The second half of the lesson covers the key concepts involved in Stable Diffusion: We talk about some of the nifty tweaks available when using Stable Diffusion in Diffusers, and show how to use them: guidance scale (for varying the amount the prompt is used), negative prompts (for removing concepts from an image), image initialisation (for starting with an existing image), textual inversion (for adding your own concepts to generated images), Dreambooth (an alternative approach to textual inversion). We explain how to use its many features, and discuss options for accessing the GPU resources needed to use the library. It has many features and is very flexible. Diffusers is (in our opinion!) the best library available at the moment for image generation. This lesson starts with a tutorial on how to use pipelines in the Diffusers library to generate images. In many lessons we’ll include a challenge for you to complete, some of which involve trying novel research directions where you’ll be venturing into the academic unknown.Studying in groups has been shown to be more effective for most people than studying alone On there are many other students you can collaborate with, and many folks are looking for study groups or study buddies.If there’s mathematical or coding concepts that we use that you’re not comfortable with, don’t be afraid to seek out other tutorials to help fill in your gaps. If you find yourself unsure about some of the foundational deep learning ideas refered to in the lessons, we’d suggest going back to study the lessons in part 1 that cover those ideas
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