![]() ![]() Notably, there’s a dedicated DD Discord forum, a DD subreddit, and a lively user community on twitter. Thousands of folks are studying and learning AI art, and there are many resources available to connect with and learn from other DD users and AI artists. Also, DD has dozens of controls, with complex interactions and few limits, so it’s easy to get bad results. It should be no surprise then, that learning the tools will take work and focus. Beyond that, experiment!Īlso, while there are animation controls in DD, you should begin by learning how to create still images, as those skills transfer directly to animations.Ĭreating art with AI is magical and complex, and is constantly being developed by data scientists and programmers. I recommend you first try out the default settings in the notebook, to confirm that the notebook runs properly and there are no errors with your setup. Depending on the settings used and the processor available, DD can take between 5 minutes to an hour or longer to render a single image.įine tuning your prompt and parameters is complex and time consuming, so taking a methodological approach will benefit you. The general approach for using DD is to pick a text prompt, tune the parameters, then run the notebook to create an image. Most of DD’s controls are numerical and control various aspects of the CLIP model and the diffusion curve. We’re focused on the knobs and levers to drive Disco Diffusion. ![]() It will require you to study, but won’t be covered in detail here in this guide. Creating a good text prompt for AI art is a nuanced, challenging task that takes much trial and error and practice. The content of the image is generally controlled by the text used in a ‘prompt’, a sentence, phrase, or series of descriptive words that tells CLIP what you want to see. As you can see in the image sequence above, the images get progressively clearer over the range of steps, as the diffusion denoising process is guided toward the desired image by CLIP. The example image took 250 diffusion steps to complete. Steps 1, 50, 100, 150, and 200 of the diffusion process Initially, the image is just a blurry mess, but as DD advances through the iteration timesteps, coarse and then fine details of the image will emerge. Diffusion will ‘denoise’ the existing image, and DD will display its ‘current estimate’ of what the final image would look like. Each iteration, or step, CLIP will evaluate the existing image against the prompt, and provide a ‘direction’ to the diffusion process. The image above was created in DD using just the text prompt: “A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.”ĭiffusion is an iterative process. When combined, CLIP uses its image identification skills to iteratively guide the diffusion denoising process toward an image that closely matches a text prompt. DD Diffusion Process (vastly simplified)ĭiffusion is a mathematical process for removing noise from an image. Rather, this guide is to help you understand how to use basic DD controls to create your own images, and to provide some insight on how all of the parameters affect CLIP and Diffusion behavior. However, the storied history and complex internal workings of CLIP and Diffusion are NOT the primary topic of this document. Created by Somnai, augmented by Gandamu, and building on the work of RiversHaveWings, nshepperd, and many others. Guide created by Chris Allen on twitter) Disco Diffusion 5.2 – what is it?ĭisco Diffusion (DD) is a Google Colab Notebook which leverages an AI Image generating technique called CLIP-Guided Diffusion to allow you to create compelling and beautiful images from just text inputs. ![]()
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