Coordinate In and Value Out: Training Flow Transformers in Ambient Space (2412.03791v1)

Abstract: Flow matching models have emerged as a powerful method for generative modeling on domains like images or videos, and even on unstructured data like 3D point clouds. These models are commonly trained in two stages: first, a data compressor (i.e., a variational auto-encoder) is trained, and in a subsequent training stage a flow matching generative model is trained in the low-dimensional latent space of the data compressor. This two stage paradigm adds complexity to the overall training recipe and sets obstacles for unifying models across data domains, as specific data compressors are used for different data modalities. To this end, we introduce Ambient Space Flow Transformers (ASFT), a domain-agnostic approach to learn flow matching transformers in ambient space, sidestepping the requirement of training compressors and simplifying the training process. We introduce a conditionally independent point-wise training objective that enables ASFT to make predictions continuously in coordinate space. Our empirical results demonstrate that using general purpose transformer blocks, ASFT effectively handles different data modalities such as images and 3D point clouds, achieving strong performance in both domains and outperforming comparable approaches. ASFT is a promising step towards domain-agnostic flow matching generative models that can be trivially adopted in different data domains.

• The paper introduces ASFT, a novel one-stage approach for generative modeling that bypasses traditional latent space bottlenecks.
• It employs a modified PerceiverIO architecture with spatially aware latent vectors to parameterize velocity fields over coordinate-value pairs.
• Empirical evaluations on image and 3D datasets demonstrate ASFT's competitive performance and scalability across diverse modalities.

An Academic Overview of "Coordinate In and Value Out: Training Flow Transformers in Ambient Space"

The paper "Coordinate In and Value Out: Training Flow Transformers in Ambient Space" aims to address the complexities and limitations inherent in traditional latent space generative models by introducing a domain-agnostic alternative: Ambient Space Flow Transformers (ASFT). Traditional generative models, often reliant on pre-trained compressors, are constrained by a bifurcated training process and the necessity for domain-specific architectures. ASFT emerges as a solution to these challenges, focusing on single-stage training in the ambient space and providing a more generalizable framework applicable across various data domains.

Innovative Methodology and Design

ASFT leverages a conditionally independent point-wise training objective to operate directly in ambient space, bypassing the requirement of a pre-trained compressor stage typically seen in generative modeling. This architectural choice stands in contrast to established practices where the data first undergoes compression, confining generative operations within a bottleneck-imposed latent space. ASFT's approach allows for generative modeling directly from coordinate-value pairs, employing a neural network that parametrizes a velocity field for these pairs conditioned on the learned latent variable that captures contextual dependencies within the data.

The ASFT model is based on the PerceiverIO architecture, modified with spatially aware latent representations. This configuration involves encoding coordinate-value pairs into learnable latent vectors through cross-attention mechanisms. Notably, each latent vector is associated with "pseudo" coordinates and forms spatial-aware associations based on the coordinate values assigned to them. The decoder employs a multi-level strategy for sequential cross-attention to improve prediction fidelity progressively.

Empirical Validation and Results

The empirical evaluations demonstrate ASFT's competencies across diverse modalities, including image and 3D point cloud generation. In the domain of image generation, ASFT's performance on FFHQ-256 and LSUN-Church datasets reflects not only its competitive capability against domain-specific models but also its scalability with model size, yielding superior results as parameter counts increase.

For ImageNet experiments, ASFT achieves strong numerical results, maintaining a competitive stance even when juxtaposed with models that benefit from latent space generators pre-trained on extensive data collections. Notably, ASFT continues to shine in its flexibility and scalability, offering nuanced control over coordinate pairs, thus facilitating high-resolution generation without alteration in training dataset resolutions.

The domain-agnostic prowess of ASFT is highlighted further by its performance in 3D point cloud generation on the ShapeNet and Objaverse datasets. Compared to LION, a state-of-the-art latent diffusion model for 3D shape generation, ASFT demonstrates superior adaptability, efficiency, and performance, devoid of requirements for domain-specific architectures or hyper-parameters typically contingent in traditional models.

Implications and Future Trajectories

ASFT's proposition and success have significant implications in the field of generative models and beyond. Through its unified training paradigm, ASFT simplifies the generative modeling process while expanding the usability of models across different data types without necessitating domain-specific adaptations. This facilitates easier adoption in varied research and practical applications.

Looking forward, potential enhancements may include exploring training efficiency optimizations and experimenting with architectures that co-train multiple data domains, thereby advancing towards a holistic, multi-modal generative framework. Moreover, the resolution-agnostic capabilities of ASFT suggest interesting lines of investigation into training efficiency by leveraging low-resolution datasets, which could, in turn, ease the computational and data acquisition burdens in resource-intensive domains.

In summation, ASFT represents a significant methodological development in the landscape of flow-based generative models, offering a streamlined, performance-efficient, and scalable framework suited for a wide array of data-driven applications. Its ambient space-oriented approach presents a versatile and powerful alternative, warranting further exploration and refinement as the generative modeling field continues to evolve.