Paper Citation Record · LEDGER

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models

As of 11 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 6 inbound Pith citation observations for arXiv:2502.01639.

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

pith.paper-citation-record.v1
2502.01639 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:48:16.035237Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

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

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T10:47:12.543726Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T15:18:32.876727Z

Reference resolution

52 of 52 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ad44ff71-fb66-43b4-bab7-5a4e822199e4 · outbound

This paper cites Im- age2stylegan: How to embed images into the stylegan latent space? In Proceedings of the IEEE/CVF inter- national conference on computer vision, pages 4432– 4441, 2019.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Im- age2stylegan: How to embed images into the stylegan latent space? In Proceedings of the IEEE/CVF inter- national conference on computer vision, pages 4432– 4441, 2019

Reference 1

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Observation ce0d3944-28f7-429a-870c-d44cf044ca7f · outbound

This paper cites Styleflow: Attribute-conditioned explo- ration of stylegan-generated images using conditional continuous normalizing flows.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Styleflow: Attribute-conditioned explo- ration of stylegan-generated images using conditional continuous normalizing flows

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.903736Z

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Observation faddb737-686e-4dbb-881c-f0f30de49277 · outbound

This paper cites Introducing claude 3.5 sonnet, 2024.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Introducing claude 3.5 sonnet, 2024

Reference 3

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Observation 40ee7794-abba-4a37-8f3e-e92b3b5d606e · outbound

This paper cites Announcing state-of-the-art flux.1 dev and schnell models, 2024.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Announcing state-of-the-art flux.1 dev and schnell models, 2024

Reference 4

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Observation b5f413a7-415e-4990-b629-daab3d2d53dd · outbound

This paper cites Ledits++: Limitless im- age editing using text-to-image models.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Ledits++: Limitless im- age editing using text-to-image models

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.858109Z

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Observation e42d9cf0-1fd9-4de4-9398-ed52f94a2d08 · outbound

This paper cites Large scale gan training for high fidelity natural image synthesis.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Large scale gan training for high fidelity natural image synthesis

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.841317Z

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Observation e308a0d0-ad3f-4524-ac27-14ea7835c338 · outbound

This paper cites Instructpix2pix: Learning to follow image edit- ing instructions.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Instructpix2pix: Learning to follow image edit- ing instructions

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.825772Z

Source-reported events for the cited work

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Observation 4d2e7c94-3433-4a22-8d8e-d1816c15aaaf · outbound

This paper cites Training-free Regional Prompting for Diffusion Transformers.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Training-free Regional Prompting for Diffusion Transformers

Reference 8

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Source-reported events for the cited work

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Observation fa9a6217-feca-4b13-bfc7-05e140de3b3e · outbound

This paper cites Noiseclr: A con- trastive learning approach for unsupervised discovery of interpretable directions in diffusion models.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Noiseclr: A con- trastive learning approach for unsupervised discovery of interpretable directions in diffusion models

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.810117Z

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Observation 84f8f5de-2749-405b-8cb7-5a9c55ef692d · outbound

This paper cites Turboedit: Text-based image editing using few-step diffusion models, 2024.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Turboedit: Text-based image editing using few-step diffusion models, 2024

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T14:48:15.832113Z

Source-reported events for the cited work

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Observation 519271ea-0d51-419e-ac59-df46444d78ee · outbound

This paper cites Diffusion models beat gans on image synthesis.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Diffusion models beat gans on image synthesis

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.783346Z

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Observation 9941adf8-5f26-4116-8425-87f359dd85d1 · outbound

This paper cites Interpreting the Weight Space of Customized Diffusion Models.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Interpreting the Weight Space of Customized Diffusion Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T14:48:15.841402Z

Source-reported events for the cited work

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Observation 21c09cfa-581a-40ba-8b4d-5fe02a910e02 · outbound

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

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Scaling rectified flow transformers for high-resolution image synthesis

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.767447Z

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Observation 4d14e808-d7e2-4243-99b3-1bed27729e57 · outbound

This paper cites Dreamsim: Learning new dimensions of hu- man visual similarity using synthetic data.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Dreamsim: Learning new dimensions of hu- man visual similarity using synthetic data

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.752425Z

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Observation 0ef45419-85ae-497b-bd79-a56ee675a532 · outbound

This paper cites Stylegan-nada: Clip-guided domain adaptation of im- age generators.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Stylegan-nada: Clip-guided domain adaptation of im- age generators

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.736597Z

Source-reported events for the cited work

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Observation 154b2911-b21f-44db-9e0c-9f9dedad6105 · outbound

This paper cites Concept sliders: Lora adaptors for precise control in diffusion models.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Concept sliders: Lora adaptors for precise control in diffusion models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.720448Z

Source-reported events for the cited work

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Observation b96fa78a-d3d6-4344-a493-619dd3645d43 · outbound

This paper cites Generative adversar- ial nets.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Generative adversar- ial nets

