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Stable Diffusion Models (SDMs) have shown remarkable proficiency in image synthesis.
Energy and Policy Considerations for Deep Learning in NLP
Strubell, E.; Ganesh, A.; and McCallum, A. 2019 · 1906
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Energy and Policy Considerations for Deep Learning in NLP
Strubell, E.; Ganesh, A.; and McCallum, A. 2019 · 1906
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Microsoft coco: Common objects in context
Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; and Zitnick, C. L. 2014 · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; and Zitnick, C. L. 2014 · 2014
Earlier work this paper cites.
Improved Techniques for Training GANs
Salimans, T.; Goodfellow, I.; Zaremba, W.; Cheung, V.; Radford, A.; and Chen, X. 2016 · 2016
Earlier work this paper cites.
Improved Techniques for Training GANs
Salimans, T.; Goodfellow, I.; Zaremba, W.; Cheung, V.; Radford, A.; and Chen, X. 2016 · 2016
Earlier work this paper cites.
Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices
Teerapittayanon, S.; McDanel, B.; and Kung, H. 2017 · 2017
Earlier work this paper cites.
Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices
Teerapittayanon, S.; McDanel, B.; and Kung, H. 2017 · 2017
Earlier work this paper cites.
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Heusel, M.; Ramsauer, H.; Unterthiner, T.; Nessler, B.; and Hochreiter, S. 2018 · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R.; Isola, P.; Efros, A. A.; Shechtman, E.; and Wang, O. 2018 · 2018
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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Heusel, M.; Ramsauer, H.; Unterthiner, T.; Nessler, B.; and Hochreiter, S. 2018 · 2018
Earlier work this paper cites.
The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R.; Isola, P.; Efros, A. A.; Shechtman, E.; and Wang, O. 2018 · 2018
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Dynamic Adaptive DNN Surgery for Inference Acceleration on the Edge
Hu, C.; Bao, W.; Wang, D.; and Liu, F. 2019 · 2019
Earlier work this paper cites.
Dynamic Adaptive DNN Surgery for Inference Acceleration on the Edge
Hu, C.; Bao, W.; Wang, D.; and Liu, F. 2019 · 2019
Earlier work this paper cites.
Datamix: Efficient privacy-preserving edge-cloud inference
Liu, Z.; Wu, Z.; Gan, C.; Zhu, L.; and Han, S. 2020 · 2020
Earlier work this paper cites.
Denoising Diffusion Implicit Models
Song, J.; Meng, C.; and Ermon, S. 2020 · 2020
Earlier work this paper cites.
Datamix: Efficient privacy-preserving edge-cloud inference
Liu, Z.; Wu, Z.; Gan, C.; Zhu, L.; and Han, S. 2020 · 2020
Earlier work this paper cites.
Denoising Diffusion Implicit Models
Song, J.; Meng, C.; and Ermon, S. 2020 · 2020
Earlier work this paper cites.
ediff-i: Text-to-image diffusion models with an ensemble of expert denoisers
Balaji, Y.; Nah, S.; Huang, X.; Vahdat, A.; Song, J.; Zhang, Q.; Kreis, K.; Aittala, M.; Aila, T.; Laine, S.; et al. 2022 · 2022
Earlier work this paper cites.
CLIPScore: A Reference-free Evaluation Metric for Image Captioning
Hessel, J.; Holtzman, A.; Forbes, M.; Bras, R. L.; and Choi, Y. 2022 · 2022
Earlier work this paper cites.
SRDiff: Single image super-resolution with diffusion probabilistic models
Li, H.; Yang, Y.; Chang, M.; Chen, S.; Feng, H.; Xu, Z.; Li, Q.; and Chen, Y. 2022 · 2022
Earlier work this paper cites.
Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps
Lu, C.; Zhou, Y.; Bao, F.; Chen, J.; Li, C.; and Zhu, J. 2022 · 2022
Earlier work this paper cites.
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
Saharia, C.; Chan, W.; Saxena, S.; Li, L.; Whang, J.; Denton, E.; Ghasemipour, S. K. S.; Ayan, B. K.; Mahdavi, S. S.; Lopes, R. G.; Salimans, T.; Ho, J.; Fleet, D. J.; and Norouzi, M. 2022 · 2022
Earlier work this paper cites.
Progressive Distillation for Fast Sampling of Diffusion Models
Salimans, T.; and Ho, J. 2022 · 2022
Earlier work this paper cites.
LAION-5B: An open large-scale dataset for training next generation image-text models
Schuhmann, C.; Beaumont, R.; Vencu, R.; Gordon, C.; Wightman, R.; Cherti, M.; Coombes, T.; Katta, A.; Mullis, C.; Wortsman, M.; Schramowski, P.; Kundurthy, S.; Crowson, K.; Schmidt, L.; Kaczmarczyk, R.; and Jitsev, J. 2022 · 2022
Earlier work this paper cites.
