To accelerate sampling, diffusion models (DMs) are often distilled into
generators that directly map noise to data in a single step. In this approach,
the resolution of the generator is fundamentally limited by that of the teacher
DM. To overcome this limitation, we propose Progressive Growing of Diffusion
Autoencoder (PaGoDA), a technique to progressively grow the resolution of the
generator beyond that of the original teacher DM. Our key insight is that a
pre-trained, low-resolution DM can be used to deterministically encode
high-resolution data to a structured latent space by solving the PF-ODE forward
in time (data-to-noise), starting from an appropriately down-sampled image.
Using this frozen encoder in an auto-encoder framework, we train a decoder by
progressively growing its resolution. From the nature of progressively growing
decoder, PaGoDA avoids re-training teacher/student models when we upsample the
student model, making the whole training pipeline much cheaper. In experiments,
we used our progressively growing decoder to upsample from the pre-trained
model's 64x64 resolution to generate 512x512 samples, achieving 2x faster
inference compared to single-step distilled Stable Diffusion like LCM. PaGoDA
also achieved state-of-the-art FIDs on ImageNet across all resolutions from
64x64 to 512x512. Additionally, we demonstrated PaGoDA's effectiveness in
solving inverse problems and enabling controllable generation.

通过渐进式扩展扩散自编码器 (PaGoDA) 技术，将生成器的分辨率逐步提高，从而有效克服了教师扩散模型分辨率的限制，并在图像生成、逆问题解决和可控生成等方面取得了显著的实验效果。

PaGoDA: 从低分辨率扩散指导者逐步生成一步生成器

PaGoDA: Progressive Growing of a One-Step Generator from a  Low-Resolution Diffusion Teacher

Recent 3D human generative models have achieved remarkable progress by
learning 3D-aware GANs from 2D images. However, existing 3D human generative
methods model humans in a compact 1D latent space, ignoring the articulated
structure and semantics of human body topology. In this paper, we explore more
expressive and higher-dimensional latent space for 3D human modeling and
propose StructLDM, a diffusion-based unconditional 3D human generative model,
which is learned from 2D images. StructLDM solves the challenges imposed due to
the high-dimensional growth of latent space with three key designs: 1) A
semantic structured latent space defined on the dense surface manifold of a
statistical human body template. 2) A structured 3D-aware auto-decoder that
factorizes the global latent space into several semantic body parts
parameterized by a set of conditional structured local NeRFs anchored to the
body template, which embeds the properties learned from the 2D training data
and can be decoded to render view-consistent humans under different poses and
clothing styles. 3) A structured latent diffusion model for generative human
appearance sampling. Extensive experiments validate StructLDM's
state-of-the-art generation performance and illustrate the expressiveness of
the structured latent space over the well-adopted 1D latent space. Notably,
StructLDM enables different levels of controllable 3D human generation and
editing, including pose/view/shape control, and high-level tasks including
compositional generations, part-aware clothing editing, 3D virtual try-on, etc.
Our project page is at: this https URL

本研究提出了一种新的 3D 人体生成模型 StructLDM，通过在高维潜空间中建模人体的语义结构和拓扑，从而实现可控的 3D 人体生成和编辑，包括姿势 / 视角 / 形状控制、合成生成、服装编辑和虚拟试穿等。

StructLDM：三维人体生成的结构化潜在扩散

StructLDM: Structured Latent Diffusion for 3D Human Generation

In this paper we propose Structuring AutoEncoders (SAE). SAEs are neural
networks which learn a low dimensional representation of data which are
additionally enriched with a desired structure in this low dimensional space.
While traditional Autoencoders have proven to structure data naturally they
fail to discover semantic structure that is hard to recognize in the raw data.
The SAE solves the problem by enhancing a traditional Autoencoder using weak
supervision to form a structured latent space. In the experiments we
demonstrate, that the structured latent space allows for a much more efficient
data representation for further tasks such as classification for sparsely
labeled data, an efficient choice of data to label, and morphing between
classes. To demonstrate the general applicability of our method, we show
experiments on the benchmark image datasets MNIST, Fashion-MNIST, DeepFashion2
and on a dataset of 3D human shapes.

本文提出了一种称为 Structuring AutoEncoders 的神经网络，其可以使用弱监督形成结构化的低维空间，从而更有效地表示并分类数据。在基准图像数据集 MNIST、Fashion-MNIST、DeepFashion2 以及 3D 人体形状数据集上进行的实验表明，结构潜空间可以为进一步的分类任务和分类数据的有效选择的操作提供更高效的表征。