Text-to-image diffusion models have remarkably excelled in producing diverse high-quality and photo-realistic images. This advancement has spurred a growing interest in incorporating specific identities into generated content. Most current methods employ an inversion approach to embed a target visual concept into the text embedding space using a single reference image. However the newly synthesized faces either closely resemble the reference image in terms of facial attributes such as expression or exhibit a reduced capacity for identity preservation. Text descriptions intended to guide the facial attributes of the synthesized face may fall short owing to the intricate entanglement of identity information with identity-irrelevant facial attributes derived from the reference image. To address these issues we present the novel use of the extended StyleGAN embedding space \mathcal W _+ to achieve enhanced identity preservation and disentanglement for diffusion models. By aligning this semantically meaningful human face latent space with text-to-image diffusion models we succeed in maintaining high fidelity in identity preservation coupled with the capacity for semantic editing. Additionally we propose new training objectives to balance the influences of both prompt and identity conditions ensuring that the identity-irrelevant background remains \lxm negligibly affected during facial attribute modifications. Extensive experiments reveal that our method adeptly generates personalized text-to-image outputs that are not only compatible with prompt descriptions but also amenable to common StyleGAN editing directions in diverse settings. Our code and model are available at https://github.com/csxmli2016/w-plus-adapter.
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Diffusion-FOF: Single-View Clothed Human Reconstruction via Diffusion-Based Fourier Occupancy Field
PDF ↗Reconstructing a clothed human from a single-view image has several challenging issues including flexibly representing various body shapes and poses estimating complete 3D geometry and consistent texture and achieving more fine-grained details. To address them we propose a new diffusion-based Fourier occupancy field method to improve the human representing ability and the geometry generating ability. First we estimate the back-view image from the given reference image by incorporating a style consistency constraint. Then we extract multi-scale features of the two images as conditional and design a diffusion model to generate the Fourier occupancy field in the wavelet domain. We refine the initial estimated Fourier occupancy field with image features as conditions to improve the geometric accuracy. Finally the reference and estimated back-view images are mapped onto the human model creating a textured clothed human model. Substantial experiments are conducted and the experimental results show that our method outperforms the state-of-the-art methods in geometry and texture reconstruction performance.
This paper unravels the potential of sketches for diffusion models addressing the deceptive promise of direct sketch control in generative AI. We importantly democratise the process enabling amateur sketches to generate precise images living up to the commitment of "what you sketch is what you get". A pilot study underscores the necessity revealing that deformities in existing models stem from spatial-conditioning. To rectify this we propose an abstraction-aware framework utilising a sketch adapter adaptive time-step sampling and discriminative guidance from a pre-trained fine-grained sketch-based image retrieval model working synergistically to reinforce fine-grained sketch-photo association. Our approach operates seamlessly during inference without the need for textual prompts; a simple rough sketch akin to what you and I can create suffices! We welcome everyone to examine results presented in the paper and its supplementary. Contributions include democratising sketch control introducing an abstraction-aware framework and leveraging discriminative guidance validated through extensive experiments.
Crowd counting is a fundamental problem in crowd analysis which is typically accomplished by estimating a crowd density map and summing over the density values. However this approach suffers from background noise accumulation and loss of density due to the use of broad Gaussian kernels to create the ground truth density maps. This issue can be overcome by narrowing the Gaussian kernel. However existing approaches perform poorly when trained with ground truth density maps with broad kernels. To deal with this limitation we propose using conditional diffusion models to predict density maps as diffusion models show high fidelity to training data during generation. With that we present CrowdDiff that generates the crowd density map as a reverse diffusion process. Furthermore as the intermediate time steps of the diffusion process are noisy we incorporate a regression branch for direct crowd estimation only during training to improve the feature learning. In addition owing to the stochastic nature of the diffusion model we introduce producing multiple density maps to improve the counting performance contrary to the existing crowd counting pipelines. We conduct extensive experiments on publicly available datasets to validate the effectiveness of our method. CrowdDiff outperforms existing \sota crowd counting methods on several public crowd analysis benchmarks with significant improvements. CrowdDiff project is available at: https://dylran.github.io/crowddiff.github.io.
