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Paper Citation Record · LEDGER

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization

As of 7 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2505.20975.

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

pith.paper-citation-record.v1
2505.20975 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:50:03.326257Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

40 of 40 outbound references displayed

  • verified exact0
  • verified fuzzy32
  • unresolved8
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation eb256cc1-8e04-4a68-9fed-b83fac120b93 · outbound

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

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 1

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unresolved
no resolver link, observed 2026-08-07T13:49:58.881550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:49:58.881550Z digest=sha256:79b0c5571011e1ddf9590b7a2a13c58a17ada5b456cd47cc52a42c979f6bb013

Observation 4dce72ee-5fb4-4748-ac46-41c630dbe6b0 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Photorealistic text-to-image diffusion models with deep language understanding,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:10.841388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:49:58.984243Z digest=sha256:1a2486395f5bb7a9a1ca4149cfaf481e2b392401b5a2da6281ea4dbbd8d912b7

Observation 7b0ee646-87d5-44a6-9a2b-0aa77edec3b9 · outbound

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

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization High-resolution image synthesis with latent diffusion models,

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T13:49:59.098373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:49:59.098373Z digest=sha256:28c9ec32f8fe2346f0f99b710a6e08d85dea9ead7d93783fb230c161b93aa79d

Observation 14897731-80bb-43ac-b432-b1616b9ea7f2 · outbound

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

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:10.575399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:49:59.219039Z digest=sha256:5b87d4e6fa796947ef62a2229274db13165f5283d53abf4a8d12d3ef9390a848

Observation 389028f6-7a34-4ab4-9f35-b82564ddf24b · outbound

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

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization An image is worth one word: Personalizing text-to-image generation using textual inversion,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:10.344119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:49:59.298870Z digest=sha256:7487ed674b9a2681b363641b98d5f722278bdcd1c56c57c15adbd536dc41e722

Observation 39c5280c-19fc-4c8f-a351-7dbb3394c3e0 · outbound

This paper cites Id-aligner: Enhancing identity- preserving text-to-image generation with reward feedback learning,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Id-aligner: Enhancing identity- preserving text-to-image generation with reward feedback learning,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:10.184311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:49:59.414470Z digest=sha256:61c80410967fc2c4950534d215839478bad9ed35766df4cab52d08ada82c35ff

Observation 472f1d7a-5c0b-4db0-ad71-4ccd6a72ac73 · outbound

This paper cites Proximal preference optimization for diffusion models,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Proximal preference optimization for diffusion models,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:09.977946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:49:59.523808Z digest=sha256:54d4c7131a431cf69eff9c8e5e0ea779b26b1e1b7350b2e3c36151ee44db4d4b

Observation 880df1b6-ebca-4fd1-976c-22feece3d93d · outbound

This paper cites Versat2i: Improving text-to-image models with versatile reward,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Versat2i: Improving text-to-image models with versatile reward,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:09.819079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:49:59.624519Z digest=sha256:111f2f17e9677856425681a21c4582dd81a025de7c049fec8dc6bba0402c39c0

Observation 1e634ca9-de88-4210-be35-9a7b6bb85f74 · outbound

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

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Imagereward: Learning and evaluating human preferences for text-to-image generation,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:09.632234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:49:59.712742Z digest=sha256:762e6efb1bc458d2fb1b087f25ed8fc571d5c97a41561cd42d9046f1dc82dc3e

Observation 8a77b5d1-75cf-4d22-8770-4ec05998c8bf · outbound

This paper cites Elucidating optimal reward-diversity tradeoffs in text-to-image diffusion models,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Elucidating optimal reward-diversity tradeoffs in text-to-image diffusion models,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:09.414794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:49:59.776123Z digest=sha256:0f050251a8f609278ea11b25e9f073650e437e3f2fccc89f7f79f9ac4d2275fc

Observation 222836f8-4a79-4c69-aef5-ca4ccefa34d4 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Direct preference optimization: Your language model is secretly a reward model,

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T13:49:59.901706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:49:59.901706Z digest=sha256:c46b280e8d45ca20bede8b03a7e941e3e90fec1ec3009f7a9e942c6bf23ec441

Observation df87d232-5a89-487e-ac82-e6f24f725d75 · outbound

This paper cites Svdiff: Compact parameter space for diffusion fine-tuning,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Svdiff: Compact parameter space for diffusion fine-tuning,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:09.241389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:00.010803Z digest=sha256:2a5faf6ab02ccb5382378f251d644644f9a3f8fd5de6546b01b242b6617bd549

Observation 3d6b4452-6d5a-4c37-b998-991325db4b5b · outbound

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

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Multi-concept customization of text-to-image diffusion,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:09.049956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:00.111502Z digest=sha256:f916171abc7d7b51f7c0c799ba754fb7e3d39443810138acf5be29c72d5c9f53

Observation cfef7280-f73b-4106-85f6-93e22fe74bb3 · outbound

This paper cites Idadapter: Learning mixed features for tuning-free personalization of text-to-image models,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Idadapter: Learning mixed features for tuning-free personalization of text-to-image models,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:08.874938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:00.217842Z digest=sha256:c3b63a616e82f3484e7024503b35984bc27f26d6eb7f64c766c5c7caf2f595a9

Observation cf50bf33-2535-401d-b5a9-7d2973804dae · outbound

This paper cites Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:08.578301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:00.301585Z digest=sha256:b23e06c130086515eccef294d3638a447523bea59af516982ddc7af784913f0e

Observation 93a898bc-4ddf-4047-8e95-48b54b40839a · outbound

This paper cites Hyperdreambooth: Hypernetworks for fast personalization of text-to-image models,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Hyperdreambooth: Hypernetworks for fast personalization of text-to-image models,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:08.265001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:00.413711Z digest=sha256:efa0396aa45fd27437c178bc90496b5d60e545136f34eda8599d2d240e873c12

Observation dcd9344f-e251-4125-8c00-339d0145f7a9 · outbound

This paper cites Blip-diffusion: Pre-trained subject representation for controllable text-to-image generation and editing,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Blip-diffusion: Pre-trained subject representation for controllable text-to-image generation and editing,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:07.944540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:00.538526Z digest=sha256:8036ac25b6a4e78fa69cc9dd5c43e120dfdcb85a30a690be376c6334a318f0c5

Observation 4bbfeafb-d449-4e1a-aa8a-17142dd34de4 · outbound

This paper cites Subject-diffusion:open domain personalized text-to-image generation without test-time fine-tuning,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Subject-diffusion:open domain personalized text-to-image generation without test-time fine-tuning,

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:07.684279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:00.660756Z digest=sha256:a4a67663eda6390c158d2f6926170f54305bfe9005491a576e9629d47f539f7b

Observation 9434dca1-6378-4738-9323-31076e436920 · outbound

This paper cites Enhancing diffusion models with text-encoder reinforcement learning,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Enhancing diffusion models with text-encoder reinforcement learning,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:07.367850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:00.779672Z digest=sha256:50b666a3b24fdc9cbd4be93f9e707cfb6c4c6f4125485e44b357fdbaa49a19ee

Observation 310ec11a-3b5a-4253-94e5-929a10616563 · outbound

This paper cites Towards better alignment: Training diffusion models with reinforcement learning against sparse rewards,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Towards better alignment: Training diffusion models with reinforcement learning against sparse rewards,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:07.092319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:00.875943Z digest=sha256:6e6596de17de78518461365f1fdfdcdd822917a56ca3331d54b3e3ac3657e045

Observation 00fc7862-dce2-4282-983e-59fb79e86688 · outbound

This paper cites Diffusion model alignment using direct preference optimization,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Diffusion model alignment using direct preference optimization,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:06.749823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:00.973645Z digest=sha256:d724b7b3fd5c7e0852a9766c581893ae76f36618799cff753d6fc0c511f0073f

Observation 4ad55f5e-0294-46e9-9cc9-a33b42547b38 · outbound

This paper cites Tuning timestep-distilled diffusion model using pairwise sample optimization,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Tuning timestep-distilled diffusion model using pairwise sample optimization,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:06.346983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:01.097950Z digest=sha256:1e46b0ef1f6b999df718d42c77335d9fabe2c3835866ecb5413e8d3e9d40fbba

Observation 00b7e375-6fe9-4fb0-8182-8340284dadf4 · outbound

This paper cites Patchdpo: Patch-level dpo for finetuning-free personalized image generation,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Patchdpo: Patch-level dpo for finetuning-free personalized image generation,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:05.980663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:01.224934Z digest=sha256:d2b742bed2fd4ac3e99c6c07a877947a288f7131f42308c50220487e03cd7f1a

Observation e96ac835-2e05-408a-8909-8ebbd0feb3a8 · outbound

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

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization High-resolution image synthesis with latent diffusion models,

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:05.689612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:01.341393Z digest=sha256:8f0810ee0f0345193edf32602a38fa47214e1db0c25a608edc9e41360d151d5e

Observation 263e868b-cf4b-44e5-b4c5-c8243145012f · outbound

This paper cites Auto-encoding variational bayes,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Auto-encoding variational bayes,

Reference 25

Resolution
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no resolver link, observed 2026-08-07T13:50:01.459728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:50:01.459728Z digest=sha256:6a9fac417b8e19bcfce85852b804df535bbce5aaf3c43c8dfea9696683196168

Observation d75def03-a022-41f8-a71d-48be5cc0786b · outbound

This paper cites Denoising diffusion implicit models,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Denoising diffusion implicit models,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:05.386149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:01.546265Z digest=sha256:9d3c72faa30d6397b0267c0865063c2badaabe4a7045a244f32c9be640c5037b

Observation 15324aed-3188-478a-8d29-9325a053613e · outbound

This paper cites Training language models to follow instructions with human feedback,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Training language models to follow instructions with human feedback,

