Paper Citation Record · LEDGER

Personalized Preference Fine-tuning of Diffusion Models

As of 12 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 1 inbound Pith citation observation for arXiv:2501.06655.

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

pith.paper-citation-record.v1
2501.06655 v1

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:01:56.983860Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T02:10:27.595446Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T02:11:15.507458Z

Reference resolution

58 of 58 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 80b4e279-aa31-439a-899a-67d400449641 · outbound

This paper cites Training a helpful and harmless assistant with reinforcement learning from human feedback, 2022.

Personalized Preference Fine-tuning of Diffusion Models Training a helpful and harmless assistant with reinforcement learning from human feedback, 2022

Reference 1

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Observation 11ccb589-d7bb-403e-88a1-1c13b6dbb5f8 · outbound

This paper cites Training diffusion models with reinforce- ment learning.

Personalized Preference Fine-tuning of Diffusion Models Training diffusion models with reinforce- ment learning

Reference 2

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Observation 0d479af6-b79e-4898-980f-c75eb1d6c8a8 · outbound

This paper cites Rank analysis of incomplete block designs: I.

Personalized Preference Fine-tuning of Diffusion Models Rank analysis of incomplete block designs: I

Reference 3

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Observation 02a8b859-bc42-4fbf-af9c-a4a42630185e · outbound

This paper cites MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?.

Personalized Preference Fine-tuning of Diffusion Models MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?

Reference 4

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Observation 08d764e2-9357-4438-a47e-8273fdb161d0 · outbound

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

Personalized Preference Fine-tuning of Diffusion Models Directly Fine-Tuning Diffusion Models on Differentiable Rewards

Reference 5

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Observation 6335e94c-865a-4728-b1d6-7a6fee45554f · outbound

This paper cites Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack.

Personalized Preference Fine-tuning of Diffusion Models Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack

Reference 6

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Observation 9af1c40a-0a4a-4c50-9f01-467a7be47e3e · outbound

This paper cites Scaling recti- fied flow transformers for high-resolution image synthesis.

Personalized Preference Fine-tuning of Diffusion Models Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 7

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Observation b6204bad-1559-4d8a-880a-4a68245e5f9b · outbound

This paper cites KTO: Model Alignment as Prospect Theoretic Optimization.

Personalized Preference Fine-tuning of Diffusion Models KTO: Model Alignment as Prospect Theoretic Optimization

Reference 8

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Observation aed6b77a-595b-4031-80db-00aba444f1e4 · outbound

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

Personalized Preference Fine-tuning of Diffusion Models Re- inforcement learning for fine-tuning text-to-image diffusion models

Reference 9

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Observation fda3e7df-8b1b-44b4-80fc-a1782413b35b · outbound

This paper cites Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration.

Personalized Preference Fine-tuning of Diffusion Models Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration

Reference 10

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Observation 524dc754-8756-43cf-88a3-90627a438383 · outbound

This paper cites Improving image generation with better captions.

Personalized Preference Fine-tuning of Diffusion Models Improving image generation with better captions

Reference 11

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Observation e6143af7-6ee1-4957-8053-ce2d57296c04 · outbound

This paper cites Denoising diffu- sion probabilistic models.

Personalized Preference Fine-tuning of Diffusion Models Denoising diffu- sion probabilistic models

Reference 12

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Observation a49d083d-685e-4531-b974-ae449c863abd · outbound

This paper cites Open- clip, 2021.

Personalized Preference Fine-tuning of Diffusion Models Open- clip, 2021

Reference 13

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Observation 18404cea-1de1-4b8f-bd59-57d8f47d9e48 · outbound

This paper cites Kingma, Tim Salimans, Ben Poole, and Jonathan Ho.

Personalized Preference Fine-tuning of Diffusion Models Kingma, Tim Salimans, Ben Poole, and Jonathan Ho

Reference 14

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Observation 8e863f60-8155-4b9e-abb8-db48af3d89c7 · outbound

This paper cites The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models.

Personalized Preference Fine-tuning of Diffusion Models The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models

Reference 15

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Observation ac6f5f4f-50c2-45bc-9594-ea151d361e4e · outbound

This paper cites Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation.

Personalized Preference Fine-tuning of Diffusion Models Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation

Reference 16

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Observation a378b1e1-b689-4496-a5bc-8965658db3ca · outbound

This paper cites Large language models are zero-shot reasoners, 2023.

Personalized Preference Fine-tuning of Diffusion Models Large language models are zero-shot reasoners, 2023

Reference 17

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Observation f1ceb44e-e509-4680-a7cb-f7e7039c58ff · outbound

This paper cites Aligning Text-to-Image Models using Human Feedback.

