During each training step, we sample xt, t, and a randomd < 1, then take two sequential steps with the shortcut model. The concatenation of these two steps is then used as the target to train the model at 2d. Note that the second step is queried at x′t+d under the denoising ...
Distribution Matching Distillation (DMD): DMD核心将预训练的扩散去噪器(diffusion denoiser)转化为快速的“一步”(one-step)图像生成器,同时保持生成图像的高质量。 预训练模型及一步模型生成器: 预训练基础模型(Pretrained base model): 假设存在一个已经预训练好的扩散模型 base,该模型能够将高斯噪声样本逐步去噪,...
Combined with a simple regression loss to match the output of the multi-step diffusion model, our method outperforms all published few-step diffusion approaches, reaching 2.62 FID on ImageNet 64x64 and 11.49 FID on zero-shot COCO-30k, comparable to Stable Diffusion but orders of magnitude ...
具体来说,初始化第二个编码器,标记为条件编码器,可以使用Stable Diffusion编码器的权重,或者使用具有随机初始化权重的轻量级网络。该控制编码器接收输入图像 x,并通过残差连接将特征图输出到预训练的Stable Diffusion模型中的多个分辨率。这种方法在控制扩散模型方面取得了显著的成果。然而,如上图3所示,在one-step模型的...
While recent methods have successfully transformed diffusion models into one-step generators, they neglect model size reduction, limiting their applicability in compute-constrained scenarios. This paper aims to develop small, efficient one-step diffusion models based on the powerful rectified flow framework...
One-step image-to-image with Stable Diffusion turbo: sketch2image, day2night, and more - GaParmar/img2img-turbo
This paper introduces Ali-AUG, a novel single-step diffusion model for efficient labeled data augmentation in industrial applications. Our method addresses the challenge of limited labeled data by generating synthetic, labeled images with precise feature insertion. Ali-AUG utilizes a stable diffusion arc...
SwiftBrush: One-Step Text-to-Image Diffusion Model with Variational Score Distillation (CVPR 2024) - VinAIResearch/SwiftBrush
We propose EM Distillation (EMD), a maximum likelihood-based approach that distills a diffusion model to a one-step generator model with minimal loss of perceptual quality. Our approach is derived through the lens of Expectation-Maximization (EM), where the generator parameters are updated using ...
In this work, we propose OSDFace, a novel one-step diffusion model for face restoration. Specifically, we propose a visual representation embedder (VRE) to better capture prior information and understand the input face. In VRE, low-quality faces are processed by a visual tokenizer and ...