From Noise to Nuance: Advances in Deep Generative Image Models | Paper Citation Record

Observation 03ec1d70-57d1-464f-96f0-7f7ca3e93816 · outbound

This paper cites Generative adversar ial net- works,.

From Noise to Nuance: Advances in Deep Generative Image Models Generative adversar ial net- works,

Reference 1

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Observation 6ecf419c-948a-4e1e-98d9-2fee62c5766a · outbound

This paper cites Unsupervised repr esentation learning with deep convolutional generative adversarial n etworks,.

From Noise to Nuance: Advances in Deep Generative Image Models Unsupervised repr esentation learning with deep convolutional generative adversarial n etworks,

Reference 2

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This paper cites Denoising diffusion proba bilistic mod- els,.

From Noise to Nuance: Advances in Deep Generative Image Models Denoising diffusion proba bilistic mod- els,

Reference 3

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

From Noise to Nuance: Advances in Deep Generative Image Models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 4

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This paper cites Diffusion models beat gans on image synthesis,.

From Noise to Nuance: Advances in Deep Generative Image Models Diffusion models beat gans on image synthesis,

Reference 5

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

From Noise to Nuance: Advances in Deep Generative Image Models Imagenet: A large-scale hierarchical image database,

Reference 6

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

From Noise to Nuance: Advances in Deep Generative Image Models LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

Reference 7

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This paper cites In-datacenter performance analysis of a tensor processing unit,.

From Noise to Nuance: Advances in Deep Generative Image Models In-datacenter performance analysis of a tensor processing unit,

Reference 8

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Observation cf17e39f-b0ad-425d-89d8-a0b053d18df1 · outbound

This paper cites Scaling Laws for Neural Language Models.

From Noise to Nuance: Advances in Deep Generative Image Models Scaling Laws for Neural Language Models

Reference 9

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Observation ba41cd94-c9ac-4885-a5b1-ff8b1f4118cd · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

From Noise to Nuance: Advances in Deep Generative Image Models Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 10

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Observation 34dddb2c-d668-45be-b2b4-1a38eeb78a4c · outbound

This paper cites Scaling Language Models: Methods, Analysis & Insights from Training Gopher.

From Noise to Nuance: Advances in Deep Generative Image Models Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 11

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Observation 6f7b9738-90c4-4027-8037-f4d49b1ee4f3 · outbound

This paper cites Attention is all you need,.

From Noise to Nuance: Advances in Deep Generative Image Models Attention is all you need,

Reference 12

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Observation 15f9479e-168e-4425-8754-6824f8fb7473 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

From Noise to Nuance: Advances in Deep Generative Image Models Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 13

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Observation 793f6563-6aef-42b0-892a-d37679c98a62 · outbound

This paper cites Photorealistic text-to-image diffusion models with dee p lan- guage understanding,.

From Noise to Nuance: Advances in Deep Generative Image Models Photorealistic text-to-image diffusion models with dee p lan- guage understanding,

Reference 14

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Observation 22af2acf-65ce-4c15-99a0-127c2fb4651c · outbound

This paper cites Zero-shot text-to-image generation,.

From Noise to Nuance: Advances in Deep Generative Image Models Zero-shot text-to-image generation,

Reference 15

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Observation 98aecbca-cec6-429f-8a71-34f8b0b3286b · outbound

This paper cites High-resolution image synthesis with latent diffusion mo dels,.

From Noise to Nuance: Advances in Deep Generative Image Models High-resolution image synthesis with latent diffusion mo dels,

Reference 16

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This paper cites Improved denoising diffu sion prob- abilistic models,.

From Noise to Nuance: Advances in Deep Generative Image Models Improved denoising diffu sion prob- abilistic models,

Reference 17

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Observation f95f2d35-0bdf-4856-ad37-1b886cadec1b · outbound

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

From Noise to Nuance: Advances in Deep Generative Image Models Learning transferable visual models from natural language supervision,

Reference 18

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This paper cites Alias-free generative adversarial networks,.

From Noise to Nuance: Advances in Deep Generative Image Models Alias-free generative adversarial networks,

Reference 19

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This paper cites Surveying the mllm landscape: A meta- review of current surveys,.

