Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability | Paper Citation Record

Observation 50395098-7029-4b84-932f-dbb924ffe11b · outbound

This paper cites A-star: Test-time attention segregation and retention for text-to-image synthesis.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability A-star: Test-time attention segregation and retention for text-to-image synthesis

Reference 1

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Observation 8c4417fd-db50-476e-8fec-d1e53627e459 · outbound

This paper cites Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models

Reference 2

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Observation a2fd1c9c-0bae-47d3-952e-9700e76a9222 · outbound

This paper cites A cat is a cat (not a dog!): Unraveling information mix- ups in text-to-image encoders through causal analysis and embedding optimization.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability A cat is a cat (not a dog!): Unraveling information mix- ups in text-to-image encoders through causal analysis and embedding optimization

Reference 3

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Observation 80b9afac-efe3-44ce-978e-0eca6a619284 · outbound

This paper cites PIXART-{\delta}: Fast and Controllable Image Generation with Latent Consistency Models.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability PIXART-{\delta}: Fast and Controllable Image Generation with Latent Consistency Models

Reference 4

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Observation 01d889e3-6bb1-4cec-8ba8-2f0b3d1d38f3 · outbound

This paper cites Perception pri- oritized training of diffusion models.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Perception pri- oritized training of diffusion models

Reference 5

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Observation e38ea864-e589-42c3-a652-dd43642285b0 · outbound

This paper cites Swiftbrush v2: Make your one-step diffusion model better than its teacher.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Swiftbrush v2: Make your one-step diffusion model better than its teacher

Reference 6

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Observation 99436bfd-bbb7-49b8-a086-a06a40d2cc0d · outbound

This paper cites Learning universal policies via text-guided video genera- tion.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Learning universal policies via text-guided video genera- tion

Reference 7

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Observation ba861456-3ce5-4e73-b087-bd5095f04955 · outbound

This paper cites ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization

Reference 8

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Observation 2ba5eb88-bef7-4130-962e-2ff74c142337 · outbound

This paper cites Training-free struc- tured diffusion guidance for compositional text-to-image syn- thesis.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Training-free struc- tured diffusion guidance for compositional text-to-image syn- thesis

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:04:06.791792Z

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Observation cb1b8b77-0413-49a5-b07d-30b8f0a5710e · outbound

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

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability An image is worth one word: Personalizing text-to-image generation using textual inversion

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:04:06.781595Z

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Observation 90371ca1-c8b2-452d-9ca1-70995f1954c9 · outbound

This paper cites How does selective mechanism im- prove self-attention networks? In Proceedings of the 58th Annual Meeting of the Association for Computational Lin- guistics, pages 2986–2995, 2020.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability How does selective mechanism im- prove self-attention networks? In Proceedings of the 58th Annual Meeting of the Association for Computational Lin- guistics, pages 2986–2995, 2020

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:04:06.773595Z

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Observation 79fa5a3b-efdd-432d-9505-7bdd42d698e4 · outbound

This paper cites Geneval: An object-focused framework for evaluating text- to-image alignment.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Geneval: An object-focused framework for evaluating text- to-image alignment

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:04:06.764368Z

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Observation 5b0c0257-252c-440f-aa66-1602fd737b88 · outbound

This paper cites Towards practical plug-and-play diffusion models.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Towards practical plug-and-play diffusion models

Reference 13

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Observation 5e2716a4-0331-49e3-82f6-1005c920578d · outbound

This paper cites Parameter-Efficient Transfer Learning with Diff Pruning.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Parameter-Efficient Transfer Learning with Diff Pruning

Reference 14

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Observation d2d46e4e-b84c-4aa0-9631-c4eb93f2512b · outbound

This paper cites I2v-adapter: A general image-to-video adapter for diffusion models.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability I2v-adapter: A general image-to-video adapter for diffusion models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:04:06.744805Z

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Observation bf237b6d-cdc0-4bb7-968d-6bca8f62641e · outbound

This paper cites FreeStyle: Free Lunch for Text-guided Style Transfer using Diffusion Models.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability FreeStyle: Free Lunch for Text-guided Style Transfer using Diffusion Models

Reference 16

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Observation 64b7c177-8c5d-4096-a857-1e62d78157d0 · outbound

This paper cites Deep residual learning for image recognition.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Deep residual learning for image recognition

Reference 17

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Observation 85a6b9a3-aa26-44f4-8c74-83a794fb5cf8 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 18

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Observation 93b933c5-f30a-449d-bc04-d29cf3cbd9bf · outbound

This paper cites Classifier-Free Diffusion Guidance.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Classifier-Free Diffusion Guidance

Reference 19

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Observation c23e1077-c63d-40a8-811e-196676efedd1 · outbound

This paper cites Denoising dif- fusion probabilistic models.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Denoising dif- fusion probabilistic models

