Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis | Paper Citation Record

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This paper cites GPT-4 Technical Report.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis GPT-4 Technical Report

Reference 1

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This paper cites Maskgit: Masked generative image transformer.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Maskgit: Masked generative image transformer

Reference 2

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This paper cites SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer

Reference 3

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This paper cites Flow Matching in Latent Space.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Flow Matching in Latent Space

Reference 4

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This paper cites Imagenet: A large-scale hierarchical image database.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Imagenet: A large-scale hierarchical image database

Reference 5

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This paper cites Diffusion models beat gans on image synthesis.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Diffusion models beat gans on image synthesis

Reference 6

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This paper cites Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens

Reference 7

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This paper cites MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizer.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizer

Reference 8

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This paper cites Generative adversarial nets.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Generative adversarial nets

Reference 9

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This paper cites Rethinking the objectives of vector- quantized tokenizers for image synthesis.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Rethinking the objectives of vector- quantized tokenizers for image synthesis

Reference 10

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This paper cites Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis

Reference 11

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This paper cites Masked autoencoders are scalable vision learners.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Masked autoencoders are scalable vision learners

Reference 12

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Observation 438627f9-f79e-4d63-9689-e9c934ca404e · outbound

This paper cites Acdit: Interpolating autoregressive con- ditional modeling and diffusion transformer.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Acdit: Interpolating autoregressive con- ditional modeling and diffusion transformer

Reference 13

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Observation 53ed039c-2ff2-46a5-8a2c-02524a020f09 · outbound

This paper cites Auto-encoding vari- ational bayes, 2013.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Auto-encoding vari- ational bayes, 2013

Reference 14

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Observation 2c6b71d6-6bb7-47fe-9e94-52c6d830322a · outbound

This paper cites Autoregressive image generation using residual quantization.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Autoregressive image generation using residual quantization

Reference 15

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This paper cites Autoregressive image generation without vec- tor quantization.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Autoregressive image generation without vec- tor quantization

Reference 16

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This paper cites Flow Matching for Generative Modeling.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Flow Matching for Generative Modeling

Reference 17

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This paper cites DeepSeek-V3 Technical Report.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis DeepSeek-V3 Technical Report

Reference 18

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This paper cites Sit: Explor- ing flow and diffusion-based generative models with scalable interpolant transformers.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Sit: Explor- ing flow and diffusion-based generative models with scalable interpolant transformers

Reference 19

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This paper cites Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps

Reference 20

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This paper cites Training language models to follow instructions with human feedback.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Training language models to follow instructions with human feedback

Reference 21

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This paper cites RandAR: Decoder-only Autoregressive Visual Generation in Random Orders.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis RandAR: Decoder-only Autoregressive Visual Generation in Random Orders

Reference 22

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This paper cites Scalable diffusion models with transformers.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Scalable diffusion models with transformers

Reference 23

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This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 24

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This paper cites TokenFlow: Unified Image Tokenizer for Multimodal Understanding and Generation.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis TokenFlow: Unified Image Tokenizer for Multimodal Understanding and Generation

Reference 25

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This paper cites FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching

Reference 26

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Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis High-resolution image synthesis with latent diffusion models

Reference 27

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This paper cites Scalable Image Tokenization with Index Backpropagation Quantization.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Scalable Image Tokenization with Index Backpropagation Quantization

Reference 28

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This paper cites LMFusion: Adapting Pretrained Language Models for Multimodal Generation.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis LMFusion: Adapting Pretrained Language Models for Multimodal Generation

Reference 29

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This paper cites Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 30

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Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis HART: Efficient Visual Generation with Hybrid Autoregressive Transformer

Reference 31

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Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Chameleon: Mixed-Modal Early-Fusion Foundation Models

Reference 32

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Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Gemini: A Family of Highly Capable Multimodal Models

Reference 33

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Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Visual autoregressive modeling: Scalable image generation via next-scale prediction

Reference 34

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Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis MetaMorph: Multimodal Understanding and Generation via Instruction Tuning

Reference 35

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Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis LLaMA: Open and Efficient Foundation Language Models

Reference 36

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Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Emu3: Next-Token Prediction is All You Need

Reference 37

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Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Parallelized Autoregressive Visual Generation

Reference 38

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Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis MaskBit: Embedding-free Image Generation via Bit Tokens

Reference 39

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Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation

Reference 40

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Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation

Reference 41

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Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Reference 42

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Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 43

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Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Vector-quantized Image Modeling with Improved VQGAN

Reference 44

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Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation

Reference 45

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Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Randomized Autoregressive Visual Generation

Reference 46

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Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis An image is worth 32 tokens for reconstruction and generation

Reference 47

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Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Reference 48

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This paper cites E-CAR: Efficient Continuous Autoregressive Image Generation via Multistage Modeling.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis E-CAR: Efficient Continuous Autoregressive Image Generation via Multistage Modeling

Reference 49

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Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Fast Training of Diffusion Models with Masked Transformers

Reference 50

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This paper cites Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

Reference 51

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This paper cites Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%

Reference 52

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