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Paper Citation Record · LEDGER

Diffusion Models with Adaptive Negative Sampling Without External Resources

As of 8 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2508.02973.

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

pith.paper-citation-record.v1
2508.02973 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T04:52:34.538179Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

  • verified exact0
  • verified fuzzy28
  • unresolved11
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a0641dd0-ce37-452a-a3a5-68d95da70c7a · outbound

This paper cites Negative prompt: Stable diffusion webui.https://github.com/AUTOMATIC1111/ stable - diffusion - webui / wiki / Negative - prompt.

Diffusion Models with Adaptive Negative Sampling Without External Resources Negative prompt: Stable diffusion webui.https://github.com/AUTOMATIC1111/ stable - diffusion - webui / wiki / Negative - prompt

Reference 1

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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.

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Observation b7a45112-0b6e-4e80-bc7c-f25c99c06839 · outbound

This paper cites Segmentation-free guidance for text-to-image dif- fusion models, 2024.

Diffusion Models with Adaptive Negative Sampling Without External Resources Segmentation-free guidance for text-to-image dif- fusion models, 2024

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-06T04:52:39.393928Z

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.

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Observation 349087a7-0369-4695-b2fd-64fc55ce3f30 · outbound

This paper cites Retrieval-augmented diffusion models.

Diffusion Models with Adaptive Negative Sampling Without External Resources Retrieval-augmented diffusion models

Reference 3

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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.

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Observation a3f0f2de-ad9f-4726-807f-184125b58364 · outbound

This paper cites Coyo-700m: Image-text pair dataset.https : / / github.

Diffusion Models with Adaptive Negative Sampling Without External Resources Coyo-700m: Image-text pair dataset.https : / / github

Reference 4

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verified fuzzy
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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.

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Observation 2814d498-0aaa-4fb0-8731-5c808dac491c · outbound

This paper cites Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models, 2023.

Diffusion Models with Adaptive Negative Sampling Without External Resources Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models, 2023

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-06T04:52:38.716628Z

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.

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Observation 1d547997-94f2-4e24-a3c6-e30ea79084cf · outbound

This paper cites Improving image syn- thesis with diffusion-negative sampling, 2024.

Diffusion Models with Adaptive Negative Sampling Without External Resources Improving image syn- thesis with diffusion-negative sampling, 2024

Reference 6

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verified fuzzy
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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.

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Observation 2027beec-4dc9-4971-a923-2ec52e518edd · outbound

This paper cites Compositional visual generation with energy based models.

Diffusion Models with Adaptive Negative Sampling Without External Resources Compositional visual generation with energy based models

Reference 7

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raw_fallback, observed 2026-08-06T04:52:38.214571Z

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.

source=pdf_text observed=2026-08-06T04:52:32.073547Z digest=sha256:2d76e88a3e3a6107412654f20c087ebdce2e778c8b16bf2c2e111475ab0fa896

Observation 071454ce-1c10-4c59-9592-3d7356ba4266 · outbound

This paper cites Training-free structured diffusion guidance for compositional text-to-image synthesis, 2023.

Diffusion Models with Adaptive Negative Sampling Without External Resources Training-free structured diffusion guidance for compositional text-to-image synthesis, 2023

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:32.149151Z digest=sha256:fe79d0a69ee486fd96d1f1d282766531813cc9c62d8ebd6a3d7c47269fb061a3

Observation e4902864-c828-49cc-ae43-c2eb9bcd394e · outbound

This paper cites Expressive text-to-image generation with rich text.

Diffusion Models with Adaptive Negative Sampling Without External Resources Expressive text-to-image generation with rich text

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-06T04:52:38.012556Z

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.

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Observation 503b970d-5cbe-4c85-af26-a55332363e58 · outbound

This paper cites Clipscore: A reference-free evaluation met- ric for image captioning, 2022.

Diffusion Models with Adaptive Negative Sampling Without External Resources Clipscore: A reference-free evaluation met- ric for image captioning, 2022

Reference 10

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verified fuzzy
raw_fallback, observed 2026-08-06T04:52:37.819853Z

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.

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Observation 591c779e-4760-4808-ab00-e39554ade386 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in neural information processing systems, 30, 2017.