Diffusers: State-of-the-art diffusion models
von Platen, P.; Patil, S.; Lozhkov, A.; Cuenca, P.; Lambert, N.; Rasul, K.; Davaadorj, M.; Nair, D.; Paul, S.; Berman, W.; Xu, Y.; Liu, S.; and Wolf, T. 2022 · 2022
Earlier work this paper cites.
Sustainable AI: Environmental Implications, Challenges and Opportunities
Wu, C.-J.; Raghavendra, R.; Gupta, U.; Acun, B.; Ardalani, N.; Maeng, K.; Chang, G.; Behram, F. A.; Huang, J.; Bai, C.; Gschwind, M.; Gupta, A.; Ott, M.; Melnikov, A.; Candido, S.; Brooks, D.; Chauhan, G.; Lee, B.; Lee, H.-H. S.; Akyildiz, B.; Balandat, M.; Spisak, J.; Jain, R.; Rabbat, M.; and Hazelwood, K. 2022 · 2022
Earlier work this paper cites.
ediff-i: Text-to-image diffusion models with an ensemble of expert denoisers
Balaji, Y.; Nah, S.; Huang, X.; Vahdat, A.; Song, J.; Zhang, Q.; Kreis, K.; Aittala, M.; Aila, T.; Laine, S.; et al. 2022 · 2022
Earlier work this paper cites.
CLIPScore: A Reference-free Evaluation Metric for Image Captioning
Hessel, J.; Holtzman, A.; Forbes, M.; Bras, R. L.; and Choi, Y. 2022 · 2022
Cited alongside, same era.
SRDiff: Single image super-resolution with diffusion probabilistic models
Li, H.; Yang, Y.; Chang, M.; Chen, S.; Feng, H.; Xu, Z.; Li, Q.; and Chen, Y. 2022 · 2022
Cited alongside, same era.
Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps
Lu, C.; Zhou, Y.; Bao, F.; Chen, J.; Li, C.; and Zhu, J. 2022 · 2022
Cited alongside, same era.
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
Saharia, C.; Chan, W.; Saxena, S.; Li, L.; Whang, J.; Denton, E.; Ghasemipour, S. K. S.; Ayan, B. K.; Mahdavi, S. S.; Lopes, R. G.; Salimans, T.; Ho, J.; Fleet, D. J.; and Norouzi, M. 2022 · 2022
Cited alongside, same era.
Progressive Distillation for Fast Sampling of Diffusion Models
Salimans, T.; and Ho, J. 2022 · 2022
Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference
Luo, S.; Tan, Y.; Huang, L.; Li, J.; and Zhao, H. 2023 · 2023
Later among the works it cites.
On distillation of guided diffusion models
Meng, C.; Rombach, R.; Gao, R.; Kingma, D.; Ermon, S.; Ho, J.; and Salimans, T. 2023 · 2023
Later among the works it cites.
A Survey on Collaborative DNN Inference for Edge Intelligence
Ren, W.-Q.; Qu, Y.-B.; Dong, C.; Jing, Y.-Q.; Sun, H.; Wu, Q.-H.; and Guo, S. 2023 · 2023
Later among the works it cites.
StyleGAN-T: unlocking the power of GANs for fast large-scale text-to-image synthesis
Sauer, A.; Karras, T.; Laine, S.; Geiger, A.; and Aila, T. 2023 · 2023
Later among the works it cites.
segmind-small-sd
Segmind. 2023 · 2023
Later among the works it cites.
Diffusion probabilistic model made slim
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Cited alongside, same era.
LAION-5B: An open large-scale dataset for training next generation image-text models
Schuhmann, C.; Beaumont, R.; Vencu, R.; Gordon, C.; Wightman, R.; Cherti, M.; Coombes, T.; Katta, A.; Mullis, C.; Wortsman, M.; Schramowski, P.; Kundurthy, S.; Crowson, K.; Schmidt, L.; Kaczmarczyk, R.; and Jitsev, J. 2022 · 2022
Cited alongside, same era.
Diffusers: State-of-the-art diffusion models
von Platen, P.; Patil, S.; Lozhkov, A.; Cuenca, P.; Lambert, N.; Rasul, K.; Davaadorj, M.; Nair, D.; Paul, S.; Berman, W.; Xu, Y.; Liu, S.; and Wolf, T. 2022 · 2022
Cited alongside, same era.
Sustainable AI: Environmental Implications, Challenges and Opportunities
Wu, C.-J.; Raghavendra, R.; Gupta, U.; Acun, B.; Ardalani, N.; Maeng, K.; Chang, G.; Behram, F. A.; Huang, J.; Bai, C.; Gschwind, M.; Gupta, A.; Ott, M.; Melnikov, A.; Candido, S.; Brooks, D.; Chauhan, G.; Lee, B.; Lee, H.-H. S.; Akyildiz, B.; Balandat, M.; Spisak, J.; Jain, R.; Rabbat, M.; and Hazelwood, K. 2022 · 2022
Cited alongside, same era.
Realistic-Vision-V5.1
2023 · 2023
Cited alongside, same era.
Structural pruning for diffusion models
Fang, G.; Ma, X.; and Wang, X. 2023 · 2023
Cited alongside, same era.
