STARFlow2 presents an autoregressive flow-based architecture for unified multimodal text-image generation by interleaving a VLM stream with a TarFlow stream via residual skips and a unified latent space.
Bridging continuous and discrete tokens for autoregressive visual generation.arXiv preprint arXiv:2503.16430
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An initial continuous autoencoder training phase prevents dimensional collapse in VQ-VAEs and yields lower reconstruction and perceptual losses.
NSVQ mitigates codebook collapse in large-codebook VQ by addressing encoder drift via non-stationary loss, replacement, and staged freezing, improving rFID from 2.39 to 2.10 on ImageNet-1k while achieving 100% utilization.
citing papers explorer
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STARFlow2: Bridging Language Models and Normalizing Flows for Unified Multimodal Generation
STARFlow2 presents an autoregressive flow-based architecture for unified multimodal text-image generation by interleaving a VLM stream with a TarFlow stream via residual skips and a unified latent space.
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Continuous First, Discrete Later: VQ-VAEs Without Dimensional Collapse
An initial continuous autoencoder training phase prevents dimensional collapse in VQ-VAEs and yields lower reconstruction and perceptual losses.
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NSVQ: Mitigating Codebook Collapse by Stabilizing Encoder Drift in Vector Quantization
NSVQ mitigates codebook collapse in large-codebook VQ by addressing encoder drift via non-stationary loss, replacement, and staged freezing, improving rFID from 2.39 to 2.10 on ImageNet-1k while achieving 100% utilization.