Reference 20

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Observation 8a5ea60a-3936-4a00-961e-ead8d7bef9aa · outbound

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

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Lora: Low- rank adaptation of large language models

Reference 21

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Observation e3b75344-5edd-4919-889c-a91aa37eab0e · outbound

This paper cites SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation

Reference 22

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Observation b9775ff9-a574-4316-80a8-1322d7230956 · outbound

This paper cites Densely connected convolutional net- works.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Densely connected convolutional net- works

Reference 23

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Observation cdae7521-c7a8-4546-981b-24ce84df12fc · outbound

This paper cites T2i-compbench: A comprehensive benchmark for open- world compositional text-to-image generation.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability T2i-compbench: A comprehensive benchmark for open- world compositional text-to-image generation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T00:04:06.233546Z

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Observation 13453236-14d0-47f1-9f2b-5982c5c50c55 · outbound

This paper cites ReVersion: Diffusion-Based Relation Inversion from Images.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability ReVersion: Diffusion-Based Relation Inversion from Images

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T00:04:06.236478Z

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Observation f4102c64-346b-4ac9-9a21-956c953f1275 · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Scaling up visual and vision-language representation learning with noisy text supervision

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:04:06.705393Z

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Observation 3dc7ea51-5698-416d-94b1-0392a5a2fd70 · outbound

This paper cites Progressive growing of GANs for improved quality, stabil- ity, and variation.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Progressive growing of GANs for improved quality, stabil- ity, and variation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:04:06.696620Z

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Observation f43cc1fd-e3ed-4b14-a9fc-bcc4e8db160b · outbound

This paper cites Alias-free generative adversarial networks.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Alias-free generative adversarial networks

Reference 28

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Observation 9a1ef8bb-b1ba-4ffc-8224-fbf6ae678ea2 · outbound

This paper cites Text2video-zero: Text- to-image diffusion models are zero-shot video generators.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Text2video-zero: Text- to-image diffusion models are zero-shot video generators

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:04:06.682676Z

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Observation a6d26e0f-72d2-498e-883b-554611bf0634 · outbound

This paper cites Similarity of neural network represen- tations revisited.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Similarity of neural network represen- tations revisited

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:04:06.674630Z

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Observation 836f5fb5-1f66-4b4d-b602-489c9337e147 · outbound

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

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Multi-concept customization of text- to-image diffusion

Reference 31

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Observation 8a8f9917-f669-4dee-9fcc-aca55f480667 · outbound

This paper cites Faster diffusion: Rethinking the role of unet encoder in dif- fusion models.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Faster diffusion: Rethinking the role of unet encoder in dif- fusion models

Reference 32

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Observation 3a75b534-476f-410e-af3b-f280917dbf38 · outbound

This paper cites Photomaker: Customizing realistic human photos via stacked id embedding.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Photomaker: Customizing realistic human photos via stacked id embedding

Reference 33

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Observation 3392d012-3767-4520-a445-957cd373abcb · outbound

This paper cites Microsoft coco: Common objects in context.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Microsoft coco: Common objects in context

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:04:06.640811Z

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Observation 5fa27deb-e0fc-457c-9380-b32a5870a1d9 · outbound

This paper cites DoRA: Weight-Decomposed Low-Rank Adaptation.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 35

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Observation 3cc3e847-3097-45ca-94ad-4c23c9e4d069 · outbound

This paper cites Decoupled Weight Decay Regularization.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Decoupled Weight Decay Regularization

Reference 36

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Observation 5add8455-8640-48c1-9c3a-8012c554f287 · outbound

This paper cites Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps

Reference 37

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Observation 61c01511-81fd-4409-a177-863e6291ee8a · outbound

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

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Reference 38

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Observation 3597ccd4-793f-468f-957a-bd31567209d4 · outbound

This paper cites The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling

Reference 39

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Observation a9b54056-16cc-4d23-a628-c70b56c09ce4 · outbound

This paper cites Deepcache: Accelerating diffusion models for free.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Deepcache: Accelerating diffusion models for free

Reference 40

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Observation f393a04b-79a5-43fc-9090-2b5f16032087 · outbound

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

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models

Reference 41

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Observation fc801f39-0f4c-404e-8352-18e6ab9be48c · outbound

This paper cites Swiftbrush: One- step text-to-image diffusion model with variational score dis- tillation.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Swiftbrush: One- step text-to-image diffusion model with variational score dis- tillation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:04:06.616168Z

Source-reported events for the cited work

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Observation 26d2b25e-7ec6-40aa-869d-c0fef311a981 · outbound

This paper cites Not All Noises Are Created Equally:Diffusion Noise Selection and Optimization.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Not All Noises Are Created Equally:Diffusion Noise Selection and Optimization

Reference 43

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Observation 8d9193b3-c400-4d31-9984-ba7844fe6c38 · outbound

