Paper Citation Record · LEDGER

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models

As of 20 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 5 inbound Pith citation observations for arXiv:2505.11245.

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

pith.paper-citation-record.v1
2505.11245 v1

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

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measured 61 of 61 standing notices

One-hop event checks from named stored sources.

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measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

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Reference resolution

56 of 56 outbound references displayed

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External citation measurements

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Outbound references

Observation a15a131d-0764-4e7d-bff8-13120126e402 · outbound

This paper cites write newline.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models write newline

Reference 1

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Observation 1d9f6897-e943-46ca-a1a9-ee98c3de27ee · outbound

This paper cites write newline.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models write newline

Reference 2

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Observation c1cbd387-16bb-42d9-bf0c-7990c2c4be32 · outbound

This paper cites Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance

Reference 3

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This paper cites GS-DiT: Advancing Video Generation with Pseudo 4D Gaussian Fields through Efficient Dense 3D Point Tracking.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models GS-DiT: Advancing Video Generation with Pseudo 4D Gaussian Fields through Efficient Dense 3D Point Tracking

Reference 4

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Observation 95ae684a-5c61-4c2e-a8d5-5028c69bae4f · outbound

This paper cites Training Diffusion Models with Reinforcement Learning.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Training Diffusion Models with Reinforcement Learning

Reference 5

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Observation 1afb6fb9-2c72-479a-bfd2-502e67347fbd · outbound

This paper cites Videocrafter2: Overcoming data limitations for high-quality video diffusion models.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Videocrafter2: Overcoming data limitations for high-quality video diffusion models

Reference 6

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Observation be05ee3a-872b-4ca5-9ea6-5b0298f3ed50 · outbound

This paper cites Directly Fine-Tuning Diffusion Models on Differentiable Rewards.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Directly Fine-Tuning Diffusion Models on Differentiable Rewards

Reference 7

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Observation 36ac2c35-4e78-4f30-9954-5bbf8f3712d7 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Diffusion models beat gans on image synthesis

Reference 8

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This paper cites badhand, 2024.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models badhand, 2024

Reference 9

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Observation 7e3115c7-a364-404b-8e73-c977f4c96ec0 · outbound

This paper cites Reinforcement learning for fine-tuning text-to-image diffusion models.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Reinforcement learning for fine-tuning text-to-image diffusion models

Reference 10

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Observation 7e545e04-974d-4ac3-a3a2-6718a170ccca · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 11

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Observation c881dafb-d5d5-4fe7-a587-6b4822750965 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Classifier-Free Diffusion Guidance

Reference 12

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Observation dde778e2-9b20-4f64-b817-abe8e2b52486 · outbound

This paper cites Denoising diffusion probabilistic models.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Denoising diffusion probabilistic models

Reference 13

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Observation 6bfb5355-2718-48fc-8633-268d79247398 · outbound

This paper cites Smoothed energy guidance: Guiding diffusion models with reduced energy curvature of attention.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Smoothed energy guidance: Guiding diffusion models with reduced energy curvature of attention

Reference 14

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Observation 8123b6c7-7a79-4c76-ae99-41beef3045d4 · outbound

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

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Elucidating the design space of diffusion-based generative models

Reference 15

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Observation 5c2f45dc-36a4-4748-b82c-d510df5e7153 · outbound

This paper cites Guiding a Diffusion Model with a Bad Version of Itself.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Guiding a Diffusion Model with a Bad Version of Itself

Reference 16

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Observation b18720e3-8419-423f-9c79-037f3083d488 · outbound

This paper cites Repurposing diffusion-based image generators for monocular depth estimation.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Repurposing diffusion-based image generators for monocular depth estimation

Reference 17

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Observation 6183c8b0-9da7-4562-9f33-650d4ec4559a · outbound

This paper cites Variational diffusion models.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Variational diffusion models

Reference 18

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Observation 2ac2f968-5711-4e74-8925-f8be78e24bed · outbound

This paper cites Pick-a-pic: An open dataset of user preferences for text-to-image generation.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Pick-a-pic: An open dataset of user preferences for text-to-image generation

Reference 19

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Observation 7fb2a181-aadf-42ca-a3ee-1917cb97c0bd · outbound

This paper cites Unleashing Vecset Diffusion Model for Fast Shape Generation.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Unleashing Vecset Diffusion Model for Fast Shape Generation

Reference 20

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Rich human feedback for text-to-image generation

Reference 21

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Observation 77b3bdfb-1759-4933-b8d6-8468d7b8d193 · outbound

This paper cites Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization

Reference 22

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Observation dfe7b766-6782-4d9f-a803-b1dcc3f7b2f1 · outbound

