The emergence of text-to-image diffusion models marked a revolutionary breakthrough in generative AI. However, training a text-to-image model to consistently reproduce the same subject remains a challenging task. Existing methods often require costly setups, lengthy fine-tuning processes and struggle to generate diverse, text-aligned images. Moreover, the increasing size of diffusion models over the years highlights a scalability challenge for previous fine-tuning methods, as tuning on these large models is even more costly. To address these limitations, we present Stencil.
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