Imagic: Text-Based Real Image Editing with Diffusion Models
Kawar, B.; Zada, S.; Lang, O.; Tov, O.; Chang, H.; Dekel, T.; Mosseri, I.; and Irani, M. 2023 · 2023
Cited alongside, same era.
BK-SDM: A Lightweight, Fast, and Cheap Version of Stable Diffusion
Kim, B.-K.; Song, H.-K.; Castells, T.; and Choi, S. 2023 · 2023
Cited alongside, same era.
Yang, X.; Zhou, D.; Feng, J.; and Wang, X. 2023 · 2023
Later among the works it cites.
Adding Conditional Control to Text-to-Image Diffusion Models
Zhang, L.; Rao, A.; and Agrawala, M. 2023 · 2023
Later among the works it cites.
Estimating the environmental impact of Generative-AI services using an LCA-based methodology
Berthelot, A.; Caron, E.; Jay, M.; and Lefèvre, L. 2024 · 2024
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LD-Pruner: Efficient Pruning of Latent Diffusion Models using Task-Agnostic Insights
Castells, T.; Song, H.-K.; Kim, B.-K.; and Choi, S. 2024 · 2024
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Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Esser, P.; Kulal, S.; Blattmann, A.; Entezari, R.; Müller, J.; Saini, H.; Levi, Y.; Lorenz, D.; Sauer, A.; Boesel, F.; Podell, D.; Dockhorn, T.; English, Z.; Lacey, K.; Goodwin, A.; Marek, Y.; and Rombach, R. 2024 · 2024
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High-Fidelity Diffusion-Based Image Editing
Hou, C.; Wei, G.; and Chen, Z. 2024 · 2024
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SDXL-Lightning: Progressive Adversarial Diffusion Distillation
Lin, S.; Wang, A.; and Yang, X. 2024 · 2024
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ConditionVideo: Training-Free Condition-Guided Video Generation
Peng, B.; Chen, X.; Wang, Y.; Lu, C.; and Qiao, Y. 2024 · 2024
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SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
Podell, D.; English, Z.; Lacey, K.; Blattmann, A.; Dockhorn, T.; Müller, J.; Penna, J.; and Rombach, R. 2024 · 2024
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Tian, Y.; Zhang, Z.; Yang, Y.; Chen, Z.; Yang, Z.; Jin, R.; Quek, T. Q.; and Wong, K.-K. 2024 · 2024
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Denoising Diffusion Step-aware Models
Yang, S.; Chen, Y.; Luozhou, W.; Liu, S.; and Chen, Y.-C. 2024 · 2024
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Cross-Attention Makes Inference Cumbersome in Text-to-Image Diffusion Models
Zhang, W.; Liu, H.; Xie, J.; Faccio, F.; Shou, M. Z.; and Schmidhuber, J. 2024 · 2024
Closest in time.
Estimating the environmental impact of Generative-AI services using an LCA-based methodology
Berthelot, A.; Caron, E.; Jay, M.; and Lefèvre, L. 2024 · 2024
Closest in time.
LD-Pruner: Efficient Pruning of Latent Diffusion Models using Task-Agnostic Insights
Castells, T.; Song, H.-K.; Kim, B.-K.; and Choi, S. 2024 · 2024
Closest in time.
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Esser, P.; Kulal, S.; Blattmann, A.; Entezari, R.; Müller, J.; Saini, H.; Levi, Y.; Lorenz, D.; Sauer, A.; Boesel, F.; Podell, D.; Dockhorn, T.; English, Z.; Lacey, K.; Goodwin, A.; Marek, Y.; and Rombach, R. 2024 · 2024
Closest in time.
High-Fidelity Diffusion-Based Image Editing
Hou, C.; Wei, G.; and Chen, Z. 2024 · 2024
Closest in time.
SDXL-Lightning: Progressive Adversarial Diffusion Distillation
Lin, S.; Wang, A.; and Yang, X. 2024 · 2024
Closest in time.
ConditionVideo: Training-Free Condition-Guided Video Generation
Peng, B.; Chen, X.; Wang, Y.; Lu, C.; and Qiao, Y. 2024 · 2024
Closest in time.
SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
Podell, D.; English, Z.; Lacey, K.; Blattmann, A.; Dockhorn, T.; Müller, J.; Penna, J.; and Rombach, R. 2024 · 2024
Closest in time.
Tian, Y.; Zhang, Z.; Yang, Y.; Chen, Z.; Yang, Z.; Jin, R.; Quek, T. Q.; and Wong, K.-K. 2024 · 2024
Closest in time.
Denoising Diffusion Step-aware Models
Yang, S.; Chen, Y.; Luozhou, W.; Liu, S.; and Chen, Y.-C. 2024 · 2024
Closest in time.
Cross-Attention Makes Inference Cumbersome in Text-to-Image Diffusion Models
Zhang, W.; Liu, H.; Xie, J.; Faccio, F.; Shou, M. Z.; and Schmidhuber, J. 2024 · 2024
Closest in time.