Image Generation with a Sphere Encoder

Kaiyu Yue, Menglin Jia, Ji Hou, Tom Goldstein

Image Generation with a Sphere Encoder: 19 upvotes on Hugging Face Daily Papers, #8 of 31 papers on 2026-02-26. Day-by-day upvote history.

We introduce the Sphere Encoder, an efficient generative framework capable of producing images in a single forward pass and competing with many-step diffusion models using fewer than five steps. Our approach works by learning an encoder that maps natural images uniformly onto a spherical latent space, and a decoder that maps random latent vectors back to the image space. Trained solely through image reconstruction losses, the model generates an image by simply decoding a random point on the sphere. Our architecture naturally supports conditional generation, and looping the encoder/decoder a few times can further enhance image quality. Across several datasets, the sphere encoder approach yields performance competitive with state of the art diffusions, but with a small fraction of the inference cost. Project page is available at https://sphere-encoder.github.io .

Paper page on Hugging Face · arXiv

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