Image Generation with a Sphere Encoder
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 .
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Kaiyu Yue
claimed_verified
2026-03-09T10:40:18.610Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/640d0dbc8036cc2142273a83/cicTWJVqqvQv_DgDucWgY.jpeg
Kaiyu Yue
kaiyuyue
user
kaiyuyue
69966aaf1268a6b79e0d0254
Menglin Jia
69966aaf1268a6b79e0d0255
Ji Hou
69966aaf1268a6b79e0d0256
Tom Goldstein
Image Generation with a Sphere Encoder
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 .
The Sphere Encoder is an efficient generative model that produces images in a single forward pass by mapping images to a spherical latent space and decoding from random points on that sphere, achieving diffusion-like quality with significantly reduced inference costs.
sphere encoder generative framework spherical latent space encoder-decoder architecture image reconstruction losses conditional generation inference cost