Paper Citation Record · LEDGER

ZoomLDM: Latent Diffusion Model for multi-scale image generation

As of 21 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 1 inbound Pith citation observation for arXiv:2411.16969.

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

pith.paper-citation-record.v1
2411.16969 v2

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:47:31.480458Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-19T13:32:06.974109Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T13:32:19.158292Z

Reference resolution

59 of 59 outbound references displayed

verified exact2
verified fuzzy38
unresolved17
parse uncertain0
malformed identifier2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 745184d6-ba42-4e07-9e15-fe1d6b5a98a7 · outbound

This paper cites Bach: Grand challenge on breast cancer histology images.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Bach: Grand challenge on breast cancer histology images

Reference 1

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Observation 96b08c88-4e06-4eff-8d36-8827c5930e73 · outbound

This paper cites Diffinfinite: Large mask-image synthesis via parallel random patch diffusion in histopathology.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Diffinfinite: Large mask-image synthesis via parallel random patch diffusion in histopathology

Reference 2

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Observation 61717d4f-4b05-4603-b2bc-598989e4fd61 · outbound

This paper cites Improving image generation with better captions.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Improving image generation with better captions

Reference 3

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Observation 8fa6b664-6226-477c-be6d-3fa448bc5c90 · outbound

This paper cites The can- cer genome atlas pan-cancer analysis project.

ZoomLDM: Latent Diffusion Model for multi-scale image generation The can- cer genome atlas pan-cancer analysis project

Reference 4

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Observation 7cb20d57-f05e-4d6d-844b-daf8cc32c181 · outbound

This paper cites Conceptual 12m: Pushing web-scale image-text pre- training to recognize long-tail visual concepts.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Conceptual 12m: Pushing web-scale image-text pre- training to recognize long-tail visual concepts

Reference 5

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Observation aa3063c1-32ac-4cc6-80e6-684702c232cc · outbound

This paper cites Pixart- σ: Weak-to-strong training of diffu- sion transformer for 4k text-to-image generation, 2024.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Pixart- σ: Weak-to-strong training of diffu- sion transformer for 4k text-to-image generation, 2024

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:32.592570Z

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Observation 552f21a2-854d-4f31-94a3-8641118c005e · outbound

This paper cites Scaling vision transformers to gigapixel images via hierarchical self-supervised learning.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Scaling vision transformers to gigapixel images via hierarchical self-supervised learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:32.577439Z

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Observation b162b141-d843-41e6-96c4-906296b6b947 · outbound

This paper cites A General-Purpose Self-Supervised Model for Computational Pathology.

ZoomLDM: Latent Diffusion Model for multi-scale image generation A General-Purpose Self-Supervised Model for Computational Pathology

Reference 8

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Observation 38cde92f-dff6-48cd-92e5-f5c1160701d5 · outbound

This paper cites Towards a general-purpose foundation model for com- putational pathology.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Towards a general-purpose foundation model for com- putational pathology

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:32.561870Z

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Observation 96cdbea4-2bd9-425f-bfe2-b62c219402e1 · outbound

This paper cites Prompt-tuning latent diffusion models for inverse problems.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Prompt-tuning latent diffusion models for inverse problems

Reference 10

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Observation 7ba610b3-556f-4680-bb15-e09e5d8efb00 · outbound

This paper cites Diffusion models beat gans on image synthesis.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Diffusion models beat gans on image synthesis

Reference 11

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Observation d281811c-b548-4341-8ca0-c9b40c72d79c · outbound

This paper cites Tweedie’s formula and selection bias.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Tweedie’s formula and selection bias

Reference 12

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Observation 32ce2275-0c8d-454c-9c9c-6e9ca18de803 · outbound

This paper cites Generate Your Own Scotland: Satellite Image Generation Conditioned on Maps.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Generate Your Own Scotland: Satellite Image Generation Conditioned on Maps

Reference 13

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Observation c43370b7-970e-448b-b5d0-42313d87ccde · outbound

This paper cites Scaling self-supervised learning for histopathology with masked image modeling.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Scaling self-supervised learning for histopathology with masked image modeling

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:32.503377Z

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Observation c590b367-75a0-4b14-8d05-e0a361d92632 · outbound

This paper cites An image is worth one word: Personalizing text-to-image generation using textual inversion.