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

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Learning transferable visual models from natural language supervi- sion

Reference 44

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Observation 0fb81ef7-a857-4252-805a-ed6b6c2e418a · outbound

This paper cites X-adapter: Adding universal compatibility of plugins for up- graded diffusion model.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability X-adapter: Adding universal compatibility of plugins for up- graded diffusion model

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:04:06.600755Z

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Observation b14ea657-08cc-454c-8833-720e7da76c6c · outbound

This paper cites Linguistic binding in dif- fusion models: Enhancing attribute correspondence through attention map alignment.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Linguistic binding in dif- fusion models: Enhancing attribute correspondence through attention map alignment

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:04:06.592520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation eed944bc-3b3b-4310-b3fc-ec471117e325 · outbound

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

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability High-resolution image synthesis with latent diffusion models

Reference 47

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Observation 66a8be3d-4d20-4cc5-a228-85cd4b08da86 · outbound

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

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Dreambooth: Fine tuning text-to-image diffusion models for subject-driven gen- eration

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-16T00:04:06.303276Z

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Observation bd0c44c4-8478-4554-a23b-b60e4e83eed7 · outbound

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

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-16T00:04:06.306438Z

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Observation 1ff19ed6-55e9-443c-9f5c-5aaf36602559 · outbound

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

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:04:06.572446Z

Source-reported events for the cited work

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Observation 41207144-cbc8-4912-bd76-21c89e47c4df · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-16T00:04:06.311826Z

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Observation 5ac4706c-0e83-4f28-aff9-31ebecff81d6 · outbound

This paper cites Freeu: Free lunch in diffusion u-net.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Freeu: Free lunch in diffusion u-net

Reference 52

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Observation 35f79f44-3a72-466a-a838-7759eb41d86b · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-16T00:04:06.318938Z

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Observation 5ffdabf4-7fe1-4ecc-99fa-029e97afe11e · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Deep unsupervised learning using nonequilibrium thermodynamics

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-16T00:04:06.322111Z

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Observation ecee6b54-ef50-471d-8339-a07bea8ceda5 · outbound

This paper cites Denoising diffusion implicit models.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Denoising diffusion implicit models

Reference 55

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Observation fd72c546-7202-4f68-9a7b-d0ace7d1e3d8 · outbound

This paper cites Generative modeling by estimating gradients of the data distribution.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Generative modeling by estimating gradients of the data distribution

Reference 56

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Observation a3b0cc4d-df97-46eb-be32-c650dd69f2de · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Score-Based Generative Modeling through Stochastic Differential Equations

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-16T00:04:06.329600Z

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Observation 72c51818-6799-481e-9624-0774053d8e03 · outbound

This paper cites Going deeper with convolutions.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Going deeper with convolutions

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-16T00:04:06.332829Z

Source-reported events for the cited work

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Observation 07cda11c-d9b9-4765-8232-3bd842a97928 · outbound

This paper cites Multi-Concept T2I-Zero: Tweaking Only The Text Embeddings and Nothing Else.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Multi-Concept T2I-Zero: Tweaking Only The Text Embeddings and Nothing Else

Reference 59

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Unavailable: canonical work link unavailable.

Observation 28ab690a-b9bd-404b-b34f-b09162836583 · outbound

This paper cites Visualizing data using t-sne.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Visualizing data using t-sne

Reference 60

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Observation e884cb23-bdc2-490f-8e38-077c5bf7ad4c · outbound

This paper cites Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 61

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Unavailable: canonical work link unavailable.

Observation 2a434e74-f221-49fc-b93f-988416c2f301 · outbound

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

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Imagereward: Learning and evaluating human preferences for text-to-image generation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:04:06.533322Z

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Observation 547037a4-4aef-479d-ad8b-2e3d836d84d4 · outbound

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

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-16T00:04:06.348232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64b4cd2b-c5ce-4468-a4a6-6ad6228d8a31 · outbound

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

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Adding conditional control to text-to-image diffusion models

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:04:06.525801Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a4838097-0b32-4bc9-89d9-ca66957472b0 · outbound

This paper cites gddim: Generalized denoising diffusion implicit models.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability gddim: Generalized denoising diffusion implicit models

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:04:06.518627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dfb4b6e4-a023-4e9b-8f63-88bad54051d4 · outbound

This paper cites Real- world image variation by aligning diffusion inversion chain.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Real- world image variation by aligning diffusion inversion chain

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:04:06.510652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 77a1c4d7-4150-4b3a-9a84-9dcfcebb3225 · outbound

This paper cites Learning deep features for discrimi- native localization.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability Learning deep features for discrimi- native localization

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:04:06.502799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c164ce64-3015-4397-8518-112ab0d33e57 · outbound

This paper cites StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation.

Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-16T00:04:06.362848Z

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