From Noise to Nuance: Advances in Deep Generative Image Models Surveying the mllm landscape: A meta- review of current surveys,

Reference 20

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This paper cites A survey of accelerator architectures for deep neural networks,.

From Noise to Nuance: Advances in Deep Generative Image Models A survey of accelerator architectures for deep neural networks,

Reference 21

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This paper cites The age of generative ai and ai-generated everything,.

From Noise to Nuance: Advances in Deep Generative Image Models The age of generative ai and ai-generated everything,

Reference 22

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This paper cites Llms and diffusion models in ui/ux: Advancing human-computer interaction and design,.

From Noise to Nuance: Advances in Deep Generative Image Models Llms and diffusion models in ui/ux: Advancing human-computer interaction and design,

Reference 23

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This paper cites Lightweight g enerative adversarial networks for text-guided image manipulation,.

From Noise to Nuance: Advances in Deep Generative Image Models Lightweight g enerative adversarial networks for text-guided image manipulation,

Reference 24

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

From Noise to Nuance: Advances in Deep Generative Image Models Dpm-sol ver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps,

Reference 25

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This paper cites The emergence of deepfake technology: A review,.

From Noise to Nuance: Advances in Deep Generative Image Models The emergence of deepfake technology: A review,

Reference 26

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This paper cites The Cat and Mouse Game: The Ongoing Arms Race Between Diffusion Models and Detection Methods.

From Noise to Nuance: Advances in Deep Generative Image Models The Cat and Mouse Game: The Ongoing Arms Race Between Diffusion Models and Detection Methods

Reference 27

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This paper cites Easily a ccessible text-to-image generation ampliﬁes demographic stereotyp es at large scale,.

From Noise to Nuance: Advances in Deep Generative Image Models Easily a ccessible text-to-image generation ampliﬁes demographic stereotyp es at large scale,

Reference 28

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This paper cites The creativity of text-to-image gen eration,.

From Noise to Nuance: Advances in Deep Generative Image Models The creativity of text-to-image gen eration,

Reference 29

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This paper cites Jailbreaking and Mitigation of Vulnerabilities in Large Language Models.

From Noise to Nuance: Advances in Deep Generative Image Models Jailbreaking and Mitigation of Vulnerabilities in Large Language Models

Reference 30

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This paper cites Scalable diffusion models with t ransformers,.

From Noise to Nuance: Advances in Deep Generative Image Models Scalable diffusion models with t ransformers,

Reference 31

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This paper cites Sd-dit: Unleashing the power of self-supervised discrimi nation in diffusion transformer,.

From Noise to Nuance: Advances in Deep Generative Image Models Sd-dit: Unleashing the power of self-supervised discrimi nation in diffusion transformer,

Reference 32

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This paper cites Scaling Autoregressive Models for Content-Rich Text-to-Image Generation.

From Noise to Nuance: Advances in Deep Generative Image Models Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Reference 33

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This paper cites Muse: Text-To-Image Generation via Masked Generative Transformers.

From Noise to Nuance: Advances in Deep Generative Image Models Muse: Text-To-Image Generation via Masked Generative Transformers

Reference 34

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This paper cites Cogview: Mastering text-to-image generation via transformers,.

From Noise to Nuance: Advances in Deep Generative Image Models Cogview: Mastering text-to-image generation via transformers,

Reference 35

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This paper cites Cogview2: Faste r and better text-to-image generation via hierarchical transfo rmers,.

From Noise to Nuance: Advances in Deep Generative Image Models Cogview2: Faste r and better text-to-image generation via hierarchical transfo rmers,

Reference 36

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This paper cites CogView3: Finer and Faster Text-to-Image Generation via Relay Diffusion.

From Noise to Nuance: Advances in Deep Generative Image Models CogView3: Finer and Faster Text-to-Image Generation via Relay Diffusion

Reference 37

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This paper cites Auto-Encoding Variational Bayes.

From Noise to Nuance: Advances in Deep Generative Image Models Auto-Encoding Variational Bayes

Reference 38

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

From Noise to Nuance: Advances in Deep Generative Image Models SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 39

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This paper cites Adve rsarial diffusion distillation,.

