AI technology has evolved significantly, with different approaches for text and image generation. While large language models function by predicting the next token in a sequence, image generators like Stable Diffusion utilize a technique called diffusion. This process begins with an image and a prompt, systematically adding noise and learning the degeneration steps. During generation, the model starts from noise and reconstructs an image. Nathan Barry’s tiny-diffusion offers a simple demo that visualizes this process. It focuses on characters, allowing users to see the denoising steps, and operates with 10.7 million parameters. Pretrained on Tiny Shakespeare, its output has a Shakespearean touch. The initial training took about 30 minutes using four NVIDIA A100s, and users can retrain the model with custom datasets. Interested in exploring diffusion further? Music serves as an inspiring method for image prompts. For more information, delve into resources on AI image generation.
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