Adversarial Concept Distillation for One-Step Diffusion Personalization | Paper Citation Record

Observation 2e3b79da-118e-49a4-8d2f-436dd5e9e40d · outbound

This paper cites GPT-4 Technical Report.

Adversarial Concept Distillation for One-Step Diffusion Personalization GPT-4 Technical Report

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-18T04:50:53.763501Z

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Observation b333d053-8077-4f33-84aa-df27f4ba1665 · outbound

This paper cites Stellar: Systematic Evaluation of Human-Centric Personalized Text-to-Image Methods.

Adversarial Concept Distillation for One-Step Diffusion Personalization Stellar: Systematic Evaluation of Human-Centric Personalized Text-to-Image Methods

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:50:53.757387Z

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Observation 9da23b19-9b27-40c7-8198-6e09c5d787be · outbound

This paper cites An image is worth multiple words: Multi-attribute inversion for constrained text-to-image synthesis.

Adversarial Concept Distillation for One-Step Diffusion Personalization An image is worth multiple words: Multi-attribute inversion for constrained text-to-image synthesis

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:52:24.273998Z

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Observation ef12195a-2c17-4b61-bc2a-2f327c03db3f · outbound

This paper cites A neural space-time representation for text-to-image personalization.ACM Transactions on Graphics (TOG), 42(6):1–10.

Adversarial Concept Distillation for One-Step Diffusion Personalization A neural space-time representation for text-to-image personalization.ACM Transactions on Graphics (TOG), 42(6):1–10

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:52:24.260450Z

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Observation 43f0a5bc-6a0c-4cdf-9ebe-d5fa5a5d5386 · outbound

This paper cites an unresolved cited work.

Adversarial Concept Distillation for One-Step Diffusion Personalization Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-05-18T04:52:24.271188Z

Source-reported events for the cited work

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Observation 311afe8a-0618-42c6-bd19-8a344ed4130e · outbound

This paper cites Kandinsky 3.0 Technical Report.

Adversarial Concept Distillation for One-Step Diffusion Personalization Kandinsky 3.0 Technical Report

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:50:53.744856Z

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Observation f7f68c5b-2c23-4b16-9f25-7ad7bc77e581 · outbound

This paper cites Break-a- scene: Extracting multiple concepts from a single image.SIGGRAPH Asia 2023.

Adversarial Concept Distillation for One-Step Diffusion Personalization Break-a- scene: Extracting multiple concepts from a single image.SIGGRAPH Asia 2023

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:52:24.266205Z

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Observation 2639d13d-3b52-4d63-8640-59f1ee825839 · outbound

This paper cites Colorpeel: Color prompt learning with diffusion models via color and shape disentanglement.

Adversarial Concept Distillation for One-Step Diffusion Personalization Colorpeel: Color prompt learning with diffusion models via color and shape disentanglement

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:52:24.279350Z

Source-reported events for the cited work

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Observation 24ac5ecf-dda6-4645-b925-d0c440b4c5d3 · outbound

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

Adversarial Concept Distillation for One-Step Diffusion Personalization Emerging properties in self-supervised vision transformers

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:52:24.268796Z

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Observation c0e41a5b-6e48-4ca3-93c1-64f368c05aa1 · outbound

This paper cites Efficient geometry- aware 3d generative adversarial networks.

Adversarial Concept Distillation for One-Step Diffusion Personalization Efficient geometry- aware 3d generative adversarial networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:52:24.276801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 52d119ce-1930-42b7-b741-8ea8dd5fcf5d · outbound

This paper cites XVerse: Consistent Multi-Subject Control of Identity and Semantic Attributes via DiT Modulation.

Adversarial Concept Distillation for One-Step Diffusion Personalization XVerse: Consistent Multi-Subject Control of Identity and Semantic Attributes via DiT Modulation

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:50:53.769526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b47198b6-a3f2-49de-80a7-5c3276e1e038 · outbound

This paper cites Disenbooth: Identity-preserving disentangled tuning for subject-driven text-to-image generation.International Conference on Learning Representations.