From Noise to Nuance: Advances in Deep Generative Image Models Adve rsarial diffusion distillation,

Reference 40

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This paper cites Scaling rectiﬁed ﬂow transformers for high-resolution image synthesis,.

From Noise to Nuance: Advances in Deep Generative Image Models Scaling rectiﬁed ﬂow transformers for high-resolution image synthesis,

Reference 41

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This paper cites Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation.

From Noise to Nuance: Advances in Deep Generative Image Models Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation

Reference 42

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This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

From Noise to Nuance: Advances in Deep Generative Image Models Progressive Distillation for Fast Sampling of Diffusion Models

Reference 43

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This paper cites SDXL-Lightning: Progressive Adversarial Diffusion Distillation.

From Noise to Nuance: Advances in Deep Generative Image Models SDXL-Lightning: Progressive Adversarial Diffusion Distillation

Reference 44

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This paper cites Google research imagen 2 update.

From Noise to Nuance: Advances in Deep Generative Image Models Google research imagen 2 update

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From Noise to Nuance: Advances in Deep Generative Image Models Google deepmind imagen 3 update

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This paper cites From word vectors to multimodal embeddings: Techniques, applications, and future directions for large language models,.

From Noise to Nuance: Advances in Deep Generative Image Models From word vectors to multimodal embeddings: Techniques, applications, and future directions for large language models,

Reference 47

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This paper cites Dall-e 3: Ai that can create images from text wi th improved prompt following.

From Noise to Nuance: Advances in Deep Generative Image Models Dall-e 3: Ai that can create images from text wi th improved prompt following

Reference 48

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This paper cites Consistency Models.

From Noise to Nuance: Advances in Deep Generative Image Models Consistency Models

Reference 49

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From Noise to Nuance: Advances in Deep Generative Image Models Consistency Models Made Easy

Reference 50

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This paper cites Q-diffusion: Quantizing diffusion models,.

From Noise to Nuance: Advances in Deep Generative Image Models Q-diffusion: Quantizing diffusion models,

Reference 51

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This paper cites Post-traini ng quantiza- tion on diffusion models,.

From Noise to Nuance: Advances in Deep Generative Image Models Post-traini ng quantiza- tion on diffusion models,

Reference 52

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This paper cites EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models.

From Noise to Nuance: Advances in Deep Generative Image Models EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models

Reference 53

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From Noise to Nuance: Advances in Deep Generative Image Models EfficientQAT: Efficient Quantization-Aware Training for Large Language Models

Reference 54

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This paper cites Mixed Precision Training.

From Noise to Nuance: Advances in Deep Generative Image Models Mixed Precision Training

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From Noise to Nuance: Advances in Deep Generative Image Models Adaptive quantization for deep neural network,

Reference 56

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This paper cites Parameter-efﬁcie nt transfer learning for nlp,.

From Noise to Nuance: Advances in Deep Generative Image Models Parameter-efﬁcie nt transfer learning for nlp,

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

From Noise to Nuance: Advances in Deep Generative Image Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 58

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This paper cites Qlora: Efﬁcient ﬁnetuning of quantized llms,.

From Noise to Nuance: Advances in Deep Generative Image Models Qlora: Efﬁcient ﬁnetuning of quantized llms,

Reference 59

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This paper cites Diffstyler: Diffusion-based localized image s tyle transfer,.

From Noise to Nuance: Advances in Deep Generative Image Models Diffstyler: Diffusion-based localized image s tyle transfer,

Reference 60

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Observation 04c68576-721c-43c2-b64d-e7a56823de9f · outbound

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

From Noise to Nuance: Advances in Deep Generative Image Models LCM-LoRA: A Universal Stable-Diffusion Acceleration Module

Reference 61

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Observation 2978468b-673e-4b52-b2a5-8fd60824503f · outbound

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

From Noise to Nuance: Advances in Deep Generative Image Models T2i- adapter: Learning adapters to dig out more controllable abi lity for text- to-image diffusion models,

Reference 62

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Observation 2af8e51d-338a-4525-9655-7c1ad98ccf7d · outbound

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

From Noise to Nuance: Advances in Deep Generative Image Models IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

Reference 63

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Observation 53e2f0bb-d333-41db-82e8-7bfb1ef6901c · outbound

This paper cites Optimizing prompts f or text-to-image generation,.

