The article discusses the challenges posed by non-deterministic outputs of diffusion models in text-to-image generation and the reliance on manual labor or pre-existing images for character creation. The author presents a method for generating consistent characters using open-source diffusion models and running training loops on a moderate GPU without additional resources. By generating multiple images from a text prompt using a text-to-image model and embedding them into a semantic feature space, cohesive clusters can be selected for further refinement. This approach aims to address the limitations of current methods and provide a more efficient and reliable solution for character generation in various applications such as story creation, asset design, game development, and advertising. The article also includes images and references from external sources to support the discussion on the proposed method.
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Source link: https://ai.gopubby.com/consistent-character-generation-using-text-to-image-diffusion-8b8e1998d429?source=rss——stable_diffusion-5
Generating stable characters consistently with diffusion XL #charactergeneration
