QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation | Paper Citation Record

Observation a541bfcb-7c50-4443-968e-3307e22c0b3e · outbound

This paper cites Cross-image attention for zero- shot appearance transfer.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Cross-image attention for zero- shot appearance transfer

Reference 1

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Observation e8d11688-1995-4805-9d8e-c1394eb894f4 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Emerg- ing properties in self-supervised vision transformers

Reference 2

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Observation 9c31251b-d2aa-412a-bdf0-8d9010fd679a · outbound

This paper cites Training-Free Layout Control with Cross-Attention Guidance.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Training-Free Layout Control with Cross-Attention Guidance

Reference 3

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Observation e05015f5-d6d7-4390-ac28-76b393d03458 · outbound

This paper cites The approximation of one ma- trix by another of lower rank.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation The approximation of one ma- trix by another of lower rank

Reference 4

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Observation 572e40bf-e39b-487b-b863-5dbc66722755 · outbound

This paper cites Diffusion self-guidance for control- lable image generation.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Diffusion self-guidance for control- lable image generation

Reference 5

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Observation fce48e39-cd5f-4ea8-ba49-2f77625fd912 · outbound

This paper cites Scaling recti- fied flow transformers for high-resolution image synthesis.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 6

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Observation 8435a61d-8fdc-463f-80eb-4fc07db4b984 · outbound

This paper cites Lora-x: Bridging foundation models with training-free cross-model adaptation.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Lora-x: Bridging foundation models with training-free cross-model adaptation

Reference 7

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Observation ab06d568-9f66-4979-bf57-d8a5a9c17728 · outbound

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

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Implicit style-content separation using b-lora

Reference 8

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Observation 992bdb3c-2323-43d6-b1a6-9f046440f492 · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 9

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Observation 3e9cf544-ea7a-4140-a6dc-a7253bd908f5 · outbound

This paper cites Singular value de- composition and least squares solutions.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Singular value de- composition and least squares solutions

Reference 10

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Observation 8477a5f4-cdc1-48a6-a648-a93bc4ef4819 · outbound

This paper cites Matrix computations johns hopkins university press.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Matrix computations johns hopkins university press

Reference 11

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Observation 9a5a833a-3973-41ba-ae4f-df68bbeecfc9 · outbound

This paper cites Mix-of-show: Decentralized low-rank adaptation for multi-concept customization of diffusion models.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Mix-of-show: Decentralized low-rank adaptation for multi-concept customization of diffusion models

Reference 12

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Observation 6e6d14a4-3f01-492a-9431-2e9f0f6c7633 · outbound

This paper cites Svdiff: Compact param- eter space for diffusion fine-tuning.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Svdiff: Compact param- eter space for diffusion fine-tuning

Reference 13

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Observation 3f89a281-a45c-4644-b7f6-0d6b104d036c · outbound

This paper cites Prompt-to-Prompt Image Editing with Cross Attention Control.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Prompt-to-Prompt Image Editing with Cross Attention Control

Reference 14

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Observation 564ec7c0-e7eb-47c9-8bf4-40d9926f49de · outbound

This paper cites Style aligned image generation via shared atten- tion.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Style aligned image generation via shared atten- tion

Reference 15

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Observation 426101a5-34af-40a1-9050-881b70a40205 · outbound

This paper cites Denoising dif- fusion probabilistic models.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Denoising dif- fusion probabilistic models

Reference 16

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Observation a1533005-fdf9-4ac4-a359-0cf4c7416e5b · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation LoRA: Low-Rank Adaptation of Large Language Models

Reference 17

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Observation f267968c-3bf7-4bf0-9f39-fd8ed9680a9f · outbound

This paper cites Zero-shot text-guided object gen- eration with dream fields.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Zero-shot text-guided object gen- eration with dream fields

Reference 18

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Observation a10551c6-0f6a-4771-a7e1-e3eb4818479f · outbound

