Paper Citation Record · LEDGER

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing

As of 9 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2507.21690.

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

pith.paper-citation-record.v1
2507.21690 v1

Coverage vector

measured 41 of 41 reference resolution

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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.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation 542956ff-2fd0-4c2f-8db6-93271872c6fc · outbound

This paper cites MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation

Reference 1

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Observation fc1fbe79-3a6b-4fe8-b652-184f41bb02f8 · outbound

This paper cites Demystifying MMD GANs.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Demystifying MMD GANs

Reference 2

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Observation 8323f574-6d94-4678-84dd-2ea69f39d5e6 · outbound

This paper cites In- structpix2pix: Learning to follow image editing instructions.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing In- structpix2pix: Learning to follow image editing instructions

Reference 3

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Observation 470c9cc5-65c5-4b06-8e38-b228c521a547 · outbound

This paper cites Any-resolution training for high- resolution image synthesis.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Any-resolution training for high- resolution image synthesis

Reference 4

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Observation faab2c4a-7a9a-433d-91d0-b5731022b0c1 · outbound

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

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing PixArt-\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation

Reference 5

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Observation f0720e94-fe54-4269-8d4d-cc5df95c1290 · outbound

This paper cites Scalable high-resolution pixel-space image syn- thesis with hourglass diffusion transformers.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Scalable high-resolution pixel-space image syn- thesis with hourglass diffusion transformers

Reference 6

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Observation 55e7070e-0519-41f5-8238-7a93c9d540ec · outbound

This paper cites Demofusion: Democratising high- resolution image generation with no $$$.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Demofusion: Democratising high- resolution image generation with no $$$

Reference 7

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Observation e4ddbd3b-799d-443f-b7b8-42de1679fd00 · outbound

This paper cites Matryoshka diffusion models.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Matryoshka diffusion models

Reference 8

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Observation 8a69b30e-c988-4b4a-809a-8798ca02c644 · outbound

This paper cites Make a cheap scaling: A self-cascade diffusion model for higher-resolution adapta- tion.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Make a cheap scaling: A self-cascade diffusion model for higher-resolution adapta- tion

Reference 9

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Observation 4baa49d7-5330-433a-85c7-511d84275f92 · outbound

This paper cites Scalecrafter: Tuning-free higher- resolution visual generation with diffusion models.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Scalecrafter: Tuning-free higher- resolution visual generation with diffusion models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:05.780160Z

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Observation c6e2db09-85a0-4325-8383-47ae4f57ac50 · outbound

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

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Prompt-to-Prompt Image Editing with Cross Attention Control

Reference 11

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Observation 0d875a70-1628-4bda-8f67-dc2e0b8388f9 · outbound

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

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 12

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Observation 60346c58-c3d3-4663-bd65-48600777e8c7 · outbound

This paper cites Denoising diffu- sion probabilistic models.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Denoising diffu- sion probabilistic models

Reference 13

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Observation a5e12a35-2151-4329-b6f7-e731be1d8df9 · outbound

This paper cites Cascaded diffu- sion models for high fidelity image generation.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Cascaded diffu- sion models for high fidelity image generation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:05.733831Z

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Observation 7575a515-2440-480e-bf53-55ba464edaf5 · outbound

This paper cites sim- ple diffusion: End-to-end diffusion for high resolution im- ages.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing sim- ple diffusion: End-to-end diffusion for high resolution im- ages

Reference 15

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Observation a077ff74-f4b1-4c0c-8f1a-34491cf03e1a · outbound

This paper cites One more step: A versatile plug-and-play module for rectifying diffusion schedule flaws and enhancing low-frequency controls.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing One more step: A versatile plug-and-play module for rectifying diffusion schedule flaws and enhancing low-frequency controls

Reference 16

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Observation 8ecf1bdb-5732-4295-88d9-431fc34eb50e · outbound

This paper cites FouriScale: A Frequency Perspective on Training-Free High-Resolution Image Synthesis.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing FouriScale: A Frequency Perspective on Training-Free High-Resolution Image Synthesis

Reference 17

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Observation d74e3e64-f22f-4ef1-959a-ea6e1a6f42dd · outbound

This paper cites Training- free diffusion model adaptation for variable-sized text-to- image synthesis.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Training- free diffusion model adaptation for variable-sized text-to- image synthesis

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:05.700700Z

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Observation 3db7c7c9-d0bb-4091-8e74-24d47828dec8 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Elucidating the design space of diffusion-based generative models

Reference 19

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Observation f6078d6f-f38b-49ec-af8c-534303811cbf · outbound

This paper cites Musiq: Multi-scale image quality transformer.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Musiq: Multi-scale image quality transformer

