TagsArticle Source
Stable Diffusion - What, Why, How?
Sources
- Colab Notebook - https://colab.research.google.com/drive/1_kbRZPTjnFgViPrmGcUsaszEdYa8XTpq?usp=sharing
- Blog post - https://stability.ai/blog/stable-diffusion-public-release
- Source Code - https://github.com/CompVis/stable-diffusion
- Hugging Face Models - https://huggingface.co/spaces/stabilityai/stable-diffusion
- Paper - https://arxiv.org/pdf/2112.10752.pdf
Abstract
Stable Diffusion is a text-based image generation machine learning model released by Stability.AI. It’s default ability generated image from text, but the model is open source which means that it can also do much more. In this video I explain how Stable Diffusion works at a high level, briefly talk about how it is different from other Diffusion-based models, compare it to DALL-E 2, and mess around with the code.
Diffusion Models
Diffusion Models are generative models just like GANs. In recent times many state-of-the-art works have been released that build on top of diffusion models such as #dalle or #imagen. In this video I give a detailed explanation of how they work. At first I explain the fundamental idea of these models and later we dive deep into the math part. I try to explain all of this on a really easy & intuitive level. After the math derivation, we look at the results from different papers and how they compare to other methods.
Diffusion models explained in 4-difficulty levels
In this video, we will take a close look at diffusion models. Diffusion models are being used in many domains but they are most famous for image generation. You might have seen diffusion models at work through Dall-e 2 and Imagen.