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.704023Z

Source-reported events for the cited work

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Observation ac88cbfe-92c3-44e6-bbb7-2bee67972e7d · outbound

This paper cites Towards a framework for human-ai interaction patterns in co-creative gan appli- cations.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Towards a framework for human-ai interaction patterns in co-creative gan appli- cations

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.688348Z

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Observation 55dd9fcf-0376-4d3e-8f40-9c88ae945dbc · outbound

This paper cites Ganspace: Discovering inter- pretable gan controls.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Ganspace: Discovering inter- pretable gan controls

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.673116Z

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Observation 1ff04984-05e8-4eee-b9e1-845b2aacddff · outbound

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

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Prompt-to-Prompt Image Editing with Cross Attention Control

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T14:48:15.878559Z

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Observation b5c77873-12cb-4be8-bb60-24c8c6914b0c · outbound

This paper cites Style aligned image generation via 9 shared attention.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Style aligned image generation via 9 shared attention

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.654295Z

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Observation 2b59a164-cdd1-4f7a-b359-2d427d5a683a · outbound

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

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 22

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Source-reported events for the cited work

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Observation f4e3085f-88fa-40d1-9bfb-416ccefa0767 · outbound

This paper cites On modeling human-computer co- creativity.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models On modeling human-computer co- creativity

Reference 23

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Observation a798ba2c-23e8-4302-8ae8-c0250b8aad31 · outbound

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

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 24

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Observation e52cf6bc-fee3-4cd2-bb7c-f588c499998f · outbound

This paper cites Image synthesis style studies, 2022.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Image synthesis style studies, 2022

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.621975Z

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Observation 0a971e42-e83c-40a7-b17f-a2dc92d48675 · outbound

This paper cites Improving image generation with better captions.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Improving image generation with better captions

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.604877Z

Source-reported events for the cited work

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Observation d5091c7f-0bf2-4aa4-b48e-0cdbc8c1236d · outbound

This paper cites Scaling up gans for text-to-image synthesis.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Scaling up gans for text-to-image synthesis

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.588006Z

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Observation 626bfd10-cf2c-4efe-96d3-f16c888345bd · outbound

This paper cites A style- based generator architecture for generative adversar- ial networks.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models A style- based generator architecture for generative adversar- ial networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.568052Z

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Observation c2c81de7-a78a-413d-9f6d-00bc5ddb81a8 · outbound

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

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Multi-concept cus- tomization of text-to-image diffusion

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.552138Z

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Observation 2228cad8-e253-4eb3-82c7-b68a7dfa809e · outbound

This paper cites Photomaker: Cus- tomizing realistic human photos via stacked id embed- ding.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Photomaker: Cus- tomizing realistic human photos via stacked id embed- ding

Reference 30

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Observation 3264bb9b-bc96-425b-b18b-8e1d7ce13f78 · outbound

This paper cites Unsupervised compo- sitional concepts discovery with text-to-image genera- tive models.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Unsupervised compo- sitional concepts discovery with text-to-image genera- tive models

Reference 31

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Observation 53b82a3b-55fb-409b-b37f-003c02cb988b · outbound

This paper cites Zero- shot image-to-image translation.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Zero- shot image-to-image translation

Reference 32

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Observation e77d3eb6-a41a-4ba4-87d2-57a65a9862a0 · outbound

This paper cites Styleclip: Text-driven manipulation of stylegan imagery.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Styleclip: Text-driven manipulation of stylegan imagery

Reference 33

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Observation 746712f6-857d-4b3d-a8bb-8f218a301290 · outbound

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

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 34

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Observation 9871517b-9483-4acb-a460-26f41a5a865c · outbound

This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 35

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Observation 5cd292a6-18e9-44dc-b5b0-f91d645f9413 · outbound

This paper cites Learning transferable visual models from natural language supervision.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Learning transferable visual models from natural language supervision

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.453519Z

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Observation d08f788c-62f8-4bdb-9dc3-6b9c69162fe9 · outbound

This paper cites Stable diffusion 2.0 release, 2022.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Stable diffusion 2.0 release, 2022

Reference 37

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Observation 07351f98-5961-4881-9c79-7928a2e84e5c · outbound

This paper cites Stable diffusion v1-4 model card, 2022.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Stable diffusion v1-4 model card, 2022

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.418920Z

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Observation e739000f-0168-4108-9014-5f670fd5e598 · outbound

This paper cites Stable diffusion v2 model card, 2022.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Stable diffusion v2 model card, 2022

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.403426Z

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Observation 8e63244c-929a-496c-9fd4-62b0ee7df00e · outbound

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

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models High- resolution image synthesis with latent diffusion mod- els

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.387167Z

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Observation 3b34bd37-1cc8-49ca-868a-5cb96d977ffa · outbound

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

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 41

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Observation 540f27ec-8df2-4740-a13b-543e733e1a62 · outbound