This paper cites Decor:decomposition and projection of text embeddings for text-to-image cus- tomization, 2024.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Decor:decomposition and projection of text embeddings for text-to-image cus- tomization, 2024

Reference 19

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Observation 13acff7f-ab3d-49fd-9ae4-f213ef8ceb1b · outbound

This paper cites Visual Style Prompting with Swapping Self-Attention.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Visual Style Prompting with Swapping Self-Attention

Reference 20

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Observation 1bf968c1-7eb0-40e9-a732-851b547bf9ae · outbound

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

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Elucidating the design space of diffusion-based generative models

Reference 21

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Observation ba698d32-55c5-4383-89f8-80b087a1400f · outbound

This paper cites Understanding diffusion objectives as the elbo with simple data augmentation.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Understanding diffusion objectives as the elbo with simple data augmentation

Reference 22

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Observation 29b310b3-727c-4975-bf05-1cd5826e8ac7 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Adam: A Method for Stochastic Optimization

Reference 23

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Observation f055f284-15c2-47de-81e9-72873002de1a · outbound

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

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Multi-concept customization of text-to-image diffusion

Reference 24

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This paper cites an unresolved cited work.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Unresolved cited work

Reference 25

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Observation 8d495464-3f65-4e73-b42b-0dbaebbc7cb4 · outbound

This paper cites Stylestudio: Text-driven style transfer with selective control of style elements.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Stylestudio: Text-driven style transfer with selective control of style elements

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:05.438115Z

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Observation bdbb7b66-8533-4ce1-9b88-d5c861c1860d · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 27

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Observation 387e80fc-ebb1-4d57-afca-5e6064d1511e · outbound

This paper cites Vb-lora: Extreme parameter efficient fine-tuning with vector banks.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Vb-lora: Extreme parameter efficient fine-tuning with vector banks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:05.421655Z

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Observation 7d57f4b1-d466-40c7-8e08-2a1bbdbaa835 · outbound

This paper cites SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors

Reference 29

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This paper cites Flow Matching for Generative Modeling.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Flow Matching for Generative Modeling

Reference 30

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Observation 49f2578c-a30c-4809-ba48-052151abb376 · outbound

This paper cites UnZipLoRA: Separating Content and Style from a Single Image.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation UnZipLoRA: Separating Content and Style from a Single Image

Reference 31

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Observation 7d7b612f-3bdb-457a-9725-b01f81ddc2ca · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 32

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Observation c844aeeb-aa71-4810-8042-a3c5000156fa · outbound

This paper cites AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts

Reference 33

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Observation d11ae02c-4ed9-4db6-a01f-b884ffe236a2 · outbound

This paper cites Tuning-free long video generation via global-local collaborative diffu- sion, 2025.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Tuning-free long video generation via global-local collaborative diffu- sion, 2025

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:05.403015Z

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Observation 84c188bf-08e8-4ef1-802d-8544bbf3b596 · outbound

This paper cites Adams bashforth moulton solver for inversion and editing in rectified flow, 2025.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Adams bashforth moulton solver for inversion and editing in rectified flow, 2025

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:05.386129Z

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Observation 84c9ca87-3d38-4304-8731-f00d3522e515 · outbound

This paper cites Pissa: Prin- cipal singular values and singular vectors adaptation of large language models.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Pissa: Prin- cipal singular values and singular vectors adaptation of large language models

Reference 36

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Observation 6e96b33b-ce85-45fb-858b-f94b70adda6d · outbound

This paper cites Null-text inversion for editing real im- ages using guided diffusion models.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Null-text inversion for editing real im- ages using guided diffusion models

Reference 37

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Observation 2126d501-d5dc-4086-89b6-e691c680938f · outbound

This paper cites T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:05.341004Z

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Observation 07f8b18e-596a-44d4-a2a9-807ad4ec63ae · outbound

This paper cites PACE: marrying the generalization of PArameter-efficient fine-tuning with con- sistency regularization.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation PACE: marrying the generalization of PArameter-efficient fine-tuning with con- sistency regularization

