分布式训练

Existing NeRF-based methods for large scene reconstruction often have limitations in visual quality and rendering speed. While the recent 3D Gaussian Splatting works well on small-scale and object-centric scenes scaling it up to large scenes poses challenges due to limited video memory long optimization time and noticeable appearance variations. To address these challenges we present VastGaussian the first method for high-quality reconstruction and real-time rendering on large scenes based on 3D Gaussian Splatting. We propose a progressive partitioning strategy to divide a large scene into multiple cells where the training cameras and point cloud are properly distributed with an airspace-aware visibility criterion. These cells are merged into a complete scene after parallel optimization. We also introduce decoupled appearance modeling into the optimization process to reduce appearance variations in the rendered images. Our approach outperforms existing NeRF-based methods and achieves state-of-the-art results on multiple large scene datasets enabling fast optimization and high-fidelity real-time rendering.

We propose a neural reflectance model (NeuBRDF) that offers highly versatile material representation yet with light memory and neural computation consumption towards achieving real-time rendering. The results depicted in Fig. 1 rendered at full HD resolution on a contemporary desktop machine demonstrate that our system achieves real-time performance with a wide variety of appearances which is approached by the following two designs. Firstly recognizing that the bidirectional reflectance is distributed in a sparse high-dimensional space we propose to project the BRDF into two low-dimensional components i.e. two hemisphere feature-grids for incoming and outgoing directions respectively. Secondly we distribute learnable neural reflectance primitives on our highly-tailored spherical surface grid. These primitives offer informative features for each hemisphere component and reduce the complexity of the feature learning network leading to fast evaluation. These primitives are centrally stored in a codebook and can be shared across multiple grids and even across materials based on low-cost indices stored in material-specific spherical surface grids. Our NeuBRDF agnostic to the material provides a unified framework for representing a variety of materials consistently. Comprehensive experimental results on measured BRDF compression Monte Carlo simulated BRDF acceleration and extension to spatially varying effects demonstrate the superior quality and generalizability achieved by the proposed scheme.

Gradient sparsification and quantization offer a promising prospect to alleviate the communication overhead problem in distributed learning. However direct combination of the two results in suboptimal solutions due to the fact that sparsification and quantization haven't been learned together. In this paper we propose Joint Sparsification-Quantization (JointSQ) inspired by the discovery that sparsification can be treated as 0-bit quantization regardless of architectures. Specifically we mathematically formulate JointSQ as a mixed-precision quantization problem expanding the solution space. It can be solved by the designed MCKP-Greedy algorithm. Theoretical analysis demonstrates the minimal compression noise of JointSQ and extensive experiments on various network architectures including CNN RNN and Transformer also validate this point. Under the introduction of computation overhead consistent with or even lower than previous methods JointSQ achieves a compression ratio of 1000xon different models while maintaining near-lossless accuracy and brings 1.4xto 2.9xspeedup over existing methods.

Diffusion models have achieved great success in synthesizing high-quality images. However generating high-resolution images with diffusion models is still challenging due to the enormous computational costs resulting in a prohibitive latency for interactive applications. In this paper we propose DistriFusion to tackle this problem by leveraging parallelism across multiple GPUs. Our method splits the model input into multiple patches and assigns each patch to a GPU. However naively implementing such an algorithm breaks the interaction between patches and loses fidelity while incorporating such an interaction will incur tremendous communication overhead. To overcome this dilemma we observe the high similarity between the input from adjacent diffusion steps and propose Displaced Patch Parallelism which takes advantage of the sequential nature of the diffusion process by reusing the pre-computed feature maps from the previous timestep to provide context for the current step. Therefore our method supports asynchronous communication which can be pipelined by computation. Extensive experiments show that our method can be applied to recent Stable Diffusion XL with no quality degradation and achieve up to a 6.1x speedup on eight NVIDIA A100s compared to one. Our code is publicly available at https://github.com/mit-han-lab/distrifuser.

We present an approach for analyzing grouping information contained within a neural network's activations permitting extraction of spatial layout and semantic segmentation from the behavior of large pre-trained vision models. Unlike prior work our method conducts a wholistic analysis of a network's activation state leveraging features from all layers and obviating the need to guess which part of the model contains relevant information. Motivated by classic spectral clustering we formulate this analysis in terms of an optimization objective involving a set of affinity matrices each formed by comparing features within a different layer. Solving this optimization problem using gradient descent allows our technique to scale from single images to dataset-level analysis including in the latter both intra- and inter-image relationships. Analyzing a pre-trained generative transformer provides insight into the computational strategy learned by such models. Equating affinity with key-query similarity across attention layers yields eigenvectors encoding scene spatial layout whereas defining affinity by value vector similarity yields eigenvectors encoding object identity. This result suggests that key and query vectors coordinate attentional information flow according to spatial proximity (a `where' pathway) while value vectors refine a semantic category representation (a `what' pathway).