SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models | Paper Citation Record

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Unresolved cited work

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This paper cites Building normalizing flows with stochastic interpolants.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Building normalizing flows with stochastic interpolants

Reference 2

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This paper cites Stochastic interpolants: A unifying framework for flows and diffusions.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Stochastic interpolants: A unifying framework for flows and diffusions

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

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Coyo-700m: Image-text pair dataset

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This paper cites Emerging properties in self-supervised vision transformers.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Emerging properties in self-supervised vision transformers

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This paper cites Neural ordinary differential equations.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Neural ordinary differential equations

Reference 6

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This paper cites Learning a similarity metric discriminatively, with application to face verification.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Learning a similarity metric discriminatively, with application to face verification

Reference 7

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This paper cites Flow matching in latent space.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Flow matching in latent space

Reference 8

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This paper cites Imagenet: A large-scale hierarchical image database.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Imagenet: A large-scale hierarchical image database

Reference 9

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This paper cites Nice: Non-linear independent components estimation.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Nice: Non-linear independent components estimation

Reference 10

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This paper cites Density estimation using real nvp.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Density estimation using real nvp

Reference 11

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This paper cites The use of multiple measurements in taxonomic problems.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models The use of multiple measurements in taxonomic problems

Reference 12

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This paper cites Discriminatory analysis: nonparametric discrimination, consistency properties.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Discriminatory analysis: nonparametric discrimination, consistency properties

Reference 13

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This paper cites Implicit style-content separation using b-lora.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Implicit style-content separation using b-lora

Reference 14

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This paper cites Diffusion Models and Representation Learning: A Survey.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Diffusion Models and Representation Learning: A Survey

Reference 15

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

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models An image is worth one word: Personalizing text-to-image generation using textual inversion

Reference 16

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This paper cites Sliderspace: Decomposing the visual capabilities of diffusion models, 2025.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Sliderspace: Decomposing the visual capabilities of diffusion models, 2025

Reference 17

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This paper cites Gatys, Alexander S.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Gatys, Alexander S

Reference 18

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This paper cites Gatys, Alexander S.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Gatys, Alexander S

Reference 19

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This paper cites Depthfm: Fast monocular depth estimation with flow matching.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Depthfm: Fast monocular depth estimation with flow matching

Reference 20

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This paper cites Flowtok: Flowing seamlessly across text and image tokens.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Flowtok: Flowing seamlessly across text and image tokens

Reference 21

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This paper cites Momentum Contrast for Unsupervised Visual Representation Learning.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Momentum Contrast for Unsupervised Visual Representation Learning

Reference 22

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This paper cites Prompt-to-prompt image editing with cross attention control.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Prompt-to-prompt image editing with cross attention control

Reference 23

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This paper cites Style aligned image generation via shared attention.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Style aligned image generation via shared attention

Reference 24

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This paper cites CLIPScore: A Reference-free Evaluation Metric for Image Captioning.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 25

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Denoising diffusion probabilistic models

Reference 26

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This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 27

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Unresolved cited work

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Gpt-4o system card

Reference 29

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Product quantization for nearest neighbor search

Reference 30

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This paper cites Perceptual losses for real-time style transfer and super-resolution.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Perceptual losses for real-time style transfer and super-resolution

Reference 31

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Recognizing Image Style

Reference 32

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Elucidating the design space of diffusion-based generative models

Reference 33

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Glow: Generative flow with invertible 1x1 convolutions

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This paper cites Rethinking style transfer: From pixels to parameterized brushstrokes.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Rethinking style transfer: From pixels to parameterized brushstrokes

Reference 35

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Unresolved cited work

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Learning linear transformations for fast image and video style transfer

Reference 37

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Flow matching for generative modeling

Reference 38

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Unresolved cited work

Reference 39

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Improved baselines with visual instruction tuning

Reference 40

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This paper cites Flowing from words to pixels: A noise-free framework for cross-modality evolution.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Flowing from words to pixels: A noise-free framework for cross-modality evolution

Reference 41

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Flow straight and fast: Learning to generate and transfer data with rectified flow

Reference 42

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers

Reference 43

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This paper cites Some methods for classification and analysis of multivariate observations.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Some methods for classification and analysis of multivariate observations

Reference 44

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models An introduction to information retrieval

Reference 45

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations

Reference 46

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Action matching: Learning stochastic dynamics from samples

Reference 47

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Representation Learning with Contrastive Predictive Coding

Reference 48

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Deadiff: An efficient stylization diffusion model with disentangled representations

Reference 49

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Learning transferable visual models from natural language supervision

Reference 50

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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Zero-shot text-to-image generation

Reference 51

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This paper cites Hierarchical text-conditional image generation with clip latents, 2022.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Hierarchical text-conditional image generation with clip latents, 2022

Reference 52

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

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models High-resolution image synthesis with latent diffusion models

Reference 53

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This paper cites Aladin: All layer adaptive instance normalization for fine-grained style similarity.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Aladin: All layer adaptive instance normalization for fine-grained style similarity

Reference 54

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This paper cites Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature

Reference 55

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This paper cites Improving deep metric learning by divide and conquer.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Improving deep metric learning by divide and conquer

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:35:10.339167Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 52737977-3c49-4815-b576-5c13767bd552 · outbound

This paper cites LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

Reference 57

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Source-reported events for the cited work

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Observation cc013455-e114-4cdc-bd93-c96c334bb17e · outbound

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

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T04:35:08.132986Z

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Observation 2d76afc8-c043-4841-b857-d8fcabe8dfc9 · outbound

This paper cites Boosting latent diffusion with flow matching.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Boosting latent diffusion with flow matching