Diffusion Models with Adaptive Negative Sampling Without External Resources Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in neural information processing systems, 30, 2017

Reference 11

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no resolver link, observed 2026-08-06T04:52:32.411019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 56b2d8c6-31ac-4400-9b7f-d30b65f78277 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Diffusion Models with Adaptive Negative Sampling Without External Resources Classifier-Free Diffusion Guidance

Reference 12

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unresolved
no resolver link, observed 2026-08-06T04:52:32.492418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:32.492418Z digest=sha256:7c0237d902f0ae052409b0e63a1c7d755113a956409d3a20580dd102d62a981b

Observation d25a8f9f-e677-4f4b-9cd0-4190c44466cb · outbound

This paper cites Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

Diffusion Models with Adaptive Negative Sampling Without External Resources Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 13

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unresolved
no resolver link, observed 2026-08-06T04:52:32.615529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:32.615529Z digest=sha256:794f03008bd6c08acf7aec1c9cef55d700b1fd88804cc4a4f48ceea0b7c65367

Observation 5e4502eb-32fc-4f01-8d03-f8dafccbbe7f · outbound

This paper cites Elucidating the design space of diffusion-based generative models, 2022.

Diffusion Models with Adaptive Negative Sampling Without External Resources Elucidating the design space of diffusion-based generative models, 2022

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:37.531472Z

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.

source=pdf_text observed=2026-08-06T04:52:32.696580Z digest=sha256:602842bcafd2199f55c97593efb8dab78948851a9e6c243c2e8d04c10343a9b1

Observation 642c99e8-23cb-49c4-81f9-ed72025e4480 · outbound

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

Diffusion Models with Adaptive Negative Sampling Without External Resources Pick-a-pic: An open dataset of user preferences for text-to-image generation

Reference 15

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no resolver link, observed 2026-08-06T04:52:32.796277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:32.796277Z digest=sha256:a7335147deccc651aaf8a58848d3a42188740ea83cae1044ce5b54f9a3cc0c0e

Observation 1af1344e-5b21-4dbe-8fa5-7f8d23ecb470 · outbound

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

Diffusion Models with Adaptive Negative Sampling Without External Resources Multi-concept customization of text-to-image diffusion

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:37.363899Z

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.

source=pdf_text observed=2026-08-06T04:52:32.874658Z digest=sha256:47573704262a54575378409837464f0fb2e657cdb586086e8a174369195e45ef

Observation db369a41-c03c-48eb-b6f4-ec54f6dce4a6 · outbound

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

Diffusion Models with Adaptive Negative Sampling Without External Resources Gligen: Open-set grounded text-to-image generation

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:37.195235Z

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.

source=pdf_text observed=2026-08-06T04:52:32.987822Z digest=sha256:7b319aa1673c9d3932c48db1ec8805b721c45980b658e615978b14b1cdef5665

Observation 79c43292-985f-43ec-8a0b-e665a013cc5b · outbound

This paper cites Compositional visual generation with composable diffusion models.

Diffusion Models with Adaptive Negative Sampling Without External Resources Compositional visual generation with composable diffusion models

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:37.032231Z

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.

source=pdf_text observed=2026-08-06T04:52:33.076357Z digest=sha256:e3380f9955411cb8ebb3c1cc1d1cf85ee4ccc5d7eba6aa5c85cc5b54990ab70b

Observation deb1bed9-8d31-4439-b888-9fc379ce8d02 · outbound

This paper cites Grounded Text-to-Image Synthesis with Attention Refocusing.

Diffusion Models with Adaptive Negative Sampling Without External Resources Grounded Text-to-Image Synthesis with Attention Refocusing

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:33.142771Z digest=sha256:e7321a6d07e81d706deec27027ebfe292e473c03577b54837698ef3945c26e28

Observation 611784f0-a768-4f7a-9e4a-88b81dd0cb64 · outbound

This paper cites Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023.

Diffusion Models with Adaptive Negative Sampling Without External Resources Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:36.827822Z

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.

source=pdf_text observed=2026-08-06T04:52:33.232616Z digest=sha256:0a27f80c2f72562362f51215bdfd7cd8e316ee31403bda0726b7960219e49b77

Observation a6be1a63-2a7d-4d4b-ad6c-2a2e26fd5382 · outbound

This paper cites Zero-shot text-to-image generation.

