Paper Citation Record · LEDGER

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer

As of 15 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 2 inbound Pith citation observations for arXiv:2411.10781.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.10781 v2

Coverage vector

measured 57 of 57 reference resolution

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:36:11.820004Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-15T15:31:11.142210Z

Reference resolution

57 of 57 outbound references displayed

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External citation measurements

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Outbound references

Observation f8fe0f60-0207-4716-acf2-5fb0f2be6991 · outbound

This paper cites GPT-4 Technical Report.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer GPT-4 Technical Report

Reference 1

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Observation 7807e726-37ed-4bb9-9317-22757df2ef80 · outbound

This paper cites Zigzag diffusion sampling: The path to success ls zigzag.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Zigzag diffusion sampling: The path to success ls zigzag

Reference 2

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Observation 25edc504-926a-446f-af51-64f28bedcf01 · outbound

This paper cites Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis

Reference 3

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Observation f3d85b88-e98c-46fb-b2e9-345f0c8818c5 · outbound

This paper cites Token merging for fast sta- ble diffusion.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Token merging for fast sta- ble diffusion

Reference 4

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Observation 3a0cc937-e97c-47b3-954f-c881011eee9a · outbound

This paper cites Maskgit: Masked generative image transformer.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Maskgit: Masked generative image transformer

Reference 5

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Observation d67cac3b-6c36-465c-bfae-26d1df098170 · outbound

This paper cites Muse: Text-To-Image Generation via Masked Generative Transformers.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Muse: Text-To-Image Generation via Masked Generative Transformers

Reference 6

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Observation 7a8cdf3f-2fd5-4f62-a0d1-ea8cba030f0b · outbound

This paper cites Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers

Reference 7

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Observation d2f17968-6e81-40bd-a313-b0ccdd9b841a · outbound

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

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens

Reference 8

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This paper cites Geneval: An object-focused framework for evaluating text- to-image alignment.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Geneval: An object-focused framework for evaluating text- to-image alignment

Reference 9

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Observation 70a10bcd-6919-48de-aa1b-87e1b4d226de · outbound

This paper cites Clipscore: A reference-free evaluation met- ric for image captioning, 2022.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Clipscore: A reference-free evaluation met- ric for image captioning, 2022

Reference 10

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Observation 5274d7fa-7819-4421-8183-650976f42a71 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equi- librium.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Gans trained by a two time-scale update rule converge to a local nash equi- librium

Reference 11

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This paper cites Classifier-free diffusion guidance.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Classifier-free diffusion guidance

Reference 12

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This paper cites Denoising diffu- sion probabilistic models.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Denoising diffu- sion probabilistic models

Reference 13

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This paper cites T2i-compbench: A comprehensive bench- mark for open-world compositional text-to-image genera- tion.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer T2i-compbench: A comprehensive bench- mark for open-world compositional text-to-image genera- tion

Reference 14

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Observation 18d2f4f9-76f7-4449-bf70-971ee6b5cf98 · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Quantization and training of neural networks for efficient integer-arithmetic-only inference

Reference 15

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Observation 39d840ca-c1cf-4af1-9c5b-514619772348 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Elucidating the design space of diffusion-based generative models

Reference 16

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Observation 961d8206-d2dd-4719-830c-9cc024a4c887 · outbound

This paper cites Auto-Encoding Variational Bayes.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Auto-Encoding Variational Bayes

Reference 17

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Unresolved cited work

Reference 18

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This paper cites Laion-aesthetics.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Laion-aesthetics

Reference 19

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Observation 4c646a68-e3f4-4003-8536-fd7a2dfc973a · outbound

This paper cites Minimizing Trajectory Curvature of ODE-based Generative Models.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Minimizing Trajectory Curvature of ODE-based Generative Models

Reference 20

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Observation 7c4c8868-d152-4b96-bcef-60e58c1ebea9 · outbound

This paper cites Microsoft coco: Common objects in context.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Microsoft coco: Common objects in context

