Paper Citation Record · LEDGER

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation

As of 15 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 1 inbound Pith citation observation for arXiv:2501.03490.

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

pith.paper-citation-record.v1
2501.03490 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:56:07.941714Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:20:03.542467Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T15:20:04.115181Z

Reference resolution

61 of 61 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7de209d1-609b-4dea-8356-51dfb5a66030 · outbound

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

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Multi- concept customization of text-to-image diffusion,

Reference 1

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Observation 17f929f1-9a58-4e8c-919c-54d824721f9b · outbound

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

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Dreambooth: Fine tuning text-to-image diffusion models for subject- driven generation,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:07.639905Z

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Observation 4387ae30-3531-4969-b106-557b6c313235 · outbound

This paper cites Paint by example: Exemplar-based image editing with diffusion models,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Paint by example: Exemplar-based image editing with diffusion models,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.834475Z

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Observation c24f6e30-f154-4fa9-815c-80f08f3ac860 · outbound

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

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 4

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Observation b19cc6d9-f0fa-47b9-9a9b-8e490ee4cdcc · outbound

This paper cites Blip-diffusion: Pre-trained subject representa- tion for controllable text-to-image generation and editing,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Blip-diffusion: Pre-trained subject representa- tion for controllable text-to-image generation and editing,

Reference 5

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Observation 02e3a208-db6f-49db-b564-890f63a8da68 · outbound

This paper cites AnyDoor: Zero-shot Object-level Image Customization.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation AnyDoor: Zero-shot Object-level Image Customization

Reference 7

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Observation 507af8f7-d69f-4830-a34f-99ca62ea1037 · outbound

This paper cites Compositional gan: Learning image-conditional binary composition,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Compositional gan: Learning image-conditional binary composition,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.802754Z

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Observation 31f2056f-333a-4559-b1d9-94febbf47efc · outbound

This paper cites Painterly image har- monization using diffusion model,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Painterly image har- monization using diffusion model,

Reference 9

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Observation e9c2d73b-59dc-447a-bd3a-e9a942e89f9f · outbound

This paper cites Learning object placement via dual-path graph completion,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Learning object placement via dual-path graph completion,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.772525Z

Source-reported events for the cited work

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Observation a716b768-b8b4-497f-b3b1-b66efba431bf · outbound

This paper cites Bridging composite and real: towards end-to-end deep image matting,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Bridging composite and real: towards end-to-end deep image matting,

Reference 11

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Observation 14e339c8-fa07-4b9d-95c9-130deb36e274 · outbound

This paper cites Countering Language Drift via Visual Grounding.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Countering Language Drift via Visual Grounding

Reference 12

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Observation 6bbe005a-19ee-4fa3-b5cf-41c9e2f9ebca · outbound

This paper cites Countering language drift with seeded iterated learning,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Countering language drift with seeded iterated learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.747215Z

Source-reported events for the cited work

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Observation 6b7ac6d6-1010-4df4-8417-42e1691559c8 · outbound

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

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Adding conditional control to text-to-image diffusion models,

Reference 14

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Source-reported events for the cited work

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Observation 49a87905-e020-45a4-95c1-eaaaeabf27af · outbound

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

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation High- resolution image synthesis with latent diffusion models,

Reference 15

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Observation 524e131e-d475-4a08-9941-c64bb2cb9132 · outbound

This paper cites Mat: Mask- aware transformer for large hole image inpainting,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Mat: Mask- aware transformer for large hole image inpainting,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.711245Z

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Observation 6a5755f2-b5bb-480f-9df7-0db61c97e652 · outbound

This paper cites Repaint: Inpainting using denoising diffusion probabilistic models,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Repaint: Inpainting using denoising diffusion probabilistic models,

Reference 17

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Source-reported events for the cited work

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Observation 23e6db53-1882-4dd0-8c6b-0ad0e9bc7514 · outbound

This paper cites Imagen editor and editbench: Advancing and evaluating text-guided image inpainting,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Imagen editor and editbench: Advancing and evaluating text-guided image inpainting,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.685209Z

Source-reported events for the cited work

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Observation fb86e0dd-974f-4589-b552-715c91a6dc6b · outbound

