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Paper Citation Record · LEDGER

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples

As of 18 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 4 inbound Pith citation observations for arXiv:2505.22002.

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

pith.paper-citation-record.v1
2505.22002 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:23:13.232281Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T12:32:13.061231Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T00:14:04.763204Z

Reference resolution

53 of 53 outbound references displayed

  • verified exact0
  • verified fuzzy1
  • unresolved51
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 555c8067-fad8-46a3-a03e-ee56fb3339ac · outbound

This paper cites a cat playing chess.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples a cat playing chess

Reference 2

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raw_fallback, observed 2026-08-07T13:23:15.369378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T13:23:13.076365Z digest=sha256:cf936fe6503aaf9b5b9ab62748af3a4211741186cd5029142a482e642929c491

Observation 65feb258-915e-4f25-925e-efff7574de75 · outbound

This paper cites an unresolved cited work.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Unresolved cited work

Reference 3

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source=pdf_text observed=2026-08-07T13:23:13.232281Z digest=sha256:cd64c3ac4908d12f9d5eac491dd2ade29827f7a95e1ffff811d4fdf097ef9d97

Observation 25782779-e3d9-4e4a-a606-4aa42e6df935 · outbound

This paper cites MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing

Reference 4

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source=pdf_text observed=2026-08-07T13:23:01.535445Z digest=sha256:41bc2ab1abba90bfb537fedaae4b995a8cc8ca140ac24ff0e57ad3855f0341e1

Observation 3a8cff3d-d226-4972-ad8c-cd17f12c0b71 · outbound

This paper cites Diffusion Policy: Visuomotor Policy Learning via Action Diffusion.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

Reference 5

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source=pdf_text observed=2026-08-07T13:23:01.681084Z digest=sha256:e54eec097a627fd4e82a65c20b533713524b767715a3cb6fa442758d273070e1

Observation cab5368b-98e2-4836-adbe-30abf0c65cf7 · outbound

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

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Directly Fine-Tuning Diffusion Models on Differentiable Rewards

Reference 6

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source=pdf_text observed=2026-08-07T13:23:01.857245Z digest=sha256:3751d1e528e929013f0b5e1a60c1f8f924c1ba6b890871a50e6d3b075c82f0bc

Observation 5ecb4595-6c47-4649-8996-0a1dfef93ef1 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

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source=pdf_text observed=2026-08-07T13:23:02.243017Z digest=sha256:badb2d2feef3f91542cc08ead7951880232bb559794cf209fa2ec593b0d88319

Observation bfadfe4b-fe21-42a6-a158-e37ddc665651 · outbound

This paper cites DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models

Reference 9

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source=pdf_text observed=2026-08-07T13:23:02.418961Z digest=sha256:11b3b6565511ee28524b3af3b9f934b3de59d335d9989340472c007e7733e413

Observation eb3fc34d-7ebe-4f2a-a66d-134fd9f89aa2 · outbound

This paper cites Prompt-to-Prompt Image Editing with Cross Attention Control.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Prompt-to-Prompt Image Editing with Cross Attention Control

Reference 10

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source=pdf_text observed=2026-08-07T13:23:02.599283Z digest=sha256:b91174c37ad6f40b3c1c18540e9bd8ade523cb66993ca09776c1ad7d77c58cb5

Observation 2ff9c380-b6e0-4d01-9d5e-d3f75dc064f0 · outbound

This paper cites CLIPScore: A Reference-free Evaluation Metric for Image Captioning.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 11

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source=pdf_text observed=2026-08-07T13:23:02.807042Z digest=sha256:952e806fda8902c9b0cd6b3a7893d94decc735246808af2f68597dfd7457af37

Observation 055f8554-e976-4e3a-89e4-d991cee6599f · outbound

This paper cites Classifier-Free Diffusion Guidance.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Classifier-Free Diffusion Guidance

Reference 12

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source=pdf_text observed=2026-08-07T13:23:02.952670Z digest=sha256:371b56f40696338a964eea97b76f7668a0900b951425457ef6081072e0c3f151

Observation d9ec8f4e-18d2-40a3-ba0c-ce9d6d23c0b7 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples LoRA: Low-Rank Adaptation of Large Language Models

Reference 14

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source=pdf_text observed=2026-08-07T13:23:03.292947Z digest=sha256:5bd0baf3ecf2c0d88f4f812a86e615db03c688b5e35cfd26ca15475658cfe8b9

Observation a414adea-0be0-4f8e-8f7a-dc34d30893c4 · outbound

This paper cites doi: 10.1145/3703155.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples doi: 10.1145/3703155

