Latent diffusion paper. On the one hand, the binary Jul 18, 2023 · Instead of generating an image, we propose to generate background, foreground, layer mask, and the composed image simultaneously. The diffusion model works on the latent space, which makes it a lot easier to train. ), we propose Latent Consistency Models (LCMs), enabling swift inference with minimal steps on any pre-trained LDMs, including Stable Diffusion Jan 26, 2023 · Currently, applying diffusion models in pixel space of high resolution images is difficult. In configs/latent-diffusion/ we provide configs for training LDMs on the LSUN-, CelebA-HQ, FFHQ and ImageNet datasets. Apr 16, 2023 · Stable Diffusion背後的技術：高效、高解析又易控制的Latent Diffusion Model. It is based on paper High-Resolution Image Synthesis with Latent Diffusion Models. The LDM3D model is fine-tuned on a dataset of tuples containing an RGB image, depth map and caption, and validated through extensive experiments. 7x faster and has a better FID score by at least 1. Our solution leverages a recent text-to-image Latent Diffusion Model (LDM), which speeds up diffusion by operating in a lower-dimensional latent space. Research Paper DrawBench. Our best results are obtained by training on a weighted variational bound designed May 18, 2023 · This research paper proposes a Latent Diffusion Model for 3D (LDM3D) that generates both image and depth map data from a given text prompt, allowing users to generate RGBD images from text prompts. Stable Diffusion is a latent diffusion model conditioned on the (non-pooled) text embeddings of a CLIP ViT-L/14 text encoder. Here, we apply the LDM paradigm to high-resolution video generation, a particu-larly resource-intensive task. We show that explicitly generating image Aug 28, 2023 · View a PDF of the paper titled InstructME: An Instruction Guided Music Edit And Remix Framework with Latent Diffusion Models, by Bing Han and 7 other authors View PDF HTML (experimental) Abstract: Music editing primarily entails the modification of instrument tracks or remixing in the whole, which offers a novel reinterpretation of the original Apr 11, 2023 · Mask-conditioned latent diffusion for generating gastrointestinal polyp images. 近年，生成式模型 (generative model) 用於圖像生成展現了驚人的成果， 最知名的 Text-to-Image with Stable Diffusion. Despite advances in feed-forward AST methods, their limited Mar 23, 2023 · End-to-End Diffusion Latent Optimization Improves Classifier Guidance. In order to protect vulnerable road users (VRUs), such as pedestrians or cyclists, it is essential that intelligent transportation systems (ITS) accurately identify them. Riegler, Vajira Thambawita. Smooth. New stable diffusion finetune ( Stable unCLIP 2. Transparent Image Layer Diffusion using Latent Transparency Resources. We first pre-train an LDM on images only Nov 15, 2023 · 5. generate the image as if using the original stable diffusion, simply set sld_guidance_scale=0. While some users wish to preserve distinct content structures, others might favor a more pronounced stylization. The method learns a "latent transparency" that encodes alpha channel transparency into the latent manifold of a pretrained latent diffusion model. In order to model a substantial number of tokens extracted from videos, four efficient variants are introduced from the perspective of decomposing the Latent Diffusion Models. Leveraging latent diffusion models, our method achieves high performance and can serve as a strong baseline for multiple cross-modality medical image synthesis tasks. Our latent diffusion models (LDMs) achieve a new state of the art for image Jan 31, 2024 · View a PDF of the paper titled AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error, by Jonas Ricker and 2 other authors View PDF Abstract: With recent text-to-image models, anyone can generate deceptively realistic images with arbitrary contents, fueling the growing threat of visual disinformation. Therefore, in this paper, we Jan 16, 2024 · Summary: This paper presents SDXL, a latent diffusion model for text-to-image synthesis. Jul 4, 2023 · Abstract. How does an AI generate images from text? How do Latent Diffusion Models work? Dec 8, 2023 · Smooth Diffusion: Crafting Smooth Latent Spaces in Diffusion Models Jiayi Guo*, Xingqian Xu*, Yifan Pu, Zanlin Ni, Chaofei Wang, Manushree Vasu, Shiji Song, Gao Huang, Humphrey Shi. Read Paper See Code. Specifically, to tailor the pre-trained latent diffusion model to operate on the RAW domain, we train a set of lightweight taming modules to inject the RAW information into the diffusion denoising Apr 11, 2023 · Mask-conditioned latent diffusion for generating gastrointestinal polyp images. LDM-4 performs at least 2. Dec 2, 2023 · In this paper, we propose to leverage the pre-trained latent diffusion model to perform the neural ISP for enhancing extremely low-light images. Recent works such as DreamFusion and Magic3D have shown great success in generating 3D content using NeRFs and text prompts, but the Sep 1, 2023 · Abstract. Apr 18, 2023 · Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. The key advantage of latent diffusion models for image generation is that they are able to generate highly detailed and realistic images from text descriptions. We model the bi-directional mappings between an image and the corresponding latent binary representation by training an auto-encoder with a Bernoulli encoding distribution. Nov 24, 2023 · In this paper, we introduce a generative model, namely the latent diffusion model (LDM), to generate degradation-free prior to enhance the regression-based deep unfolding method. 