We present DiSR-NeRF a diffusion-guided framework for view-consistent super-resolution (SR) NeRF. Unlike prior works we circumvent the requirement for high-resolution (HR) reference images by leveraging existing powerful 2D super-resolution models. Nonetheless independent SR 2D images are often inconsistent across different views. We thus propose Iterative 3D Synchronization (I3DS) to mitigate the inconsistency problem via the inherent multi-view consistency property of NeRF. Specifically our I3DS alternates between upscaling low-resolution (LR) rendered images with diffusion models and updating the underlying 3D representation with standard NeRF training. We further introduce Renoised Score Distillation (RSD) a novel score-distillation objective for 2D image resolution. Our RSD combines features from ancestral sampling and Score Distillation Sampling (SDS) to generate sharp images that are also LR-consistent. Qualitative and quantitative results on both synthetic and real-world datasets demonstrate that our DiSR-NeRF can achieve better results on NeRF super-resolution compared with existing works. Code and video results available at the project website.
SmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal Large Language Models
PDF ↗Current instruction-based image editing methods such as InstructPix2Pix often fail to produce satisfactory results in complex scenarios due to their dependence on the simple CLIP text encoder in diffusion models. To rectify this this paper introduces SmartEdit a novel approach of instruction-based image editing that leverages Multimodal Large Language Models (MLLMs) to enhance its understanding and reasoning capabilities. However direct integration of these elements still faces challenges in situations requiring complex reasoning. To mitigate this we propose a Bidirectional Interaction Module (BIM) that enables comprehensive bidirectional information interactions between the input image and the MLLM output. During training we initially incorporate perception data to boost the perception and understanding capabilities of diffusion models. Subsequently we demonstrate that a small amount of complex instruction editing data can effectively stimulate SmartEdit's editing capabilities for more complex instructions. We further construct a new evaluation dataset Reason-Edit specifically tailored for complex instruction-based image editing. Both quantitative and qualitative results on this evaluation dataset indicate that our SmartEdit surpasses previous methods paving the way for the practical application of complex instruction-based image editing.
In this paper we introduce Fairy a minimalist yet robust adaptation of image-editing diffusion models enhancing them for video editing applications. Our approach centers on the concept of anchor-based cross-frame attention a mechanism that implicitly propagates diffusion features across frames ensuring superior temporal coherence and high-fidelity synthesis. Fairy not only addresses limitations of previous models including memory and processing speed. It also improves temporal consistency through a unique data augmentation strategy. This strategy renders the model equivariant to affine transformations in both source and target images. Remarkably efficient Fairy generates 120-frame 512x384 videos (4-second duration at 30 FPS) in just 14 seconds outpacing prior works by at least 44x. A comprehensive user study involving 1000 generated samples confirms that our approach delivers superior quality decisively outperforming established methods.
Perturbing Attention Gives You More Bang for the Buck: Subtle Imaging Perturbations That Efficiently Fool Customized Diffusion Models
PDF ↗Diffusion models (DMs) embark a new era of generative modeling and offer more opportunities for efficient generating high-quality and realistic data samples. However their widespread use has also brought forth new challenges in model security which motivates the creation of more effective adversarial attackers on DMs to understand its vulnerability. We propose CAAT a simple but generic and efficient approach that does not require costly training to effectively fool latent diffusion models (LDMs). The approach is based on the observation that cross-attention layers exhibits higher sensitivity to gradient change allowing for leveraging subtle perturbations on published images to significantly corrupt the generated images. We show that a subtle perturbation on an image can significantly impact the cross-attention layers thus changing the mapping between text and image during the fine-tuning of customized diffusion models. Extensive experiments demonstrate that CAAT is compatible with diverse diffusion models and outperforms baseline attack methods in a more effective (more noise) and efficient (twice as fast as Anti-DreamBooth and Mist) manner.