Reference 16

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source=pdf_text observed=2026-08-07T13:23:03.630103Z digest=sha256:bf3f73a7bdfe3ee4b5cd1c8b824fcdcbda1a948c3cbc0c7f5f6fb41555ffa935

Observation e7ae4da1-4a0c-4d71-853f-c81917c6b9bb · outbound

This paper cites CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching

Reference 17

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source=pdf_text observed=2026-08-07T13:23:03.828401Z digest=sha256:acbc7f037f914e9777535faaf4d20cff425006f1253195a13d84fae1ce0fe8c8

Observation 8254a454-61fc-4f23-810f-42734a3871a0 · outbound

This paper cites Imagic: Text-Based Real Image Editing with Diffusion Models.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Imagic: Text-Based Real Image Editing with Diffusion Models

Reference 18

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source=pdf_text observed=2026-08-07T13:23:03.995514Z digest=sha256:2af91f64d9875edbfe8b53fadf421603d320a1a241307f82bc4e89a0296a0ab6

Observation 1c346a8e-77b2-476e-a1e4-e65f339be3a7 · outbound

This paper cites SDPO: Segment-Level Direct Preference Optimization for Social Agents.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples SDPO: Segment-Level Direct Preference Optimization for Social Agents

Reference 19

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source=pdf_text observed=2026-08-07T13:23:04.117228Z digest=sha256:c393f2e0f26284cc5be296a43037a3273c5b8259ff660ba22cc84186db0eeff8

Observation 1e1734a6-acea-491d-8039-6c7dd6a19905 · outbound

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

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Aligning Text-to-Image Models using Human Feedback

Reference 20

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source=pdf_text observed=2026-08-07T13:23:04.252641Z digest=sha256:d239a041350dee5b1629ac0ac8fa5d3ecef7a94f24ad726de617cb76d563fcd9

Observation b462b511-c8d5-45d8-b1b3-5ee1f1631a3a · outbound

This paper cites Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment

Reference 21

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source=pdf_text observed=2026-08-07T13:23:04.358322Z digest=sha256:96df40a5a31c8dbd494e81f72f258a5b95839a09fa4573e216689a7a253aa29c

Observation 75c08bd0-bfac-4552-af51-3094f9366dc1 · outbound

This paper cites Null-text Inversion for Editing Real Images using Guided Diffusion Models.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Null-text Inversion for Editing Real Images using Guided Diffusion Models

Reference 23

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source=pdf_text observed=2026-08-07T13:23:04.667462Z digest=sha256:f3323cde3d71b425c3ff48c6f810cce344977316b2780af15bf2b109e60d706c

Observation 4233c6f0-a7fb-49ef-8a49-d276128fed8e · outbound

This paper cites Fast Prompt Alignment for Text-to-Image Generation.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Fast Prompt Alignment for Text-to-Image Generation

Reference 24

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source=pdf_text observed=2026-08-07T13:23:04.804389Z digest=sha256:22cd030e33ed822f50dd68a86d06f35d28bc2a9343172ff9caa8783f1f545b3c

Observation b20144f8-927e-42f5-9f2a-04f10f3efd2a · outbound

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

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Training language models to follow instructions with human feedback

Reference 25

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source=pdf_text observed=2026-08-07T13:23:05.343879Z digest=sha256:5b223ca579a0caec8237fd739d9ce38bd34c5f67f6040bed9ed6c1903e334766

Observation b8f4a08b-dbe4-4c3b-bc45-18239e48d035 · outbound

This paper cites Scalable Diffusion Models with Transformers.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Scalable Diffusion Models with Transformers

Reference 26

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source=pdf_text observed=2026-08-07T13:23:07.321643Z digest=sha256:a8152c729a894902ec5cf3067e4000312926dc2d8ed6bbdf80476dc5cb7069a7

Observation e10309dc-8029-45d0-88bd-1aefc24c8ca9 · outbound

This paper cites DreamFusion: Text-to-3D using 2D Diffusion.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples DreamFusion: Text-to-3D using 2D Diffusion

Reference 27

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source=pdf_text observed=2026-08-07T13:23:07.456286Z digest=sha256:086a2fcd14fee5457433b2c34ef62d5fbfa4eb31a6d00e1da873849d8e5f0a52

Observation bbb02988-905c-4a91-a752-c27ffe2b99b0 · outbound

This paper cites Diffusion Autoencoders: Toward a Meaningful and Decodable Representation.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Diffusion Autoencoders: Toward a Meaningful and Decodable Representation