Adversarial Concept Distillation for One-Step Diffusion Personalization Disenbooth: Identity-preserving disentangled tuning for subject-driven text-to-image generation.International Conference on Learning Representations

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:52:24.263469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c4310ed7-13f7-40c0-a3bf-247b66d76374 · outbound

This paper cites Subject-driven text-to-image generation via apprenticeship learning.

Adversarial Concept Distillation for One-Step Diffusion Personalization Subject-driven text-to-image generation via apprenticeship learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.875968Z

Source-reported events for the cited work

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Observation 7471d467-5dd9-4e63-b20c-940b63bdd091 · outbound

This paper cites Re-imagen: Retrieval- augmented text-to-image generator.

Adversarial Concept Distillation for One-Step Diffusion Personalization Re-imagen: Retrieval- augmented text-to-image generator

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.922654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f553b8b3-21c2-479c-98c4-728a44db0a8c · outbound

This paper cites Fine-tuning visual autoregressive models for subject-driven generation.Proceedings of the International Conference on Computer Vision.

Adversarial Concept Distillation for One-Step Diffusion Personalization Fine-tuning visual autoregressive models for subject-driven generation.Proceedings of the International Conference on Computer Vision

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.984127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5f64e2ff-893d-4ef5-bbe3-9c24b21776c4 · outbound

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

Adversarial Concept Distillation for One-Step Diffusion Personalization Idadapter: Learning mixed features for tuning-free personalization of text-to-image models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.765057Z

Source-reported events for the cited work

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Observation 22c6bd7e-dcf2-4ea1-a2ba-56892cb0fec0 · outbound

This paper cites Swiftbrush v2: Make your one-step diffusion model better than its teacher.European Conference on Computer Vision.

Adversarial Concept Distillation for One-Step Diffusion Personalization Swiftbrush v2: Make your one-step diffusion model better than its teacher.European Conference on Computer Vision

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.760753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e85975e9-6bd5-4037-acaf-e99e88c2b588 · outbound

This paper cites Emerging Properties in Unified Multimodal Pretraining.

Adversarial Concept Distillation for One-Step Diffusion Personalization Emerging Properties in Unified Multimodal Pretraining

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-05-18T04:50:53.622865Z

Source-reported events for the cited work

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Observation 28e86c35-909e-4081-8cec-49065405f3ef · outbound

This paper cites Freecustom: Tuning-free customized image generation for multi-concept composition.

Adversarial Concept Distillation for One-Step Diffusion Personalization Freecustom: Tuning-free customized image generation for multi-concept composition

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.792953Z

Source-reported events for the cited work

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Observation 99b3295e-631f-4406-81cb-86933e3d038f · outbound

This paper cites DreamArtist++: Controllable One-Shot Text-to-Image Generation via Positive-Negative Adapter.

Adversarial Concept Distillation for One-Step Diffusion Personalization DreamArtist++: Controllable One-Shot Text-to-Image Generation via Positive-Negative Adapter

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:50:53.636864Z

Source-reported events for the cited work

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Observation d865cc08-cfc3-4ffc-a8dc-4df63aa07343 · outbound

This paper cites An image is worth one word: Personalizing text-to-image generation using textual inversion.International Conference on Learning Representations.

Adversarial Concept Distillation for One-Step Diffusion Personalization An image is worth one word: Personalizing text-to-image generation using textual inversion.International Conference on Learning Representations

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.994834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c10f0506-d936-448f-9631-0ee430a9b297 · outbound

This paper cites Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models.

Adversarial Concept Distillation for One-Step Diffusion Personalization Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:50:53.669077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c1a52294-0177-4853-b9ae-a4e57d8513c7 · outbound

This paper cites Lcm-lookahead for encoder-based text-to-image personalization.

Adversarial Concept Distillation for One-Step Diffusion Personalization Lcm-lookahead for encoder-based text-to-image personalization

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:56.006451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 430eadad-35e9-43cd-a716-dd326b777293 · outbound

This paper cites Tokenverse: Versatile multi-concept personalization in token modulation space.Proceedings of the International Conference on Computer Vision.

