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Paper Citation Record · LEDGER

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization

As of 14 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2412.09169.

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

pith.paper-citation-record.v1
2412.09169 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

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measured 52 of 52 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

52 of 52 outbound references displayed

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  • verified fuzzy17
  • unresolved33
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External citation measurements

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Outbound references

Observation 6abf3ce1-0fc1-4585-9e66-cade3f1ed61b · outbound

This paper cites GPT-4 Technical Report.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization GPT-4 Technical Report

Reference 1

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Observation 05091123-4b78-4585-805b-61c7167667a1 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Emerg- ing properties in self-supervised vision transformers

Reference 2

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Observation f55e3cce-729b-4734-a5aa-c94d0d73a681 · outbound

This paper cites Muse: Text- to-image generation via masked generative transformers.Pro- ceedings of the 40th International Conference on Machine Learning, 202, 2023.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Muse: Text- to-image generation via masked generative transformers.Pro- ceedings of the 40th International Conference on Machine Learning, 202, 2023

Reference 3

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Observation c39e965c-c7e6-467d-82a6-427ecabe59d6 · outbound

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

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 4

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Observation dd103b17-723e-4290-a97a-a3dab91d6054 · outbound

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

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 5

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Observation 3e2065f8-880a-4439-b594-0c319622c675 · outbound

This paper cites Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings

Reference 6

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Observation 2210236e-35e6-4444-b19c-cc8f9f8c091c · outbound

This paper cites Diffusion with offset noise.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Diffusion with offset noise

Reference 7

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 9dfc8f43-1fdc-4076-b008-308d32662fc9 · outbound

This paper cites Svdiff: Compact parame- ter space for diffusion fine-tuning.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Svdiff: Compact parame- ter space for diffusion fine-tuning

Reference 8

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Observation 63c368b0-50b7-493a-9960-4bb07f209e24 · outbound

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

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Prompt-to-Prompt Image Editing with Cross Attention Control

Reference 9

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Observation 49e03aab-9ede-4888-8791-208be8bba359 · outbound

This paper cites Style aligned image generation via shared at- tention.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Style aligned image generation via shared at- tention

Reference 10

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Observation 72e7401d-0db1-4cc9-9367-ee023af79602 · outbound

This paper cites Classifier-Free Diffusion Guidance.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Classifier-Free Diffusion Guidance

Reference 11

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Observation 51f3ba18-c26f-4e0a-b0a9-2bfcb89acbf0 · outbound

This paper cites Denoising dif- fusion probabilistic models.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Denoising dif- fusion probabilistic models

Reference 12

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Observation 1cb55a0e-eaf0-4361-bf05-e74ea142e237 · outbound

This paper cites Parameter-efficient transfer learning for nlp.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Parameter-efficient transfer learning for nlp

Reference 13

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Observation 8d681222-4bd6-46c6-8701-b4d1093c7ad2 · outbound

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

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization LoRA: Low-Rank Adaptation of Large Language Models

Reference 14

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Observation 4f866657-1142-4578-acc2-8e8496cf47c0 · outbound

This paper cites Visual Style Prompting with Swapping Self-Attention.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Visual Style Prompting with Swapping Self-Attention

Reference 15

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Observation 777d0831-162a-49f8-9146-c6f3a1c151cb · outbound

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

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Pick-a-pic: An open dataset of user preferences for text-to-image generation

Reference 16

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Observation bf6c9dc0-fec1-489c-95e2-59d9e3f4674e · outbound

This paper cites The singular value decompo- sition: Its computation and some applications.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization The singular value decompo- sition: Its computation and some applications

Reference 17

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Observation d2dd89bc-c3dd-4254-b222-37e289ec3394 · outbound

This paper cites Multi-concept customization of text- to-image diffusion.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Multi-concept customization of text- to-image diffusion

Reference 18

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Observation 8b1c45a7-4a7f-4eb8-b6c2-dff7110c0b72 · outbound

This paper cites Direct Consistency Optimization for Robust Customization of Text-to-Image Diffusion Models.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Direct Consistency Optimization for Robust Customization of Text-to-Image Diffusion Models

Reference 19

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Observation 7e8f2be3-ff63-4f17-8430-4ef8ba0dae76 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 20

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Observation 00959386-1a3f-4f0d-b0d1-3e0f06d1463e · outbound

This paper cites Get What You Want, Not What You Don't: Image Content Suppression for Text-to-Image Diffusion Models.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Get What You Want, Not What You Don't: Image Content Suppression for Text-to-Image Diffusion Models

Reference 21

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Observation c05a419f-5f39-483f-9232-156f1ada8828 · outbound

This paper cites P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 22

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Observation 4fff388b-3814-4a2f-9fd3-303361462914 · outbound

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DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Gpt understands, too

Reference 23

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Observation d55225e2-64ca-498f-9808-568dc03e7afc · outbound

This paper cites Locating and editing factual associations in gpt.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Locating and editing factual associations in gpt

Reference 24

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Observation 74bdf3c0-7d46-4625-8175-69924779c337 · outbound

This paper cites Negative-prompt Inversion: Fast Image Inversion for Editing with Text-guided Diffusion Models.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Negative-prompt Inversion: Fast Image Inversion for Editing with Text-guided Diffusion Models

Reference 25

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Observation e1c54c64-d54c-4141-a1a4-a31dd0a10bdf · outbound

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DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Null-text inversion for editing real im- ages using guided diffusion models

Reference 26

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Source-reported events for the cited work

