Diffusion model face generation However, the vast amount of research in this field makes it difficult for readers to learn the key Controlled generation. Existing diffusion-based generative methods on de novo 3D molecule generation face two major challenges. e. License: The CreativeML OpenRAIL M license is an Open RAIL M license, adapted from the work that BigScience and the RAIL Initiative are jointly carrying in the area of responsible AI licensing. See translation + Reply. This space uses the open-source Shap-E model, a recent diffusion model from OpenAI to generate 3D models from text. Prior methods using explicit face models, like 3D morphable models (3DMM) and facial landmarks, often fall short in generating high-fidelity videos due to their lack of appearance-aware motion representation. To associate your repository with the face-generation topic, It also allows for additionally performing Textual Inversion. This guide will show you how to use SVD to generate short This work presents an autoregressive diffusion model that requires only one identity image and audio sequence to generate a video of a realistic talking head, capable of hallucinating head movements, facial expressions, such as blinks, and preserving a given background. generating and editing This technical report presents a diffusion model based framework for face swapping between two portrait images. 9. Because I found that the community is trying to include the video-driven methods into the talking face generation scope, though it is originally termed as Face Reenactment. In this paper, we To fill this gap, we propose a novel speech-to-face generation framework, which leverages a Speech-Conditioned Latent Diffusion Model, called SCLDM. The challenging task, traditionally having relied heavily on digital craftspersons, remains yet to be explored. Tanzila Rahman, Shweta Mahajan, Hsin-Ying Lee, Jian Ren, Sergey Tulyakov, Leonid Sigal. 3. The code provides a facility to do In this work, we present Collaborative Diffusion, where pre-trained uni-modal diffusion models collaborate to achieve multi-modal face generation and editing without re-training. Unconditional image generation is a popular application of diffusion models that generates images that look like those in the dataset used for training. In recent years, diffusion models have been studied and proven to be more reasonable and effective than previous methods. Paper Code GANimation: Anatomically-aware Facial Animation from a To comprehensively investigate the bad face issue, we first empirically evaluate the face quality of generations from the prevalent Stable Diffusion V1. In this paper, we introduce a generative framework for generating 3D facial expression sequences In recent years, the field of talking faces generation has attracted considerable attention, with certain methods adept at generating virtual faces that convincingly imitate human expressions. Diffusion models are becoming increasingly popular in synthetic data generation and image editing applications. However, we anticipate that future advancements in T2I diffusion Step 1: Generate a 3D Model Start by visiting the Shap-E Hugging Face Space here or down below. 1. Updated Dec 3, 2024; generative-adversarial-network generative-model image-generation face-recognition dataset-generation mae face-generation identity-generator face-editing. Related work An Introduction to Diffusion Models: Introduction to Diffusers and Diffusion Models From Scratch: December 12, 2022: Fine-Tuning and Guidance: Fine-Tuning a Diffusion Model on New Data and Adding Guidance: December 21, 2022: Stable Diffusion: Exploring a Powerful Text-Conditioned Latent Diffusion Model: January 2023 (TBC) Doing More with Diffusion driven 3D face animation model. See also the article about the BLOOM Open RAIL license on which our Stable Video Diffusion (SVD) Image-to-Video is a diffusion model that takes in a still image as a conditioning frame, and generates a video from it. The fundamental differences between talking and singing Model type: Diffusion-based text-to-image generation model. 000 identities with 50 samples each, 10. We discuss the hottest trends about diffusion models, help each other with contributions, personal projects or just hang out ☕. Recent developments in diffusion-based generative models allow for more realistic and stable data synthesis and their performance on image and video generation has surpassed that of other Recent advancements in generative models have significantly enhanced talking face video generation, yet singing video generation remains underexplored. Thus we propose a novel approach called TEx-Face (TExt & Expression-to-Face) that addresses these challenges by dividing the task into three components, i. 03396: null: 2023-07-26: Learning Landmarks Motion from Speech for Speaker-Agnostic 3D Talking Heads Generation: Federico Nocentini et. To do this, we combine the strengths of Generative Adversarial networks (GANs) and diffusion models (DMs) by employing the multimodal features in the DM into the latent space of the pretrained Diffusion probabilistic models have demonstrated great potential in producing realistic-looking super-resolution (SR) images. This technology typically Denoising diffusion models have shown great potential in multiple research areas. For example, AnimateDiff inserts a motion modeling module into a frozen text-to-image