Adversarial Concept Distillation for One-Step Diffusion Personalization Tokenverse: Versatile multi-concept personalization in token modulation space.Proceedings of the International Conference on Computer Vision

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.894210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 90a0cf92-6347-4d3f-ada3-e06dfa454861 · outbound

This paper cites Mix-of-show: Decentralized low-rank adap- tation for multi-concept customization of diffusion models.Advances in Neural Information Processing Systems, 36.

Adversarial Concept Distillation for One-Step Diffusion Personalization Mix-of-show: Decentralized low-rank adap- tation for multi-concept customization of diffusion models.Advances in Neural Information Processing Systems, 36

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.883214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 08343f59-b572-41cb-920f-361234eb5be5 · outbound

This paper cites Pulid: Pure and lightning id customization via contrastive alignment.Advances in neural information processing systems, 37:36777–36804.

Adversarial Concept Distillation for One-Step Diffusion Personalization Pulid: Pure and lightning id customization via contrastive alignment.Advances in neural information processing systems, 37:36777–36804

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.886892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 15be3381-2b4d-4050-b07d-f2e984cb8eaa · outbound

This paper cites Infinity: Scaling bitwise autoregressive modeling for high-resolution image synthesis.

Adversarial Concept Distillation for One-Step Diffusion Personalization Infinity: Scaling bitwise autoregressive modeling for high-resolution image synthesis

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.897776Z

Source-reported events for the cited work

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Observation 5a3a4a65-9153-4e28-9627-2039764bcba3 · outbound

This paper cites Svdiff: Compact parameter space for diffusion fine-tuning.Proceedings of the International Conference on Computer Vision.

Adversarial Concept Distillation for One-Step Diffusion Personalization Svdiff: Compact parameter space for diffusion fine-tuning.Proceedings of the International Conference on Computer Vision

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.926285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9e89a15a-1954-4795-803a-c598dfa6de29 · outbound

This paper cites Multiscale sliced wasserstein distances as perceptual color difference measures.

Adversarial Concept Distillation for One-Step Diffusion Personalization Multiscale sliced wasserstein distances as perceptual color difference measures

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.942084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ac058f2e-2721-45fd-b5bd-9cbbc29b0293 · outbound

This paper cites Prompt-to-prompt image editing with cross attention control.International Conference on Learning Representations.

Adversarial Concept Distillation for One-Step Diffusion Personalization Prompt-to-prompt image editing with cross attention control.International Conference on Learning Representations

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.945731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ee8d2f5f-f941-4528-8931-db58ed6ea9a4 · outbound

This paper cites Denoising diffusion probabilistic models.Advances in Neural Information Processing Systems, 33:6840–6851.

Adversarial Concept Distillation for One-Step Diffusion Personalization Denoising diffusion probabilistic models.Advances in Neural Information Processing Systems, 33:6840–6851

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:56.010448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3c5fdc89-34db-45c7-9eb5-eae715f4b798 · outbound

This paper cites Classdiffusion: More aligned personalization tuning with explicit class guidance.

Adversarial Concept Distillation for One-Step Diffusion Personalization Classdiffusion: More aligned personalization tuning with explicit class guidance

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.833199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1eebdab3-f4cd-4077-89c2-a3c475cdbb93 · outbound

This paper cites ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity Preserving.

Adversarial Concept Distillation for One-Step Diffusion Personalization ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity Preserving

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T04:50:53.662603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8ce02585-9787-44d7-8310-3e3f4a295ea5 · outbound

This paper cites Resolving multi-condition confusion for finetuning-free personalized image generation.Proceedings of the Conference on Artificial Intelligence.

Adversarial Concept Distillation for One-Step Diffusion Personalization Resolving multi-condition confusion for finetuning-free personalized image generation.Proceedings of the Conference on Artificial Intelligence

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.825913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bf1391f0-2157-4b99-96b8-adce5c770dd6 · outbound

This paper cites Taming Encoder for Zero Fine-tuning Image Customization with Text-to-Image Diffusion Models.