From Noise to Nuance: Advances in Deep Generative Image Models Optimizing prompts f or text-to-image generation,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-11T17:33:17.906027Z

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Observation 066ec47d-8e97-4acf-bf14-4b5894d0dc28 · outbound

This paper cites Sparsity-and hybrid ity-inspired visual parameter-efﬁcient ﬁne-tuning for medical diagnos is,.

From Noise to Nuance: Advances in Deep Generative Image Models Sparsity-and hybrid ity-inspired visual parameter-efﬁcient ﬁne-tuning for medical diagnos is,

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T17:33:17.909750Z

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Observation 2fae8c87-b4f0-46ad-b981-48d072ca126f · outbound

This paper cites Zero: M emory optimizations toward training trillion parameter models,.

From Noise to Nuance: Advances in Deep Generative Image Models Zero: M emory optimizations toward training trillion parameter models,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-11T17:33:17.915133Z

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Observation 21cbe4f9-f7f2-4005-b835-441f02feee26 · outbound

This paper cites Efﬁcient large-scale language model training on gpu clus ters using megatron-lm,.

From Noise to Nuance: Advances in Deep Generative Image Models Efﬁcient large-scale language model training on gpu clus ters using megatron-lm,

Reference 67

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Observation b1caf7f2-bdb7-447a-8e3d-940e75636eeb · outbound

This paper cites xDiT: an Inference Engine for Diffusion Transformers (DiTs) with Massive Parallelism.

From Noise to Nuance: Advances in Deep Generative Image Models xDiT: an Inference Engine for Diffusion Transformers (DiTs) with Massive Parallelism

Reference 68

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Observation be0a2d13-e0eb-40cd-bb7d-a5f06ad55e03 · outbound

This paper cites Deep generative model for imag e inpainting with local binary pattern learning and spatial a ttention,.

From Noise to Nuance: Advances in Deep Generative Image Models Deep generative model for imag e inpainting with local binary pattern learning and spatial a ttention,

Reference 69

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Observation ecc82349-c306-4fd0-8f04-66441d2dfacd · outbound

This paper cites Gen erative image inpainting with contextual attention,.

From Noise to Nuance: Advances in Deep Generative Image Models Gen erative image inpainting with contextual attention,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-11T17:33:17.938019Z

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Observation 73254ffd-cd26-4a63-aeab-3b2a94d33bad · outbound

This paper cites GRIG: Few-Shot Generative Residual Image Inpainting.

From Noise to Nuance: Advances in Deep Generative Image Models GRIG: Few-Shot Generative Residual Image Inpainting

Reference 71

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Observation d7b0bd61-6ee9-445d-bebc-432d4aa7c034 · outbound

This paper cites Latentpaint : Image inpainting in latent space with diffusion models,.

From Noise to Nuance: Advances in Deep Generative Image Models Latentpaint : Image inpainting in latent space with diffusion models,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:33:20.010105Z

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Observation fcbaa0a9-1f01-400b-bddc-34d1690f6c15 · outbound

This paper cites Repaint: Inpainting using denoising diffusio n probabilis- tic models,.

From Noise to Nuance: Advances in Deep Generative Image Models Repaint: Inpainting using denoising diffusio n probabilis- tic models,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:33:19.990915Z

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Observation 94b398ab-9fe8-49ab-94c1-a221fdfcf59f · outbound

This paper cites Gradpaint: Gradi ent-guided inpainting with diffusion models,.

From Noise to Nuance: Advances in Deep Generative Image Models Gradpaint: Gradi ent-guided inpainting with diffusion models,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:33:19.970650Z

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Observation 65b5d348-be7f-40dc-ab1d-482fae254faa · outbound

This paper cites Inout: Diverse image outpainting via gan inversio n,.