Reproducible pipelines. In contrast to previous work that generates samples by swapping the two face regions, our method masks the face partially and then feeds it into our model (MCDM), outputting images that have hardly recognizable artifacts which prompts the face Realistic Speech-to-Face Generation with Speech-Conditioned Latent Diffusion Model with Face Prior Jinting Wang1, Li Liu1*, Jun Wang2, Hei Victor Cheng3 1The Hong Kong University of Science and Technology (Guangzhou) 2Tencent AI Lab 3Aarhus University October 6, 2023 Abstract Speech-to-face generation is an intriguing area of research Realistic Speech-to-Face Generation with Speech-Conditioned Latent Diffusion Model with Face Prior Jinting Wang1, Li Liu1*, Jun Wang2, Hei Victor Cheng3 1The Hong Kong University of Science and Technology (Guangzhou) 2Tencent AI Lab 3Aarhus University October 6, 2023 Abstract Speech-to-face generation is an intriguing area of research This paper aims to apply text-to-image models to design a customizable facial generation model that is improved upon the Stable Diffusion model by incorporating LoRA (Low-Rank Adaptation) principles for style constraints. Model Details Developed by: Seth Forsgren, Hayk Martiros; Model type: Diffusion-based text-to-image generation model; Language EmoTalker is proposed, an emotionally editable portraits animation approach based on the diffusion model that modifies the denoising process to ensure preservation of the original portrait’s identity during inference and is effective in generating high-quality, emotionally customizable facial expressions. Model Editing. To address this, in the second stage of training, we provided the mean and standard deviation of the motion as conditional guidance, enabling the model to Face generation has made significant progress with large-scale diffusion models, attracting widespread interest. Then the latent diffusion model takes a prompt and the noisy latent image, predicts the added noise, and removes the predicted noise from the initial latent image to get This weights here are intended to be used with the 🧨 Diffusers library. and 2. Abstract. For the sake of explaination, let's say Semantic Image Synthesis (SIS) is among the most popular and effective techniques in the field of face generation and editing, thanks to its good generation quality and the versatility is brings along. This technique works by only training weights in the cross-attention layers, and it uses a special word to represent the newly learned concept. Existing methods often rely on time-consuming one-by-one optimization approaches, which are not efficient for modeling the same distribution content, e. However, existing methods ignore the potential of text modal, and their generators mainly follow the source-oriented feature rearrange paradigm coupled with Auto-regressive models for code generation from natural language have a similar limitation: they do not easily allow reconsidering earlier tokens generated. Edify Image supports a wide range of applications, including text-to-image synthesis, 4K upsampling, ControlNets, 360 HDR panorama generation, and finetuning for More recently, diffusion models (DMs) have emerged as the leading method in text-to-image generation [9, 1]. Specifically, the face attribute disentanglement module is proposed to disentangle eye blinking and lip motion features, where the lip motion features are synchronized In this paper, we present a versatile face generative model that uses text and visual inputs. 2) We enrich the diffusion model with motion frames and audio embeddings in order to maintain the consistency of generated images. In this free course, you will: 👩🎓 Study the theory behind diffusion models; 🧨 Learn how to generate images and audio with the popular 🤗 Diffusers library; 🏋️♂️ Train your own diffusion models from scratch; 📻 image-editing image-generation face-generation multi-modality face-editing diffusion-models aigc stable-diffusion latent-diffusion-models gen-ai Updated Nov 28, 2023 Python In this paper, a solution for face generation using diffusion models conditioned by both attributes and masks is introduced. In the newest FaceChain FACT (Face Adapter with deCoupled Training) version, with only 1 Generating synthetic datasets for training face recognition models is challenging because dataset generation entails more than creating high fidelity images. The success of Generative Adversarial Networks (GANs) in face generation is particularly notable. 5 (SD1. Diffusion models partially solve this problem and are able to generate diverse samples given the same condition. 2306. Face generation is the task of generating (or interpolating) new faces from an existing dataset. Würstchen’s biggest benefits come from the fact that it can generate images much faster than models like Stable Diffusion XL, while using a lot less memory! So for all of us who don’t have A100s lying around, this will come in handy. Generating synthetic datasets for training face recognition models is challenging because The editability of our method is currently constrained by the T2I model we employ, as the proposed SIVE process relies on the attention map generated by the T2I model. In this post, we will explore various techniques and models for generating highly 2. Facial generation technology uses computer algorithms and artificial intelligence techniques to generate realistic facial images. Like Textual Inversion, DreamBooth, and LoRA, Custom Diffusion only requires a few (~4-5) example images. In this paper, we propose a novel audio-driven diffusion method for generating high-resolution realistic videos of talking heads with the help of the denoising diffusion model. This affects the performance of face recogni-tion models trained with synthetic data and evaluated with real data, as observed in [33]. This issue is particularly pronounced when trying to generate Deep generative models have shown impressive results in generating realistic images of faces. 