Reference 28

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source=pdf_text observed=2026-08-07T13:23:07.623938Z digest=sha256:ebf0ef21c0ba553041d8eed6226b044c51da0a6f044dc432caf402be0f6f414d

Observation 8bb66da3-0afc-4d37-824e-6ae84e45fd9d · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Learning Transferable Visual Models From Natural Language Supervision

Reference 29

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source=pdf_text observed=2026-08-07T13:23:08.111582Z digest=sha256:9258836cc03d2c163127dcf58bf57c08435a9c36f71ca16692a590db1bb6d75b

Observation 720f7d71-1a73-46b3-a8a9-4c93d02750ad · outbound

This paper cites Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Reference 30

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source=pdf_text observed=2026-08-07T13:23:09.937975Z digest=sha256:35fc80e8f96d22c35646ee2bb851f94d033957e12a7420907d35e28aeaf103ea

Observation 095c8792-5f6a-4fc5-ad5f-b4aefe4509f1 · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples High-Resolution Image Synthesis with Latent Diffusion Models

Reference 31

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source=pdf_text observed=2026-08-07T13:23:10.048591Z digest=sha256:cce9e5990c0a34e2845df971f25afab13300782b69a384e9526a39ab61e08804

Observation 9f173aeb-507a-483f-937d-b3c8f7d7073f · outbound

This paper cites DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing

Reference 34

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source=pdf_text observed=2026-08-07T13:23:10.427239Z digest=sha256:9c27a49ee67e6e589a8f7ad6491a702d98191de9cd6cdabc875511a9862509c3

Observation d0401a6a-68df-461d-8c5e-40b135bef54c · outbound

This paper cites Deep Unsupervised Learning using Nonequilibrium Thermodynamics.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Reference 35

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source=pdf_text observed=2026-08-07T13:23:10.578152Z digest=sha256:e2085e230fb01cda4cb42f753860102db88efbf3c1bd4ed62d3d4abc73b9c9df

Observation cb9426ff-2214-4f9a-8080-116c14c56d80 · outbound

This paper cites Denoising Diffusion Implicit Models.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Denoising Diffusion Implicit Models

Reference 36

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source=pdf_text observed=2026-08-07T13:23:10.730864Z digest=sha256:0747c6563c92dba522f67b6ca54f1206bb6baf771e4e502529f9b098d04e4454

Observation 797b8c62-23d6-4688-8e36-80a60f2f74d9 · outbound

This paper cites Improved Techniques for Training Score-Based Generative Models.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Improved Techniques for Training Score-Based Generative Models

Reference 37

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source=pdf_text observed=2026-08-07T13:23:10.901070Z digest=sha256:1111a092d2730177f98b6d09fecc6b3917c952f7d3e086de3322653f9d5e34ce

Observation e6f651d2-0b8c-43b9-b898-1407a1a25645 · outbound

This paper cites an unresolved cited work.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Unresolved cited work

Reference 38

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source=pdf_text observed=2026-08-07T13:23:11.090175Z digest=sha256:08a45bca596cec1fd8f29e853e32cd16fd5a7d7053218459738f16c66626f3e2

Observation 1567cb64-eba7-4cfd-a546-41f62aa51bc2 · outbound

This paper cites How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs

Reference 39

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source=pdf_text observed=2026-08-07T13:23:11.230813Z digest=sha256:12284bc76c8f7bc24fe257674970e8e6fb5e977a391374b4026dc70af86d6805

Observation a340ab0d-fd6b-4896-8517-6484cb316afc · outbound

This paper cites Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation

Reference 40

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source=pdf_text observed=2026-08-07T13:23:11.429291Z digest=sha256:5a909d6c1ed0286796505d6d5694f82e78d7acfe6496f788869a87d5c6dea4c0

Observation ae06d4f7-f05e-4e6c-a785-ccc839ac2678 · outbound

This paper cites Attention Is All You Need.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Attention Is All You Need

Reference 41

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source=pdf_text observed=2026-08-07T13:23:11.567660Z digest=sha256:06df6faeed4654c6e586b1756b02ec7064d2c302c7221f1dc56bf7711b37a904

Observation ebb1aaea-ca66-4040-b019-22d9a329bd5b · outbound

This paper cites Diffusion Model Alignment Using Direct Preference Optimization.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Diffusion Model Alignment Using Direct Preference Optimization