Reference 20

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Observation aa011418-f23b-461f-9053-4ff4acbfd929 · outbound

This paper cites Arbitrary-scale image gen- eration and upsampling using latent diffusion model and im- plicit neural decoder.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Arbitrary-scale image gen- eration and upsampling using latent diffusion model and im- plicit neural decoder

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:05.675955Z

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Observation e19f827d-7fb7-4ebe-8ef4-5c4dc701083a · outbound

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

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Pick-a-pic: An open dataset of user preferences for text-to-image generation

Reference 22

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Observation 587da6c5-60c0-4582-8b84-7c33b3ba866d · outbound

This paper cites The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale

Reference 23

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Observation a6fce39a-5be3-46e0-82d9-8bfb4d6d7e05 · outbound

This paper cites Syncdiffusion: Coherent montage via synchronized joint diffusions.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Syncdiffusion: Coherent montage via synchronized joint diffusions

Reference 24

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Observation 6b7f0a65-94bb-4538-80cb-80da2922d12a · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 25

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Observation 6515d94f-1180-492a-be03-32062bded7ef · outbound

This paper cites Microsoft coco: Common objects in context.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Microsoft coco: Common objects in context

Reference 26

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Source-reported events for the cited work

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Observation 6a2fb718-52a7-40ff-a114-7d021de9cf1d · outbound

This paper cites Accdiffusion: An accurate method for higher-resolution im- age generation.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Accdiffusion: An accurate method for higher-resolution im- age generation

Reference 27

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Observation de3b78c3-6a33-46af-9519-9fb1477ce189 · outbound

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

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations

Reference 28

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Source-reported events for the cited work

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Observation 17266596-43d9-4fdf-a919-7af0b5904480 · outbound

This paper cites Sdxl: Improving latent diffusion models for high-resolution image synthesis.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Sdxl: Improving latent diffusion models for high-resolution image synthesis

Reference 29

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Observation 558b4ff9-6cae-45a4-904c-4ef541f630a6 · outbound

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

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing High-resolution image synthesis with latent diffusion models

Reference 30

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Observation 6a4dbc82-0c76-4c41-a274-daf65f0d7b8b · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:05.606900Z

Source-reported events for the cited work

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Observation fbbf20d2-f2e5-4875-beef-ef7f70d00c3b · outbound

This paper cites ResMaster: Mastering High-Resolution Image Generation via Structural and Fine-Grained Guidance.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing ResMaster: Mastering High-Resolution Image Generation via Structural and Fine-Grained Guidance

Reference 32

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Source-reported events for the cited work

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Observation 1bbc9825-2ebf-471d-b95d-3d9357a138c9 · outbound

This paper cites Denois- ing diffusion implicit models.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Denois- ing diffusion implicit models

Reference 33

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Observation 86211265-c858-4720-8019-c05b9d25787e · outbound

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

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Score-Based Generative Modeling through Stochastic Differential Equations

Reference 34

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Observation 740b70e8-c058-401d-9518-7d2ea7a363dd · outbound

This paper cites Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation Models.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation Models

Reference 35

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Source-reported events for the cited work

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Observation 9a8de881-78db-4769-9d97-d0979e5b59ff · outbound

This paper cites Ex- ploring clip for assessing the look and feel of images.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Ex- ploring clip for assessing the look and feel of images

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:05.590094Z

Source-reported events for the cited work

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Observation 7c2ef370-561e-4575-be45-23a024e4b9c3 · outbound

This paper cites Resshift: Efficient diffusion model for image super- resolution by residual shifting.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Resshift: Efficient diffusion model for image super- resolution by residual shifting

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:05.581695Z

Source-reported events for the cited work

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Observation 43d9f271-2783-4f3b-a8bc-6e7cb06037b2 · outbound

This paper cites Designing a practical degradation model for deep blind image super-resolution.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Designing a practical degradation model for deep blind image super-resolution

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:05.573782Z

Source-reported events for the cited work

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Observation 47a798a4-4449-4154-88b9-150d64b8cb5f · outbound

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

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Adding conditional control to text-to-image diffusion models

Reference 39

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Observation 9adcc1a5-8b42-4a20-a64b-c8ab8a187704 · outbound

This paper cites HiDiffusion: Unlocking Higher-Resolution Creativity and Efficiency in Pretrained Diffusion Models.

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing HiDiffusion: Unlocking Higher-Resolution Creativity and Efficiency in Pretrained Diffusion Models

Reference 40

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Source-reported events for the cited work

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Observation 9ab0e27c-d0ad-4b50-8b99-9c783534f0d2 · outbound

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

APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing Uni-controlnet: All-in-one control to text-to-image diffusion models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:37:05.560283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

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