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[upd format] Third-party Usage and Research
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@iFighting
iFighting committed Jan 12, 2025
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Expand Up @@ -163,47 +163,28 @@ We'll provide the sampling script later.

(`Note please report accuracy numbers and provide trained models in your new repository to facilitate others to get sense of correctness and model behavior`)

[12/30/2024] Next Token Prediction Towards Multimodal Intelligence: https://github.com/LMM101/Awesome-Multimodal-Next-Token-Prediction

[12/30/2024] Varformer: Adapting VAR’s Generative Prior for Image Restoration: https://arxiv.org/abs/2412.21063

[12/22/2024] Distilled Decoding 1: One-step Sampling of Image Auto-regressive Models with Flow Matching: https://github.com/imagination-research/distilled-decoding

[12/19/2024] FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching: https://github.com/OliverRensu/FlowAR

[12/13/2024] 3D representation in 512-Byte: Variational tokenizer is the key for autoregressive 3D generation: https://github.com/sparse-mvs-2/VAT

[12/19/2024] FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching: https://github.com/OliverRensu/FlowAR

[12/9/2024] CARP: Visuomotor Policy Learning via Coarse-to-Fine Autoregressive Prediction: https://carp-robot.github.io/

[12/5/2024] Infinity ∞: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis: https://github.com/FoundationVision/Infinity

[12/5/2024] Switti: Designing Scale-Wise Transformers for Text-to-Image Synthesis: https://github.com/yandex-research/switti

[12/4/2024] TokenFlow🚀: Unified Image Tokenizer for Multimodal Understanding and Generation: https://github.com/ByteFlow-AI/TokenFlow

[12/3/2024] XQ-GAN🚀: An Open-source Image Tokenization Framework for Autoregressive Generation: https://github.com/lxa9867/ImageFolder

[11/28/2024] CoDe: Collaborative Decoding Makes Visual Auto-Regressive Modeling Efficient: https://github.com/czg1225/CoDe

[11/27/2024] SAR3D: Autoregressive 3D Object Generation and Understanding via Multi-scale 3D VQVAE: https://github.com/cyw-3d/SAR3D

[11/26/2024] LiteVAR: Compressing Visual Autoregressive Modelling with Efficient Attention and Quantization: https://arxiv.org/abs/2411.17178

[11/28/2024] Scalable Autoregressive Monocular Depth Estimation: https://arxiv.org/abs/2411.11361

[11/15/2024] M-VAR: Decoupled Scale-wise Autoregressive Modeling for High-Quality Image Generation: https://github.com/OliverRensu/MVAR

[10/14/2024] HART: Efficient Visual Generation with Hybrid Autoregressive Transformer: https://github.com/mit-han-lab/hart

[10/3/2024] ImageFolder🚀: Autoregressive Image Generation with Folded Tokens: https://github.com/lxa9867/ImageFolder

[07/25/2024] ControlVAR: Exploring Controllable Visual Autoregressive Modeling: https://github.com/lxa9867/ControlVAR

[07/3/2024] VAR-CLIP: Text-to-Image Generator with Visual Auto-Regressive Modeling: https://github.com/daixiangzi/VAR-CLIP

[06/16/2024] STAR: Scale-wise Text-to-image generation via Auto-Regressive representations: https://arxiv.org/abs/2406.10797
| **Time** | **Research** | **Link** |
|--------------|--------------------------------------------------------------------------------------------------|--------------------------------------------------------------------|
| [12/30/2024] | Next Token Prediction Towards Multimodal Intelligence | https://github.com/LMM101/Awesome-Multimodal-Next-Token-Prediction |
| [12/30/2024] | Varformer: Adapting VAR’s Generative Prior for Image Restoration |https://arxiv.org/abs/2412.21063 |
| [12/22/2024] | Distilled Decoding 1: One-step Sampling of Image Auto-regressive Models with Flow Matching | https://github.com/imagination-research/distilled-decoding |
| [12/19/2024] | FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching | https://github.com/OliverRensu/FlowAR |
| [12/13/2024] | 3D representation in 512-Byte: Variational tokenizer is the key for autoregressive 3D generation | https://github.com/sparse-mvs-2/VAT |
| [12/9/2024] | CARP: Visuomotor Policy Learning via Coarse-to-Fine Autoregressive Prediction | https://carp-robot.github.io/ |
| [12/5/2024] | Infinity ∞: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis | https://github.com/FoundationVision/Infinity |
| [12/5/2024] | Switti: Designing Scale-Wise Transformers for Text-to-Image Synthesis | https://github.com/yandex-research/switti |
| [12/4/2024] | TokenFlow🚀: Unified Image Tokenizer for Multimodal Understanding and Generation | https://github.com/ByteFlow-AI/TokenFlow |
| [12/3/2024] | XQ-GAN🚀: An Open-source Image Tokenization Framework for Autoregressive Generation | https://github.com/lxa9867/ImageFolder |
| [11/28/2024] | CoDe: Collaborative Decoding Makes Visual Auto-Regressive Modeling Efficient | https://github.com/czg1225/CoDe |
| [11/28/2024] | Scalable Autoregressive Monocular Depth Estimation | https://arxiv.org/abs/2411.11361 |
| [11/27/2024] | SAR3D: Autoregressive 3D Object Generation and Understanding via Multi-scale 3D VQVAE | https://github.com/cyw-3d/SAR3D |
| [11/26/2024] | LiteVAR: Compressing Visual Autoregressive Modelling with Efficient Attention and Quantization | https://arxiv.org/abs/2411.17178 |
| [11/15/2024] | M-VAR: Decoupled Scale-wise Autoregressive Modeling for High-Quality Image Generation | https://github.com/OliverRensu/MVAR |
| [10/14/2024] | HART: Efficient Visual Generation with Hybrid Autoregressive Transformer | https://github.com/mit-han-lab/hart |
| [10/3/2024] | ImageFolder🚀: Autoregressive Image Generation with Folded Tokens | https://github.com/lxa9867/ImageFolder |
| [07/25/2024] | ControlVAR: Exploring Controllable Visual Autoregressive Modeling | https://github.com/lxa9867/ControlVAR |
| [07/3/2024] | VAR-CLIP: Text-to-Image Generator with Visual Auto-Regressive Modeling | https://github.com/daixiangzi/VAR-CLIP |
| [06/16/2024] | STAR: Scale-wise Text-to-image generation via Auto-Regressive representations | https://arxiv.org/abs/2406.10797 |


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