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Pseudo Numerical Methods for Diffusion Models on Manifolds

Reference 23

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This paper cites OSV: One Step is Enough for High-Quality Image to Video Generation.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models OSV: One Step is Enough for High-Quality Image to Video Generation

Reference 24

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This paper cites badprompt, 2023.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models badprompt, 2023

Reference 25

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Observation c61c1cfb-627f-4fef-b729-3797cd27e1d7 · outbound

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

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Training language models to follow instructions with human feedback

Reference 26

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Scalable diffusion models with transformers

Reference 27

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 28

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Aligning Text-to-Image Diffusion Models with Reward Backpropagation

Reference 29

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Video Diffusion Alignment via Reward Gradients

Reference 30

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Markov decision processes: discrete stochastic dynamic programming

Reference 31

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Direct preference optimization: Your language model is secretly a reward model

Reference 32

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models High-resolution image synthesis with latent diffusion models

Reference 33

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Laion-aesthetics

Reference 34

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Proximal Policy Optimization Algorithms

Reference 35

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Rethinking the spatial inconsistency in classifier-free diffusion guidance

Reference 36

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Motion-i2v: Consistent and controllable image-to-video generation with explicit motion modeling

Reference 37

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Make-A-Video: Text-to-Video Generation without Text-Video Data

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Journeydb: A benchmark for generative image understanding

Reference 39

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Reinforcement learning: An introduction

Reference 40

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Diffusion model alignment using direct preference optimization

Reference 41

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Phased Consistency Models

Reference 42

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Animatelcm: Computation-efficient personalized style video generation without personalized video data

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:59:20.594788Z

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Zola: Zero-shot creative long animation generation with short video model

Reference 44

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data

Reference 45

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Be-your-outpainter: Mastering video outpainting through input-specific adaptation

Reference 46

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Rectified Diffusion: Straightness Is Not Your Need in Rectified Flow

Reference 47

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models I made stable diffusion xl smarter by finetuning it on bad ai-generated images, 2023

Reference 48

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 49

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Deep Reward Supervisions for Tuning Text-to-Image Diffusion Models

Reference 50

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Imagereward: Learning and evaluating human preferences for text-to-image generation

Reference 51

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Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Using human feedback to fine-tune diffusion models without any reward model

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:59:20.527357Z

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Observation 86d100cb-df52-40ba-8b5d-5b1a790724d2 · outbound

This paper cites From slow bidirectional to fast causal video generators.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models From slow bidirectional to fast causal video generators

Reference 53

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Observation b4a61981-a084-451b-b037-9404cfb05e99 · outbound

This paper cites Trans4D: Realistic Geometry-Aware Transition for Compositional Text-to-4D Synthesis.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Trans4D: Realistic Geometry-Aware Transition for Compositional Text-to-4D Synthesis

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T20:59:20.030764Z

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Observation aa8d5719-4ad9-4887-8b6a-e6431b798718 · outbound

This paper cites Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T20:59:20.035498Z

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Observation f9ffeeee-4a93-4d19-ac12-1a6b81b39cf5 · outbound

This paper cites Large-scale Reinforcement Learning for Diffusion Models.

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models Large-scale Reinforcement Learning for Diffusion Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-15T20:59:20.040602Z

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Pith citing papers

Observation 5e9fb47e-8cf5-4aeb-8a80-ab15447a6eb6 · inbound

C$^2$FG: Control Classifier-Free Guidance via Score Discrepancy Analysis cites this paper.

C$^2$FG: Control Classifier-Free Guidance via Score Discrepancy Analysis Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-15T15:06:10.187298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e86f708e-61de-465c-a0a6-5afe122c1045 · inbound

C$^2$FG: Control Classifier-Free Guidance via Score Discrepancy Analysis cites this paper.

C$^2$FG: Control Classifier-Free Guidance via Score Discrepancy Analysis Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-21T12:04:09.483758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 98b9e50e-7989-4e60-842a-63d31d3e8b8f · inbound

Not all tokens contribute equally to diffusion learning cites this paper.

Not all tokens contribute equally to diffusion learning Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:40:53.735080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5a511123-109e-46fe-94d1-4a86ca7bc60e · inbound

Towards General Preference Alignment: Diffusion Models at Nash Equilibrium cites this paper.

Towards General Preference Alignment: Diffusion Models at Nash Equilibrium Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:56:06.944544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ac0e41fe-3e81-4fae-9c11-95a837ea453f · inbound

Offline Preference Optimization for Rectified Flow with Noise-Tracked Pairs cites this paper.

Offline Preference Optimization for Rectified Flow with Noise-Tracked Pairs Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:11:15.584291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.