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Paper Citation Record · LEDGER

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models

As of 16 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2411.10800.

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

pith.paper-citation-record.v1
2411.10800 v1

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measured 33 of 33 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-12T19:21:50.615568Z

measured 33 of 33 standing notices

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

measured 0 of 0 inbound itemization

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Reference resolution

33 of 33 outbound references displayed

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Outbound references

Observation d59c934a-0038-44a7-83d1-7b81e0ce57a5 · outbound

This paper cites Universal Guidance for Diffusion Models.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Universal Guidance for Diffusion Models

Reference 3

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Observation 87a08015-fa52-4bcc-b0d4-e156fc1b40b2 · outbound

This paper cites InstructPix2Pix: Learning to Follow Image Editing Instructions.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models InstructPix2Pix: Learning to Follow Image Editing Instructions

Reference 4

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Observation 09dd6f0b-5094-4bbb-8383-b47006f8ff38 · outbound

This paper cites MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing

Reference 5

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Observation 8b6c1793-7cd5-4eb8-b36a-6fe66e2bf8b0 · outbound

This paper cites Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors

Reference 7

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Observation 803fb587-7f38-4ced-928b-89449090127c · outbound

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

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 8

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Observation 4be13321-0715-4680-a00a-d7b4890e7919 · outbound

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

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Prompt-to-Prompt Image Editing with Cross Attention Control

Reference 9

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Observation a4bfc2b0-ae4c-4d4e-8a1f-c66b6f855572 · outbound

This paper cites Imagic: Text-Based Real Image Editing with Diffusion Models.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Imagic: Text-Based Real Image Editing with Diffusion Models

Reference 12

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Observation df74b9bc-f278-4c25-8ee2-3482c75b9cb2 · outbound

This paper cites DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation

Reference 13

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Observation 4cfafba1-5bc8-411b-96d9-5476cef0372b · outbound

This paper cites SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations

Reference 15

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Observation d9c4baa8-e8bf-45e5-b0de-4122de8852d7 · outbound

This paper cites T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models

Reference 16

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Observation 8094e049-6e1b-4663-af06-4433f2141386 · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 17

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Observation c08fe681-b04d-4863-9e6c-46d3c5565e83 · outbound

This paper cites Zero-shot Image-to-Image Translation.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Zero-shot Image-to-Image Translation

Reference 18

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Observation 84ebe27e-feeb-422e-b15e-c755052655d8 · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Learning Transferable Visual Models From Natural Language Supervision

Reference 19

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Observation a8a2d776-07b0-4ab7-b494-b36ab9f93770 · outbound

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

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 21

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Observation c861c9e6-0906-4b06-bc93-6b6742e767a6 · outbound

This paper cites Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

Reference 22

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Observation e8484a97-79b2-4d06-8a6a-f9f6239e2da4 · outbound

This paper cites Deep Unsupervised Learning using Nonequilibrium Thermodynamics.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Reference 23

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Observation 34dfcad9-87ff-4dab-97c2-5f382898c276 · outbound

This paper cites Solving Inverse Problems in Medical Imaging with Score-Based Generative Models.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Solving Inverse Problems in Medical Imaging with Score-Based Generative Models

Reference 25

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Observation a3c931f3-6942-4f95-b141-dc51d315d42a · outbound

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

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 26

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Observation b47fa4b4-81cd-4607-b142-e56fdbe2fd3a · outbound

This paper cites Dequantization and Color Transfer with Diffusion Models.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Dequantization and Color Transfer with Diffusion Models

Reference 27

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Observation ca093982-a4a8-48b4-9205-9c5b1345e96e · outbound

This paper cites Pretraining is All You Need for Image-to-Image Translation.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Pretraining is All You Need for Image-to-Image Translation

Reference 28

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Observation 446c94bd-6845-4381-9b98-1e794364efee · outbound

This paper cites Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesis.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesis

Reference 29

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Observation e0bfa1cd-2a7a-4039-bfe5-bc336627c4f6 · outbound

This paper cites FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model

Reference 30

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Observation 17ea2ea7-b44f-4c2a-99fe-b26d6cfd4ed6 · outbound

This paper cites ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models

Reference 31

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Observation 328c3289-2eab-43ac-8557-75719b835199 · outbound

This paper cites Energy-based Generative Adversarial Network.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Energy-based Generative Adversarial Network

Reference 32

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Observation 17dbe4c4-e971-4d88-9c59-bed3599cdb4d · outbound

This paper cites Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models

Reference 33

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Observation 6fa2507b-5da8-4439-b277-f948981f4efa · outbound

This paper cites Microsoft COCO: Common Objects in Context.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Microsoft COCO: Common Objects in Context

Reference 2015

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Observation d32a52cd-0cfd-4db3-926d-7989793f08e8 · outbound

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

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 2016

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Observation 5da000a6-6f28-4bb8-a419-c89eaface675 · outbound

This paper cites Stacked Generative Adversarial Networks.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Stacked Generative Adversarial Networks

Reference 2017

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Observation 4de3df3a-42d6-4be5-b9a1-66d23656e70c · outbound

This paper cites Progressive Growing of GANs for Improved Quality, Stability, and Variation.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Progressive Growing of GANs for Improved Quality, Stability, and Variation

Reference 2018

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Observation 969690fe-1d89-4b42-be2e-7c653219ddbe · outbound

This paper cites Generative Modeling by Estimating Gradients of the Data Distribution.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Generative Modeling by Estimating Gradients of the Data Distribution

Reference 2020

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Observation 288e34ae-5753-47c0-852e-3ddaf77428ba · outbound

This paper cites Diffusion Models Beat GANs on Image Synthesis.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models Diffusion Models Beat GANs on Image Synthesis

Reference 2021

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Observation 35029932-ddb2-4333-ac5b-3b9fab0d011f · outbound

This paper cites In 2022 IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR).

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models In 2022 IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR)

Reference 2022

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Observation f5adf989-fc3c-4316-b157-b22b8200277b · outbound

This paper cites An Image is Worth Multiple Words: Multi-attribute Inversion for Constrained Text-to-Image Synthesis.

Test-time Conditional Text-to-Image Synthesis Using Diffusion Models An Image is Worth Multiple Words: Multi-attribute Inversion for Constrained Text-to-Image Synthesis

Reference 2023

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