Reference 21

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This paper cites Alignment of dif- fusion models: Fundamentals, challenges, and future.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Alignment of dif- fusion models: Fundamentals, challenges, and future

Reference 22

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This paper cites Lumina-mGPT: Illuminate Flexible Photorealistic Text-to-Image Generation with Multimodal Generative Pretraining.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Lumina-mGPT: Illuminate Flexible Photorealistic Text-to-Image Generation with Multimodal Generative Pretraining

Reference 23

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This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 24

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This paper cites DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models

Reference 25

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Dpm-solver: A fast ode solver for diffusion 9 probabilistic model sampling in around 10 steps

Reference 26

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This paper cites On Distillation of Guided Diffusion Models.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer On Distillation of Guided Diffusion Models

Reference 27

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Null-text inversion for editing real im- ages using guided diffusion models

Reference 28

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This paper cites Learning to reason with llms, 2024.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Learning to reason with llms, 2024

Reference 29

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Scalable diffusion models with transformers

Reference 30

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Sdxl: Improving latent diffusion models for high-resolution image synthesis

Reference 31

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This paper cites Not All Noises Are Created Equally:Diffusion Noise Selection and Optimization.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Not All Noises Are Created Equally:Diffusion Noise Selection and Optimization

Reference 32

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Learning transferable visual models from natural language supervision, 2021

Reference 33

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Progressive distillation for fast sampling of diffusion models

Reference 34

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Improved tech- niques for training gans

Reference 35

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Neural Machine Translation of Rare Words with Subword Units

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Catch-Up Distillation: You Only Need to Train Once for Accelerating Sampling

Reference 37

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion models

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Denois- ing diffusion implicit models

Reference 39

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Consistency Models

Reference 40

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Score-based generative modeling through stochastic differential equa- tions

Reference 41

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Introducing stable diffusion 3.5

Reference 42

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Roformer: Enhanced transformer with rotary position embedding

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 44

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Gemma: Open Models Based on Gemini Research and Technology

Reference 45

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This paper cites Accelerating Auto-regressive Text-to-Image Generation with Training-free Speculative Jacobi Decoding.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Accelerating Auto-regressive Text-to-Image Generation with Training-free Speculative Jacobi Decoding

Reference 47

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer LLaMA: Open and Efficient Foundation Language Models

Reference 48

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Neural discrete representation learning

Reference 49

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This paper cites Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis, 2023.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis, 2023

Reference 50

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 51

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Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Imagere- ward: Learning and evaluating human preferences for text- to-image generation, 2023

Reference 52

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This paper cites Fast sampling of dif- fusion models with exponential integrator.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Fast sampling of dif- fusion models with exponential integrator

Reference 53

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This paper cites Exploring self-attention for image recognition.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Exploring self-attention for image recognition

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:23:17.416375Z

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Observation ce95bff6-f0db-4e05-b98b-96b9c16df326 · outbound

This paper cites Dpm- solver-v3: Improved diffusion ode solver with empirical model statistics.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Dpm- solver-v3: Improved diffusion ode solver with empirical model statistics

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:23:17.351560Z

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Observation 41fc00f4-01e6-4286-8a74-08a07e9ffa3e · outbound

This paper cites an unresolved cited work.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Unresolved cited work

Reference 56

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Observation d96a7b7c-4aa2-4d1f-9484-254c0c450cf1 · outbound

This paper cites an unresolved cited work.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-12T19:23:17.315558Z

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Observation d4dfc0b0-a77d-459d-83da-da13d6066890 · outbound

This paper cites an unresolved cited work.

Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer Unresolved cited work

Reference 58

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Pith citing papers

Observation facd7dd1-b936-4c9e-b537-2c1757926930 · inbound

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection cites this paper.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer

Reference 41

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Observation 67ae4d6f-148e-4521-b18c-5743fe7fe898 · inbound

Reflective Flow Sampling Enhancement cites this paper.

Reflective Flow Sampling Enhancement Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer

Reference 46

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