT (Visual Direction Transfer for Diffusion), a framework that expands the editing vocabulary by capturing latent semantics directly from image-edit pairs. ViDiT learns the underlying transformation by optimizing a single, global, and continuous editing direction from a small set of before-and-after'' examples. This optimization process transfers visual changes into the diffusion model's conditioning space, allowing for detailed edits that text alone cannot easily describe. ViDiT operates on a Learn Once'' principle, which completely eliminates the need for model fine-tuning or expensive per-image optimization during inference. Once learned, these continuous directions enable ``Edit Anywhere'' capabilities, allowing users to apply highly disentangled manipulations, such as changes in facial features, animal attributes, or artistic styles, to any image in a zero-shot manner with granular control over the edit intensity. Quantitative and qualitative evaluations demonstrate that ViDiT outperforms existing text-based editing methods in maintaining input faithfulness while achieving precise, scalable attribute control.