This paper cites Cloneofsimo/lora: Using low-rank adapta- tion to quickly fine-tune diffusion models.s.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Cloneofsimo/lora: Using low-rank adapta- tion to quickly fine-tune diffusion models.s

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.358516Z

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Observation 43b0e881-10d0-4c23-b225-229b6baf9655 · outbound

This paper cites Adversarial diffusion distillation.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Adversarial diffusion distillation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.343225Z

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Observation 4398faa2-49f4-40ff-933e-0841f8e1bf06 · outbound

This paper cites Facenet: A unified embedding for face recog- nition and clustering.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Facenet: A unified embedding for face recog- nition and clustering

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.327188Z

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Observation 6e4ec73e-fd28-49e5-a13d-ab614cd1ab32 · outbound

This paper cites Interfacegan: Interpreting the disentangled face representation learned by gans.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Interfacegan: Interpreting the disentangled face representation learned by gans

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.312176Z

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Observation 46c05361-41f2-4657-94de-df49d76cb495 · outbound

This paper cites Instantbooth: Personalized text-to-image generation without test-time finetuning.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Instantbooth: Personalized text-to-image generation without test-time finetuning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.293593Z

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Observation ccef0954-00db-4c42-aff7-7ffc31fd38b3 · outbound

This paper cites Stylespace analysis: Disentangled controls for style- gan image generation.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Stylespace analysis: Disentangled controls for style- gan image generation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.277108Z

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Observation 5a4280b8-100d-4c58-a87c-f7e33d75105c · outbound

This paper cites Turboedit: Instant text-based image editing.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Turboedit: Instant text-based image editing

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.260177Z

Source-reported events for the cited work

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Observation 9bef35cc-240a-40b9-a0b1-9d9fc5784848 · outbound

This paper cites Inversion-free image editing with natu- ral language.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Inversion-free image editing with natu- ral language

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.241656Z

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Observation 580fe796-5fd3-4700-8ffb-00eed1295f1d · outbound

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

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

Reference 50

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7cfafa4e-f20b-4273-ae69-8b5e7559c176 · outbound

This paper cites Improved Distribution Matching Distillation for Fast Image Synthesis.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models Improved Distribution Matching Distillation for Fast Image Synthesis

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-09T14:48:16.028108Z

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Observation e9552ccb-f88f-45bc-8eea-6bf7c5b0b787 · outbound

This paper cites person” show higher varia- tion in CLIP space compared to rarer concepts like “waterfalls.

SliderSpace: Decomposing the Visual Capabilities of Diffusion Models person” show higher varia- tion in CLIP space compared to rarer concepts like “waterfalls

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:48:16.223970Z

Source-reported events for the cited work

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Pith citing papers

Observation fac0574d-fe0b-49c5-9c08-8fb6de32b46f · inbound

Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing cites this paper.

Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing SliderSpace: Decomposing the Visual Capabilities of Diffusion Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T10:47:12.543726Z

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Observation 295c4180-7685-4c88-97c8-0479dadc5a20 · inbound

LatentGandr: Visual Exploration of Generative AI Latent Space via Local Embeddings cites this paper.

LatentGandr: Visual Exploration of Generative AI Latent Space via Local Embeddings SliderSpace: Decomposing the Visual Capabilities of Diffusion Models

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:41:08.615438Z

Source-reported events for the cited work

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

Observation 7c4e459c-cf48-44a7-b40d-32c98eb67b21 · inbound

STRIDE: Training-Free Diversity Guidance via PCA-Directed Feature Perturbation in Single-Step Diffusion Models cites this paper.

STRIDE: Training-Free Diversity Guidance via PCA-Directed Feature Perturbation in Single-Step Diffusion Models SliderSpace: Decomposing the Visual Capabilities of Diffusion Models

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:57:09.278330Z

Source-reported events for the cited work

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

Observation 80d65b35-2dea-4183-ae99-083bd2bd3b99 · inbound

Show Me Examples: Inferring Visual Concepts from Image Sets cites this paper.

Show Me Examples: Inferring Visual Concepts from Image Sets SliderSpace: Decomposing the Visual Capabilities of Diffusion Models

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T15:18:32.878403Z

Source-reported events for the cited work

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

Observation 6242054c-ae2e-4dc6-a363-fc89c5f671c4 · inbound

Show Me Examples: Inferring Visual Concepts from Image Sets cites this paper.

Show Me Examples: Inferring Visual Concepts from Image Sets SliderSpace: Decomposing the Visual Capabilities of Diffusion Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-12T08:05:49.527175Z

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Observation 9330b7be-03d1-4733-a074-996158755f62 · inbound

Show Me Examples: Inferring Visual Concepts from Image Sets cites this paper.

Show Me Examples: Inferring Visual Concepts from Image Sets SliderSpace: Decomposing the Visual Capabilities of Diffusion Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T09:02:24.462657Z

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