Paper Citation Record · LEDGER

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback

As of 19 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2412.00122.

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

pith.paper-citation-record.v1
2412.00122 v1

Coverage vector

measured 46 of 46 reference resolution

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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External citation measurements

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Outbound references

Observation ccd8d2f9-66a1-41e0-84bf-2044df0d7590 · outbound

This paper cites an unresolved cited work.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Unresolved cited work

Reference 1

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This paper cites Universal guidance for diffusion models.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Universal guidance for diffusion models

Reference 2

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This paper cites Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models

Reference 3

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This paper cites Training-free layout control with cross-attention guidance.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Training-free layout control with cross-attention guidance

Reference 4

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Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Unresolved cited work

Reference 5

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Observation bdb8bece-cc94-4320-879a-92c2d2fd5404 · outbound

This paper cites RAFT: reward ranked finetuning for generative foundation model alignment.Trans.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback RAFT: reward ranked finetuning for generative foundation model alignment.Trans

Reference 6

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This paper cites Optimizing DDPM sampling with shortcut fine-tuning.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Optimizing DDPM sampling with shortcut fine-tuning

Reference 7

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Observation 96b34492-18c9-42d2-9df6-2f116b14dd29 · outbound

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

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models

Reference 8

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This paper cites You only look at one sequence: Rethinking transformer in vision through object detection.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback You only look at one sequence: Rethinking transformer in vision through object detection

Reference 9

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This paper cites Akula, Pradyumna Narayana, Sugato Basu, Xin Eric Wang, and William Yang Wang.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Akula, Pradyumna Narayana, Sugato Basu, Xin Eric Wang, and William Yang Wang

Reference 10

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Observation 3f705913-c013-44f7-99e3-65d965c5f320 · outbound

This paper cites Benchmarking Spatial Relationships in Text-to-Image Generation.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Benchmarking Spatial Relationships in Text-to-Image Generation

Reference 11

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Observation 86f3d7e3-0639-481e-bc11-2dad4519df83 · outbound

This paper cites Optimizing prompts for text-to-image generation.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Optimizing prompts for text-to-image generation

Reference 12

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

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Clipscore: A reference-free evaluation met- ric for image captioning

Reference 13

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This paper cites spaCy 2: Natural lan- guage understanding with Bloom embeddings, convolutional neural networks and incremental parsing.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback spaCy 2: Natural lan- guage understanding with Bloom embeddings, convolutional neural networks and incremental parsing

Reference 14

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Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Unresolved cited work

Reference 15

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Observation 0324b0f6-8216-4330-977e-9353e5679c5f · outbound

This paper cites T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation

Reference 16

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

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching

Reference 17

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This paper cites RealignDiff: Boosting Text-to-Image Diffusion Model with Coarse-to-fine Semantic Re-alignment.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback RealignDiff: Boosting Text-to-Image Diffusion Model with Coarse-to-fine Semantic Re-alignment

Reference 18

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This paper cites Dif- fusionclip: Text-guided diffusion models for robust image manipulation.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Dif- fusionclip: Text-guided diffusion models for robust image manipulation

Reference 19

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Observation 7a43d818-a623-4946-8db7-fab4cf806b67 · outbound

This paper cites Dense text-to-image generation with attention modulation.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Dense text-to-image generation with attention modulation

Reference 20

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Observation eb93f159-7ab7-4966-a76a-11dbf5483723 · outbound

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

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Pick-a-pic: An open dataset of user preferences for text-to-image generation

Reference 21

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Observation 7d9bd878-39f9-437a-bef3-7b7898122a12 · outbound

This paper cites Maskgan: Towards diverse and interactive facial image ma- nipulation.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Maskgan: Towards diverse and interactive facial image ma- nipulation

Reference 22

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

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Aligning Text-to-Image Models using Human Feedback

Reference 23

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Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Unresolved cited work

Reference 24

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Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Unresolved cited work

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This paper cites GLIGEN: open-set grounded text-to-image generation.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback GLIGEN: open-set grounded text-to-image generation

Reference 26

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This paper cites Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll ´ar, and C.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll ´ar, and C

Reference 27

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This paper cites Training diffusion models towards diverse image generation with reinforcement learning.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Training diffusion models towards diverse image generation with reinforcement learning

Reference 28

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Observation 228aa5e7-8b43-44f5-b293-40d3fe83ecaf · outbound

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

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 29

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This paper cites Linguistic bind- ing in diffusion models: Enhancing attribute correspondence through attention map alignment.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Linguistic bind- ing in diffusion models: Enhancing attribute correspondence through attention map alignment

Reference 30

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Observation 15e5d1eb-4a9b-4910-abb3-7d2d728eeb2c · outbound

This paper cites High-resolution image syn- thesis with latent diffusion models.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback High-resolution image syn- thesis with latent diffusion models

Reference 31

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This paper cites Denton, Seyed Kamyar Seyed Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Denton, Seyed Kamyar Seyed Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J

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This paper cites LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

Reference 33

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Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback LAION-5B: an open large-scale dataset for training next generation image-text models

Reference 34

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Observation 41dcfde7-92a2-4a9a-8669-0810f5c04f82 · outbound

This paper cites DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback

Reference 35

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This paper cites Tokencompose: Text-to-image diffusion with token-level supervision.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Tokencompose: Text-to-image diffusion with token-level supervision

Reference 36

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Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Gonzalez, Boyi Li, and Trevor Darrell

Reference 37

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

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 38

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This paper cites Human preference score: Better aligning text-to- image models with human preference.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Human preference score: Better aligning text-to- image models with human preference

Reference 39

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Observation ba6ce854-926d-4d51-908e-f55e50f9d0b6 · outbound

This paper cites Deep Reward Supervisions for Tuning Text-to-Image Diffusion Models.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Deep Reward Supervisions for Tuning Text-to-Image Diffusion Models

Reference 40

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Observation 424c2e83-e082-40f4-91b7-2831abce7b13 · outbound

This paper cites Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:41:23.175165Z

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Observation e78c2f37-09e6-421b-a157-8015001e4b29 · outbound

This paper cites Imagere- ward: Learning and evaluating human preferences for text- to-image generation.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Imagere- ward: Learning and evaluating human preferences for text- to-image generation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:41:23.156952Z

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Observation 11a18690-e9f9-4842-b39d-5c72898e4bc2 · outbound

This paper cites Mastering text-to-image diffu- sion: Recaptioning, planning, and generating with multi- modal llms.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Mastering text-to-image diffu- sion: Recaptioning, planning, and generating with multi- modal llms

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:41:23.140834Z

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Observation 1356b476-c0f9-4c58-a0af-20a52ca6b00d · outbound

This paper cites A dense reward view on aligning text-to-image diffusion with prefer- ence.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback A dense reward view on aligning text-to-image diffusion with prefer- ence

Reference 44

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Observation 46c769ad-a8ff-4b22-b68b-d5b5b464a9ce · outbound

This paper cites Freedom: Training-free energy-guided condi- tional diffusion model.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Freedom: Training-free energy-guided condi- tional diffusion model

Reference 45

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Observation 3edc5e74-684d-49ba-875f-2e2635808369 · outbound

This paper cites Fixed Category & Incremental Quantity.

Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback Fixed Category & Incremental Quantity

Reference 46

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Pith citing papers

No inbound Pith citation observations are available.