Despite the remarkable process of talking-head-based avatar-creating solutions directly generating anchor-style videos with full-body motions remains challenging. In this study we propose Make-Your-Anchor a novel system necessitating only a one-minute video clip of an individual for training subsequently enabling the automatic generation of anchor-style videos with precise torso and hand movements. Specifically we finetune a proposed structure-guided diffusion model on input video to render 3D mesh conditions into human appearances. We adopt a two-stage training strategy for the diffusion model effectively binding movements with specific appearances. To produce arbitrary long temporal video we extend the 2D U-Net in the frame-wise diffusion model to a 3D style without additional training cost and a simple yet effective batch-overlapped temporal denoising module is proposed to bypass the constraints on video length during inference. Finally a novel identity-specific face enhancement module is introduced to improve the visual quality of facial regions in the output videos. Comparative experiments demonstrate the effectiveness and superiority of the system in terms of visual quality temporal coherence and identity preservation outperforming SOTA diffusion/non-diffusion methods. Project page: https://github.com/ICTMCG/Make-Your-Anchor.
Stable Diffusion has established itself as a foundation model in generative AI artistic applications receiving widespread research and application. Some recent fine-tuning methods have made it feasible for individuals to implant personalized concepts onto the basic Stable Diffusion model with minimal computational costs on small datasets. However these innovations have also given rise to issues like facial privacy forgery and artistic copyright infringement. In recent studies researchers have explored the addition of imperceptible adversarial perturbations to images to prevent potential unauthorized exploitation and infringements when personal data is used for fine-tuning Stable Diffusion. Although these studies have demonstrated the ability to protect images it is essential to consider that these methods may not be entirely applicable in real-world scenarios. In this paper we systematically evaluate the use of perturbations to protect images within a practical threat model. The results suggest that these approaches may not be sufficient to safeguard image privacy and copyright effectively. Furthermore we introduce a purification method capable of removing protected perturbations while preserving the original image structure to the greatest extent possible. Experiments reveal that Stable Diffusion can effectively learn from purified images over all protective methods.
Given a clothing image and a person image an image-based virtual try-on aims to generate a customized image that appears natural and accurately reflects the characteristics of the clothing image. In this work we aim to expand the applicability of the pre-trained diffusion model so that it can be utilized independently for the virtual try-on task. The main challenge is to preserve the clothing details while effectively utilizing the robust generative capability of the pre-trained model. In order to tackle these issues we propose StableVITON learning the semantic correspondence between the clothing and the human body within the latent space of the pre-trained diffusion model in an end-to-end manner. Our proposed zero cross-attention blocks not only preserve the clothing details by learning the semantic correspondence but also generate high-fidelity images by utilizing the inherent knowledge of the pre-trained model in the warping process. Through our proposed novel attention total variation loss and applying augmentation we achieve the sharp attention map resulting in a more precise representation of clothing details. StableVITON outperforms the baselines in qualitative and quantitative evaluation showing promising quality in arbitrary person images. Our code is available at https://github.com/rlawjdghek/StableVITON.
Diffusion models currently dominate the field of data-driven image synthesis with their unparalleled scaling to large datasets. In this paper we identify and rectify several causes for uneven and ineffective training in the popular ADM diffusion model architecture without altering its high-level structure. Observing uncontrolled magnitude changes and imbalances in both the network activations and weights over the course of training we redesign the network layers to preserve activation weight and update magnitudes on expectation. We find that systematic application of this philosophy eliminates the observed drifts and imbalances resulting in considerably better networks at equal computational complexity. Our modifications improve the previous record FID of 2.41 in ImageNet-512 synthesis to 1.81 achieved using fast deterministic sampling. As an independent contribution we present a method for setting the exponential moving average (EMA) parameters post-hoc i.e. after completing the training run. This allows precise tuning of EMA length without the cost of performing several training runs and reveals its surprising interactions with network architecture training time and guidance.
Diffusion models have made significant advances in generating high-quality images but their application to video generation has remained challenging due to the complexity of temporal motion. Zero-shot video editing offers a solution by utilizing pre-trained image diffusion models to translate source videos into new ones. Nevertheless existing methods struggle to maintain strict temporal consistency and efficient memory consumption. In this work we propose a novel approach to enhance temporal consistency in generated videos by merging self-attention tokens across frames. By aligning and compressing temporally redundant tokens across frames our method improves temporal coherence and reduces memory consumption in self-attention computations. The merging strategy matches and aligns tokens according to the temporal correspondence between frames facilitating natural temporal consistency in generated video frames. To manage the complexity of video processing we divide videos into chunks and develop intra-chunk local token merging and inter-chunk global token merging ensuring both short-term video continuity and long-term content consistency. Our video editing approach seamlessly extends the advancements in image editing to video editing rendering favorable results in temporal consistency over state-of-the-art methods.