ZoomLDM: Latent Diffusion Model for multi-scale image generation An image is worth one word: Personalizing text-to-image generation using textual inversion

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:32.488403Z

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Observation 5cd5f979-1855-4674-8623-4cfb5eac3f12 · outbound

This paper cites Fast constrained sampling in pre-trained diffusion models.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Fast constrained sampling in pre-trained diffusion models

Reference 16

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Source-reported events for the cited work

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Observation 933b7599-907c-49df-b612-1693a971288f · outbound

This paper cites Learned representation-guided diffusion models for large-image generation.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Learned representation-guided diffusion models for large-image generation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:32.472506Z

Source-reported events for the cited work

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Observation 2cedbcf6-5db9-41c6-995f-a10ec9aec9e1 · outbound

This paper cites Diffusion- based generation of histopathological whole slide images at a gigapixel scale.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Diffusion- based generation of histopathological whole slide images at a gigapixel scale

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:32.457639Z

Source-reported events for the cited work

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Observation 80dbda3e-5bd1-4b46-b4a8-fe8d9f7249bb · outbound

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

ZoomLDM: Latent Diffusion Model for multi-scale image generation Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 19

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Source-reported events for the cited work

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Observation 26e20607-de04-4798-a204-56bf3d52e413 · outbound

This paper cites Classifier-Free Diffusion Guidance.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Classifier-Free Diffusion Guidance

Reference 20

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Source-reported events for the cited work

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Observation d939bf22-7747-4342-8537-66cbb66d45bf · outbound

This paper cites Denoising dif- fusion probabilistic models.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Denoising dif- fusion probabilistic models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:32.433091Z

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Observation 29bde00a-637f-49a5-a875-8553528fbb9a · outbound

This paper cites Attention-based deep multiple instance learning.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Attention-based deep multiple instance learning

Reference 22

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Observation c88ffc21-2ce9-431d-aa41-3bb496f6e4fa · outbound

This paper cites Ex- plainable ai for computational pathology identifies model 9 limitations and tissue biomarkers.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Ex- plainable ai for computational pathology identifies model 9 limitations and tissue biomarkers

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:32.284762Z

Source-reported events for the cited work

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Observation 583aac8f-53e9-45ce-99ce-ebf4ec29f226 · outbound

This paper cites Si-mil: Taming deep mil for self-interpretability in gigapixel histopathology.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Si-mil: Taming deep mil for self-interpretability in gigapixel histopathology

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:32.268334Z

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Observation 8531211d-63f6-4923-98b3-272001e044a9 · outbound

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

ZoomLDM: Latent Diffusion Model for multi-scale image generation The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:32.253404Z

Source-reported events for the cited work

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Observation 5dd83b67-3586-4d26-8850-dff77fe2b9d3 · outbound

This paper cites ∞-brush: Controllable large image synthesis with diffusion models in infinite dimensions, 2024.

ZoomLDM: Latent Diffusion Model for multi-scale image generation ∞-brush: Controllable large image synthesis with diffusion models in infinite dimensions, 2024

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:32.236288Z

Source-reported events for the cited work

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Observation 6b837751-4f0c-4bf5-abeb-c4e3a9b27590 · outbound

This paper cites Dual-stream multiple instance learning network for whole slide image classifica- tion with self-supervised contrastive learning.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Dual-stream multiple instance learning network for whole slide image classifica- tion with self-supervised contrastive learning

Reference 27

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Observation dfc5cb55-c337-4f3d-97ad-b38b1cf5de33 · outbound

This paper cites A visual- language foundation model for computational pathology.

ZoomLDM: Latent Diffusion Model for multi-scale image generation A visual- language foundation model for computational pathology

Reference 28

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Source-reported events for the cited work

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Observation 185096fb-862e-4f1b-a03e-d5e01ebc4e5a · outbound

This paper cites A morphology focused diffusion probabilistic model for synthesis of histopathology images.

ZoomLDM: Latent Diffusion Model for multi-scale image generation A morphology focused diffusion probabilistic model for synthesis of histopathology images

Reference 29

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Observation 1bb7643b-4bae-4fea-9090-b062c0920791 · outbound

This paper cites A multimodal comparison of latent denois- ing diffusion probabilistic models and generative adversarial networks for medical image synthesis.