From Noise to Nuance: Advances in Deep Generative Image Models Inout: Diverse image outpainting via gan inversio n,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:33:19.954920Z

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Observation 5d92696b-e5fe-4dda-ae83-dabbe423a5f7 · outbound

This paper cites Painting outside as inside: Edge guided image outpainting via bidirec- tional rearrangement with progressive step learning,.

From Noise to Nuance: Advances in Deep Generative Image Models Painting outside as inside: Edge guided image outpainting via bidirec- tional rearrangement with progressive step learning,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:33:19.937063Z

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Observation 32f24867-aaec-46aa-ab66-084c64fef47d · outbound

This paper cites Towards reliable image outpainting: Learning structure-aware multimodal fusion with depth guidance,.

From Noise to Nuance: Advances in Deep Generative Image Models Towards reliable image outpainting: Learning structure-aware multimodal fusion with depth guidance,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:33:19.918684Z

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Observation d78f7174-db61-49fa-967d-6cc2d362ad6c · outbound

This paper cites Em ergent correspondence from image diffusion,.

From Noise to Nuance: Advances in Deep Generative Image Models Em ergent correspondence from image diffusion,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:33:19.902011Z

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Observation d28f42f2-dbf9-43e2-b827-324163efc19d · outbound

This paper cites Zero-1-to-3: Zero-shot one image to 3d object ,.

From Noise to Nuance: Advances in Deep Generative Image Models Zero-1-to-3: Zero-shot one image to 3d object ,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:33:19.883337Z

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Observation 2c2f0805-2dbe-4e13-982c-683465ee12eb · outbound

This paper cites Wonder3d: Single image to 3d using cross-domain diffusion,.

From Noise to Nuance: Advances in Deep Generative Image Models Wonder3d: Single image to 3d using cross-domain diffusion,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:33:19.862786Z

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Observation ac786a30-8546-4ac1-b9e2-be4b3ad9f5fa · outbound

This paper cites Make-it-3d: High-ﬁdelity 3d creation from a single image w ith diffu- sion prior,.

From Noise to Nuance: Advances in Deep Generative Image Models Make-it-3d: High-ﬁdelity 3d creation from a single image w ith diffu- sion prior,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:33:19.846515Z

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Observation c60e56bb-7bf1-4104-ab32-8e125ce0b80c · outbound

This paper cites Adding conditional c ontrol to text-to-image diffusion models,.

From Noise to Nuance: Advances in Deep Generative Image Models Adding conditional c ontrol to text-to-image diffusion models,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:33:19.823009Z

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Observation d2af0bef-a05e-44f3-bb3c-13dcc3f33860 · outbound

This paper cites ControlNet-XS: Rethinking the Control of Text-to-Image Diffusion Models as Feedback-Control Systems.

From Noise to Nuance: Advances in Deep Generative Image Models ControlNet-XS: Rethinking the Control of Text-to-Image Diffusion Models as Feedback-Control Systems

Reference 83

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Observation 944dfbb4-73b6-4f92-9b4b-da18fc0d23a2 · outbound

This paper cites Uni-controlnet: All-in-one control to text-to-ima ge diffusion models,.

From Noise to Nuance: Advances in Deep Generative Image Models Uni-controlnet: All-in-one control to text-to-ima ge diffusion models,

Reference 84

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Observation 309b61eb-7439-4968-8e0e-a7abc9033f95 · outbound

This paper cites StyleDrop: Text-to-Image Generation in Any Style.

From Noise to Nuance: Advances in Deep Generative Image Models StyleDrop: Text-to-Image Generation in Any Style

Reference 85

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Observation 6543d28d-ead1-4476-b7ca-d369949ed463 · outbound

This paper cites Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate.

From Noise to Nuance: Advances in Deep Generative Image Models Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate

Reference 86

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:33:18.801676Z

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Observation 5f1d7eca-9602-417e-9ad9-7fe36d5b6862 · outbound

This paper cites Clipstyler: Image style transfer w ith a single text condition,.

From Noise to Nuance: Advances in Deep Generative Image Models Clipstyler: Image style transfer w ith a single text condition,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:33:19.787253Z

Source-reported events for the cited work

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Observation 13a4748c-9da6-44d0-a62e-52b0a829dc7b · outbound

This paper cites StyleShot: A Snapshot on Any Style.