Adversarial Concept Distillation for One-Step Diffusion Personalization Taming Encoder for Zero Fine-tuning Image Customization with Text-to-Image Diffusion Models

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:50:53.565443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5b89bd42-0d42-4017-8868-f3043e6a0fe4 · outbound

This paper cites Infiniteyou: Flexible photo recrafting while preserving your identity.Proceedings of the International Conference on Computer Vision.

Adversarial Concept Distillation for One-Step Diffusion Personalization Infiniteyou: Flexible photo recrafting while preserving your identity.Proceedings of the International Conference on Computer Vision

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.796800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5ba84376-2b05-44b2-8419-090b2e729d18 · outbound

This paper cites Omg: Occlusion-friendly personalized multi-concept generation in diffusion models.

Adversarial Concept Distillation for One-Step Diffusion Personalization Omg: Occlusion-friendly personalized multi-concept generation in diffusion models

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.804344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 422b84ec-3a9c-40b8-be45-b21ea26d8b92 · outbound

This paper cites Generating multi- image synthetic data for text-to-image customization.

Adversarial Concept Distillation for One-Step Diffusion Personalization Generating multi- image synthetic data for text-to-image customization

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Resolution: verified exact; arxiv_id, observed 2026-05-18T04:50:53.656372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a3c30249-e2e9-4818-963e-21dd7f8f8e56 · outbound

This paper cites Multi- concept customization of text-to-image diffusion.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

Adversarial Concept Distillation for One-Step Diffusion Personalization Multi- concept customization of text-to-image diffusion.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.839706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2383c79f-1755-453c-b223-c8b6185aaae6 · outbound

This paper cites Ensembling off-the-shelf models for gan training.

Adversarial Concept Distillation for One-Step Diffusion Personalization Ensembling off-the-shelf models for gan training

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.998198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 65f0c6b4-bbb5-4e81-aa29-4024920e4728 · outbound

This paper cites Flux.https://github.com/black-forest-labs/flux.

Adversarial Concept Distillation for One-Step Diffusion Personalization Flux.https://github.com/black-forest-labs/flux

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.934060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5406362b-147f-44f9-832f-08db9e78b145 · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.International Conference on Machine Learning.

Adversarial Concept Distillation for One-Step Diffusion Personalization Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.International Conference on Machine Learning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.937919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 78533fa8-8f5e-4616-af1d-7bac508773da · outbound

This paper cites Photomaker: Customizing realistic human photos via stacked id embedding.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

Adversarial Concept Distillation for One-Step Diffusion Personalization Photomaker: Customizing realistic human photos via stacked id embedding.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.949373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ee3fee2d-43b8-4bb5-9461-141dbb0aacb6 · outbound

This paper cites A Comprehensive Survey on Visual Concept Mining in Text-to-image Diffusion Models.

Adversarial Concept Distillation for One-Step Diffusion Personalization A Comprehensive Survey on Visual Concept Mining in Text-to-image Diffusion Models

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Resolution: verified exact; arxiv_id, observed 2026-05-18T04:50:53.732584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 29385e10-0063-4c01-8c32-f33c3519fcdf · outbound

This paper cites Distilled decoding 1: One-step sampling of image auto-regressive models with flow matching.International Conference on Learning Representations.

Adversarial Concept Distillation for One-Step Diffusion Personalization Distilled decoding 1: One-step sampling of image auto-regressive models with flow matching.International Conference on Learning Representations

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Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.890400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0ee02bd0-6d20-431a-b442-87c23376afc9 · outbound

This paper cites Cones: Concept neurons in diffusion models for customized generation.

Adversarial Concept Distillation for One-Step Diffusion Personalization Cones: Concept neurons in diffusion models for customized generation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.901429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a9f60eae-5756-4f6b-835c-94c66b17b3e0 · outbound

This paper cites Customizable image synthesis with multiple subjects.

