Paper Citation Record · LEDGER

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps

As of 22 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 2 inbound Pith citation observations for arXiv:2411.15236.

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

pith.paper-citation-record.v1
2411.15236 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:08:54.669287Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:45:53.297589Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T15:58:37.263440Z

Reference resolution

41 of 41 outbound references displayed

verified exact2
verified fuzzy11
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 860654df-32b1-470a-a7be-77ce9b923569 · outbound

This paper cites A-star: Test-time attention segregation and retention for text-to-image synthesis.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps A-star: Test-time attention segregation and retention for text-to-image synthesis

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:08:56.587696Z

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Observation 00464a84-ec3f-481f-9cba-3fb45053ed2d · outbound

This paper cites AlignIT: Enhancing Prompt Alignment in Customization of Text-to-Image Models.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps AlignIT: Enhancing Prompt Alignment in Customization of Text-to-Image Models

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:08:55.655150Z

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Observation f3a26ef5-c45c-459a-a889-f967ed3a35fd · outbound

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

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models

Reference 3

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Observation 86b56c1a-ed34-4e6f-a549-f9508e3e5072 · outbound

This paper cites PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Reference 4

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Observation c1746190-6bc2-4565-81e2-59ffb069b285 · outbound

This paper cites PixArt-\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps PixArt-\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T15:08:53.596085Z

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Observation 338f1936-24c5-4ed3-94a4-fa7ee20f7b0b · outbound

This paper cites Dall-eval: Probing the reasoning skills and social biases of text-to- image generation models.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Dall-eval: Probing the reasoning skills and social biases of text-to- image generation models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:08:56.459247Z

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Observation a614f833-0c21-4618-8e76-d663731cda2e · outbound

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

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 7

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Observation 38e3d833-4cdc-45bd-8fd6-117feeac26d1 · outbound

This paper cites Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis

Reference 8

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Source-reported events for the cited work

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Observation 735bb36e-7c43-4f19-bb83-85f56785282f · outbound

This paper cites When Attention Sink Emerges in Language Models: An Empirical View.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps When Attention Sink Emerges in Language Models: An Empirical View

Reference 9

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Source-reported events for the cited work

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Observation 9f994c93-3122-47c8-bd26-2ca7ec81dc13 · outbound

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

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Prompt-to-Prompt Image Editing with Cross Attention Control

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T15:08:53.768421Z

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Observation e0a854e5-19ca-4449-8159-8bf6b8d2fed6 · outbound

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

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps spacy 2: Natural lan- guage understanding with bloom embeddings, convolutional neural networks and incremental parsing

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:08:56.362672Z

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Observation ce474741-af47-465b-bc87-cbc72ca61bd7 · outbound

This paper cites ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment

Reference 12

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Observation 7ffb49e3-94ef-49ad-a930-5195b5240431 · outbound

This paper cites Tifa: Accu- rate and interpretable text-to-image faithfulness evaluation with question answering.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Tifa: Accu- rate and interpretable text-to-image faithfulness evaluation with question answering

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:08:56.288975Z

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Observation 4139fcd9-6cf9-4359-9bca-762418452722 · outbound

This paper cites MC$^2$: Multi-concept Guidance for Customized Multi-concept Generation.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps MC$^2$: Multi-concept Guidance for Customized Multi-concept Generation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T15:08:53.854867Z

Source-reported events for the cited work

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Observation 166d9dcb-4ec2-453d-b425-f61bd04fea16 · outbound

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

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Dense text-to-image generation with attention modulation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T15:08:53.879934Z

Source-reported events for the cited work

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Observation 9be8d80a-c345-45a0-a1d6-eb7b3402a058 · outbound

This paper cites mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T15:08:53.954810Z

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Observation 978e7c52-b3e2-417e-9378-8a35852e42b8 · outbound

This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 17

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Observation cd0d00d4-ea19-4ec4-9c31-f439499692a8 · outbound

This paper cites Microsoft coco: Common objects in context.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Microsoft coco: Common objects in context

Reference 18

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Observation 37b1587b-5b1e-4954-a3ad-5feb3b425a7a · outbound

This paper cites Improving Text-to-Image Consistency via Automatic Prompt Optimization.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Improving Text-to-Image Consistency via Automatic Prompt Optimization

Reference 19

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Observation c62ebd91-f847-4e46-a36a-22593e22af9e · outbound

This paper cites Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models!.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models!