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:05.326280Z

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Observation 1de085c5-93b2-482e-b5e3-fd505c5a54e7 · outbound

This paper cites Improved denoising diffusion probabilistic models.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Improved denoising diffusion probabilistic models

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:05.310581Z

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Observation 64346013-1958-49a2-bdb5-117c1d57d62f · outbound

This paper cites Finding and editing multi-modal neurons in pre-trained transformers.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Finding and editing multi-modal neurons in pre-trained transformers

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:05.295850Z

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Observation 13f68b8c-a210-4522-baf1-de28a536b1d3 · outbound

This paper cites Scalable diffusion models with transformers.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Scalable diffusion models with transformers

Reference 42

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Observation da1bdf10-0ca7-418a-80f9-91d11595337d · outbound

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

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Orthogonal adaptation for modular customization of diffusion models

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:05.264367Z

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Observation a8ee56f5-5e2e-4f13-9049-8f0f3e586e33 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 44

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Observation c6665d04-b0a7-4f28-b93d-d0f725d7905a · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Learning transferable visual models from natural language supervi- sion

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:05.249630Z

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Observation 974cfd5c-d2c4-47f8-9adc-4890e4373387 · outbound

This paper cites Dreambooth3d: Subject-driven text-to-3d generation.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Dreambooth3d: Subject-driven text-to-3d generation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:05.234295Z

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Observation 2f8d51d4-c7e2-4c11-870d-51cda2940553 · outbound

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

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation High-resolution image syn- thesis with latent diffusion models

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:05.218239Z

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Observation 3b149572-743d-4557-8413-4b4c748c9691 · outbound

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

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:05.200790Z

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Observation db660adc-2e6c-440a-8dc1-844cc6ea95e9 · outbound

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

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Ziplora: Any subject in any style by effectively merging loras

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:05.184997Z

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Observation 0f9f0cea-3907-492f-bfda-c4c86d535afe · outbound

This paper cites Unleashing the power of task-specific directions in parameter efficient fine-tuning.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Unleashing the power of task-specific directions in parameter efficient fine-tuning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:05.167648Z

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Observation 9175b758-4485-4af7-9931-ef3fa936b744 · outbound

This paper cites Loraclr: Contrastive adaptation for customization of diffusion models.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Loraclr: Contrastive adaptation for customization of diffusion models

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:05.150550Z

Source-reported events for the cited work

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

Observation e0f1280e-bd5c-4334-ba62-0b4844c4c128 · outbound

This paper cites Styledrop: Text-to-image synthesis of any style.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Styledrop: Text-to-image synthesis of any style

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:05.134473Z

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Observation 53986774-6b4f-4efe-966d-ec03e35a639e · outbound

This paper cites Denois- ing diffusion implicit models.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Denois- ing diffusion implicit models

Reference 53

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Observation 73b48815-f31b-4497-9583-0d8d4344cad2 · outbound

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

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Score-based generative modeling through stochastic differential equa- tions

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:04.255036Z

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Observation 8f162f8e-5b3d-462d-9a43-931bda2e554d · outbound

This paper cites Vl-adapter: Parameter-efficient transfer learning for vision-and-language tasks.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Vl-adapter: Parameter-efficient transfer learning for vision-and-language tasks

Reference 55

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QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:53:05.087068Z

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Observation 146db99d-528a-4855-a4ed-c6fe8f21d339 · outbound

This paper cites Hydralora: An asymmetric lora architec- ture for efficient fine-tuning.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Hydralora: An asymmetric lora architec- ture for efficient fine-tuning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:05.071382Z

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

Observation c71c9468-08ed-4957-876e-783709db2442 · outbound

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

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Plug-and-play diffusion features for text-driven image-to-image translation

Reference 58

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Observation 0d05f1b2-54aa-4cd6-a456-c8fa8697276a · outbound

This paper cites Attention is all you need.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Attention is all you need

Reference 59

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Observation 20aa414e-3004-44e5-9908-6a3ebef68df2 · outbound