Diffusion Models with Adaptive Negative Sampling Without External Resources Zero-shot text-to-image generation

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:36.740198Z

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.

source=pdf_text observed=2026-08-06T04:52:33.331751Z digest=sha256:afe99ebbc643329225dd1c3418333d81b2e2565efe6d45b52cb4aa1436fed6d9

Observation 28542aa7-d271-4513-a09a-41da96310ada · outbound

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

Diffusion Models with Adaptive Negative Sampling Without External Resources Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 22

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no resolver link, observed 2026-08-06T04:52:33.371018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:33.371018Z digest=sha256:d568d3854c6f4ecd9e4d8352b6deaaf25e43299e1ed1f5580f4680630e957042

Observation e00bb6d5-8ddd-471d-a904-aedfb3f18836 · outbound

This paper cites Linguistic bind- ing in diffusion models: Enhancing attribute correspondence through attention map alignment, 2023.

Diffusion Models with Adaptive Negative Sampling Without External Resources Linguistic bind- ing in diffusion models: Enhancing attribute correspondence through attention map alignment, 2023

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:36.559145Z

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.

source=pdf_text observed=2026-08-06T04:52:33.458780Z digest=sha256:b54db727da2e19ca9b322ec1beae0f184ad72df2f0d65d82014caab4595a5e17

Observation 7bb9da86-31b6-4a7e-89a7-a218e210058e · outbound

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

Diffusion Models with Adaptive Negative Sampling Without External Resources High-resolution image synthesis with latent diffusion models

Reference 24

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no resolver link, observed 2026-08-06T04:52:33.512523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:33.512523Z digest=sha256:58cb891185904e0177c4e608703721e6dc5c16818dbdaeda3dfe98b8638ae1d3

Observation 106ff50a-c801-4d9e-bf2b-6cc26d585996 · outbound

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

Diffusion Models with Adaptive Negative Sampling Without External Resources Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:36.376329Z

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.

source=pdf_text observed=2026-08-06T04:52:33.604813Z digest=sha256:8b1ac2a549fd764be4d5460184b0033dcec9c75cefd275aae77b033e7aad45d1

Observation f63d7b67-68fa-482f-94dd-a8bf95ad5c36 · outbound

This paper cites Berg, and Li Fei-Fei.

Diffusion Models with Adaptive Negative Sampling Without External Resources Berg, and Li Fei-Fei

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:36.200741Z

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.

source=pdf_text observed=2026-08-06T04:52:33.689783Z digest=sha256:34031c1c46fa33271ba34564ac68be6fd2598bc93179d565aabaccf6cae37372

Observation 9883a506-d069-488d-a287-313d46519eb2 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.Advances in Neural Information Processing Systems, 35:36479–36494, 2022.

Diffusion Models with Adaptive Negative Sampling Without External Resources Photorealistic text-to-image diffusion models with deep language understanding.Advances in Neural Information Processing Systems, 35:36479–36494, 2022

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:36.073934Z

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.

source=pdf_text observed=2026-08-06T04:52:33.735322Z digest=sha256:98d9d9acbaae0594d186ef99ce59de10705a2c004cc27638ecccb31cc1480a1f

Observation f394791f-bf25-4ec7-9d5a-176c9d726655 · outbound

This paper cites Improved techniques for training gans, 2016.

Diffusion Models with Adaptive Negative Sampling Without External Resources Improved techniques for training gans, 2016

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:35.920278Z

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.

source=pdf_text observed=2026-08-06T04:52:33.770754Z digest=sha256:d20f5473996b556f48e63d24257335958997362a83f9466c4bb8f75ae2e68130

Observation 51acbde5-9501-4e86-9b6a-2e398527c3ed · outbound

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

Diffusion Models with Adaptive Negative Sampling Without External Resources Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:35.795602Z

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.

source=pdf_text observed=2026-08-06T04:52:33.826580Z digest=sha256:e53fbf59da1a34f884d788f52b3119a5fc80e336180e0234dfbe51d6bb51d3c1

Observation 87bcda5c-04c1-4637-b5aa-332eac3425c3 · outbound

This paper cites Denoising Diffusion Implicit Models.

Diffusion Models with Adaptive Negative Sampling Without External Resources Denoising Diffusion Implicit Models

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-06T04:52:33.890126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:33.890126Z digest=sha256:76959affd67a4ac05464e112c64f00e1f421ef83b6821c22d98f21b98e63adc1

Observation 791a26fe-3dfc-4ccc-a42b-1a58030cd623 · outbound

This paper cites Generative modeling by esti- mating gradients of the data distribution.Advances in neural information processing systems, 32, 2019.