1 achieves slightly Arc2Face builds upon a pretrained Stable Diffusion model, yet adapts it to the task of ID-to-face generation, conditioned solely on ID vectors. However, prevailing GAN-based methods suffer from unnatural distortions and artifacts due to sophisticated motion deformation. generation frameworks (Karras et al. We propose a face dataset generation method that can generate both a large number of Highlights •A diffusion model for face generation maximizing fidelity, quality and diversity. In this Multimodal-driven talking face generation refers to animating a portrait with the given pose, expression, and gaze transferred from the driving image and video, or estimated from the text and audio. However, existing methods ignore the potential of text modal, and their generators mainly follow the source-oriented feature rearrange paradigm coupled with Conditioned Latent Diffusion Model for face UV-texture generation (UV-IDM) to generate photo-realistic textures based on the Basel Face Model (BFM). Despite the great progress, existing diffusion models mainly focus on uni-modal control, i. Talking Face Generation Existing works on audio-driven talking face generation Abstract: Talking face generation has historically struggled to produce head movements and natural facial expressions without guidance from additional reference videos. Existing face forgery datasets have limitations in generating high-quality facial images and addressing the challenges posed by evolving generative techniques. In this work, we present Collaborative Diffusion, where pre-trained uni-modal diffusion models collaborate to achieve multi-modal face generation and editing without re Multimodal Conditioned face image generation and face super-resolution are significant areas of research. Related work A Dual Condition Face Generator (DCFace) based on a diffusion model that enables DCFace to consistently produce face images of the same subject under different styles with precise control and provide higher verification accuracies compared to previous works. The Riffusion model was created by fine-tuning the Stable-Diffusion-v1-5 checkpoint. FaceChain is a novel framework for generating identity-preserved human portraits. Since majority heavy atoms in molecules allow connections to multiple atoms through single bonds, solely using pair-wise distance to model molecule geometries is insufficient. In this paper, we investigate the presence of bias in diffusion-based face generation This Stable diffusion checkpoint allows you to generate pixel art sprite sheets from four different angles. It supports multi-concept training by design. In this free course, you will: 📻 Fine-tune existing diffusion models on new datasets; 🗺 Explore conditional generation and guidance; 🧑🔬 Create your own custom diffusion model pipelines; Finetuning a diffusion model on new data and adding guidance. Chan, Yuming Jiang, Ziwei Liu. However, the realism doesn’t necessarily guarantee that the SR images are faithful to the ground truth high- resolution images. In the face domain, SynFace [57] studied the efficacy of using DiscoFaceGAN [15] for synthetic face generation. The model first projects input images to a latent space using an autoencoder and then trains a diffusion model on this latent space. Recently, DigiFace-1M [5] studied the efficacy of 3D model based face rendering in combination with image augmenta-tions to create a synthetic dataset. 3) Our approach is robust in terms of generalization, invariant on the source of identity frames and audio recordings. Model type: Diffusion-based text-to-image generation model. You will also see how we can create diffusion models that take additional inputs as We introduce a novel approach for high-resolution talking head generation from a single image and audio input. Generation begins with random noise, but this is gradually refined over a number of steps until an output image emerges. In recent years, the field of talking faces generation has Custom Diffusion. When choreographing high-quality dance movements, people need to take into account not only the musical context but also the nearby music-aligned dance motions. DMs define how information spreads and evolves over time, by modeling the 🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX. Typically, the best results are obtained from finetuning a pretrained In this paper, we propose an efficient, fast, versatile and LoRA-compatible distillation method to accelerate the generation of pre-trained diffusion models: Flash Diffusion. GANs managed to generate high-quality, high-fidelity images when conditioned on semantic masks, but they still lack the ability to diversify their output. 2 Diffusion Models for Face Generation (DM) Diffusion models are a type of machine learning models that describe the evolution of a quantity over time, inspired by diffusion processes in physics. CVPR 2023. , the diffusion process is driven by only one modality of condition. 