Motion2VecSets: 4D Latent Vector Set Diffusion for Non-rigid Shape Reconstruction and Tracking
PDF ↗We introduce Motion2VecSets a 4D diffusion model for dynamic surface reconstruction from point cloud sequences. While existing state-of-the-art methods have demonstrated success in reconstructing non-rigid objects using neural field representations conventional feed-forward networks encounter challenges with ambiguous observations from noisy partial or sparse point clouds. To address these challenges we introduce a diffusion model that explicitly learns the shape and motion distribution of non-rigid objects through an iterative denoising process of compressed latent representations. The diffusion-based priors enable more plausible and probabilistic reconstructions when handling ambiguous inputs. We parameterize 4D dynamics with latent sets instead of using global latent codes. This novel 4D representation allows us to learn local shape and deformation patterns leading to more accurate non-linear motion capture and significantly improving generalizability to unseen motions and identities. For more temporally-coherent object tracking we synchronously denoise deformation latent sets and exchange information across multiple frames. To avoid computational overhead we designed an interleaved space and time attention block to alternately aggregate deformation latents along spatial and temporal domains. Extensive comparisons against state-of-the-art methods demonstrate the superiority of our Motion2VecSets in 4D reconstruction from various imperfect observations.
Diffusion models have shown remarkable results for image generation editing and inpainting. Recent works explore diffusion models for 3D shape generation with neural implicit functions i.e. signed distance function and occupancy function. However they are limited to shapes with closed surfaces which prevents them from generating diverse 3D real-world contents containing open surfaces. In this work we present UDiFF a 3D diffusion model for unsigned distance fields (UDFs) which is capable to generate textured 3D shapes with open surfaces from text conditions or unconditionally. Our key idea is to generate UDFs in spatial-frequency domain with an optimal wavelet transformation which produces a compact representation space for UDF generation. Specifically instead of selecting an appropriate wavelet transformation which requires expensive manual efforts and still leads to large information loss we propose a data-driven approach to learn the optimal wavelet transformation for UDFs. We evaluate UDiFF to show our advantages by numerical and visual comparisons with the latest methods on widely used benchmarks. Page: https://weiqi-zhang.github.io/UDiFF.
Lodge: A Coarse to Fine Diffusion Network for Long Dance Generation Guided by the Characteristic Dance Primitives
PDF ↗We propose Lodge a network capable of generating extremely long dance sequences conditioned on given music. We design Lodge as a two-stage coarse to fine diffusion architecture and propose the characteristic dance primitives that possess significant expressiveness as intermediate representations between two diffusion models. The first stage is global diffusion which focuses on comprehending the coarse-level music-dance correlation and production characteristic dance primitives. In contrast the second-stage is the local diffusion which parallelly generates detailed motion sequences under the guidance of the dance primitives and choreographic rules. In addition we propose a Foot Refine Block to optimize the contact between the feet and the ground enhancing the physical realism of the motion. Code available at https://li-ronghui.github.io/lodge
Consistent3D: Towards Consistent High-Fidelity Text-to-3D Generation with Deterministic Sampling Prior
PDF ↗Score distillation sampling (SDS) and its variants have greatly boosted the development of text-to-3D generation but are vulnerable to geometry collapse and poor textures yet. To solve this issue we first deeply analyze the SDS and find that its distillation sampling process indeed corresponds to the trajectory sampling of a stochastic differential equation (SDE): SDS samples along an SDE trajectory to yield a less noisy sample which then serves as a guidance to optimize a 3D model. However the randomness in SDE sampling often leads to a diverse and unpredictable sample which is not always less noisy and thus is not a consistently correct guidance explaining the vulnerability of SDS. Since for any SDE there always exists an ordinary differential equation (ODE) whose trajectory sampling can deterministically and consistently converge to the desired target point as the SDE we propose a novel and effective "Consistent3D" method that explores the ODE deterministic sampling prior for text-to-3D generation. Specifically at each training iteration given a rendered image by a 3D model we first estimate its desired 3D score function by a pre-trained 2D diffusion model and build an ODE for trajectory sampling. Next we design a consistency distillation sampling loss which samples along the ODE trajectory to generate two adjacent samples and uses the less noisy sample to guide another more noisy one for distilling the deterministic prior into the 3D model. Experimental results show the efficacy of our Consistent3D in generating high-fidelity and diverse 3D objects and large-scale scenes as shown in Fig. 1. The codes are available at https://github.com/sail-sg/Consistent3D.