Personalized Preference Fine-tuning of Diffusion Models Aligning Text-to-Image Models using Human Feedback

Reference 18

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Observation 46a4ea93-30a3-4a98-a285-c7706f219412 · outbound

This paper cites Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation.

Personalized Preference Fine-tuning of Diffusion Models Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation

Reference 19

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Observation 88d7ab99-7b94-4b1d-b854-299054cfac5a · outbound

This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

Personalized Preference Fine-tuning of Diffusion Models LLaVA-OneVision: Easy Visual Task Transfer

Reference 21

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Observation 83b40789-fa98-4c24-b8ea-1383e607eeec · outbound

This paper cites Aligning Diffusion Models by Optimizing Human Utility.

Personalized Preference Fine-tuning of Diffusion Models Aligning Diffusion Models by Optimizing Human Utility

Reference 22

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Observation 86bcab01-244e-41f2-9e0d-f48c4995ccfa · outbound

This paper cites Personalized Language Modeling from Personalized Human Feedback.

Personalized Preference Fine-tuning of Diffusion Models Personalized Language Modeling from Personalized Human Feedback

Reference 23

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Observation 6865cb0c-5ecb-4c54-acb8-dc7a061c702e · outbound

This paper cites Decoupled Weight Decay Regularization.

Personalized Preference Fine-tuning of Diffusion Models Decoupled Weight Decay Regularization

Reference 24

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Observation da66c3f9-2f68-4797-af64-5cff0a030d07 · outbound

This paper cites T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models.

Personalized Preference Fine-tuning of Diffusion Models T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models

Reference 25

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This paper cites GPT-4 Technical Report.

Personalized Preference Fine-tuning of Diffusion Models GPT-4 Technical Report

Reference 26

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This paper cites W ¨urstchen: An ef- ficient architecture for large-scale text-to-image diffusion models.

Personalized Preference Fine-tuning of Diffusion Models W ¨urstchen: An ef- ficient architecture for large-scale text-to-image diffusion models

Reference 27

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Observation 4034e8cc-46fe-49a3-ae4d-b29d5d13c1bc · outbound

This paper cites Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning.

Personalized Preference Fine-tuning of Diffusion Models Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning

Reference 28

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Observation c615ca92-8839-4e91-985e-a66306c22b1a · outbound

This paper cites SDXL: Improving latent diffusion models for high-resolution image synthesis.

Personalized Preference Fine-tuning of Diffusion Models SDXL: Improving latent diffusion models for high-resolution image synthesis

Reference 29

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Observation aeba61ac-9706-4aa7-a86d-e6cdd596d963 · outbound

This paper cites Aligning Text-to-Image Diffusion Models with Reward Backpropagation.

Personalized Preference Fine-tuning of Diffusion Models Aligning Text-to-Image Diffusion Models with Reward Backpropagation

Reference 30

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Observation 8f3d906d-6eac-4439-915c-bff9d6643479 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Personalized Preference Fine-tuning of Diffusion Models Learning transferable visual models from natural language supervi- sion

Reference 31

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Observation 2aacc67d-0c1a-4a9a-8d15-96493a439e55 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

Personalized Preference Fine-tuning of Diffusion Models Direct preference optimization: Your language model is secretly a reward model

Reference 32

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Observation 6aa7b3b5-f885-46e8-b93c-c0a771725732 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Personalized Preference Fine-tuning of Diffusion Models Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 33

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Personalized Preference Fine-tuning of Diffusion Models High-resolution image synthesis with latent diffusion models

Reference 34

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Observation d4409820-6666-40d9-b545-3deb632950dd · outbound

This paper cites Photorealistic text-to-image diffusion models with deep lan- guage understanding.

Personalized Preference Fine-tuning of Diffusion Models Photorealistic text-to-image diffusion models with deep lan- guage understanding

Reference 35

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Observation 8dd029ff-d460-4ea2-873a-6f0bcaac5c7a · outbound

This paper cites Whose Opinions Do Language Models Reflect?.

Personalized Preference Fine-tuning of Diffusion Models Whose Opinions Do Language Models Reflect?

Reference 36

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Personalized Preference Fine-tuning of Diffusion Models CLIP MLP aesthetic score predictor

Reference 37

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This paper cites LAION-5b: An open large-scale dataset for train- ing next generation image-text models.

Personalized Preference Fine-tuning of Diffusion Models LAION-5b: An open large-scale dataset for train- ing next generation image-text models

Reference 38

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This paper cites A Picture is Worth a Thousand Words: Principled Recaptioning Improves Image Generation.