1) [], and SDXL []. Related Work Diffusion Models. Talking face generation has historically struggled to produce head movements and natural facial Stable Diffusion's latest models are very good at generating hyper-realistic images, but they can struggle with accurately generating human faces. We applied the concept of "style loss" from Diffusion models arise as a powerful generative tool recently. B. Model Details Developed by: Robin Rombach, Patrick Esser. Unit 3: Stable Diffusion Our proposed model, Implicit Face Motion Diffusion Model (IF-MDM), employs implicit motion to encode human faces into appearance-aware compressed facial latents, enhancing video generation. , 2021): introduces cascaded diffusion, which comprises a pipeline of multiple diffusion models that generate images of increasing resolution for high-fidelity image synthesis Facial generation technology uses computer algorithms and artificial intelligence techniques to generate realistic facial images. To achieve optimal results, this paper utilizes diffusion models as Expressive 3D Facial Animation Generation Based on Local-to-global Latent Diffusion [TVCG 2024] Wenfeng Song*, Xuan Wang, Yiming Jiang, Shuai Li*, Aimin Hao, Xia Hou, and Hong Qin. generating and editing faces by superiority of Collaborative Diffusion in multi-modal face generation and editing. Latent diffusion models (LDM) [27] apply diffusion model in the latent space of powerful pre-trained autoencoders to save computational resources while retaining the generation quality. 4. , IP-Adapter, ControlNet, and Stable Diffusion’s inpainting pipeline, for face feature encoding, multi-conditional generation, and face inpainting respectively. 🖼️ Here's an example: This model was The rapid progress in deep learning has given rise to hyper-realistic facial forgery methods, leading to concerns related to misinformation and security risks. 11\%$ on average in $4$ out of $5$ test Face animation has achieved much progress in computer vision. FLAME is low-dimensional but more expressive than the FaceWarehouse model and the Basel Face Model. •Analysis of perception priorit Therefore, we propose a Facial Decoupled Diffusion model for Talking head generation called FD2Talk, which fully leverages the advantages of diffusion models and decouples the complex facial details through multi-stages. Our novel Patch-wise style extractor and Time-step dependent ID loss enables DCFace to consistently produce face images of the same subject under different styles with precise control. Our method employs a latent mapping strategy that faces. It involves generating multiple images of same subjects under different factors (\textit{e. Compared with previous GAN-based approaches, by taking advantage of the diffusion model for the face swapping task, DiffFace achieves better benefits such as training stability, high fidelity, diversity of the samples, and controllability. The proposed solution has been trained by re-weighting the loss terms of an LDM in a perception-prioritized fashion showing that this allows achieving a higher quality of the generated samples. It starts with a Gaussian noise distribution and itera-tively applies a sequence of diffusion steps. (b) Face Editing. edu Anil Jain jain@msu. However, common diffusion frameworks suffer from Figure 2: Bias amplification by diffusion models in face generation (Binary Gender attribute - Male/Female) Figure 3: Illustration of our method to mitigate the bias in diffusion model. Despite abundant efforts are devoted to video quality and lip synchronization, most existing works do not take the unignorable aspect of facial emotional expression CIAGAN [22] detects face keypoints using a histogram of oriented gradients (HOG) model. edu Michigan State University East Lansing, MI 48824 Abstract Generating synthetic datasets for training face recogni-tion models is challenging because dataset generation en- Multimodal Conditioned face image generation and face super-resolution are significant areas of research. To combat this, we present DiffusionFace, the first In this work, we present Collaborative Diffusion, where pre-trained uni-modal diffusion models collaborate to achieve multi-modal face generation and editing without re-training. You can use the Riffusion model directly, or try the Riffusion web app. However, these models can amplify existing biases and propagate them to downstream applications. While generative approaches such as video Voice-to-Face Generation: Couple of Self-Supervised Representation Learning with Diffusion Model Abstract: In this study, we explore the challenging task of generating facial images from unheard voices, aiming to synthesize similar faces that correspond to the voice identity. Previous works have Multimodal Conditioned face image generation and face super-resolution are significant areas of research. Assuming that you followed 1. Besides, I introduce facial guidance optimization Compared with previous GAN-based approaches, by taking advantage of the diffusion model for the face swapping task, DiffFace achieves better benefits such as training stability, high fidelity, and controllability. This technology typically employs deep learning models such as Generative Adversarial Networks (GANs) and diffusion models, learning