0 license Activity. However, training diffusion models in the pixel space are both data-intensive and computationally demanding, which restricts their applicability as priors for high-dimensional real-world data such as medical images. Stable UnCLIP 2. Nov 21, 2023 · We present Stable Video Diffusion — a latent video diffusion model for high-resolution, state-of-the-art text-to-video and image-to-video generation. Training can be started by running Training can be started by running CUDA_VISIBLE_DEVICES= < GPU_ID > python main. Here, we apply the LDM paradigm to high-resolution video generation, a particularly resource-intensive task. Oct 11, 2022 · Diffusion models have achieved unprecedented performance in generative modeling. 2 Feb 2024 · Marco Pasini , Maarten Grachten , Stefan Lattner ·. , 2022). com Mar 4, 2024 · We present OOTDiffusion, a novel network architecture for realistic and controllable image-based virtual try-on (VTON). 1. Jun 29, 2023 · View a PDF of the paper titled Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion Models, by Simian Luo and 3 other authors View PDF Abstract: The Video-to-Audio (V2A) model has recently gained attention for its practical application in generating audio directly from silent videos, particularly in video/film production. Recently, latent diffusion models trained for 2D image synthesis have been turned into generative video models by inserting temporal layers and finetuning them on small, high-quality We propose an all-in-one image restoration system with latent diffusion, named AutoDIR, which can automatically detect and restore images with multiple unknown degradations. One benefit of the proposed problem is to enable better Oct 6, 2023 · Latent Diffusion models (LDMs) have achieved remarkable results in synthesizing high-resolution images. We tackle the task of text-to-3D creation with pre-trained latent-based NeRFs (NeRFs that generate 3D objects given input latent code). Compared to previous direct-synthesis-based works, our proposed LFDM can better synthesize spatial details and temporal motion by fully Abstract. introduced a latent space encoding of pixels that allows high resolution images to be generated by diffusion at a lower computational cost [24], and this approach was used to train Nov 20, 2023 · View a PDF of the paper titled Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model, by Chunming He and 8 other authors View PDF HTML (experimental) Abstract: Illumination degradation image restoration (IDIR) techniques aim to improve the visibility of degraded images and mitigate the adverse effects Mar 24, 2023 · In this paper, we propose an approach for cI2V using novel latent flow diffusion models (LFDM) that synthesize an optical flow sequence in the latent space based on the given condition to warp the given image. io/ Latent Diffusion Models (LDM) Paper: High-Resolution Image Synthesis with Latent Diffusion Models. Roman Macháček, Leila Mozaffari, Zahra Sepasdar, Sravanthi Parasa, Pål Halvorsen, Michael A. 6x than a standard diffusion model. Our latent diffusion models (LDMs) achieve new state of the art scores for image inpainting and class-conditional image synthesis and highly competitive performance on various tasks, including unconditional image generation, text-to-image synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Nonetheless, the need to accommodate diverse and subjective user preferences poses a significant challenge. Bram Wallace, Akash Gokul, Stefano Ermon, Nikhil Naik. Our code is available at \url {https://github. Siddarth Venkatraman, Shivesh Khaitan, Ravi Tej Akella, John Dolan, Jeff Schneider, Glen Berseth. They use a pre-trained auto-encoder and train the diffusion U Dec 20, 2021 · By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. LSD: Object-Centric Slot Diffusion Jindong Jiang, Fei Deng, Gautam Singh, Sungjin Ahn NeurIPS 2023 🌟Spotlight🌟 Project page: https://latentslotdiffusion. , Gaussian) latent space of GANs, VAEs, and normalizing flows. 