Stable diffusion, a recent breakthrough in generative modeling, has emerged as a powerful technique for text-to-image generation. Unlike traditional methods, stable diffusion leverages continuous diffusion processes, making it more robust and stable during training. By applying this approach, the model can progressively generate realistic images from textual descriptions by iteratively refining the synthesized samples. This methodology allows for greater control over the image generation process and facilitates the production of high-quality images with fine-grained details and improved diversity.
Join this session and learn:
- Principle of Diffusion models.
- Model score function of images with UNet model
- Understanding prompt through contextualized word embedding
- How text influences image through cross attention
- Improve efficiency by adding an autoencoder.
- Learn how to use Automatic 1111 to generate images from text using Stable Diffusion