Adversarial Concept Distillation for One-Step Diffusion Personalization Customizable image synthesis with multiple subjects

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.909796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 27d469b6-9f13-4c8b-952e-40c217705001 · outbound

This paper cites Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps.Advances in Neural Information Processing Systems, 35:5775–5787.

Adversarial Concept Distillation for One-Step Diffusion Personalization Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps.Advances in Neural Information Processing Systems, 35:5775–5787

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.953597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d1c8c594-b5e5-46c1-a947-2eb3153154f0 · outbound

This paper cites Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference.

Adversarial Concept Distillation for One-Step Diffusion Personalization Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-05-18T04:50:53.601436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3548b993-8b2e-481a-b630-ee13786c7597 · outbound

This paper cites LCM-LoRA: A Universal Stable-Diffusion Acceleration Module.

Adversarial Concept Distillation for One-Step Diffusion Personalization LCM-LoRA: A Universal Stable-Diffusion Acceleration Module

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:50:53.580581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e69b97ed-f288-417e-b6fb-b38cc2f181d1 · outbound

This paper cites Subject-diffusion: Open domain personalized text-to-image generation without test-time fine-tuning.Proceedings of the ACM SIGGRAPH Conference on Computer Graphics.

Adversarial Concept Distillation for One-Step Diffusion Personalization Subject-diffusion: Open domain personalized text-to-image generation without test-time fine-tuning.Proceedings of the ACM SIGGRAPH Conference on Computer Graphics

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.862296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 35bc656d-ec00-47c0-acc8-b57d01720081 · outbound

This paper cites Overview of intelligent video coding: from model-based to learning-based approaches.

Adversarial Concept Distillation for One-Step Diffusion Personalization Overview of intelligent video coding: from model-based to learning-based approaches

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.865401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a1e344d6-d5dd-4349-9efd-9c5b58bbda1a · outbound

This paper cites Unified Multi-Modal Latent Diffusion for Joint Subject and Text Conditional Image Generation.

Adversarial Concept Distillation for One-Step Diffusion Personalization Unified Multi-Modal Latent Diffusion for Joint Subject and Text Conditional Image Generation

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:50:53.616261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8de96be6-6130-4a8f-acef-ed29f0ea412c · outbound

This paper cites T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models.Proceedings of the Conference on Artificial Intelligence.

Adversarial Concept Distillation for One-Step Diffusion Personalization T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models.Proceedings of the Conference on Artificial Intelligence

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.856208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 08c28a8a-f55d-45ad-b58c-a980e926fb42 · outbound

This paper cites Dreamo: A unified framework for image customization.SIGGRAPH Asia.

Adversarial Concept Distillation for One-Step Diffusion Personalization Dreamo: A unified framework for image customization.SIGGRAPH Asia

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.859115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 96d792fe-6258-4c78-944e-9201c95e5cfa · outbound

This paper cites Swiftbrush: One-step text-to-image diffusion model with variational score distillation.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

Adversarial Concept Distillation for One-Step Diffusion Personalization Swiftbrush: One-step text-to-image diffusion model with variational score distillation.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.869029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a7140831-e3f5-4b83-837a-f519c73e3fcd · outbound

This paper cites an unresolved cited work.

Adversarial Concept Distillation for One-Step Diffusion Personalization Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-05-18T04:50:55.872221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b02bb6e2-3eab-451f-9937-7ccf7e3c5708 · outbound

This paper cites Kosmos- g: Generating images in context with multimodal large language models.International Conference on Learning Representations.

Adversarial Concept Distillation for One-Step Diffusion Personalization Kosmos- g: Generating images in context with multimodal large language models.International Conference on Learning Representations

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.879411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b09ce757-5afe-4f15-b784-81da276565e2 · outbound

This paper cites Attndreambooth: Towards text-aligned personalized text-to-image generation.Advances in Neural Information Processing Systems, 37:39869–39900.

Adversarial Concept Distillation for One-Step Diffusion Personalization Attndreambooth: Towards text-aligned personalized text-to-image generation.Advances in Neural Information Processing Systems, 37:39869–39900

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.914634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c11095bd-ed44-4223-a3ec-faebc5357cdd · outbound

This paper cites TextBoost: Boosting Text Encoder for Personalized Text-to-Image Generation.