114 watching Forks. Latent Diffusion Models (LDMs) enable high-quality im-age synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Authors created a “big” LDM-4 w/ VQ-reg w/o attn, on a fixed 387M parameters. Latent diffusion models, which operate in a much lower-dimensional space, offer a . In order to take advantage of AI solutions in endoscopy diagnostics, we must overcome the issue of limited annotations. We present DiffuseVAE, a poor voice quality. We leverage the power of pretrained latent diffusion models, designing an outfitting UNet to learn the garment detail features. data (x) into a lower Nov 23, 2022 · Latent Video Diffusion Models for High-Fidelity Long Video Generation. In this paper, we present VideoGen, a text-to-video generation approach, which can generate a high-definition video with high frame fidelity and strong temporal consistency using reference-guided latent diffusion. Feb 27, 2024 · We present LayerDiffuse, an approach enabling large-scale pretrained latent diffusion models to generate transparent images. Key to Saved searches Use saved searches to filter your results more quickly Feb 2, 2024 · Bass Accompaniment Generation via Latent Diffusion. To leverage these representations for image generation, we propose a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding. A noteworthy application is privacy-preserved open-data sharing by proposing synthetic data as surrogates of real patient data. py --base configs/latent-diffusion/ < config_spec > . Although many attempts using GANs and autoregressive models have been made in this area, the Oct 11, 2023 · View a PDF of the paper titled DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion model, by Xiaofan Li and 1 other authors View PDF Abstract: With the increasing popularity of autonomous driving based on the powerful and unified bird's-eye-view (BEV) representation, a demand for high-quality and Jun 14, 2023 · Latent diffusion models achieve state-of-the-art performance on a variety of generative tasks, such as image synthesis and image editing. Apr 24, 2023 · The prior works on TTA either pre-trained a joint text-audio encoder or used a non-instruction-tuned model, such as, T5. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. This model allows for image variations and mixing operations as described in Hierarchical Text-Conditional Image Generation with CLIP Latents, and, thanks to its modularity, can be combined with other models such as KARLO. Introduced by Rombach et al. Despite the promise, these models are susceptible We validate the effectiveness of our approach for unconditional, class-conditional, and sequence-to-sequence language generation. Jan 27, 2023 · View a PDF of the paper titled Mo\^usai: Text-to-Music Generation with Long-Context Latent Diffusion, by Flavio Schneider and 3 other authors View PDF Abstract: Recent years have seen the rapid development of large generative models for text; however, much less research has explored the connection between text and another "language" of With DrawBench, we compare Imagen with recent methods including VQ-GAN+CLIP, Latent Diffusion Models, and DALL-E 2, and find that human raters prefer Imagen over other models in side-by-side comparisons, both in terms of sample quality and image-text alignment. Previous works only focus on the adversarial attacks against the encoder or the output image under white-box settings, regardless of the denoising process. Aug 28, 2023 · View a PDF of the paper titled InstructME: An Instruction Guided Music Edit And Remix Framework with Latent Diffusion Models, by Bing Han and 7 other authors View PDF HTML (experimental) Abstract: Music editing primarily entails the modification of instrument tracks or remixing in the whole, which offers a novel reinterpretation of the original Apr 13, 2022 · Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. Expand. To enhance the zero-shot capability that is important Jul 4, 2023 · This work proposes an efficient latent diffusion model for text-to-image synthesis obtained by distilling the knowledge of SDXL, and builds two efficient T2I models, called KOALA-1B&-700M, while reducing the model size up to 54% and 69% of the original SDXL model. After training the compression model, the latent representations of the training set are used as input to the diffusion model. Edit. Source: High-Resolution Image Synthesis with Latent Diffusion Models. Despite the proficiency of LDM in various applications, such as text-to-image generation, facilitated by robust text encoders and a variational autoencoder, the critical need to deploy large generative models on edge devices compels a search Dec 19, 2022 · Scalable Diffusion Models with Transformers. Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone: The increase of model parameters is mainly due to more attention blocks and a larger cross-attention context as SDXL uses a second text encoder. We propose to first encode speech signals into a phoneme-rate latent representation with a variational autoencoder enhanced by adversarial training, and then jointly model the duration and the latent representation with a diffusion model. Feb 7, 2024 · Stable Audio is based on latent diffusion, with its latent defined by a fully-convolutional variational autoencoder. However, the robustness of latent diffusion models is not well studied. Diffusion models [ 12, 28] are generative models that convert Gaussian noise into samples from a learned Binary Latent Diffusion. Highly Influenced. To this end, we introduce the This is an official PyTorch implementation of the Latent Slot Diffusion (LSD) model presented in the paper Object-Centric Slot Diffusion. In this paper, we show that a binary latent space can be explored for compact yet expressive image representations. During SDXL training, the U-Net is conditioned on image size, image cropping information, and receives training data Mar 21, 2023 · 3D-CLFusion: Fast Text-to-3D Rendering with Contrastive Latent Diffusion. We analyze the scalability of our Diffusion Transformers (DiTs) through the lens t. 23 Nov 2022 · Yingqing He , Tianyu Yang , Yong Zhang , Ying Shan , Qifeng Chen ·. In this study, we propose AudioLDM, a TTA system that is built on a latent space to learn the continuous audio representations from contrastive language-audio pretraining Dec 9, 2023 · Latent Diffusion Models (LDMs) capture the dynamic evolution of latent variables over time, blending patterns and multimodality in a generative system. Using an autoencoder to connect the original images with compressed latent spaces and a cross attention enhanced U-Net as the backbone of diffusion, latent diffusion models (LDMs) have achieved stable and high fertility image generation. We present a novel controllable system for generating single stems to accompany musical Latent Video Diffusion Models for High-Fidelity Long Video Generation. The downside is that these approaches add additional complexity to the diffusion framework. Abstract. mp4. Marvin Klemp, Kevin Rösch, Royden Wagner, Jannik Quehl, Martin Lauer. So today, I’m planning to explore image generation models centered around the paper “High-Resolution Image Synthesis with Latent Diffusion Models” (Rombach et al. It is conditioned on text prompts as well as timing embeddings, allowing for fine control over both the content and length of the generated music and sounds. We also develop an application called DepthFusion These latent diffusion models achieve new state of the art scores for image inpainting and class-conditional image synthesis and highly competitive performance on various tasks, including unconditional image generation, text-to-image synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs. LDMs are efficient diffusion models (Ho et al,2020;Song et al,2021b) by conducting the diffusion process in the latent space instead of the pixel space. Oct 1, 2023 · In this paper, we propose Make-A-Volume, a diffusion-based framework for cross-modality 3D medical image synthesis. We explore a new class of diffusion models based on the transformer architecture. 1, Hugging Face) at 768x768 resolution, based on SD2. On the other hand, standard Variational Autoencoders (VAEs) typically have access to a low-dimensional latent space but exhibit poor sample quality. To alleviate the huge computational cost required by pixel-based diffusion SR, latent-based methods utilize a feature encoder to transform the image and then implement the SR image generation in a Jun 19, 2020 · Denoising Diffusion Probabilistic Models. diff-nonloop-0319. Stable Audio is capable of rendering stereo signals of up to 95 sec at Apr 18, 2023 · View a PDF of the paper titled NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers, by Kai Shen and 8 other authors View PDF Abstract: Scaling text-to-speech (TTS) to large-scale, multi-speaker, and in-the-wild datasets is important to capture the diversity in human speech such as speaker Feb 17, 2023 · LDFA: Latent Diffusion Face Anonymization for Self-driving Applications. To achieve layered image generation, we train an autoencoder that is able to reconstruct layered images and train diffusion models on the latent representation. We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. [1] The goal of diffusion models is to learn a Sep 26, 2023 · To this end, we propose LaVie, an integrated video generation framework that operates on cascaded video latent diffusion models, comprising a base T2V model, a temporal interpolation model, and a video super-resolution model. A diffusion model consists of three major components: the forward process, the reverse process, and the sampling procedure. Subsequently, the textual representation is used to construct a latent representation of the Feb 1, 2024 · Unconditional Latent Diffusion Models Memorize Patient Imaging Data. g. Diffusion models applied to latent spaces, which are normally built with (Variational) Autoencoders. github. By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Latte employs a pre-trained variational autoencoder to encode input videos into features in latent space, where tokens are extracted from encoded features. Smooth Diffusion is a