Generative art with language+diffusion models

Adventures in DIY models
Punditry
Mechanics
UIs
Incoming
References

Generative art using modern diffusion-backed image generators. The name-brand models are DALL-E 2, stable diffusion, Midjourney etc, which are diffusion + transformer models.

CLIP presumably goes somewhere near here.

For audio stuff see music diffusion.

Adventures in DIY models

That Pokemon diffusion post: Adventures in Finetuning Stable Diffusion

Punditry

Mechanics

UIs

DiffusionBee - Stable Diffusion App for AI Art /divamgupta/diffusionbee-stable-diffusion-ui: Diffusion Bee is the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed.
invoke-ai/InvokeAI Linux, Windows and macOS
InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.
AUTOMATIC1111/stable-diffusion-webui: Stable Diffusion web UI
A browser interface based on Gradio library for Stable Diffusion.
Run on Apple Silicon
NMKD Stable Diffusion GUI - AI Image Generator by N00MKRAD
A handy GUI to run Stable Diffusion, a machine learning toolkit to generate images from text, locally on your own hardware.
It is completely uncensored and unfiltered - I am not responsibly for any of the content generated with it. No data is shared/collected by me or any third party.
NightCafe Creator
Stable Diffusion, DALL-E 2, CLIP-Guided Diffusion, VQGAN+CLIP and Neural Style Transfer are all available on NightCafe.
Playground AI

Incoming

References

Dhariwal, Prafulla, and Alex Nichol. 2021. “Diffusion Models Beat GANs on Image Synthesis.” arXiv:2105.05233 [Cs, Stat], June.

Dutordoir, Vincent, Alan Saul, Zoubin Ghahramani, and Fergus Simpson. 2022. “Neural Diffusion Processes.” arXiv.

Han, Xizewen, Huangjie Zheng, and Mingyuan Zhou. 2022. “CARD: Classification and Regression Diffusion Models.” arXiv.

Ho, Jonathan, Ajay Jain, and Pieter Abbeel. 2020. “Denoising Diffusion Probabilistic Models.” arXiv:2006.11239 [Cs, Stat], December.

Hoogeboom, Emiel, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, and Tim Salimans. 2021. “Autoregressive Diffusion Models.” arXiv:2110.02037 [Cs, Stat], October.

Nichol, Alex, and Prafulla Dhariwal. 2021. “Improved Denoising Diffusion Probabilistic Models.” arXiv:2102.09672 [Cs, Stat], February.

Platen, Patrick von, Suraj Patil, Anton Lozhkov, Pedro Cuenca, Nathan Lambert, Kashif Rasul, Mishig Davaadorj, and Thomas Wolf. 2022. “Diffusers: State-of-the-Art Diffusion Models.” GitHub.

Sohl-Dickstein, Jascha, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. “Deep Unsupervised Learning Using Nonequilibrium Thermodynamics.” arXiv:1503.03585 [Cond-Mat, q-Bio, Stat], November.

Song, Jiaming, Chenlin Meng, and Stefano Ermon. 2021. “Denoising Diffusion Implicit Models.” arXiv:2010.02502 [Cs], November.

Song, Yang, and Stefano Ermon. 2020a. “Generative Modeling by Estimating Gradients of the Data Distribution.” In Advances In Neural Information Processing Systems. arXiv.

———. 2020b. “Improved Techniques for Training Score-Based Generative Models.” In Advances In Neural Information Processing Systems. arXiv.

Yang, Ling, Zhilong Zhang, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Ming-Hsuan Yang, and Bin Cui. 2022. “Diffusion Models: A Comprehensive Survey of Methods and Applications.” arXiv.