Reference 42

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source=pdf_text observed=2026-08-07T13:23:11.682372Z digest=sha256:cf82f4d4fac7884a4c8d42c751d62fef86c68d280c2b5bbb6dc6eb53e03e7b47

Observation 422a07d5-0ade-481a-93e5-3e6f3d5c923b · outbound

This paper cites Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting

Reference 43

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Observation a3456976-8d19-41d6-8adf-41f159710c7c · outbound

This paper cites AttnGCG: Enhancing Jailbreaking Attacks on LLMs with Attention Manipulation.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples AttnGCG: Enhancing Jailbreaking Attacks on LLMs with Attention Manipulation

Reference 44

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source=pdf_text observed=2026-08-07T13:23:11.956259Z digest=sha256:b6ab10f1ff3a3454059b7a0083183bb989849be93dc90b8ce485e0208bff9778

Observation 098b5ba6-712a-4421-b688-ae65ff339045 · outbound

This paper cites Fine-Grained Human Feedback Gives Better Rewards for Language Model Training.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Fine-Grained Human Feedback Gives Better Rewards for Language Model Training

Reference 45

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source=pdf_text observed=2026-08-07T13:23:12.103502Z digest=sha256:5ff47e38a9bdf2a8f66a43facc0ac85f1c773b05d30c442accc2deb78e730b70

Observation 3060bb09-27e8-46ce-9a32-a562f8df7118 · outbound

This paper cites ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation

Reference 46

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source=pdf_text observed=2026-08-07T13:23:12.222530Z digest=sha256:e36fd5e8de84b961b8e4f44052c9ec0af62160c9c963e619b8b610728b63cd99

Observation 7e8e2fb8-3024-4f03-9424-203a8b96e992 · outbound

This paper cites GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation

Reference 47

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source=pdf_text observed=2026-08-07T13:23:12.395474Z digest=sha256:b4500912d59cd519e18306617c6e532c4eee59970535caed17bc0d63e4fdf06c

Observation c479fbc2-c7c6-46ba-9dd9-7a2e670d54f1 · outbound

This paper cites Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model

Reference 48

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source=pdf_text observed=2026-08-07T13:23:12.498356Z digest=sha256:413198c30af8f26c92b2fc229e309416e80ed9bf18b40e606f9bdd5a31fe5989

Observation d30fad13-688f-472a-8fa6-10ece99ac71b · outbound

This paper cites Align, Adapt and Inject: Sound-guided Unified Image Generation.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Align, Adapt and Inject: Sound-guided Unified Image Generation

Reference 49

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source=pdf_text observed=2026-08-07T13:23:12.619974Z digest=sha256:b9e09ef43ab79ef08a572043d4979f8fb7bf5d01b544fa475d4da8c400b2073c

Observation ec1ab486-8a66-4f9a-95ce-5a0ed0c2bd63 · outbound

This paper cites RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback

Reference 50

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source=pdf_text observed=2026-08-07T13:23:12.812692Z digest=sha256:88fd7f3507fc6eb5f2b05543d176590ef856672facca48bddbd52206c328b483

Observation c60dde6b-f476-4227-9157-f03b5e4afb38 · outbound

This paper cites LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generation.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generation

Reference 51

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source=pdf_text observed=2026-08-07T13:23:12.923543Z digest=sha256:d7883751b7b9efd7339beb6e98eef1bd729ff55dfbde120cbe00566618d72a8f

Observation 9a63898a-fc7d-4874-9a3a-2b1d974c3e6c · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 2015

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source=pdf_text observed=2026-08-07T13:23:10.190412Z digest=sha256:21eb0a81e1d8aa709b9767fc669c1aadeabf24342bdc620c7c36e037b3852af4

Observation 9f8acd2c-8092-41aa-b313-5aca31293763 · outbound

This paper cites Visual Relationship Detection with Language Priors.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Visual Relationship Detection with Language Priors

Reference 2016

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source=pdf_text observed=2026-08-07T13:23:04.486714Z digest=sha256:e6ed5ead5515de6f748d61c784493a53771a1614c12f3e6011a7ed32da30b324

Observation 479aa011-f2ea-48ec-9687-b7d731dee3bc · outbound

This paper cites Proximal Policy Optimization Algorithms.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Proximal Policy Optimization Algorithms

Reference 2017

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source=pdf_text observed=2026-08-07T13:23:10.300156Z digest=sha256:ac6a0cee38bc4987a0c9392653e4cc68b84eb646767073fd40cfd2df5536a5ed

Observation eae8015b-9296-4197-9adc-90384ab2461f · outbound

This paper cites Denoising Diffusion Probabilistic Models.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Denoising Diffusion Probabilistic Models