This paper cites Layoutdm: Transformer-based diffusion model for layout generation,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Layoutdm: Transformer-based diffusion model for layout generation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.668750Z

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Observation 471c75c9-50e5-4e5d-b815-fddb435862b1 · outbound

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

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Learning transferable visual models from natural language supervision,

Reference 20

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Observation d74b7532-9572-4ece-b580-a854ffbf74b2 · outbound

This paper cites Gligen: Open-set grounded text-to-image generation,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Gligen: Open-set grounded text-to-image generation,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.641753Z

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Observation c8449c7b-b116-465d-aff1-a46eb26e1587 · outbound

This paper cites Microsoft coco: Common objects in context,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Microsoft coco: Common objects in context,

Reference 22

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Observation 905618ed-fd07-4b0f-9c79-11b8bae24e9b · outbound

This paper cites Denoising diffusion probabilistic mod- els,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Denoising diffusion probabilistic mod- els,

Reference 23

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Observation 6a8f896d-01df-451a-9e26-ab41a15bb839 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Deep unsupervised learning using nonequilibrium thermodynamics,

Reference 24

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Observation a737daf2-7c0a-48f6-80c9-01f69e097b18 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Score-Based Generative Modeling through Stochastic Differential Equations

Reference 25

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Observation 20281740-7012-456b-8864-f5d92f1b7f75 · outbound

This paper cites Improved denoising diffusion probabilis- tic models,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Improved denoising diffusion probabilis- tic models,

Reference 26

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Observation 672f60f6-c064-46de-87e7-581021e4b8c0 · outbound

This paper cites Diffusion models beat gans on image synthesis,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Diffusion models beat gans on image synthesis,

Reference 27

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Observation f6e23de4-71ab-4a87-906f-b47e5a5a0789 · outbound

This paper cites Fastdiff: A fast conditional diffusion model for high-quality speech synthesis,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Fastdiff: A fast conditional diffusion model for high-quality speech synthesis,

Reference 28

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Observation db43b03c-ce0f-4eca-be57-513feed80894 · outbound

This paper cites DisenBooth: Identity-Preserving Disentangled Tuning for Subject-Driven Text-to-Image Generation.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation DisenBooth: Identity-Preserving Disentangled Tuning for Subject-Driven Text-to-Image Generation

Reference 29

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Observation 41ca1734-141f-45b7-b44b-5363ace9288b · outbound

This paper cites InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning

Reference 30

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Observation eb7becec-6434-4168-85d5-f61ee20bd04b · outbound

This paper cites ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation

Reference 31

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Observation 5a540e45-d1b3-413c-9718-35f3a44da445 · outbound

This paper cites Cones: Concept Neurons in Diffusion Models for Customized Generation.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Cones: Concept Neurons in Diffusion Models for Customized Generation

Reference 32

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Observation a51f0e7c-536f-4ed4-a3a7-7291ad6b6244 · outbound

This paper cites P+: Extended Textual Conditioning in Text-to-Image Generation.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation P+: Extended Textual Conditioning in Text-to-Image Generation

Reference 33

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Observation 1833d590-8ba1-46f7-92d1-83b871da6358 · outbound

This paper cites Subject-driven text-to-image generation via apprenticeship learning,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Subject-driven text-to-image generation via apprenticeship learning,

Reference 34

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Observation 71147ebd-8197-4cb5-9d3e-b100c5583cb2 · outbound

This paper cites A neural space- time representation for text-to-image personalization,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation A neural space- time representation for text-to-image personalization,

Reference 35

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This paper cites T2i- adapter: Learning adapters to dig out more controllable ability for text- to-image diffusion models,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation T2i- adapter: Learning adapters to dig out more controllable ability for text- to-image diffusion models,

Reference 36

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This paper cites Uni-controlnet: All-in-one control to text-to-image diffusion models,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Uni-controlnet: All-in-one control to text-to-image diffusion models,

Reference 37

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This paper cites Layoutvae: Stochastic scene layout generation from a label set,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Layoutvae: Stochastic scene layout generation from a label set,

Reference 38

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This paper cites LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators

Reference 39

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This paper cites Attribute- conditioned layout gan for automatic graphic design,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Attribute- conditioned layout gan for automatic graphic design,

Reference 40

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SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Variational transformer networks for layout generation,

