Stable diffusion, a generative model used in text-to-image synthesis, frequently encounters resolution-induced composition problems when generating images of varying sizes. This issue primarily stems from the model being trained on pairs of single-scale images and their corresponding text descriptions. Moreover, direct training on images of unlimited sizes is unfeasible, as it would require an immense number of text-image pairs and entail substantial computational expenses. To overcome these challenges, we propose a two-stage pipeline named Any-Size-Diffusion (ASD), designed to efficiently generate well-composed HD images of any size, while minimizing the need for high-memory GPU resources. Specifically, the initial stage, dubbed Any Ratio Adaptability Diffusion (ARAD), leverages a selected set of images with a restricted range of ratios to optimize the text-conditional diffusion model, thereby improving its ability to adjust composition to accommodate diverse image sizes. To support the creation of images at any desired size, we further introduce a technique called Fast Seamless Tiled Diffusion (FSTD) at the subsequent stage. This method allows for the rapid enlargement of the ASD output to any high-resolution size, avoiding seaming artifacts or memory overloads. Experimental results on the LAION-COCO and MM-CelebA-HQ benchmarks demonstrate that ASD can produce well-structured images of arbitrary sizes, cutting down the inference time by 2X compared to the traditional tiled algorithm. The source code is available at https://github.com/ProAirVerse/Any-Size-Diffusion.

Artifact-free super-resolution (SR) aims to translate low-resolution images into their high-resolution counterparts with a strict integrity of the original content, eliminating any distortions or synthetic details. While traditional diffusion-based SR techniques have demonstrated remarkable abilities to enhance image detail, they are prone to ar-tifact introduction during iterative procedures. Such arti-facts, ranging from trivial noise to unauthentic textures, de-viate from the true structure of the source image, thus chal-lenging the integrity of the super-resolution process. In this work, we propose Self-Adaptive Reality-Guided Diffusion (SARGD), a training-free method that delves into the latent space to effectively identify and mitigate the propagation of artifacts. Our SARGD begins by using an artifact detector to identify implausible pixels, creating a binary mask that highlights artifacts. Following this, the Reality Guidance Refinement (RGR) process refines artifacts by integrating this mask with realistic latent representations, improving alignment with the original image. Nonetheless, initial realistic-latent representations from lower-quality images result in over-smoothing in the final output. To address this, we introduce a Self-Adaptive Guidance (SAG) mechanism. It dynamically computes a reality score, enhancing the sharpness of the realistic latent. These alternating mechanisms collectively achieve artifact-free super-resolution. Extensive experiments demonstrate the superiority of our method, delivering detailed artifact-free high-resolution images while reducing sampling steps by 2 x. We release our code at https://github.com/ProAirVerse/Self-Adaptive-Guidance-Diffusion.git.

Salient object detection is a binary pixel-wise classification to distinguish objects in an image, and also have attracted many research interests in the optical Remote Sensing Images (RSIs). The existing state-of-the-art method exploits the full encoder-decoder architecture to predict salient objects in the optical RSIs, suffering from the problem of unsmooth edges and incomplete structures. To address these problems, in this paper, we propose a Boundary-Aware Network (BANet) with two-stage partial decoders sharing the same encoders for salient object detection in RSIs. Specifically, a Boundary-Aware Partial Decoder (BAD) is introduced at the first stage to focus on learning clear edges of salient objects. To solve the pixel-imbalance problem between boundary and background, an edge-aware loss is proposed to guide learning the BAD network. The resulting features are then employed in turn to enhance high-level features. Afterwards, the Structure-Aware Partial Decoder (SAD) is further introduced at the second stage to improve the structure integrity of salient objects. To alleviate the problem of incomplete structures, the structural similarity loss is further proposed to supervise learning the SAD network. In a consequence, our proposed BANet can predict salient objects with clear edges and complete structure, while reducing model parameters due to the discardment of low-level features. Besides, training a deep neural network requires a large amount of images, and the current benchmark datasets for optical remote sensing images are not large enough. Therefore, we also create a large-scale challenging dataset for salient object detection in RSIs. Extensive experiments demonstrate that our proposed BANet outperforms previous RSI SOD models on all existing benchmark datasets and our new presented dataset available at https://github.com/QingpingZheng/RSISOD.

This paper probes intrinsic factors behind typical failure cases (e.g. spatial inconsistency and boundary confusion) produced by the existing state-of-the-art method in face parsing. To tackle these problems, we propose a novel Decoupled Multi-task Learning with Cyclical Self-Regulation (DML-CSR) for face parsing. Specifically, DML-CSR designs a multi-task model which comprises face parsing, binary edge, and category edge detection. These tasks only share low-level encoder weights without high-level interactions between each other, enabling to decouple auxiliary modules from the whole network at the inference stage. To address spatial inconsistency, we develop a dynamic dual graph convolutional network to capture global contextual information without using any extra pooling operation. To handle boundary confusion in both single and multiple face scenarios, we exploit binary and category edge detection to jointly obtain generic geometric structure and fine-grained semantic clues of human faces. Besides, to prevent noisy labels from degrading model generalization during training, cyclical self-regulation is proposed to self-ensemble several model instances to get a new model and the resulting model then is used to self-distill subsequent models, through alternating iterations. Experiments show that our method achieves the new state-of-the-art performance on the Helen, CelebAMask-HQ, and Lapa datasets. The source code is available at https://github.com/deepinsight/insightface/tree/master/parsing/dml_csr.