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arXiv Detail & Related papers (2026-01-07T14:09:18Z)
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arXiv Detail & Related papers (2025-10-14T07:34:44Z)
NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale [101.57871281101747]
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arXiv Detail & Related papers (2025-07-11T09:32:45Z)
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Current conditional autoregressive image generation methods have shown promising results, yet their potential remains largely unexplored in the practical unsupervised image translation domain.<n>A critical limitation stems from the discrete quantization inherent in traditional Vector Quantization-based frameworks.<n>We propose Softmax Relaxed Quantization, a novel approach that reformulates codebook selection as a continuous probability mixing process.
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Fast Autoregressive Models for Continuous Latent Generation [49.079819389916764]
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arXiv Detail & Related papers (2025-04-24T13:57:08Z)
D2C: Unlocking the Potential of Continuous Autoregressive Image Generation with Discrete Tokens [80.75893450536577]
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arXiv Detail & Related papers (2025-03-21T13:58:49Z)
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Visual Autoregressive Modeling for Image Super-Resolution [14.935662351654601]
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We introduce FlowAR, a next scale prediction method featuring a streamlined scale design.<n>This eliminates the need for VAR's intricate multi-scale residual tokenizer.<n>We validate the effectiveness of FlowAR on the challenging ImageNet-256 benchmark.
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arXiv Detail & Related papers (2024-11-22T09:08:58Z)
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Latent-based image generative models have achieved notable success in image generation tasks. Despite sharing the same latent space, autoregressive models significantly lag behind LDMs and MIMs in image generation. We propose a simple but effective discrete image tokenizer to stabilize the latent space for image generative modeling.
arXiv Detail & Related papers (2024-10-16T12:13:17Z)
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arXiv Detail & Related papers (2024-10-02T16:05:27Z)
Progressive Text-to-Image Generation [40.09326229583334]
We present a progressive model for high-fidelity text-to-image generation. The proposed method takes effect by creating new image tokens from coarse to fine based on the existing context. The resulting coarse-to-fine hierarchy makes the image generation process intuitive and interpretable.
arXiv Detail & Related papers (2022-10-05T14:27:20Z)
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This paper presents a versatile framework for conditional image generation. It incorporates the inductive bias of CNNs and powerful sequence modeling of auto-regression. Our method achieves superior diverse image generation performance as compared with the state-of-the-art.
arXiv Detail & Related papers (2022-07-21T22:19:17Z)
Global Context with Discrete Diffusion in Vector Quantised Modelling for Image Generation [19.156223720614186]
The integration of Vector Quantised Variational AutoEncoder with autoregressive models as generation part has yielded high-quality results on image generation. We show that with the help of a content-rich discrete visual codebook from VQ-VAE, the discrete diffusion model can also generate high fidelity images with global context.
arXiv Detail & Related papers (2021-12-03T09:09:34Z)

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