用球面编码器单次前向生成高质量图像，效率远超扩散模型。
Image Generation with a Sphere Encoder
- 将图像映射到球形潜在空间，随机采点即可解码出图
- 仅需少于5步生成效果媲美多步扩散模型
- 适合追求高速生成的部署场景
我们提出球面编码器（Sphere Encoder），一种高效生成框架，可在单次前向传播中生成图像，性能媲美需多步迭代的扩散模型，且仅需少于五步。该方法通过学习一个编码器，将自然图像均匀映射至球形潜在空间，并配合解码器将随机潜在向量还原为图像。模型仅通过图像重建损失训练，生成时只需解码球面上的随机点。架构天然支持条件生成，循环运行编码器/解码器几次可进一步提升图像质量。在多个数据集上，该方法表现接近当前最优扩散模型，但推理成本仅为极小部分。项目主页见 https://sphere-encoder.github.io。
原文摘要 · Abstract (English)
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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