Adversarial Concept Distillation for One-Step Diffusion Personalization TextBoost: Boosting Text Encoder for Personalized Text-to-Image Generation

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-20T02:04:38.960030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fe871915-b300-4f51-a3b5-078d451ed977 · outbound

This paper cites $\lambda$-ECLIPSE: Multi-Concept Personalized Text-to-Image Diffusion Models by Leveraging CLIP Latent Space.

Adversarial Concept Distillation for One-Step Diffusion Personalization $\lambda$-ECLIPSE: Multi-Concept Personalized Text-to-Image Diffusion Models by Leveraging CLIP Latent Space

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:50:53.722089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 41b42182-bd8a-43e4-b41b-7f788b6ca4f9 · outbound

This paper cites Orthogonal adaptation for modular customization of diffusion models.

Adversarial Concept Distillation for One-Step Diffusion Personalization Orthogonal adaptation for modular customization of diffusion models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.829658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 62687c84-a260-4a2d-9f0a-2cc73c846186 · outbound

This paper cites Barron, and Ben Mildenhall.

Adversarial Concept Distillation for One-Step Diffusion Personalization Barron, and Ben Mildenhall

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.836090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7aa946a4-2ace-482d-9f5d-1ddcc82248f2 · outbound

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

Adversarial Concept Distillation for One-Step Diffusion Personalization Learning transferable visual models from natural language supervision

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.846713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 14726492-d7bd-4a29-9a22-381d443ee6cc · outbound

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

Adversarial Concept Distillation for One-Step Diffusion Personalization High-resolution image synthesis with latent diffusion models

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.821807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9d45713c-8efa-43dd-b982-c8b1fee88639 · outbound

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

Adversarial Concept Distillation for One-Step Diffusion Personalization U-net: Convolutional networks for biomedical image segmentation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.814591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f000b0ca-3847-4733-8e5e-0f4a94fd745f · outbound

This paper cites Ipadapter-instruct: Resolving ambiguity in image-based conditioning using instruct prompts.

Adversarial Concept Distillation for One-Step Diffusion Personalization Ipadapter-instruct: Resolving ambiguity in image-based conditioning using instruct prompts

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.811526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1037c898-baf0-40fe-a447-7f8dd505bf64 · outbound

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

Adversarial Concept Distillation for One-Step Diffusion Personalization Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.818141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation dee709ff-01c8-4c17-8ca0-77e6b255f8a4 · outbound

This paper cites HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models.

Adversarial Concept Distillation for One-Step Diffusion Personalization HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:50:53.675804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8edf59c1-4869-4a47-8e58-2165d8a094c8 · outbound

This paper cites Align Your Flow: Scaling Continuous-Time Flow Map Distillation.

Adversarial Concept Distillation for One-Step Diffusion Personalization Align Your Flow: Scaling Continuous-Time Flow Map Distillation

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:50:53.703420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4863f00e-0cf0-4f51-85ac-b8828a47e44d · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.Advances in Neural Information Processing Systems.

Adversarial Concept Distillation for One-Step Diffusion Personalization Photorealistic text-to-image diffusion models with deep language understanding.Advances in Neural Information Processing Systems

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.842939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fec51820-d0e1-47ac-aac0-b4ce90b59026 · outbound

This paper cites Progressive distillation for fast sampling of diffusion models.

Adversarial Concept Distillation for One-Step Diffusion Personalization Progressive distillation for fast sampling of diffusion models

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.808060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a21610bc-238e-441c-9aff-5fa93d870d6e · outbound

This paper cites Adversarial diffusion distillation.European Conference on Computer Vision.