Personalized Preference Fine-tuning of Diffusion Models A Picture is Worth a Thousand Words: Principled Recaptioning Improves Image Generation

Reference 39

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Personalized Preference Fine-tuning of Diffusion Models Deep unsupervised learning using nonequilibrium thermodynamics

Reference 40

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Observation b5e90beb-16ef-41d4-af4c-39db589a22d2 · outbound

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Personalized Preference Fine-tuning of Diffusion Models Denois- ing diffusion implicit models

Reference 41

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Personalized Preference Fine-tuning of Diffusion Models Generative modeling by esti- mating gradients of the data distribution

Reference 42

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Observation dfbbc879-061c-4f23-bac5-973bf4f9b080 · outbound

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Personalized Preference Fine-tuning of Diffusion Models Score-based generative modeling through stochastic differential equa- tions

Reference 43

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Personalized Preference Fine-tuning of Diffusion Models A Roadmap to Pluralistic Alignment

Reference 44

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Personalized Preference Fine-tuning of Diffusion Models Diffusion model align- ment using direct preference optimization

Reference 45

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This paper cites Chain-of-thought prompting elicits reasoning in large language models, 2023.

Personalized Preference Fine-tuning of Diffusion Models Chain-of-thought prompting elicits reasoning in large language models, 2023

Reference 46

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This paper cites Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis.

Personalized Preference Fine-tuning of Diffusion Models Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 47

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Personalized Preference Fine-tuning of Diffusion Models Human preference score: Better aligning text- to-image models with human preference

Reference 48

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Personalized Preference Fine-tuning of Diffusion Models Imagere- ward: Learning and evaluating human preferences for text- to-image generation

Reference 49

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Observation c0cf1e38-e6b8-4c1d-9ec1-0443a7fa99e4 · outbound

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Personalized Preference Fine-tuning of Diffusion Models Using human feedback to fine-tune diffusion models without any reward model

Reference 50

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Observation 1109cb91-8a6a-4f91-b330-f7dea52aa003 · outbound

This paper cites IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models.

Personalized Preference Fine-tuning of Diffusion Models IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

Reference 51

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This paper cites ICPL: Few-shot In-context Preference Learning via LLMs.

Personalized Preference Fine-tuning of Diffusion Models ICPL: Few-shot In-context Preference Learning via LLMs

Reference 52

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This paper cites Scaling autoregressive models for content-rich text-to-image generation.

Personalized Preference Fine-tuning of Diffusion Models Scaling autoregressive models for content-rich text-to-image generation

Reference 53

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Observation 6f23cb91-b839-4e24-82b9-8d3f5b50fe93 · outbound

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Personalized Preference Fine-tuning of Diffusion Models Adding conditional control to text-to-image diffusion models

Reference 54

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Observation ff086758-53aa-410a-abbe-2f37949eecce · outbound

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

Personalized Preference Fine-tuning of Diffusion Models Large-scale Reinforcement Learning for Diffusion Models

Reference 55

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d316001c-b380-4dbb-ba79-7e425421dcbe · outbound

This paper cites TX t=1 log pθ(x+ t−1|x+ t ) pref(x+ t−1|x+ t ) − log pθ(x− t−1|x− t ) pref(x− t−1|xt) #! = − log σ βEx+ 1:T ,x− 1:T T Et.

Personalized Preference Fine-tuning of Diffusion Models TX t=1 log pθ(x+ t−1|x+ t ) pref(x+ t−1|x+ t ) − log pθ(x− t−1|x− t ) pref(x− t−1|xt) #! = − log σ βEx+ 1:T ,x− 1:T T Et

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:01:57.550424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7fb4377c-1456-45e4-b20d-396eae00f1c5 · outbound

This paper cites an unresolved cited work.

Personalized Preference Fine-tuning of Diffusion Models Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:01:57.534823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f6834884-e3c5-4bd4-ad58-56520aa54465 · outbound

This paper cites an unresolved cited work.

Personalized Preference Fine-tuning of Diffusion Models Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:01:57.519451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0650e29e-e9dc-4a68-ae18-18312eabdeb1 · outbound

This paper cites an unresolved cited work.

Personalized Preference Fine-tuning of Diffusion Models Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:01:57.503218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

Observation 882f1f7b-ade3-489f-9160-f2fce10bf76e · inbound

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

Offline Preference Optimization for Rectified Flow with Noise-Tracked Pairs Personalized Preference Fine-tuning of Diffusion Models

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:11:15.509543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.