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-07T13:50:01.654923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:50:01.654923Z digest=sha256:c177b97a5bbc9208ffe613afe38d52ae1e6708a05e224bb64b02c449e53f1ff6

Observation c46f33e1-f6d0-4b01-92ee-1bc1748694a9 · outbound

This paper cites Realcustom: Narrowing real text word for real-time open-domain text-to-image customization,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Realcustom: Narrowing real text word for real-time open-domain text-to-image customization,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:05.156276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:01.763822Z digest=sha256:76a9e6984011da0eeb861ebd4f0ac78f6f8a83ca91a82160d5ef5de75045f1ab

Observation 09727e1c-d7a4-4c23-9fce-f8241ee9caa8 · outbound

This paper cites Beyond fine-tuning: A systematic study of sampling techniques in personalized image generation,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Beyond fine-tuning: A systematic study of sampling techniques in personalized image generation,

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:04.898712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:01.836610Z digest=sha256:cb1ba8a30b9eee81e9220d8bcca21e6a09d0056f72fda2b5682e0257471b3622

Observation 1fff47ed-eead-496c-b4f6-c788c9463df7 · outbound

This paper cites Photoswap: Personalized subject swapping in images,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Photoswap: Personalized subject swapping in images,

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:04.625710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:01.882440Z digest=sha256:ab33fa8531e85e7d87376124090c1da26f60f41af0aea261516f96f1d3c9bc70

Observation d4993356-3d3f-4b1a-8513-01c46ebc92ec · outbound

This paper cites Enhancing detail preservation for customized text-to- image generation: A regularization-free approach,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Enhancing detail preservation for customized text-to- image generation: A regularization-free approach,

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:04.449207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:01.912225Z digest=sha256:b30b21fc591d3d0efd5b7c061b2bece01ed74bd0b6738259e75f0dc71557dbe2

Observation 7ec7d4ff-9626-4e4f-b34f-00cf9e06c881 · outbound

This paper cites Microsoft coco: Common objects in context,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Microsoft coco: Common objects in context,

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-07T13:50:01.991656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:50:01.991656Z digest=sha256:219013822917c00e8af3abaa14a409850a1646eac52a5e559a1f2dca0fae49d3

Observation 22446074-ae31-4533-8ef4-26b09ea458c1 · outbound

This paper cites Gpt-4 technical report,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Gpt-4 technical report,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:04.302817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:02.142048Z digest=sha256:4c97fcaa8e6e0be24aea0c1b708d067cfbf4da9f543296f90b38c7238f71d5ac

Observation ec364cf1-9f4d-4a9b-934e-0da259922cf1 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Learning transferable visual models from natural language supervision,

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-07T13:50:02.254620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:50:02.254620Z digest=sha256:cb999d7708be2684bec377e4370d46e81e65e0c18ce32f5b4c64d3b201d9b30c

Observation 747bcdf2-e09c-46f8-a28b-8e4f1de0370b · outbound

This paper cites Pseudo numerical methods for diffusion models on manifolds,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Pseudo numerical methods for diffusion models on manifolds,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:04.130697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:02.479182Z digest=sha256:5e1b514c449c5701ac61979a82335fa808b287966f4529e2f8cf27e995f648d2

Observation 78ef03d5-9229-4ff6-87a4-3f07122c2194 · outbound

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

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Elucidating the design space of diffusion-based generative models,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:03.939849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:02.654638Z digest=sha256:4cdf0f25e0214d696991ef5307cb3d77827f45b845e737a85d1b2d412239353c

Observation 1ce82a0e-61e8-40e3-8ebd-e1931db00ac7 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization U-net: Convolutional networks for biomedical image segmentation,

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T13:50:02.856336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:50:02.856336Z digest=sha256:6bc73c72b3d912069477b4d560de633fee7085bd93a0ffaa33eaa7b2260bd51d

Observation 2bba11cf-9c38-4cfd-955c-7443f1d33edb · outbound

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

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Sdxl: Improving latent diffusion models for high-resolution image synthesis,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:03.759112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:03.053298Z digest=sha256:c2fa9761839eef20e392e838080bf37b2bf1aca52d4a3e937fe7fac5767ba35c

Observation 20978567-28a3-495b-b2b1-9a10329db199 · outbound

This paper cites Lora: Low-rank adaptation of large language models,.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Lora: Low-rank adaptation of large language models,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:03.685324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:03.154122Z digest=sha256:b04b40a4da5de5686e531595233d5a09dcdcb992cc2dbbeaa6e13564d7c660e4

Observation 6a402cd2-b718-4c79-8add-1a64dd607db9 · outbound

This paper cites Which image is more consistent with the text prompt?.

DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization Which image is more consistent with the text prompt?

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:50:03.674299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:50:03.326257Z digest=sha256:4b80238efe4486bca37384c4c6ce05bbd47a743a0e70dce810434e10772ba606

Pith citing papers

No inbound Pith citation observations are available.