Paper Citation Record · LEDGER

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection

As of 14 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 5 inbound Pith citation observations for arXiv:2412.10891.

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

pith.paper-citation-record.v1
2412.10891 v2

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:36:11.919194Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

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measured 5 of 5 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: arxiv_reference, observed 2026-05-20T20:59:02.034132Z

Reference resolution

61 of 61 outbound references displayed

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External citation measurements

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Outbound references

Observation 051b27e2-ff4b-4172-9b39-bb5514dae98d · outbound

This paper cites In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 11461--11471, 2022.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 11461--11471, 2022

Reference 1

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Observation 54a56d9e-632b-41b9-8c6d-2d26b33be0a4 · outbound

This paper cites GPT-4 Technical Report.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection GPT-4 Technical Report

Reference 2

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Observation d68d277a-84fc-4ef2-b96a-e066f4e5aa47 · outbound

This paper cites Universal guidance for diffusion models.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Universal guidance for diffusion models

Reference 3

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Observation 7cba13eb-7d4b-4624-8483-9839ff7493d6 · outbound

This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 4

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Observation b0757bd1-5f13-41c7-b17e-2d92f2a76085 · outbound

This paper cites CFG++: Manifold-constrained Classifier Free Guidance for Diffusion Models.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection CFG++: Manifold-constrained Classifier Free Guidance for Diffusion Models

Reference 5

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Observation 91fe0116-02e9-4455-b30c-ec573345bbdb · outbound

This paper cites Geneval: An object-focused framework for evaluating text-to-image alignment.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Geneval: An object-focused framework for evaluating text-to-image alignment

Reference 6

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Observation 682eed57-8a90-4aef-a502-a1b33cd0492a · outbound

This paper cites AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

Reference 7

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Observation 59745804-026b-49fd-bcd0-2c72ead067b9 · outbound

This paper cites Diffusion with offset noise, 2023.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Diffusion with offset noise, 2023

Reference 8

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Observation 5a1d68da-acc1-45f1-8e01-d01ef6b138b2 · outbound

This paper cites Manifold preserving guided diffusion.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Manifold preserving guided diffusion

Reference 9

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Observation 5b6844d7-c021-4456-b0cf-a69c24654ba0 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Classifier-Free Diffusion Guidance

Reference 10

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Observation 1c5a71c6-b6a6-424b-9d6d-d1640baf537e · outbound

This paper cites Denoising diffusion probabilistic models.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Denoising diffusion probabilistic models

Reference 11

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Observation f5e0154b-fa02-41d9-b3c4-a41619a5470e · outbound

This paper cites Video diffusion models.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Video diffusion models

Reference 12

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Observation 9b8d199d-9bde-4e5d-af8e-039953a84197 · outbound

This paper cites Smoothed Energy Guidance: Guiding Diffusion Models with Reduced Energy Curvature of Attention.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Smoothed Energy Guidance: Guiding Diffusion Models with Reduced Energy Curvature of Attention

Reference 13

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Observation 950f7004-cb02-43be-a832-2ecfb06ba653 · outbound

This paper cites Improving Sample Quality of Diffusion Models Using Self-Attention Guidance.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Improving Sample Quality of Diffusion Models Using Self-Attention Guidance

Reference 14

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Observation 13d20f72-85ba-487b-a00b-2e60917fde58 · outbound

This paper cites Towards Mitigating Hallucination in Large Language Models via Self-Reflection.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Towards Mitigating Hallucination in Large Language Models via Self-Reflection

Reference 15

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Observation dcca8bb8-a043-4d93-9ed6-73de088d5ece · outbound

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

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Pick-a-pic: An open dataset of user preferences for text-to-image generation

Reference 16

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Observation 7074f2af-a37d-499c-8a30-1be3f9f95158 · outbound

This paper cites Unmasked teacher: Towards training-efficient video foundation models.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Unmasked teacher: Towards training-efficient video foundation models

Reference 17

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Observation 29af3adc-1aeb-4752-b769-eb686e7c6d92 · outbound

This paper cites Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding

Reference 18

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Observation 97218303-61e8-43bc-8f73-52560054a4d9 · outbound

This paper cites Common diffusion noise schedules and sample steps are flawed.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Common diffusion noise schedules and sample steps are flawed

Reference 19

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Observation dd2f9c60-aa56-4a90-9492-5b1513da49f6 · outbound

This paper cites SDXL-Lightning: Progressive Adversarial Diffusion Distillation.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection SDXL-Lightning: Progressive Adversarial Diffusion Distillation

Reference 20

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This paper cites Microsoft coco: Common objects in context.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Microsoft coco: Common objects in context

Reference 21

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This paper cites Fetv: A benchmark for fine-grained evaluation of open-domain text-to-video generation.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Fetv: A benchmark for fine-grained evaluation of open-domain text-to-video generation

Reference 22

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This paper cites Diffusion probabilistic models for 3d point cloud generation.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Diffusion probabilistic models for 3d point cloud generation

Reference 23

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Guided image synthesis via initial image editing in diffusion model

Reference 24

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This paper cites The Lottery Ticket Hypothesis in Denoising: Towards Semantic-Driven Initialization.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection The Lottery Ticket Hypothesis in Denoising: Towards Semantic-Driven Initialization

Reference 25

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This paper cites Null-text inversion for editing real images using guided diffusion models.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Null-text inversion for editing real images using guided diffusion models