This paper cites Diffusers: State-of-the-art diffu- sion models.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Diffusers: State-of-the-art diffu- sion models

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:05.031879Z

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Observation b6b6239e-f58d-4505-8cc1-687a878f19d8 · outbound

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

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:04.302164Z

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Observation 27b46f78-86cb-4667-ae67-f325fe0352ca · outbound

This paper cites Uncovering the disentanglement capability in text- to-image diffusion models.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Uncovering the disentanglement capability in text- to-image diffusion models

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:04.308770Z

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Observation d85e0a8b-06ae-4eef-8453-7bb0bde40413 · outbound

This paper cites Freeman, Fr ´edo Durand, and Song Han.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Freeman, Fr ´edo Durand, and Song Han

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:05.003226Z

Source-reported events for the cited work

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Observation bc3982f3-a902-4b3d-a212-b6231ec366f9 · outbound

This paper cites Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:04.318614Z

Source-reported events for the cited work

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Observation 55ab22dc-167e-4473-9063-39baf7e1017d · outbound

This paper cites End-to-end chinese landscape painting creation using generative adversarial networks.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation End-to-end chinese landscape painting creation using generative adversarial networks

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:04.983350Z

Source-reported events for the cited work

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

Observation 8ca12cd8-eaa5-4176-bf1f-4f222166749f · outbound

This paper cites TV-3DG: Mastering Text-to-3D Customized Generation with Visual Prompt.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation TV-3DG: Mastering Text-to-3D Customized Generation with Visual Prompt

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:53:04.528779Z

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Observation 49350794-6a72-4afd-a8dc-324142f487ce · outbound

This paper cites Zero-shot contrastive loss for text-guided diffusion image style transfer.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Zero-shot contrastive loss for text-guided diffusion image style transfer

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:04.967204Z

Source-reported events for the cited work

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

Observation 129c04a5-3254-440c-b4fd-59558d8f32e1 · outbound

This paper cites Deconfounded video moment retrieval with causal intervention.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Deconfounded video moment retrieval with causal intervention

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:04.948294Z

Source-reported events for the cited work

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Observation 74189a8f-6404-4b30-8c01-9aa98623150f · outbound

This paper cites Video moment retrieval with cross-modal neural architecture search.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Video moment retrieval with cross-modal neural architecture search

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:04.929877Z

Source-reported events for the cited work

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

Observation e261ace9-2e00-484f-b234-45b19f71024a · outbound

This paper cites Robust video question answer- ing via contrastive cross-modality representation learning.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Robust video question answer- ing via contrastive cross-modality representation learning

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:04.912945Z

Source-reported events for the cited work

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

Observation e856d3d0-c0a8-4184-820b-645e6098f318 · outbound

This paper cites CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:04.358426Z

Source-reported events for the cited work

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Observation 6a425e35-7976-492b-9e46-79d66ca5e413 · outbound

This paper cites IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:04.364730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74874dd6-4f56-4f8a-84fe-c15ea45d1276 · outbound

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

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Adding conditional control to text-to-image diffusion models

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:04.374462Z

Source-reported events for the cited work

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Observation 8c54b8e3-2952-4ea2-9b56-d0499a1fe1b7 · outbound

This paper cites Multi-LoRA Composition for Image Generation.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Multi-LoRA Composition for Image Generation

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:04.379364Z

Source-reported events for the cited work

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Observation c5b8d3fa-5b21-4090-b7d1-e7b508fecf90 · outbound

This paper cites Egotextvqa: Towards egocentric scene-text aware video question answering.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Egotextvqa: Towards egocentric scene-text aware video question answering

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:04.881600Z

Source-reported events for the cited work

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

Observation 7d953ae7-ae2c-455d-a2b9-e71420c22f17 · outbound

This paper cites Cached multi-lora composition for multi- concept image generation.

QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation Cached multi-lora composition for multi- concept image generation

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:04.860447Z

Source-reported events for the cited work

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

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