Recent advances in generative models have sparked growing interest in moving beyond pure image generation toward transparent image generation, i.e., joint generation of image and its alpha mask. However, most existing approaches adopt a two-stage pipeline, where a diffusion-based model first generates an RGB image and a subsequent matting head predicts the alpha mask. This separation not only leads to error accumulation and inaccurate predictions but also overlooks the intrinsic correlation between the cross-modal data. In this work, we introduce Zippo, a unified diffusion framework, zipping color and transparency distributions into a single diffusion model, by learning joint distribution of RGB image and alpha mask. Zippo not only generates high-fidelity images but also produces plausible and sharp alpha masks. In practice, Zippo inflates the latent space into a unified representation that encodes cross-modal data, and builds upon it with a modality-aware diffusion process that flexibly switches between RGB and alpha domains. In this process, conditioning on one modality while denoising the other allows the model to generate RGB images from alpha masks and predict transparency from input images. In addition to single-modality prediction, we further design a modality-aware noise reassignment strategy to empower Zippo with the joint generation capability of RGB images and their corresponding alpha masks under text guidance. With these techniques, Zippo supports a wide range of transparent image generation tasks, including image-alpha joint generation, image matting, and alpha mask conditioned image generation. Extensive experiments demonstrate that Zippo not only delivers superior visual fidelity but also achieves competitive performance in visual downstream prediction, highlighting joint image-alpha modeling as a powerful alternative to traditional paradigms.

Extensive pre-training with large data is indispensable for downstream geometry and semantic visual perception tasks. Thanks to large-scale text-to-image (T2I) pretraining, recent works show promising results by simply fine-tuning T2I diffusion models for a few dense perception tasks. However, several crucial design decisions in this process still lack comprehensive justification, encompassing the necessity of the multi-step diffusion mechanism, training strategy, inference ensemble strategy, and fine-tuning data quality. In this work, we conduct a thorough investigation into critical factors that affect transfer efficiency and performance when using diffusion priors. Our key findings are: 1) High-quality fine-tuning data is paramount for both semantic and geometry perception tasks. 2) As a special case of the diffusion scheduler by setting its hyper-parameters, the multi-step generation can be simplified to a one-step fine-tuning paradigm without any loss of performance, while significantly speeding up inference. 3) Apart from fine-tuning the diffusion model with only latent space supervision, task-specific supervision can be beneficial to enhance fine-grained details. These observations culminate in the development of GenPercept, an effective deterministic one-step fine-tuning paradigm tailored for dense visual perception tasks exploiting diffusion priors. Different from the previous multi-step methods, our paradigm offers a much faster inference speed, and can be seamlessly integrated with customized perception decoders and loss functions for task-specific supervision, which can be critical for improving the fine-grained details of predictions. Comprehensive experiments on a diverse set of dense visual perceptual tasks, including monocular depth estimation, surface normal estimation, image segmentation, and matting, are performed to demonstrate the remarkable adaptability and effectiveness of our proposed method. Code: https://github.com/aim-uofa/GenPercept

With state-of-the-art performance in image synthesis especially in text-to-image generation, diffusion models (DMs) have received unprecedented attention. Despite promising application prospects, fake images generated from diffusion models are causing potential security concerns. To this end, in this work, we aim to investigate whether the generated images of DMs are different from other generated models e.g. generative adversarial networks (GANs) and whether a universal forensic classifier exists. To perform this work, we first collected a dataset consisting of 409k fake images generated from different types of DMs. Through a comprehensive analysis on this benchmark, we showcased that common forensic artifacts are shared among DMs and a forensic classifier trained for one model can generalize well for other agnostic generative models. Specifically, we first demonstrated that despite photorealism of the images generated by DMs, they still contain artifacts namely non-robust visual features which are hard for human but easy for machine to recognize. Then we studied the characteristics of the artifacts from the view of adversarial attack and unexpectedly found there exists a universal adversarial perturbation to fool the classifier. Furthermore, we devised visualization and analysis tools focusing on the spectral properties of the generated samples and adversarial features which demonstrates augmentations in the frequency domain greatly affect the performance of the detectors.