new category of diffusion models that is simultaneously high-performing and smooth. We present Stable Video Diffusion — a latent video diffusion model for high-resolution, state-of-the-art text-to-video and image-to-video generation. Jonathan Ho, Ajay Jain, Pieter Abbeel. Generative latent diffusion models hold a wide range of applications in the medical imaging domain. See full list on github. Our key insights are two-fold: 1) We reveal that the incorporation of simple temporal self-attentions, coupled with Jun 15, 2023 · Arbitrary Style Transfer (AST) aims to transform images by adopting the style from any selected artwork. We first pre-train an LDM on images only; then, we turn the image generator into a video generator by Our latent diffusion models (LDMs) achieve new state of the art scores for image inpainting and class-conditional image synthesis and highly competitive performance on various tasks, including unconditional image generation, text-to-image synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel Abstract. Instead, existing approaches focus on diffusion in lower dimensional spaces (latent diffusion), or have multiple super-resolution levels of generation referred to as cascades. Four instances of LDMs were created and trained on 215M parameters, using Places dataset. Abstract:. Although many attempts using GANs and Oct 18, 2023 · The recent use of diffusion prior, enhanced by pre-trained text-image models, has markedly elevated the performance of image super-resolution (SR). 1. com Our latent diffusion models (LDMs) achieve new state of the art scores for im-age inpainting and class-conditional image synthesis and highly competitive performance on various tasks, includ-ing unconditional image generation, text-to-image synthe-sis, and super-resolution, while significantly reducing com-putational requirements compared to pix Our latent diffusion models (LDMs) achieve new state of the art scores for image inpainting and class-conditional image synthesis and highly competitive performance on various tasks, including unconditional image generation, text-to-image synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel Our latent diffusion models (LDMs) achieve a new state of the art for image inpainting and highly competitive performance on various tasks, including unconditional image generation, semantic scene synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs. We present SDXL, a latent diffusion model for text-to-image synthesis. We first pre-train an LDM on images only arXiv. More from the Imagen family: Imagen Video Imagen Editor Latent diffusion models (LDMs)(Rombach et al,2022). Latte first extracts spatio-temporal tokens from input videos and then adopts a series of Transformer blocks to model video distribution in the latent space. However, previous studies in TTA have limited generation quality with high computational costs. Sep 12, 2023 · Reasoning with Latent Diffusion in Offline Reinforcement Learning. Subjective evaluations on LJSpeech and LibriTTS datasets May 18, 2023 · This research paper proposes a Latent Diffusion Model for 3D (LDM3D) that generates both image and depth map data from a given text prompt, allowing users to generate RGBD images from text prompts. Yingqing He, Tianyu Yang, Yong Zhang, Ying Shan, Qifeng Chen. e. Our main hypothesis is that many image restoration tasks, such as super-resolution, motion deblur, denoising, low-light enhancement, dehazing, and deraining can often be Dec 7, 2023 · This paper contributes to the existing literature as follows: first, our two latent diffusion models show better performance on the Inception Score (IS) and Fréchet Inception Distance (FID Aug 31, 2022 · How do Latent Diffusion Models work? If you want answers to these questions, we've got #StableDiffusion explained. Then a series of Transformer blocks are Oct 12, 2022 · Denoising diffusion models (DDMs) have shown promising results in 3D point cloud synthesis. Use in 🧨 Diffusers Safe Latent Diffusion is fully integrated in 🧨diffusers . To advance 3D DDMs and make them useful for digital artists, we require (i) high generation quality, (ii) flexibility for manipulation and applications such as conditional synthesis and shape interpolation, and (iii) the ability to output smooth surfaces or meshes. In machine learning, diffusion models, also known as diffusion probabilistic models or score-based generative models, are a class of latent variable generative models. Without a redundant warping process, the garment features are precisely aligned with the target human body via the proposed outfitting fusion in the Jul 16, 2023 · Diffusion models have recently emerged as powerful generative priors for solving inverse problems. We demonstrate across multiple diverse data sets that our latent language diffusion models are significantly more effective than previous diffusion language models. The textual-prompt encoder (FLAN-T5) encodes the