Adversarial Concept Distillation for One-Step Diffusion Personalization Adversarial diffusion distillation.European Conference on Computer Vision

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.788367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6b7477bc-7d2d-48ce-8dbe-95fa75fe2e92 · outbound

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

Adversarial Concept Distillation for One-Step Diffusion Personalization Ziplora: Any subject in any style by effectively merging loras

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:50:53.643209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 61c688ac-d17c-43da-8174-d057058ca57e · outbound

This paper cites Instantbooth: Personalized text-to-image generation without test-time finetuning.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

Adversarial Concept Distillation for One-Step Diffusion Personalization Instantbooth: Personalized text-to-image generation without test-time finetuning.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.778805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2aec6716-b04d-4377-a8e6-b39bd6998398 · outbound

This paper cites Loraclr: Contrastive adaptation for customization of diffusion models.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

Adversarial Concept Distillation for One-Step Diffusion Personalization Loraclr: Contrastive adaptation for customization of diffusion models.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.775631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 72d8c6a7-eb01-4e4b-89a5-6c858684293b · outbound

This paper cites Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA.

Adversarial Concept Distillation for One-Step Diffusion Personalization Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:50:53.649749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d5eb38bd-ef5a-4c06-b6eb-d8af3eb4c18a · outbound

This paper cites Denoising diffusion implicit models.

Adversarial Concept Distillation for One-Step Diffusion Personalization Denoising diffusion implicit models

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.781877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9f7748eb-0d6d-4ad7-94bf-fce3b4f96a0b · outbound

This paper cites Consistency models.

Adversarial Concept Distillation for One-Step Diffusion Personalization Consistency models

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.785034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c4d51ba8-655c-48a6-a08e-b9891f538ed6 · outbound

This paper cites Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation.

Adversarial Concept Distillation for One-Step Diffusion Personalization Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 80

Resolution: verified exact; local_arxiv, observed 2026-05-18T04:50:53.738033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1d66a29b-7fd7-4e76-8e74-dafe4577d234 · outbound

This paper cites Generative multimodal models are in-context learners.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

Adversarial Concept Distillation for One-Step Diffusion Personalization Generative multimodal models are in-context learners.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.800950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 153bc2c9-cacf-48ee-9996-eb8fdddf4faf · outbound

This paper cites Ominicontrol: Minimal and universal control for diffusion transformer.

Adversarial Concept Distillation for One-Step Diffusion Personalization Ominicontrol: Minimal and universal control for diffusion transformer

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.853252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d0289041-315a-4e4b-b868-816d3a38b433 · outbound

This paper cites Key-locked rank one editing for text-to-image personalization.

Adversarial Concept Distillation for One-Step Diffusion Personalization Key-locked rank one editing for text-to-image personalization

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:52:24.283442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e5cbc13e-e17b-458f-9bb1-40cb6f353b44 · outbound

This paper cites Visual autoregressive modeling: Scalable image generation via next-scale prediction.Advances in neural information processing systems, 37:84839–84865.

Adversarial Concept Distillation for One-Step Diffusion Personalization Visual autoregressive modeling: Scalable image generation via next-scale prediction.Advances in neural information processing systems, 37:84839–84865

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.772092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 74e337a5-a18b-4b64-957c-447b4410b81f · outbound

This paper cites An overview of large ai models and their applications.Visual Intelligence, 2(1):1–22.

Adversarial Concept Distillation for One-Step Diffusion Personalization An overview of large ai models and their applications.Visual Intelligence, 2(1):1–22

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.768423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 86b25504-b550-4b6d-a586-6e44c7c3dfaf · outbound

This paper cites Plug-and-play diffusion features for text-driven image-to-image translation.

Adversarial Concept Distillation for One-Step Diffusion Personalization Plug-and-play diffusion features for text-driven image-to-image translation

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.756876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d6c5d8b4-e1a9-423b-bf12-d91e1931c54a · outbound

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

Adversarial Concept Distillation for One-Step Diffusion Personalization P+: Extended Textual Conditioning in Text-to-Image Generation

Reference 87

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:50:53.588477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c039940f-04e9-4133-a372-3fbc90d07dc5 · outbound

This paper cites Dynamic prompt learning: Addressing cross-attention leakage for text-based image editing.Advances in Neural Information Processing Systems.