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:35:10.079433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c194d10a-5fbd-4934-a0a9-3cf90d47a28d · outbound

This paper cites Ziplora: Any subject in any style by effectively merging loras.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Ziplora: Any subject in any style by effectively merging loras

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:35:09.865210Z

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Observation 74677e87-b082-4a98-b40b-a8de75af0a95 · outbound

This paper cites Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T04:35:08.143900Z

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Observation ed3ea15b-b1fb-402b-8ac3-4458b3c2d973 · outbound

This paper cites Very deep convolutional networks for large-scale image recognition, 2015.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Very deep convolutional networks for large-scale image recognition, 2015

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:35:09.677102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 79eb81ac-e394-46a4-bda3-204f12976843 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Deep unsupervised learning using nonequilibrium thermodynamics

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T04:35:08.151083Z

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Observation 7153ecdd-c4a6-48e3-85db-20d757f8a730 · outbound

This paper cites Measuring style similarity in diffusion models.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Measuring style similarity in diffusion models

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:35:09.490700Z

Source-reported events for the cited work

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Observation 54a17369-7b34-42b5-8b04-84a51374486b · outbound

This paper cites Measuring style similarity in diffusion models, 2024 b.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Measuring style similarity in diffusion models, 2024 b

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:35:09.274573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6c8b20b2-ccb8-4896-b321-726b66caef99 · outbound

This paper cites Denoising diffusion implicit models.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Denoising diffusion implicit models

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:35:09.171118Z

Source-reported events for the cited work

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Observation c8683466-86cb-4db8-8ddc-e2840732af4a · outbound

This paper cites Score-based generative modeling through stochastic differential equations.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Score-based generative modeling through stochastic differential equations

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:35:08.961990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 085fddce-2bd0-4471-901e-1c95814a50a9 · outbound

This paper cites Cleandift: Diffusion features without noise.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Cleandift: Diffusion features without noise

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:35:08.749745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3686093c-bf83-4f11-ad56-62ee594242b7 · outbound

This paper cites Ctrloralter: Conditional loradapter for efficient 0-shot control and altering of t2i models.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Ctrloralter: Conditional loradapter for efficient 0-shot control and altering of t2i models

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:35:08.657858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b969667c-0dad-45f0-94f6-6cd9fb313e4e · outbound

This paper cites Emergent correspondence from image diffusion.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Emergent correspondence from image diffusion

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:35:08.632174Z

Source-reported events for the cited work

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Observation 1ff38868-8983-492f-8088-f514f7665c14 · outbound

This paper cites Improving and generalizing flow-based generative models with minibatch optimal transport.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Improving and generalizing flow-based generative models with minibatch optimal transport

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:35:08.603883Z

Source-reported events for the cited work

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Observation 342982d1-e033-4af9-ace6-03108ddcf661 · outbound

This paper cites Visualizing data using t-sne.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Visualizing data using t-sne

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T04:35:08.180946Z

Source-reported events for the cited work

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Observation a7fc8a73-bbd0-4318-9644-7f5efc87e09e · outbound

This paper cites P+: Extended Textual Conditioning in Text-to-Image Generation.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models P+: Extended Textual Conditioning in Text-to-Image Generation

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T04:35:08.184334Z

Source-reported events for the cited work

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Observation 03ec9312-4fc3-4506-9f7f-af572ce8ed97 · outbound

This paper cites InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T04:35:08.187882Z

Source-reported events for the cited work

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Observation 20f79f1b-7a09-4bf3-92e0-586eccdba709 · outbound

This paper cites Evaluating data attribution for text-to-image models.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Evaluating data attribution for text-to-image models

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:35:08.562570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6b459bb3-98ff-42d5-8c2c-6c44d7e977b0 · outbound

This paper cites Glstylenet: exquisite style transfer combining global and local pyramid features.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Glstylenet: exquisite style transfer combining global and local pyramid features

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:35:08.526592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cc628cf7-edb3-4ad5-9a26-c0c772569abf · outbound

This paper cites Microast: Towards super-fast ultra-resolution arbitrary style transfer.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Microast: Towards super-fast ultra-resolution arbitrary style transfer

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:35:08.500915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5639276f-c696-47b7-bb11-4a818e9357c0 · outbound

This paper cites Bam! the behance artistic media dataset for recognition beyond photography.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Bam! the behance artistic media dataset for recognition beyond photography

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:35:08.477081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ad7d99ea-1d1f-4955-9571-0b738ffaf8b3 · outbound

This paper cites Csgo: Content-style composition in text-to-image generation, 2024.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Csgo: Content-style composition in text-to-image generation, 2024

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:35:08.462957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bb4401f6-6f08-4912-8241-cb68130c264f · outbound

This paper cites A tale of two features: Stable diffusion complements dino for zero-shot semantic correspondence.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models A tale of two features: Stable diffusion complements dino for zero-shot semantic correspondence

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:35:08.452677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f21252f1-af65-43a9-9cef-be06a55e32f2 · outbound

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

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Adding conditional control to text-to-image diffusion models, 2023 b

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-06T04:35:08.212949Z

Source-reported events for the cited work

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Observation 5495ab14-1209-4394-b8db-8474efb28527 · outbound

This paper cites Domain enhanced arbitrary image style transfer via contrastive learning.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Domain enhanced arbitrary image style transfer via contrastive learning

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:35:08.434329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8634d751-90ea-4859-a102-6614fceca80f · outbound

This paper cites Style fader generative adversarial networks for style degree controllable artistic style transfer.

SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models Style fader generative adversarial networks for style degree controllable artistic style transfer

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:35:08.421665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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