input description of the audio. Stars. Oct 8, 2022 · The encoder maps the brain image to a latent representation with a size of 20 \ (\times \) 28 \ (\times \) 20. To disable safe latent diffusion, i. The ability to automatically generate music that appropriately matches an arbitrary input track is a challenging task. This is because the latent space of the image generator network captures a lot of the underlying structure and variability in the datasets, allowing the model to generate a wide range Figure 1: TANGO, as depicted in this figure, has three major components: i) textual-prompt encoder, ii) latent diffusion model (LDM), and iii) mel-spectogram/audio VAE. In this paper, we focus on enhancing the creative painting ability of current LDMs in Apr 18, 2023 · Abstract. 8k stars Watchers. It preserves the production Jan 29, 2023 · Text-to-audio (TTA) system has recently gained attention for its ability to synthesize general audio based on text descriptions. SDXL uses a larger U-Net compared to previous Stable Diffusion models, and adds a refiner module to improve visual quality of image samples. We provide a reference script for sampling, but there also exists a diffusers integration, which we expect to see more active community development. in High-Resolution Image Synthesis with Latent Diffusion Models. 1-768. Edit social preview. LDMs first employ an encoder Efrom a pre-trained variational autoencoder to compress the input data sample x∈p. Consequently, our latent diffusion model (LDM)-based approach TANGO outperforms the state-of-the-art AudioLDM on most metrics and stays comparable on the rest on AudioCaps test set, despite training the LDM on a 63 times In this paper, we propose a novel latent diffusion transformer for video generation, namely Latte, which adopts a video Transformer as the backbone. The commonly-adopted formulation of the latent code of diffusion models is a sequence of gradually denoised samples, as opposed to the simpler (e. In this paper, we develop NaturalSpeech 2, a TTS system that leverages a neural audio codec with residual vector quantizers to get the quantized latent vectors and uses a diffusion model to generate these latent vectors conditioned on text input. However, the iterative sampling process is computationally intensive and leads to slow generation. The method allows generation of single transparent images or of multiple transparent layers. AI-generated content has attracted lots of attention recently, but photo-realistic video synthesis is still challenging. This is that In this paper, we first explain the foundations of diffusion models (Section2), providing a brief but self-contained introduction to three predominant formulations: denoising diffusion probabilistic models (DDPMs) [89, 213], score-based generative models (SGMs) [218, 219], and stochastic differential equations (Score SDEs) [112, 217, 223]. Our latent diffusion models (LDMs) achieve new state-of-the-art scores for Jun 6, 2022 · In this paper, we present an accelerated solution to the task of local text-driven editing of generic images, where the desired edits are confined to a user-provided mask. Inspired by Consistency Models (song et al. Recently, latent diffusion models trained for 2D image synthesis have been turned into generative video models by inserting temporal layers and finetuning them on small, high-quality video Apr 23, 2023 · In this work, we present DiffVoice, a novel text-to-speech model based on latent diffusion. Furthermore, to overcome the large computational cost challenge in LDM, we propose a lightweight model to generate knowledge priors in deep unfolding denoiser, and Sep 30, 2022 · Artistic painting has achieved significant progress during recent years. This paper provides an alternative, Gaussian formulation of the latent space of various diffusion models Jan 5, 2024 · We propose a novel Latent Diffusion Transformer, namely Latte, for video generation. Apache-2. Sep 1, 2023 · Abstract. Readme License. yaml -t --gpus 0, Jan 2, 2022 · Diffusion probabilistic models have been shown to generate state-of-the-art results on several competitive image synthesis benchmarks but lack a low-dimensional, interpretable latent space, and are slow at generation. Classifier guidance -- using the gradients of an image classifier to steer the generations of a diffusion model -- has the potential to dramatically expand the creative control over image generation and editing. Latent diffusion models use an auto-encoder to map between image space and latent space. 2. By decomposing the image formation process Inpainting with Latent Diffusion. e. Offline reinforcement learning (RL) holds promise as a means to learn high-reward policies from a static dataset, without the need for further environment interactions. This paper aims to improve denoising Jun 1, 2022 · Rombach et al. org e-Print archive Dec 19, 2021 · Latent Diffusion Model. ur wi tc fv jd ju mc ku gu tw