Reference 26

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Observation 704d7a5e-a958-4401-af98-29192ad42f73 · outbound

This paper cites Synthetic Shifts to Initial Seed Vector Exposes the Brittle Nature of Latent-Based Diffusion Models.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Synthetic Shifts to Initial Seed Vector Exposes the Brittle Nature of Latent-Based Diffusion Models

Reference 27

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 28

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Not All Noises Are Created Equally:Diffusion Noise Selection and Optimization

Reference 29

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Layered rendering diffusion model for controllable zero-shot image synthesis

Reference 30

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Learning transferable visual models from natural language supervision

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection High-resolution image synthesis with latent diffusion models

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Align Your Steps: Optimizing Sampling Schedules in Diffusion Models

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Photorealistic text-to-image diffusion models with deep language understanding

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Improved techniques for training gans

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Generating images of rare concepts using pre-trained diffusion models

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Adversarial Diffusion Distillation

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Laion-5b: An open large-scale dataset for training next generation image-text models

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection pytorch-fid: FID Score for PyTorch

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection IV-Mixed Sampler: Leveraging Image Diffusion Models for Enhanced Video Synthesis

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Rethinking the spatial inconsistency in classifier-free diffusion guidance

Reference 42

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Reflexion: Language agents with verbal reinforcement learning

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Denoising Diffusion Implicit Models

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Diffusion model alignment using direct preference optimization

Reference 46

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation

Reference 47

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection FreeInit: Bridging Initialization Gap in Video Diffusion Models

Reference 48

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Freeinit: Bridging initialization gap in video diffusion models

Reference 49

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Observation 8be9b835-2498-4750-a5e5-b67c087793f7 · outbound

This paper cites Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 50

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Imagereward: Learning and evaluating human preferences for text-to-image generation

Reference 51

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Observation fe02becf-fa53-4480-a596-0911f02b4444 · outbound

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Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Good Seed Makes a Good Crop: Discovering Secret Seeds in Text-to-Image Diffusion Models

Reference 52

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Observation 3bca16c1-bd47-4dd7-96b0-ab3b319e3c1c · outbound

This paper cites Guidance with spherical gaussian constraint for conditional diffusion.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Guidance with spherical gaussian constraint for conditional diffusion

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:36:12.740451Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bdeccf74-37e3-4085-ba56-27344a361af3 · outbound

This paper cites Text-to-Image Rectified Flow as Plug-and-Play Priors.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Text-to-Image Rectified Flow as Plug-and-Play Priors

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-11T15:36:11.882634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28879cc1-28eb-4e87-bcde-9272823153d9 · outbound

This paper cites Scaling autoregressive models for content-rich text-to-image generation.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Scaling autoregressive models for content-rich text-to-image generation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:36:12.717157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a3d6f899-34cb-4693-bf57-79335286ed01 · outbound

This paper cites ChronoMagic-Bench: A Benchmark for Metamorphic Evaluation of Text-to-Time-lapse Video Generation.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection ChronoMagic-Bench: A Benchmark for Metamorphic Evaluation of Text-to-Time-lapse Video Generation

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T15:36:11.893082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dea64545-5e45-49f4-a608-2691b5059730 · outbound

This paper cites Golden Noise for Diffusion Models: A Learning Framework.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Golden Noise for Diffusion Models: A Learning Framework

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T15:36:11.898113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4847a85a-8797-4f85-a33b-db96244f0eca · outbound

This paper cites write newline.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection write newline

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T15:36:11.903018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 311df55f-7d94-4f2a-a84c-8561aed81f1b · outbound

This paper cites @esa (Ref.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection @esa (Ref

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T15:36:11.908874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b43a8705-999b-4eaa-9240-b2ceb7e1ec8a · outbound

This paper cites an unresolved cited work.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Unresolved cited work

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T15:36:11.913819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e97dbdbe-ba44-4201-888e-c8ccb31108bd · outbound

This paper cites an unresolved cited work.

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection Unresolved cited work

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T15:36:11.919194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation a94e8041-b270-41da-a0ca-88f9520d7e19 · inbound

Optimizing Few-Step Generation with Adaptive Matching Distillation cites this paper.

Optimizing Few-Step Generation with Adaptive Matching Distillation Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T03:42:44.895516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4415d591-67c5-4ff9-a30c-9b8c2e9b915c · inbound

Na\"ive PAINE: Lightweight Text-to-Image Generation Improvement with Prompt Evaluation cites this paper.

Na\"ive PAINE: Lightweight Text-to-Image Generation Improvement with Prompt Evaluation Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-14T22:28:35.981853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8c1ca4c-2f74-48b9-a224-a48d4d918753 · inbound

Training-Free Refinement of Flow Matching with Divergence-based Sampling cites this paper.

Training-Free Refinement of Flow Matching with Divergence-based Sampling Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T22:05:48.927038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9a57ae3a-0a0d-4e19-877b-08e7fc73c89a · inbound

$Z^2$-Sampling: Zero-Cost Zigzag Trajectories for Semantic Alignment in Diffusion Models cites this paper.

$Z^2$-Sampling: Zero-Cost Zigzag Trajectories for Semantic Alignment in Diffusion Models Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:06:15.115823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0e157345-4df4-4564-9120-5c42a6a958ab · inbound

Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making cites this paper.

Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection

Reference 267

Resolution: verified exact; arxiv_id, observed 2026-05-20T20:59:02.035978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.