Reference 41

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Observation 6a4d4e75-637d-4994-ab81-dd329740c68a · outbound

This paper cites Layouttransformer: Layout generation and completion with self-attention,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Layouttransformer: Layout generation and completion with self-attention,

Reference 42

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This paper cites Layout-bridging text-to-image synthesis,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Layout-bridging text-to-image synthesis,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.424957Z

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This paper cites Background layout generation and object knowledge transfer for text-to-image generation,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Background layout generation and object knowledge transfer for text-to-image generation,

Reference 44

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Observation 9931f9fa-61af-4d87-9f9f-0c0eb979fc16 · outbound

This paper cites Layoutllm-t2i: Eliciting layout guidance from llm for text-to-image generation,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Layoutllm-t2i: Eliciting layout guidance from llm for text-to-image generation,

Reference 45

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Observation 7b6340ee-ecbf-40c5-b81c-4478cc4287a4 · outbound

This paper cites LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models

Reference 46

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This paper cites Attention is all you need,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Attention is all you need,

Reference 47

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This paper cites Nerf: Representing scenes as neural radiance fields for view synthesis,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Nerf: Representing scenes as neural radiance fields for view synthesis,

Reference 48

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

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 49

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This paper cites On the spectral bias of neural networks,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation On the spectral bias of neural networks,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.355260Z

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This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Gans trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 51

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Observation b87feada-dda5-4334-897c-874fa388a333 · outbound

This paper cites Clipscore: A reference-free evaluation metric for image captioning,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Clipscore: A reference-free evaluation metric for image captioning,

Reference 52

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Observation 7f0254a8-e094-4942-9f6a-be9de7508741 · outbound

This paper cites Image synthesis from layout with locality-aware mask adaption,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Image synthesis from layout with locality-aware mask adaption,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.319659Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9882da85-2b29-4e33-9a35-433e3a9e9514 · outbound

This paper cites Constrained graphic layout generation via latent optimization,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Constrained graphic layout generation via latent optimization,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.303614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 78a56dc3-82e0-4f81-8ad1-d06fe206d62b · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Pytorch: An imperative style, high-performance deep learning library,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:07.907166Z

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Observation 95fd7851-cac3-46ab-bf80-72079237903b · outbound

This paper cites Adam: A Method for Stochastic Optimization.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Adam: A Method for Stochastic Optimization

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:07.911944Z

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Observation 42f614d4-7af6-476e-9d88-e9015524d7e0 · outbound

This paper cites Dataset diffusion: Diffusion-based synthetic data generation for pixel-level semantic seg- mentation,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Dataset diffusion: Diffusion-based synthetic data generation for pixel-level semantic seg- mentation,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.275701Z

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Observation 448d14bc-e049-4efd-a55e-890bde848c11 · outbound

This paper cites Image synthesis from reconfigurable layout and style,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Image synthesis from reconfigurable layout and style,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.258206Z

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Observation 676c68f8-2e66-44e4-81ae-687cb1b368f7 · outbound

This paper cites Learning layout and style reconfigurable gans for controllable image synthesis,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Learning layout and style reconfigurable gans for controllable image synthesis,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.241887Z

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Observation 7c4f1469-648c-4298-9120-8c4cf455e1d9 · outbound

This paper cites Object- centric image generation from layouts,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Object- centric image generation from layouts,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.226246Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f93553b9-a59e-4f37-b703-6609b187936a · outbound

This paper cites Layoutdiffusion: Controllable diffusion model for layout-to-image generation,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Layoutdiffusion: Controllable diffusion model for layout-to-image generation,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.210383Z

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Observation 1e0a897d-406a-4e01-956f-840b489f5178 · outbound

This paper cites Drag your gan: Interactive point-based manipulation on the generative image manifold,.

SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation Drag your gan: Interactive point-based manipulation on the generative image manifold,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.195108Z

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Pith citing papers

Observation a5415215-b39f-43aa-938a-8eaf33183f90 · inbound

T-Stars-Poster: A Framework for Product-Centric Advertising Image Design cites this paper.

T-Stars-Poster: A Framework for Product-Centric Advertising Image Design SceneBooth: Diffusion-based Framework for Subject-preserved Text-to-Image Generation

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-10T15:20:04.119093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.