Diffusion Models with Adaptive Negative Sampling Without External Resources Generative modeling by esti- mating gradients of the data distribution.Advances in neural information processing systems, 32, 2019

Reference 31

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unresolved
no resolver link, observed 2026-08-06T04:52:33.976377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:33.976377Z digest=sha256:f780d7098eef0e36dcb01649563a029e2cd885a838363cd122416a832622f7bd

Observation e81d45b0-77aa-40fe-bf9d-06011f2c318b · outbound

This paper cites Adapting diffusion models for improved prompt compliance and controllable image synthesis, 2024.

Diffusion Models with Adaptive Negative Sampling Without External Resources Adapting diffusion models for improved prompt compliance and controllable image synthesis, 2024

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:35.606202Z

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.

source=pdf_text observed=2026-08-06T04:52:34.037147Z digest=sha256:a7f709ec929e6b77c6cbdef23150d5f88b64bb6bf38d0259a24f3d7de3b51666

Observation eabad5de-4fe2-49a8-8d25-4ad884ccb70c · outbound

This paper cites Sketch-guided text-to-image diffusion models, 2022.

Diffusion Models with Adaptive Negative Sampling Without External Resources Sketch-guided text-to-image diffusion models, 2022

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:35.434415Z

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.

source=pdf_text observed=2026-08-06T04:52:34.092883Z digest=sha256:08a61b1e158e072a426555672eed9f3bd414d5d4e85b86d2227fd90ec749a5d9

Observation e1ab6f44-99eb-4593-b13c-5eebbda6fd6a · outbound

This paper cites Bayesian learning via stochas- tic gradient langevin dynamics.

Diffusion Models with Adaptive Negative Sampling Without External Resources Bayesian learning via stochas- tic gradient langevin dynamics

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:35.285132Z

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.

source=pdf_text observed=2026-08-06T04:52:34.150526Z digest=sha256:8593069c61c95769dc1b9380e927b9fb1a4027612fec29feb253cc52496d4412

Observation 6cb0804d-7cf3-43c7-b50c-e4934bd6b9fd · outbound

This paper cites Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis, 2023.

Diffusion Models with Adaptive Negative Sampling Without External Resources Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis, 2023

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-06T04:52:34.243625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:52:34.243625Z digest=sha256:e8ec0a4c2e171e9b97b24f7efba871d88bbfac0551fa20953a90de87300d3174

Observation 8a339e04-e8bf-4055-9748-7b27c9ffa1e1 · outbound

This paper cites Imagereward: learning and evaluating human preferences for text-to-image generation.

Diffusion Models with Adaptive Negative Sampling Without External Resources Imagereward: learning and evaluating human preferences for text-to-image generation

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:35.136008Z

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.

source=pdf_text observed=2026-08-06T04:52:34.305158Z digest=sha256:ee7aa9ca7cec6db4825f3e36a8ac9bfba700b0738a98f69b943d1042418d52ca

Observation e2f4a43b-4408-47af-b354-25df8bb3f732 · outbound

This paper cites Scaling autoregressive models for content-rich text-to-image generation, 2022.

Diffusion Models with Adaptive Negative Sampling Without External Resources Scaling autoregressive models for content-rich text-to-image generation, 2022

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:35.032972Z

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.

source=pdf_text observed=2026-08-06T04:52:34.396044Z digest=sha256:e396b223d0c1bdf037ec9edbfe319a61c7b382f8c2825bd64fcddf1ed5e333e2

Observation a516c474-3196-453c-b862-bd5281593fdf · outbound

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

Diffusion Models with Adaptive Negative Sampling Without External Resources Adding conditional control to text-to-image diffusion models, 2023

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:34.884202Z

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.

source=pdf_text observed=2026-08-06T04:52:34.483121Z digest=sha256:be6c58b36afc9871e25ce6c8e31b378bc60878cd8cba3d4000ba84bf9a1f24ec

Observation 42a94cbb-7c30-40eb-9c21-b40dd916cc58 · outbound

This paper cites Layoutdiffusion: Controllable diffu- sion model for layout-to-image generation.

Diffusion Models with Adaptive Negative Sampling Without External Resources Layoutdiffusion: Controllable diffu- sion model for layout-to-image generation

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T04:52:34.736182Z

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.

source=pdf_text observed=2026-08-06T04:52:34.538179Z digest=sha256:51d37681adb75e049e8d400d56fc1e38c29b72b8db0b53360f6c2cc051f65395

Pith citing papers

No inbound Pith citation observations are available.