Reference 2020

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source=pdf_text observed=2026-08-07T13:23:03.152344Z digest=sha256:6b888490db59509ba35cbf7bec4735cfc482344c60e478b7813af3d9b79818c5

Observation 36f45c74-5c15-436e-bd58-a2168113b18a · outbound

This paper cites Diffusion Models Beat GANs on Image Synthesis.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Diffusion Models Beat GANs on Image Synthesis

Reference 2021

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source=pdf_text observed=2026-08-07T13:23:02.095620Z digest=sha256:cff023aa1826fb4749f13fbed911dfaade76d0c38538eb9a3256b9e74323f36f

Observation 2f329f97-1758-46c0-bbff-d99d17cf7c00 · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Constitutional AI: Harmlessness from AI Feedback

Reference 2022

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source=pdf_text observed=2026-08-07T13:23:01.130908Z digest=sha256:5c5ad0754baaee8b94a5c2e19b289f456fbf70742f4a7fb40fbc391f90a39974

Observation fdc78d5b-3e9c-4473-89d0-cfb761724a05 · outbound

This paper cites InstructPix2Pix: Learning to Follow Image Editing Instructions.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples InstructPix2Pix: Learning to Follow Image Editing Instructions

Reference 2023

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source=pdf_text observed=2026-08-07T13:23:01.364254Z digest=sha256:a74bc4b4a506e287303dc28fc7ac851ab51358c31ed413e01d50a64ee23610d4

Observation 4a9d209e-8ca3-4856-b047-2a39ba38dc7d · outbound

This paper cites Hallucination of Multimodal Large Language Models: A Survey.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Hallucination of Multimodal Large Language Models: A Survey

Reference 2024

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source=pdf_text observed=2026-08-07T13:23:01.206270Z digest=sha256:3d760d1d6fe8b44e36d88ff7186090294d43c2900c38ea539479855cd14fdbf3

Observation 2c0a9f22-4a7e-4eab-b5c2-0342d3b9d6c4 · outbound

This paper cites Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse Rewards.

D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse Rewards

Reference 2025

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source=pdf_text observed=2026-08-07T13:23:03.425660Z digest=sha256:b8fff7e176ca341b2de5ef45dabe680464af946d1c8dca0c85e747dd745bf650

Pith citing papers

Observation 677826cb-de02-4929-9679-b1925de96318 · inbound

CAT: Causal Attention Tuning For Injecting Fine-grained Causal Knowledge into Large Language Models cites this paper.

CAT: Causal Attention Tuning For Injecting Fine-grained Causal Knowledge into Large Language Models D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples

Reference 17

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source=arxiv_source observed=2026-08-05T12:32:13.061231Z digest=sha256:13134ef2bb3f0563e58e4962705cc2ddd2f0da748601f1004b882814b8577915

Observation b9fe8573-fd92-4b3f-8bb4-0b2a1dbdbda2 · inbound

HP-Edit: A Human-Preference Post-Training Framework for Image Editing cites this paper.

HP-Edit: A Human-Preference Post-Training Framework for Image Editing D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples

Reference 13

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arxiv_id, observed 2026-05-10T03:29:21.991785Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-10T03:26:15.525307Z digest=sha256:1b9f8cdf7d15f7446637e819cf70e9ebaa92b10def7d3a04159a0e1c010233ea

Observation 23dd579a-9728-4b0b-b4c2-918929da3a5a · inbound

SpatialFusion: Endowing Unified Image Generation with Intrinsic 3D Geometric Awareness cites this paper.

SpatialFusion: Endowing Unified Image Generation with Intrinsic 3D Geometric Awareness D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples

Reference 20

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arxiv_id, observed 2026-05-12T08:46:26.784659Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-07T13:45:53.346402Z digest=sha256:bb558a2e1beaa4c521b081d20fefff7a58196d94fbe8ce080e18d6b928b3de17

Observation ef9aca14-7908-42ed-aaf8-dbb5884d17c5 · inbound

Towards Anatomically Plausible Human Image Generation via Synthetic Localized Preferences cites this paper.

Towards Anatomically Plausible Human Image Generation via Synthetic Localized Preferences D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples

Reference 9

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arxiv_id, observed 2026-06-30T00:14:04.764679Z

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source=pdf_text observed=2026-06-29T22:55:02.514189Z digest=sha256:17a7d38c5f27fee3bc13275a56127fd86762859ad68b079136e1b183bf3ac527