ZoomLDM: Latent Diffusion Model for multi-scale image generation A multimodal comparison of latent denois- ing diffusion probabilistic models and generative adversarial networks for medical image synthesis

Reference 30

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Observation 90f3f098-931d-42bd-8a7e-b27ffca93ace · outbound

This paper cites Improved denoising diffusion probabilistic models.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Improved denoising diffusion probabilistic models

Reference 31

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Observation ed51a3ab-0c5c-4849-90d8-0f867e75cf48 · outbound

This paper cites Glide: Towards photorealis- tic image generation and editing with text-guided diffusion models.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Glide: Towards photorealis- tic image generation and editing with text-guided diffusion models

Reference 32

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Observation ef498991-b0e8-4099-9556-49dfbe2d3651 · outbound

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

ZoomLDM: Latent Diffusion Model for multi-scale image generation DINOv2: Learning Robust Visual Features without Supervision

Reference 33

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Observation b1c97dce-1432-47aa-a13d-13949b8d897a · outbound

This paper cites Scalable diffusion models with transformers.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Scalable diffusion models with transformers

Reference 34

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Observation 27fa1211-77a7-43b2-bf22-d7ff0e294308 · outbound

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

ZoomLDM: Latent Diffusion Model for multi-scale image generation SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 35

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Observation 5b5d377c-6ccf-440f-9b51-81d1daefa1ae · outbound

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

ZoomLDM: Latent Diffusion Model for multi-scale image generation SDXL: Improving latent diffusion models for high-resolution image synthesis

Reference 36

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Observation 37dc68f2-cfd4-4c69-b841-6407135b7d94 · outbound

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

ZoomLDM: Latent Diffusion Model for multi-scale image generation Learning transferable visual models from natural language supervi- sion

Reference 37

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Source-reported events for the cited work

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Observation 826aa77d-1146-414f-bbec-13b1d309d7b3 · outbound

This paper cites Large scale high-resolution land cover mapping with multi- resolution data.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Large scale high-resolution land cover mapping with multi- resolution data

Reference 38

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Observation 9e2f5de7-cc46-4978-a3c9-ced634fa6f79 · outbound

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

ZoomLDM: Latent Diffusion Model for multi-scale image generation High-resolution image synthesis with latent diffusion models

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:32.091916Z

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Observation c724a7ca-529c-4436-8781-1a3da1d5a8e3 · outbound

This paper cites Image super-resolution via iterative refinement.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Image super-resolution via iterative refinement

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:32.078090Z

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Observation c6111f9d-c1bc-4adc-a106-c73b552b48c1 · outbound

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

ZoomLDM: Latent Diffusion Model for multi-scale image generation Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 41

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Observation c4d97b57-e793-44bb-9d91-0d945dbbaa1d · outbound

This paper cites RSDiff: Remote Sensing Image Generation from Text Using Diffusion Model.

ZoomLDM: Latent Diffusion Model for multi-scale image generation RSDiff: Remote Sensing Image Generation from Text Using Diffusion Model

Reference 42

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Source-reported events for the cited work

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Observation e5462259-6ddd-4193-8dd3-07c70948681a · outbound

This paper cites pytorch-fid: FID Score for PyTorch.

ZoomLDM: Latent Diffusion Model for multi-scale image generation pytorch-fid: FID Score for PyTorch

Reference 43

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Source-reported events for the cited work

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Observation 91094589-45a0-447a-bc57-97c04bd14c53 · outbound

This paper cites Denois- ing diffusion implicit models.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Denois- ing diffusion implicit models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:32.044426Z

Source-reported events for the cited work

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Observation 9c1cf17c-b2b7-44ef-9148-c418e7149671 · outbound

This paper cites National agriculture imagery program (NAIP),.

ZoomLDM: Latent Diffusion Model for multi-scale image generation National agriculture imagery program (NAIP),

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:32.029883Z

Source-reported events for the cited work

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Observation d4da514f-f7fb-40a5-9987-488fe3078cfa · outbound

This paper cites Exploiting diffusion prior for 10 real-world image super-resolution.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Exploiting diffusion prior for 10 real-world image super-resolution

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:32.001174Z

Source-reported events for the cited work

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Observation e6f49a8a-5da9-4508-8cbb-64708b1629d0 · outbound

This paper cites Transpath: Transformer-based self-supervised learning for histopatho- logical image classification.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Transpath: Transformer-based self-supervised learning for histopatho- logical image classification

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:31.986388Z

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Observation a96a550a-8682-4481-a57e-8aeb0b900939 · outbound

This paper cites ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis.

