Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:43:18.795420Z
Paper Citation Record · LEDGER
As of 10 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 1 inbound Pith citation observation for arXiv:2505.14028.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:43:18.795420Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-26T17:59:00.685869Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T03:29:31.047930Z
63 of 63 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 37c694d2-d97f-46f4-b3b7-20ef663736c6 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale https : / / github
Reference 1
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Observation 18d30c89-be6a-4b45-a64a-e27f18013e1c · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale https://openai.com/chatgpt
Reference 2
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Observation 6ecc05a1-a46e-4131-9498-7faaaa549b4a · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Artflow: Unbiased image style transfer via re- versible neural flows
Reference 3
Source-reported events for the cited work
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Observation a1c9d41b-2786-45f5-9d06-965b0b4c424b · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale InstructPix2Pix: Learning to Follow Image Editing Instructions
Reference 4
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Observation 553b2c6f-edcb-4a0c-9981-a6e07d08828a · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
Reference 5
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Observation 5edde88f-5c98-4201-9ee9-96051bf47582 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Style injec- tion in diffusion: A training-free approach for adapting large- scale diffusion models for style transfer
Reference 6
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Observation 56c5289f-c3d3-4de0-90ba-dcf3639625cb · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Arbitrary style transfer via multi-adaptation network
Reference 7
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Observation 4b303751-07ad-4a31-83cd-0c7f9eb9cb09 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Implicit Style-Content Separation using B-LoRA
Reference 8
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Observation d033ac61-991f-4bfd-8fc9-7f50e92952ce · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Im- age style transfer using convolutional neural networks
Reference 9
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Observation 88d632de-1ffa-44fa-9d9c-7e37daba0aa1 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Controlling perceptual fac- tors in neural style transfer
Reference 10
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Observation 9582ff73-330f-4e5f-9e0f-0b8f4df685a3 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Aespa-net: Aesthetic pattern-aware style transfer networks
Reference 11
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Observation 34af5529-0c63-4881-ba84-2b199b3191ee · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Diff- styler: Controllable dual diffusion for text-driven image styl- ization
Reference 12
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Observation 2cc77fd3-9343-4a1b-9be9-f4a303fe5190 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Arbitrary style transfer in real-time with adaptive instance normalization
Reference 13
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Observation b7913bce-b798-42a5-a70c-672cafeec7ae · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing
Reference 14
Source-reported events for the cited work
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Observation e9ee15c6-4076-48f8-82f7-f5c10bacf287 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Training-free Content Injection using h-space in Diffusion Models
Reference 15
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Observation 150f8db9-d524-4a2a-a30d-cf250cc4134f · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Perceptual losses for real-time style transfer and super-resolution
Reference 16
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Observation 27aacb76-777a-4d51-adce-f246833adbb2 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale StyleShot: A Snapshot on Any Style
Reference 17
Source-reported events for the cited work
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Observation 66c29c55-8479-4d01-a5f7-da18995056cd · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Style transfer by relaxed optimal transport and self-similarity
Reference 18
Source-reported events for the cited work
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Observation 85e8b297-6029-4d9f-901c-9f040deb44f2 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale StyleTokenizer: Defining Image Style by a Single Instance for Controlling Diffusion Models
Reference 19
Source-reported events for the cited work
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Observation adfeefa1-4503-4564-9145-6bc5844876a0 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Universal style transfer via feature transforms
Reference 20
Source-reported events for the cited work
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Observation 6ea37057-8b68-4cd3-87f0-1bba1af1204e · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Visual Attribute Transfer through Deep Image Analogy
Reference 21
Source-reported events for the cited work
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Observation caf117cc-bb0f-4d4a-bac7-1f376c9c39c2 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale The ArtBench Dataset: Benchmarking Generative Models with Artworks
Reference 22
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Observation bf1b51ca-9bf2-4afc-8bef-db7f3a94cc2a · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Deep photo style transfer
Reference 23
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Observation 2377b198-4807-4bd7-adaf-49e7f8a01888 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Ava: A large-scale database for aesthetic visual analysis
Reference 24
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Observation fe398633-fcca-4843-af01-20e84b47d2dd · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale DINOv2: Learning Robust Visual Features without Supervision
Reference 25
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Observation f1ab0798-b49d-4dd1-bb79-c6deebdc7e3a · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Scalable diffusion models with transformers
Reference 26
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Observation c170ce1a-bd5f-4af2-b429-b9c3b4496fcf · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Dead- iff: An efficient stylization diffusion model with disentan- gled representations
Reference 27
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Observation d005c47b-b26b-42fe-bca9-3eb1391f6c13 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Learning transferable visual models from natural language supervi- sion
Reference 28
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Observation ca22cba9-4a54-44a8-8e0d-794a12d53fc2 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Reference 29
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Observation ef9ef765-b202-46fe-b424-3c75bbf0e114 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature
Reference 30
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Observation 9911184a-6e9e-41fb-9b9b-1b16ddef0e7b · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale ViPer: Visual Personalization of Generative Models via Individual Preference Learning
Reference 31
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Observation 5696ad04-51f0-4921-a1f5-03ce46daeb72 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale StyleDrop: Text-to-Image Generation in Any Style
Reference 32
Source-reported events for the cited work
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Observation 19976cb7-9530-4ffa-8a6e-b61865139c3b · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Efficient example-based painting and synthesis of 2d directional texture
Reference 33
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Observation 2ef8e3a6-ab6b-474c-9a72-1d778b0a7c62 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation
Reference 34
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Observation 23ed2673-0583-4bc0-8139-d2af418cfed2 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale InstantStyle-Plus: Style Transfer with Content-Preserving in Text-to-Image Generation
Reference 35
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Observation c89fa597-e41f-4f4e-a5ce-92ab0e1bf8c5 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Imagen editor and editbench: Advancing and evaluating text-guided image inpainting
Reference 36
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Observation c8b65517-4363-480a-b476-eb75615c4071 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Stylediffusion: Controllable disentangled style transfer via diffusion models
Reference 37
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Observation 8e585bcf-1258-4dfd-9624-028facebacbf · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale OmniGen: Unified Image Generation
Reference 38
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Observation d7704aa5-456d-4e34-8503-9f5cdddf4d56 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale CSGO: Content-Style Composition in Text-to-Image Generation
Reference 39
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Observation 0cbd304d-7758-43a9-b9ec-c6fd9b681c1f · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale FreeTuner: Any Subject in Any Style with Training-free Diffusion
Reference 40
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Observation cad9c199-e575-4477-82a2-c444d64db424 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Unresolved cited work
Reference 41
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Observation 2b1f8615-b38a-45f5-9e6c-9097de6ab7de · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Magicbrush: A manually annotated dataset for instruction- guided image editing
Reference 42
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Observation 0d3cc214-77fe-43f6-b25f-a4d64a6901d0 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Style transfer via image component analysis
Reference 43
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Observation 20abe3e3-b967-48b2-9b75-49bc8eb90c24 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Exact feature distribution matching for arbitrary style transfer and domain generalization
Reference 44
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Observation 7c38eb3c-2539-416b-9ee7-20cd561068ce · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Do- main enhanced arbitrary image style transfer via contrastive learning
Reference 45
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Observation 9db6a5e4-c491-4924-adb9-090bd81624a4 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models
Reference 46
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Observation c2f7a5dd-21f4-4e55-999e-9839978f593e · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Inversion-based style transfer with diffusion models
Reference 47
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Observation 95bdfbab-a54f-4309-aae3-2b776c687581 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Artbank: Artistic style transfer with pre-trained diffusion model and implicit style prompt bank
Reference 48
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Observation d386d345-4f7b-49a8-8b9e-942ff37ab139 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale UltraEdit: Instruction-based Fine-Grained Image Editing at Scale
Reference 49
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Observation 3339d763-5bf4-48f5-bfdb-8925ce477863 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model
Reference 50
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Observation a0d5ca82-79ab-4f68-a4cb-9ca6c6a49b74 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Unresolved cited work
Reference 51
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Reference 52
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OmniStyle: Filtering High Quality Style Transfer Data at Scale Unresolved cited work
Reference 53
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OmniStyle: Filtering High Quality Style Transfer Data at Scale Unresolved cited work
Reference 54
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Reference 56
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OmniStyle: Filtering High Quality Style Transfer Data at Scale Unresolved cited work
Reference 57
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OmniStyle: Filtering High Quality Style Transfer Data at Scale Unresolved cited work
Reference 58
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Reference 59
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OmniStyle: Filtering High Quality Style Transfer Data at Scale Unresolved cited work
Reference 60
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Observation 6221ab9b-74e2-46ab-a2f3-361376720903 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Section 1.1 delves into the process of generating content images, while Section 1.2 elaborates on the specific details of the collected style images
Reference 61
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Observation 24ff3c48-3caf-4caa-b074-98c47cc9d2b4 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale clip-vit-large- patch14,
Reference 62
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Observation f3a59b64-d6ab-44b5-bc6a-552eabcaafe3 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale Section 3.1 focuses on quantitative evaluations, including user studies and ablation studies
Reference 63
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Observation ab3b41ea-da40-4f77-8d31-178ac1dd36e8 · outbound
OmniStyle: Filtering High Quality Style Transfer Data at Scale In contrast, the outputs generated by CAST, CSGO, and StyleShot display rich details, resulting in rel- atively higher aesthetic scores
Reference 64
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Observation ef99f212-e0c7-44f2-a988-36e61fbefdbe · inbound
FreeStyle: Free Control of Style-Content Dual-Reference Generation from Community LoRA Mining OmniStyle: Filtering High Quality Style Transfer Data at Scale
Reference 20
Source-reported events for the cited work
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