Reference 20

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Observation 22456177-2924-4e5a-80e3-d21454ed916a · outbound

This paper cites Conform: Contrast is all you need for high- fidelity text-to-image diffusion models.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Conform: Contrast is all you need for high- fidelity text-to-image diffusion models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:08:56.127872Z

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Observation 70120ce3-9e5c-4932-97f2-7d689dec5a89 · outbound

This paper cites Openai gpt-3 api [gpt-3.5-turbo], 2024.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Openai gpt-3 api [gpt-3.5-turbo], 2024

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:08:56.003417Z

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Observation 1b51419f-9f9c-4f71-bb8d-f1216a926d55 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 23

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Observation f7c116de-be8d-4031-88e7-7e733ee9434a · outbound

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

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Not All Noises Are Created Equally:Diffusion Noise Selection and Optimization

Reference 24

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Observation 1ade333e-63e3-43c6-9714-1cd9299d83c7 · outbound

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

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Learning transferable visual models from natural language supervi- sion

Reference 25

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Observation d350a184-03ee-4255-a4d6-34504c349c54 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 26

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Observation 0b906bca-8342-4e94-a1ca-9173e430b137 · outbound

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

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 27

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Observation 11e5687e-4de8-4665-8bde-81fe11a06ab5 · outbound

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

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Linguistic bind- ing in diffusion models: Enhancing attribute correspondence through attention map alignment

Reference 28

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Observation b3976570-40f6-4017-8709-f07fdd194195 · outbound

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

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps High-resolution image synthesis with latent diffusion models

Reference 29

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Observation e67aa0c6-9e1a-4956-a64a-a3190cd26d30 · outbound

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

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Photorealistic text-to-image diffusion models with deep language understanding

Reference 30

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Observation bfd31bc5-6cb1-49fd-be3d-344a8282fc4d · outbound

This paper cites Rethinking the spatial inconsistency in classifier- free diffusion guidance.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Rethinking the spatial inconsistency in classifier- free diffusion guidance

Reference 31

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Observation fd48f046-aa25-41ac-bf2b-ac475226fc33 · outbound

This paper cites Massive Activations in Large Language Models.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Massive Activations in Large Language Models

Reference 32

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Observation f95914e2-e112-4fba-9254-4cc7a3ab33f7 · outbound

This paper cites Attention is all you need.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Attention is all you need

Reference 33

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Observation 61e611f5-978f-42c8-af57-4d3effba7709 · outbound

This paper cites TokenCompose: Text-to-Image Diffusion with Token-level Supervision.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps TokenCompose: Text-to-Image Diffusion with Token-level Supervision

Reference 34

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Observation 7ea65f26-5a4c-4102-a902-2ab30b770d93 · outbound

This paper cites Efficient Streaming Language Models with Attention Sinks.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Efficient Streaming Language Models with Attention Sinks

Reference 35

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Observation 68feadc4-3997-4a2a-89c4-757edabb1c2d · outbound

This paper cites Dynamic prompt learning: Addressing cross- attention leakage for text-based image editing.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Dynamic prompt learning: Addressing cross- attention leakage for text-based image editing

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:08:55.688429Z

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Observation f24ac3fb-769c-4092-b47d-a0ea293bd3a1 · outbound

This paper cites Towards Understanding the Working Mechanism of Text-to-Image Diffusion Model.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Towards Understanding the Working Mechanism of Text-to-Image Diffusion Model

Reference 37

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Observation 5a92f757-1784-42dd-8ef2-10fe272ceac7 · outbound

This paper cites Uncovering the Text Embedding in Text-to-Image Diffusion Models.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Uncovering the Text Embedding in Text-to-Image Diffusion Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-12T15:08:54.605688Z

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Observation 988c4283-3a19-43a4-aa41-6e97888d8372 · outbound

This paper cites Scaling Autoregressive Models for Content-Rich Text-to-Image Generation.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-12T15:08:54.617831Z

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Observation 1c5deeee-8208-4051-8fa1-6835cb49f8d4 · outbound

This paper cites When and why vision-language models behave like bags-of-words, and what to do about it?.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps When and why vision-language models behave like bags-of-words, and what to do about it?

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-12T15:08:54.627473Z

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Observation 2d139588-cb0e-453b-8fe0-eca0c44a2ca8 · outbound

This paper cites Enhancing Semantic Fidelity in Text-to-Image Synthesis: Attention Regulation in Diffusion Models.

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps Enhancing Semantic Fidelity in Text-to-Image Synthesis: Attention Regulation in Diffusion Models

Reference 41

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Pith citing papers

Observation fdbcc39c-bc22-4799-9ead-0a82d02e5fc4 · inbound

Detail++: Training-Free Detail Enhancer for T2I Diffusion Models cites this paper.

Detail++: Training-Free Detail Enhancer for T2I Diffusion Models Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T14:45:53.297589Z

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Observation 46a62a12-f3ce-40f9-a558-0886d5c4ec17 · inbound

DetailAnywhere: Fashion Detail Generation via Cross-Modal Feature Alignment Distillation cites this paper.

DetailAnywhere: Fashion Detail Generation via Cross-Modal Feature Alignment Distillation Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-07-03T15:58:37.265067Z

Source-reported events for the cited work

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