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This paper cites DINOv2: Learning Robust Visual Features without Supervision.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization DINOv2: Learning Robust Visual Features without Supervision

Reference 27

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Observation c4578e8d-d70f-450d-8483-200b4edb2aef · outbound

This paper cites MoA: Mixture-of-Attention for Subject-Context Disentanglement in Personalized Image Generation.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization MoA: Mixture-of-Attention for Subject-Context Disentanglement in Personalized Image Generation

Reference 28

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DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization aMUSEd: An Open MUSE Reproduction

Reference 29

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Observation fdc1c4d8-e749-43d0-8166-55a4d1f49aa0 · outbound

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DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 30

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DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Feature projection for im- proved text classification

Reference 31

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Source-reported events for the cited work

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Observation 0b38a6b1-12ff-4cf4-88c5-cb113e756f2e · outbound

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

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Learning transferable visual models from natural language supervi- sion

Reference 32

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Observation f5689a00-1975-40be-b09a-3d64e1f806d0 · outbound

This paper cites Direct prefer- ence optimization: Your language model is secretly a reward model.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Direct prefer- ence optimization: Your language model is secretly a reward model

Reference 33

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Source-reported events for the cited work

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DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Zero-shot text-to-image generation

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization High-resolution image synthesis with latent diffusion models

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 66e33ff0-8ee6-4f80-8795-7ccde0e921e5 · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization U- net: Convolutional networks for biomedical image segmen- tation

Reference 36

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Observation de6fd442-b8ea-455f-91e2-a160ca798d38 · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven gen- eration.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Dreambooth: Fine tuning text-to-image diffusion models for subject-driven gen- eration

Reference 37

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Observation e572aa89-8684-4c05-b7f8-b4adffcb920b · outbound

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

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Photorealistic text-to-image diffusion models with deep lan- guage understanding

Reference 38

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 4f8af555-6a95-4022-92f4-e9ed39ebeba1 · outbound

This paper cites Zur theorie der linearen und nichtlinearen in- tegralgleichungen.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Zur theorie der linearen und nichtlinearen in- tegralgleichungen

Reference 39

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 0750f039-16f0-4555-b4ea-fbc9c4d2584b · outbound

This paper cites Ziplora: Any subject in any style by effectively merging loras.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Ziplora: Any subject in any style by effectively merging loras

Reference 40

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation b8fdf0a2-be5f-44e5-baae-3df02cd10ac9 · outbound

This paper cites Ziplora-pytorch.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Ziplora-pytorch

Reference 41

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 718beb27-dfed-4846-a386-29fc6264763a · outbound

This paper cites StyleDrop: Text-to-Image Generation in Any Style.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization StyleDrop: Text-to-Image Generation in Any Style

Reference 42

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Source-reported events for the cited work

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Observation 7969f504-7463-4116-882a-6f7a5f3ceeab · outbound

This paper cites Measuring Style Similarity in Diffusion Models.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Measuring Style Similarity in Diffusion Models

Reference 43

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Source-reported events for the cited work

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Observation dae98b86-35cd-4732-9eb8-1997f5aed2df · outbound

This paper cites Denoising Diffusion Implicit Models.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Denoising Diffusion Implicit Models

Reference 44

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 59c0369f-6034-4c70-ad78-6993721ffc7d · outbound

This paper cites Key-locked rank one editing for text-to-image personaliza- tion.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Key-locked rank one editing for text-to-image personaliza- tion

Reference 45

Resolution
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Source-reported events for the cited work

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Observation 9a7f870d-33c9-48d5-bd0b-4518e9775c45 · outbound

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

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 46

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Source-reported events for the cited work

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Observation da8f07e6-340f-41e9-bd3a-caec96071194 · outbound

This paper cites FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention

Reference 47

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unresolved
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Source-reported events for the cited work

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Observation 07efa52e-cdb8-43d5-b20a-d8e52f008886 · outbound

This paper cites Break-for-Make: Modular Low-Rank Adaptations for Composable Content-Style Customization.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Break-for-Make: Modular Low-Rank Adaptations for Composable Content-Style Customization

Reference 48

Resolution
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no resolver link, observed 2026-08-11T17:18:42.944369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5468fdb8-a940-44f4-a6d3-3302d7243a0e · outbound

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

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-11T17:18:42.948743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:18:42.948743Z digest=sha256:620b7809cbf00107da85c103eeed62b2ca5001223322fbd2f4df42844cf2f934

Observation 70a9245d-d3b5-4ca8-aba0-be95365187da · outbound

This paper cites Infusion: Preventing Customized Text-to-Image Diffusion from Overfitting.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Infusion: Preventing Customized Text-to-Image Diffusion from Overfitting

Reference 50

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 8af422e9-968d-43a0-ae02-91bf4f9dfb37 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Adding conditional control to text-to-image diffusion models

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T17:18:43.375303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T17:18:42.956578Z digest=sha256:6b01a68f61976b16ee18f02382a8de5a9fa13d97300396c96a1fb2c3291aa348

Observation a656a0b0-e6e6-4f57-abce-c1c705dc8443 · outbound

This paper cites Magnet: We Never Know How Text-to-Image Diffusion Models Work, Until We Learn How Vision-Language Models Function.

DECOR:Decomposition and Projection of Text Embeddings for Text-to-Image Customization Magnet: We Never Know How Text-to-Image Diffusion Models Work, Until We Learn How Vision-Language Models Function

Reference 52

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers

No inbound Pith citation observations are available.