from a large dataset of real facial images to generate new virtual facial images. In particular, alter the identity. Methodology in the recordings, so we need to use a voice-to-face model for training data preparation, and 2) to realize the new voice genera-tion, a prompt-to-face model is needed. Jan 26, 2024. Additionally, an ideal controllable 3D face generation model should consider both facial attributes and expressions. A diffusion model works by learning to denoise random Gaussian noise step-by-step. Furthermore, methods for editing expressions are often 如果您熟悉中文，可以阅读中文版本的README。. edu Xiaoming Liu liuxm@msu. Although the diffusion model has the advantage of adding various guidance techniques while guaranteeing strong generation capabilities, employing the diffusion model to face swapping has not yet been explored due to the difficulties described below. Contribute to mk-minchul/dcface development by creating an account on GitHub. An overview of our method is illustrated in Figure2. 2022), current face generation models have achieved remarkable photorealism in both 2D and 3D. Text-to-image model DCFace: Synthetic Face Generation with Dual Condition Diffusion Model Minchul Kim kimminc2@msu. We developed a new approach of diffusion modeling combined with neural style transfer for realistic face image generation. Extensive experiments UV-IDM leverages the powerful texture generation capacity of a latent diffusion model (LDM) to obtain detailed facial textures. Face landmark images and face surroundings are used to guide a conditional GAN for face generation. Diffusion Generation Diffusion model is a generative model based on the stochas-tic diffusion process to generate samples from a probability distribution (Sohl-Dickstein et al. More recently, Diffusion models have gained popularity and outperformed GAN models in multiple tasks, includ-ing image synthesis [10]. Diffusion models have recently Controlled generation. Photorealistic Video Generation with Diffusion Models (2023) Space-Time Diffusion Features for Zero-Shot Text-Driven Motion Transfer (2023) If you want recommendations for any Paper on Hugging Face checkout this Space. Recent advancements in text-to-image generation using diffusion models have significantly improved the quality of generated images and expanded the ability to depict a wide range of objects. 3. To preserve the identity during the reconstruction procedure we design an identity-conditioned module that can utilize any in-the-wild image as a robust condition for the LDM to guide texture generation. Fine-tunes the UNet component of a pretrained Stable Diffusion model on the CelebA dataset. diffusion model, which is different in that our work utilizes ID Conditional DDPM. - huggingface/diffusers Texture Diffusion This DreamBooth model is fine-tuned for diffuse textures. Diffusion models are generative models that gained significant popularity in recent years, thanks to their capabilities to produce high-quality images. Ziqi Huang, Kelvin C. But there are certain times when you want to generate the same output every time, like when MagicPrompt - Stable Diffusion This is a model from the MagicPrompt series of models, which are GPT-2 models intended to generate prompt texts for imaging AIs, in this case: Stable Diffusion. Language(s): English Stable Video Diffusions (SVD), I2VGen-XL, AnimateDiff, and ModelScopeT2V are popular models used for video diffusion. Diffusion models [20,56,58] have re-cently become a mainstream approach for image synthe-sis [9,11,38] apart from Generative Adversarial Networks (GANs) [14], and success has also been found in various (a) Face Generation. Diffusion models [23,62,64] have re-cently become a mainstream approach for image synthe-sis [11,13,43] apart from Generative Adversarial Networks (GANs) [17], and success has also been found in various To the best of our knowledge, this is the first approach that applies the diffusion model in face swapping task. Download links for the pre-trained IDiff-Face diffusion model weights: Pre-trained IDiff-Face (25% CPD) Pre-trained IDiff-Face (50% CPD) Download links for the pre-generated synthetic 10K identities x 50 images datasets from the paper: For the generation of 10. In this paper, we study the efficacy and Welcome to Unit 1 of the Hugging Face Diffusion Models Course! In this unit, The secret to diffusion models’ success is the iterative nature of the diffusion process. , 3D GAN In-version, Conditional Style Code Diffusion, and 3D Train a diffusion model Unconditional image generation is a popular application of diffusion models that generates images that look like those in the dataset used for training. Deyongz. However, existing methods face challenges related to limited generalization, To this end, we propose a Dual Condition Face Generator (DCFace) based on a diffusion model. 