From Noise to Nuance: Advances in Deep Generative Image Models StyleShot: A Snapshot on Any Style

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-11T17:33:18.038844Z

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Observation 5aa9a9bd-8c63-4537-b4d7-0a378e6cf55f · outbound

This paper cites Metropolis Theorem and Its Applications in Single Image Detail Enhancement.

From Noise to Nuance: Advances in Deep Generative Image Models Metropolis Theorem and Its Applications in Single Image Detail Enhancement

Reference 89

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:33:18.752170Z

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Observation 8e556311-82c4-457d-bc36-0c4ea4503e56 · outbound

This paper cites Multi-scale image decomposition using a l ocal statistical edge model,.

From Noise to Nuance: Advances in Deep Generative Image Models Multi-scale image decomposition using a l ocal statistical edge model,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:33:19.773939Z

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Observation 007aecf8-167e-4ace-a39a-55820318a1a4 · outbound

This paper cites CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement Task.

From Noise to Nuance: Advances in Deep Generative Image Models CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement Task

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-11T17:33:18.055157Z

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Observation 66777fd2-f9ff-4ad1-8183-17ad40b08357 · outbound

This paper cites ECAFormer: Low-light Image Enhancement using Cross Attention.

From Noise to Nuance: Advances in Deep Generative Image Models ECAFormer: Low-light Image Enhancement using Cross Attention

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-11T17:33:18.060291Z

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Observation 3e7a62bf-f393-48a5-9201-fc1b355bda7f · outbound

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

From Noise to Nuance: Advances in Deep Generative Image Models Gans trained by a two time-scale update rule converge to a lo cal nash equilibrium,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:33:19.761780Z

Source-reported events for the cited work

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

Observation 200c23a7-122f-442b-b88c-4bf65caf11ed · outbound

This paper cites On aliased resizing and surprising subtleties in gan evaluation,.

From Noise to Nuance: Advances in Deep Generative Image Models On aliased resizing and surprising subtleties in gan evaluation,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:33:19.747731Z

Source-reported events for the cited work

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

Observation 898b6efc-d526-4469-a506-02e95e928f85 · outbound

This paper cites Demystifying MMD GANs.

From Noise to Nuance: Advances in Deep Generative Image Models Demystifying MMD GANs

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-11T17:33:18.074132Z

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Observation 3fa6d0fe-cac9-41c0-8afb-7289a171feeb · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptua l metric,.

From Noise to Nuance: Advances in Deep Generative Image Models The unreasonable effectiveness of deep features as a perceptua l metric,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:33:19.725412Z

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Observation dc4875b2-12cf-406c-94b4-4cf1015ae173 · outbound

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

From Noise to Nuance: Advances in Deep Generative Image Models Imagereward: Learning and evaluating human preferences f or text- to-image generation,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:33:19.710306Z

Source-reported events for the cited work

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

Observation 59026ac4-f31a-4c37-b235-88a3e276b62b · outbound

This paper cites CLIP-AGIQA: Boosting the Performance of AI-Generated Image Quality Assessment with CLIP.

From Noise to Nuance: Advances in Deep Generative Image Models CLIP-AGIQA: Boosting the Performance of AI-Generated Image Quality Assessment with CLIP

Reference 98

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:33:18.647530Z

Source-reported events for the cited work

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

Observation 6b45f769-a6ce-4d1b-8f12-91128d61fe69 · outbound

This paper cites Human Evaluation of Text-to-Image Models on a Multi-Task Benchmark.

From Noise to Nuance: Advances in Deep Generative Image Models Human Evaluation of Text-to-Image Models on a Multi-Task Benchmark

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-11T17:33:18.094338Z

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Observation 60b8a62b-4e35-4141-97af-bbedb72cd9d0 · outbound

This paper cites Hum an-ai co- creation: evaluating the impact of large-scale text-to-im age generative models on the creative process,.

From Noise to Nuance: Advances in Deep Generative Image Models Hum an-ai co- creation: evaluating the impact of large-scale text-to-im age generative models on the creative process,

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:33:19.693968Z

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