Yang Zhao
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Text-to-image diffusion models have demonstrated remarkable capabilities in transforming textual prompts into coherent images, yet the computational cost of their inference remains a persistent challenge. To address this issue, we present UFOGen, a novel generative model designed for ultra-fast, one-step text-to-image synthesis. In contrast to conventional approaches that focus on improving samplers or employing distillation techniques for diffusion models, UFOGen adopts a hybrid methodology, integrating diffusion models with a GAN objective. Leveraging a newly introduced diffusion-GAN objective and initialization with pre-trained diffusion models, UFOGen excels in efficiently generating high-quality images conditioned on textual descriptions in a single step. Beyond traditional text-to-image generation, UFOGen showcases versatility in applications. Notably, UFOGen stands among the pioneering models enabling one-step text-to-image generation and diverse downstream tasks, presenting a significant advancement in the landscape of efficient generative models.
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An authentic face restoration system is becoming increasingly demanding in many computer vision applications, e.g., image enhancement, video communication, and taking portrait. Most of the advanced face restoration models can recover high-quality faces from low-quality ones but usually fail to faithfully generate realistic and high-frequency details that are favored by users. To achieve authentic restoration, we propose IDM, an Iteratively learned face restoration system based on denoising Diffusion Models (DDMs). We define the criterion of an authentic face restoration system, and argue that denoising diffusion models are naturally endowed with this property from two aspects: intrinsic iterative refinement and extrinsic iterative enhancement. Intrinsic learning can preserve the content well and gradually refine the high-quality details, while extrinsic enhancement helps clean the data and improve the restoration task one step further. We demonstrate superior performance on blind face restoration tasks. Beyond restoration, we find the authentically cleaned data by the proposed restoration system is also helpful to image generation tasks in terms of training stabilization and sample quality. Without modifying the models, we achieve better quality than state-of-the-art on FFHQ and ImageNet generation using either GANs or diffusion models.
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BlazeStyleGAN: A Real-Time On-Device StyleGAN
Fei Deng
Lu Wang
Chuo-Ling Chang
(2023)
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StyleGAN models have been widely adopted for generating and editing face images. Yet, few work investigated running StyleGAN models on mobile devices. In this work, we introduce BlazeStyleGAN --- to the best of our knowledge, the first StyleGAN model that can run in real-time on smartphones. We design an efficient synthesis network with the auxiliary head to convert features to RGB at each level of the generator, and only keep the last one at inference. We also improve the distillation strategy with a multi-scale perceptual loss using the auxiliary heads, and an adversarial loss for the student generator and discriminator. With these optimizations, BlazeStyleGAN can achieve real-time performance on high-end mobile GPUs. Experimental results demonstrate that BlazeStyleGAN generates high-quality face images and even mitigates some artifacts from the teacher model.
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