We establish rigorous benchmarks for visual perception robustness. Synthetic images such as ImageNet-C ImageNet-9 and Stylized ImageNet provide specific type of evaluation over synthetic corruptions backgrounds and textures yet those robustness benchmarks are restricted in specified variations and have low synthetic quality. In this work we introduce generative model as a data source for synthesizing hard images that benchmark deep models' robustness. Leveraging diffusion models we are able to generate images with more diversified backgrounds textures and materials than any prior work where we term this benchmark as ImageNet-D. Experimental results show that ImageNet-D results in a significant accuracy drop to a range of vision models from the standard ResNet visual classifier to the latest foundation models like CLIP and MiniGPT-4 significantly reducing their accuracy by up to 60%. Our work suggests that diffusion models can be an effective source to test vision models. The code and dataset are available at https://github.com/chenshuang-zhang/imagenet_d.
Image diffusion models have been utilized in various tasks such as text-to-image generation and controllable image synthesis. Recent research has introduced tuning methods that make subtle adjustments to the original models yielding promising results in specific adaptations of foundational generative diffusion models. Rather than modifying the main backbone of the diffusion model we delve into the role of skip connection in U-Net and reveal that hierarchical features aggregating long-distance information across encoder and decoder make a significant impact on the content and quality of image generation. Based on the observation we propose an efficient generative tuning framework dubbed SCEdit which integrates and edits Skip Connection using a lightweight tuning module named SC-Tuner. Furthermore the proposed framework allows for straightforward extension to controllable image synthesis by injecting different conditions with Controllable SC-Tuner simplifying and unifying the network design for multi-condition inputs. Our SCEdit substantially reduces training parameters memory usage and computational expense due to its lightweight tuners with backward propagation only passing to the decoder blocks. Extensive experiments conducted on text-to-image generation and controllable image synthesis tasks demonstrate the superiority of our method in terms of efficiency and performance. Project page: https://scedit.github.io/.
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous and Instruction-guided Driving
PDF ↗Diffusion models excel at modeling complex and multimodal trajectory distributions for decision-making and control. Reward-gradient guided denoising has been recently proposed to generate trajectories that maximize both a differentiable reward function and the likelihood under the data distribution captured by a diffusion model. Reward-gradient guided denoising requires a differentiable reward function fitted to both clean and noised samples limiting its applicability as a general trajectory optimizer. In this paper we propose DiffusionES a method that combines gradient-free optimization with trajectory denoising to optimize black-box non-differentiable objectives while staying in the data manifold. Diffusion-ES samples trajectories during evolutionary search from a diffusion model and scores them using a black-box reward function. It mutates high-scoring trajectories using a truncated diffusion process that applies a small number of noising and denoising steps allowing for much more efficient exploration of the solution space. We show that DiffusionES achieves state-of-the-art performance on nuPlan an established closed-loop planning benchmark for autonomous driving. Diffusion-ES outperforms existing sampling-based planners reactive deterministic or diffusion-based policies and reward-gradient guidance. Additionally we show that unlike prior guidance methods our method can optimize non-differentiable language-shaped reward functions generated by few-shot LLM prompting. When guided by a human teacher that issues instructions to follow our method can generate novel highly complex behaviors such as aggressive lane weaving which are not present in the training data. This allows us to solve the hardest nuPlan scenarios which are beyond the capabilities of existing trajectory optimization methods and driving policies.