2. The pipeline of our method and previous methods [12], [13] are shown in Fig. Our key insight is that diffusion models driven by different modalities are inherently complementary regarding the latent denoising steps, where bilateral connections can be established upon. Unit 3: Stable Diffusion Scalable multimodal approach for face generation and super-resolution using a conditional diffusion model Ahmed Abotaleb 1 , Mohamed W. It produces flat textures with very little visible lighting/shadows. Typically, the best results are obtained from finetuning a pretrained model on a specific dataset. Diffusion models are inherently random which is what allows it to generate different outputs every time it is run. In this work, we present an au-toregressive diffusion model that requires only one identity image and audio sequence to generate a video of a real-istic talking head. Custom Diffusion is a training technique for personalizing image generation models. Cool! + Reply. A Diffusion model consists of Recap of the Diffusion Models. However, introducing mul-tiple conditional representations (Yang, Zhuang, and Pan 2021) into face generation, particularly in the context of 3D, remains largely unexplored. Inheriting the advantages of LDM and its re-learning ability, in this paper, we employ the LDM for talking face generation to achieve high-fidelity synthesis results. Model Details Developed by: Seth Forsgren, Hayk Martiros; Model type: Diffusion-based text-to-image generation model; Language tasks into the same generator, we frame the talking face generation as a target-oriented texture transfer, instead of the source-oriented feature rearrange, and adopt a multi-conditional diffusion model to avoid unstable training of GANs, termed Texture-Geometry-aware Diffusion Model (TGDM), as shown in Fig. Why another text-to-image model? Well, this one is pretty fast and efficient. Paper See New model/pipeline to contribute exciting new diffusion models / diffusion pipelines; See New scheduler; Also, say 👋 in our public Discord channel . ai/license. Deviating from recent works that combine ID with text embeddings for zero-shot personalization of text-to-image models, we emphasize on the compactness of FR features, which can fully capture the This repository demonstrates how to fine-tune a Stable Diffusion model on the CelebA dataset and then generate new face images from a textual prompt. The state-of-the-art results for this task are located in the Image Generation parent. Face keypoints are fur-ther processed to generate an abstract face landmark image, which contains pose and expression. . Jan 30, 2024. In this paper, we investigate the presence of bias in diffusion-based face generation Visual Concept-driven Image Generation with Text-to-Image Diffusion Model. superiority of Collaborative Diffusion in multi-modal face generation and editing. Language(s): English. , faces. In particular, their ability to synthesize and modify human faces has spurred research into using generated face images in both training data augmentation and model performance assessments. Train a diffusion model Unconditional image generation is a popular application of diffusion models that generates images that look like those in the dataset used for training. Numerous efforts have been undertaken to in-corporate face priors. It involves generating multiple images of same subjects under different factors (e. Let z t Image-to-image is similar to text-to-image, but in addition to a prompt, you can also pass an initial image as a starting point for the diffusion process. Our solution is capable of A diffusion model for face generation maximizing fidelity, quality and diversity. merging another model with this Edify Image utilizes cascaded pixel-space diffusion models trained using a novel Laplacian diffusion process, in which image signals at different frequency bands are attenuated at varying rates. edu Feng Liu liufeng6@msu. We can experiment with prompts, but to get seamless, photorealistic results for faces, we may need to try new methodologies and models. In many popular diffusion models, subtle changes in inputs, both images and text prompts, can drastically change outputs. g. To achieve optimal results, this paper utilizes diffusion models as the primary engine for Text-to-image diffusion models have achieved widespread popularity due to their unprecedented image generation capability. Therefore, it is crucial to understand the sources of bias in their outputs. }, variations in pose, illumination, expression, aging and occlusion) which follows the real image conditional distribution. Our method generates realistic facial Train the diffusion model using the UNet2DModel class from the Diffusers library. Our novel Patch-wise style ex-tractor and Time-step dependent ID loss enables DCFace to This project implements a latent diffusion model for generating highly realistic facial images. 1 (b). 2301. Specifically, we separate facial details into motion and appearance. In this paper, we propose a Face Animation framework with an attribute-guided Diffusion Model (FADM), which is the first work to exploit the superior modeling Welcome to Unit 2 of the Hugging Face Diffusion Models Course! In this unit, you will learn how to use and adapt pre-trained diffusion models in new ways. Recent works attempted to go beyond the standard GAN-based framework, and started to explore Diffusion Models (DMs) for this task as these stand out with Cascaded Diffusion Models for High Fidelity Image Generation (Ho et al. Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation: Michał Stypułkowski et. Controlling outputs generated by diffusion models has been long pursued by the community and is now an active research topic. 2020; Chan et al. 1 (RV5. Generating photorealistic 3D faces from given conditions is a challenging task. Our code will be publicly Face recognition models trained on synthetic images from the proposed DCFace provide higher verification accuracies compared to previous works by $6. We propose an approach that takes the strengths of DMs and GAN and generates photo-realistic images with flexible control over facial attributes, which can be adapted to 2D and 3D domains, as illustrated in Figure 1. Please note: For commercial use, please refer to https://stability. We won’t dive deep into the theory, but understanding how a diffusion model works will come really handy when we need to pick a technique to generate synthetic data for our use case. Jadehhe. arXiv 2024. And findings show that Stable Diffusion, though initially designed for text-to-image, is an ideal base model for realizing face generation from different modalities [14]. We trained our model using the FFHQ dataset and fine-tuned it using a specialized dataset of LeBron James. To this end, we propose a Dual Condition Face Generator (DCFace) based on a diffusion model. Face recognition models trained on synthetic images from the proposed DCFace provide Unlike explicit face models or video diffusion models, implicit motion is not spatially disentangled, complicating the application of commonly used methods like lip-sync loss [3] in talking head generation [15, 35, 34]. Preliminaries: Diffusion Models Diffusion Models [30] are a family of generative models that can recover the data distribution from a Gaussian noise by learning the reverse process of a Markov Chain. These first images are my results after merging this model with another model trained on my wife. To the best of our knowledge, this is the first work to harness the In this work, we present Collaborative Diffusion, where pre-trained uni-modal diffusion models collaborate to achieve multi-modal face generation and editing without re-training. Collaborative Diffusion also supports multi-modal editing of real images with promising Abstract. ing priors into diffusion models for face restoration is also a critical focus. , variations in pose, illumination, expression, aging and occlusion) which follows the real image conditional distribution. Additionally, an ideal controllable 3D face generation model should consider both facial attributes and expressions. •A multi-conditioning mechanism using both attributes and semantic masks. Train a diffusion model. personalization face face-generation stable-diffusion id-embedding subject-driven-generation. Figure 1 shows example images generated by the pioneering text-to-image diffusion model DALL-E2 [], demonstrating extraordinary fidelity and imagination. A multi-conditioning mechanism using both attributes and semantic masks. are proposed to accelerate the sampling process of diffusion models through a class of non-Markovian diffusion processes. The VAE and Auto-regressive models for code generation from natural language have a similar limitation: they do not easily allow reconsidering earlier tokens generated. The initial image is encoded to latent space and noise is added to it. The method reaches state-of-the-art performances in terms of FID and CLIP-Score for few steps image generation on diffusion model. We use a GMM to fit the means of respective attribute classes for each channel in the reverse diffusion process. In this work, we propose a new T2I personalization diffusion model, Dense-Face, Multimodal-driven talking face generation refers to animating a portrait with the given pose, expression, and gaze transferred from the driving image and video, or estimated from the text and audio. you’ll need to login to your Hugging Face account (create one Stable Video Diffusion (SVD) is a powerful image-to-video generation model that can generate 2-4 second high resolution (576x1024) videos conditioned on an input image. In recent years, the field of talking faces generation has attracted considerable attention, with certain methods adept at generating virtual faces that convincingly imitate human expressions. Hugging Face Diffusion Models Course. But, as the underlying diffusion model is unconditional, the restored face changes considerably compared to the original person if the reverse process is run longer. EAT-Face: Emotion-Controllable Audio-Driven Talking Face Generation via Diffusion Model Abstract: Audio-driven talking face generation is a promising task with a lot of attention. For example, PGDiff [62] employs a face prior by pretraining a VQ-based restorer and using it as a target . Each model is distinct. To further unleash the users' creativity, it is desirable for the model to be controllable by multiple modalities simultaneously, e. We introduce CodeFusion, a pre-trained diffusion code generation model that addresses this limitation by iteratively denoising a complete program conditioned on the encoded natural language. [40] uses an unconditional diffusion model and starts from an intermediate stage of the reverse diffu-sion process using the output of a deterministic network. Adversarial attacks involve adding perturbations to the source image to cause misclassification by the target model, which demonstrates the potential of attacking face recognition models. Existing adversarial face image generation methods still can't achieve satisfactory performance because of low transferability and high detectability. 2015; Ho, Jain, and Abbeel 2020). you will have an id_image and style_images directory. al. Fakhr1 & Mohamed Zaki2 Multimodal Conditioned face image With the development of artificial intelligence, more and more attention has been put onto generative models, which represent the creativity, a very important aspect of intelligence. lution for talking-face generation based on diffusion mod-els. further enhance the generation capability of diffusion models. edu Michigan State University East Lansing, MI 48824 Abstract Generating synthetic datasets for training face recogni-tion models is challenging because dataset generation en- Diffusion models arise as a powerful generative tool recently. 050 identities were initially sampled We present a new multimodal face image generation method that converts a text prompt and a visual input, such as a semantic mask or scribble map, into a photorealistic face image. Read about Stable Diffusion here 🤗's Stable Diffusion blog. K. The basic framework consists of three components, i. Text-To-Image Diffusion Model: Stable Diffusion. Collaborative Diffusion for Multi-Modal Face Generation and Editing. In a series of experiments on various text generation tasks including text summarization, machine translation, and common sense generation, AR-Diffusion clearly demonstrated the superiority over existing diffusion language models and that it can be 100timessim600times faster when achieving comparable results. UV-IDM leverages the powerful texture generation capacity of a latent diffu-sion model (LDM) to obtain detailed facial textures. Nowadays, they’re widely used in image, video, and text synthesis. 5) [], Realistic Vision V5. You can find the training dataset in OneDrive. Although implicit motion lacks the spatial disentanglement of explicit models, which complicates alignment with subtle lip movements, we introduce motion You can use the Riffusion model directly, or try the Riffusion web app. The differences between human talking and singing limit the performance of existing talking face video generation models when applied to singing. Model Details Model Description (SVD) Image-to-Video is a latent diffusion model trained to generate short video clips the first diffusion model based face swapping framework in detail. To achieve optimal results, this paper utilizes diffusion models as the primary engine for EmoTalker: Emotionally Editable Talking Face Generation via Diffusion Model. To preserve the identity during the reconstruction procedure, datasets [8]. Nonetheless, current diffusion-based motion generation models often create entire motion sequences directly and unidirectionally, lacking focus on the motion with local and bidirectional enhancement. So, if you are looking for video-driven talking face generation, I would suggest Facial expression generation is one of the most challenging and long-sought aspects of character animation, with many interesting applications. However, existing methods face challenges related to limited generalization, particularly when dealing with challenging identities. Given multi-modal controls, our framework synthesizes high-quality images consistent with the input conditions. Like DreamBooth and Textual Inversion, Custom Diffusion is also used to teach a pre-trained text-to-image diffusion model about new concepts to generate outputs involving the concept(s) of interest. DCFace: Synthetic Face Generation with Dual Condition Diffusion Model Minchul Kim kimminc2@msu. However, ensuring that these models adhere closely to the text prompts remains a considerable challenge. Analysis of We present a simple mapping and a style modulation network to link two models and convert meaningful representations in feature maps and attention maps into latent codes. To do this, we combine the strengths of Generative Adversarial networks (GANs) and diffusion models (DMs) by employing the multi-modal features in the DM into the latent space The MEAD dataset, initially designed for emotionalized speech-face generation research, has been repurposed in our study to generate 3D facial animations. Use the trained model to generate new face samples and visualize them. generating and editing faces by Diffusion models arise as a powerful generative tool recently. This necessitates the conversion of the 2D MEAD dataset into a 3D MEAD dataset, Generating synthetic datasets for training face recognition models is challenging because dataset generation entails more than creating high fidelity images. Essentially, the way Stable Diffusion (SD) works is the same as we mentioned above. This paper develops a novel training-free framework namely Iterative Refinement with Controllable Diffusion models are becoming increasingly popular in synthetic data generation and image editing applications. Furthermore, methods for editing lution for talking-face generation based on diffusion mod-els. If you are looking for the weights to be loaded into the CompVis Stable Diffusion codebase, come here. We design a pipeline where human annotators rank the generated faces of the same prompt by different models and find that despite its smaller model size, RV5. We present a new multi-modal face image generation method that converts a text prompt and a visual input, such as a semantic mask or scribble map, into a photo-realistic face image. 01415: link: 2023-07-20: HyperReenact: One-Shot Reenactment via Jointly Learning to Refine and Thanks for PR from everybody! From now on, I'll occasionally include some papers about video-driven talking face generation.
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