ZoomLDM: Latent Diffusion Model for multi-scale image generation ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis

Reference 48

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Source-reported events for the cited work

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Observation 6016a46c-7f25-4bc3-bf6c-b4621583eaf7 · outbound

This paper cites Pathldm: Text conditioned latent diffusion model for histopathology.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Pathldm: Text conditioned latent diffusion model for histopathology

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:31.971620Z

Source-reported events for the cited work

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Observation 048cd28b-9227-40b9-bb07-2b8973364ffd · outbound

This paper cites Efficient Diffusion Model for Image Restoration by Residual Shifting.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Efficient Diffusion Model for Image Restoration by Residual Shifting

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-08-12T12:47:31.526108Z

Source-reported events for the cited work

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Observation a8a1e29d-250a-4e80-9660-96966f1aa482 · outbound

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

ZoomLDM: Latent Diffusion Model for multi-scale image generation Resshift: Efficient diffusion model for image super- resolution by residual shifting

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:31.957238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 971e492f-f351-4e38-982f-0399eb14cf92 · outbound

This paper cites Adding conditional control to text-to-image diffusion models, 2023.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Adding conditional control to text-to-image diffusion models, 2023

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:31.943195Z

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Observation 934aa09a-2e51-4247-89b2-d677c01bb804 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

ZoomLDM: Latent Diffusion Model for multi-scale image generation The unreasonable effectiveness of deep features as a perceptual metric

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:31.927913Z

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Observation 15f1653e-355d-4da5-bcb2-6a394aa3cfec · outbound

This paper cites However, gigapixel images also concern the remote sensing domain, where satellite images regularly are in the range of 10000 × 10000 pixels.

ZoomLDM: Latent Diffusion Model for multi-scale image generation However, gigapixel images also concern the remote sensing domain, where satellite images regularly are in the range of 10000 × 10000 pixels

Reference 55

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T12:47:31.911198Z

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Observation 27c18dbd-7692-4842-9a06-67641c7bea9a · outbound

This paper cites Table 7 shows that replacing UNI with HIPT degrades performance and further replacing the ViT summarizer network with a simple 4-layer CNN leads to a greater decline.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Table 7 shows that replacing UNI with HIPT degrades performance and further replacing the ViT summarizer network with a simple 4-layer CNN leads to a greater decline

Reference 56

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T12:47:31.894664Z

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Observation 60b4f7bd-5351-457e-a9df-6bf83d5faaa1 · outbound

This paper cites Summarizer-CDM training details Summarizer: We train the Summarizer jointly with the LDM.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Summarizer-CDM training details Summarizer: We train the Summarizer jointly with the LDM

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:31.878610Z

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Observation 5825e7fd-8e01-45c9-8662-a49472e0a067 · outbound

This paper cites (8) However, to calculate g we need e = ∂C ∂ ˆz0 which we can calculate by backpropagating through the decoder model.

ZoomLDM: Latent Diffusion Model for multi-scale image generation (8) However, to calculate g we need e = ∂C ∂ ˆz0 which we can calculate by backpropagating through the decoder model

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:31.862950Z

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Observation dd26a2e7-15a4-4dae-bab4-18998777be90 · outbound

This paper cites More super-resolution baselines In Tables 8 and 9 we provide additional baselines for the super-resolution task.

ZoomLDM: Latent Diffusion Model for multi-scale image generation More super-resolution baselines In Tables 8 and 9 we provide additional baselines for the super-resolution task

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:47:31.846648Z

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Observation c0ca3041-ab4c-4860-9af4-2c5e742627e4 · outbound

This paper cites an unresolved cited work.

ZoomLDM: Latent Diffusion Model for multi-scale image generation Unresolved cited work

Reference 2023

Resolution: unresolved; raw_fallback, observed 2026-08-12T12:47:32.015267Z

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Pith citing papers

Observation b79cd2e4-d167-4b66-8023-4b2bd6f986a6 · inbound

Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment cites this paper.

Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment ZoomLDM: Latent Diffusion Model for multi-scale image generation

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-19T13:32:19.160573Z

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