Adversarial Concept Distillation for One-Step Diffusion Personalization Dynamic prompt learning: Addressing cross-attention leakage for text-based image editing.Advances in Neural Information Processing Systems

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.752664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3b0dfde6-53ff-4b8a-afae-70629ade1940 · outbound

This paper cites InstantID: Zero-shot Identity-Preserving Generation in Seconds.

Adversarial Concept Distillation for One-Step Diffusion Personalization InstantID: Zero-shot Identity-Preserving Generation in Seconds

Reference 89

Resolution: verified exact; local_arxiv, observed 2026-05-18T04:50:53.689093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3058436c-4aaf-4684-8c2b-e6d263ebdbdf · outbound

This paper cites Ms-diffusion: Multi- subject zero-shot image personalization with layout guidance.International Conference on Learning Representations.

Adversarial Concept Distillation for One-Step Diffusion Personalization Ms-diffusion: Multi- subject zero-shot image personalization with layout guidance.International Conference on Learning Representations

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.850335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ede48adf-cf18-466e-8efa-3d206168d523 · outbound

This paper cites Minegan: effective knowledge transfer from gans to target domains with few images.

Adversarial Concept Distillation for One-Step Diffusion Personalization Minegan: effective knowledge transfer from gans to target domains with few images

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:56.002633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4972bbf3-556f-4e04-9a32-52fe44754141 · outbound

This paper cites Minegan++: Mining generative models for efficient knowledge transfer to limited data domains.International Journal of Computer Vision, 132(2):490–514.

Adversarial Concept Distillation for One-Step Diffusion Personalization Minegan++: Mining generative models for efficient knowledge transfer to limited data domains.International Journal of Computer Vision, 132(2):490–514

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:56.014561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5ffd0b04-32e3-49aa-9c25-808d83ed47f2 · outbound

This paper cites Raducanu.

Adversarial Concept Distillation for One-Step Diffusion Personalization Raducanu

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:56.017936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 43af150f-93a4-47a7-a6e2-04b42ecc9820 · outbound

This paper cites Transferring gans: generating images from limited data.

Adversarial Concept Distillation for One-Step Diffusion Personalization Transferring gans: generating images from limited data

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:52:24.294529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d7a060f1-eaf2-42f3-a74c-252ff15d48cf · outbound

This paper cites Pro- lificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation.

Adversarial Concept Distillation for One-Step Diffusion Personalization Pro- lificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.988033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fd134173-6e73-4fc6-a60b-33306a71ecbb · outbound

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

Adversarial Concept Distillation for One-Step Diffusion Personalization Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.991330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ca619714-e36c-48d0-939b-d8e4c6ae6da9 · outbound

This paper cites OmniGen2: Towards Instruction-Aligned Multimodal Generation.

Adversarial Concept Distillation for One-Step Diffusion Personalization OmniGen2: Towards Instruction-Aligned Multimodal Generation

Reference 97

Resolution: verified exact; local_arxiv, observed 2026-05-18T04:50:53.709691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 09e1d13a-23f6-4a4d-ad06-9f9249e653a2 · outbound

This paper cites Less-to-More Generalization: Unlocking More Controllability by In-Context Generation.

Adversarial Concept Distillation for One-Step Diffusion Personalization Less-to-More Generalization: Unlocking More Controllability by In-Context Generation

Reference 98

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:50:53.716606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b980bd0b-c4ff-4b51-9419-4aad15f6a7da · outbound

This paper cites Infinite-id: Identity-preserved personalization via id-semantics decoupling paradigm.

Adversarial Concept Distillation for One-Step Diffusion Personalization Infinite-id: Identity-preserved personalization via id-semantics decoupling paradigm

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T04:50:55.974015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0832eb2d-16a2-49ff-9190-b05ca4b88a7a · outbound

This paper cites Proxy- tuning: Tailoring multimodal autoregressive models for subject-driven image generation.

Adversarial Concept Distillation for One-Step Diffusion Personalization Proxy- tuning: Tailoring multimodal autoregressive models for subject-driven image generation

Reference 100

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:50:53.609102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers