We investigate the space of weights spanned by a large collection of
customized diffusion models. We populate this space by creating a dataset of
over 60,000 models, each of which is a base model fine-tuned to insert a
different person's visual identity. We model the underlying manifold of these
weights as a subspace, which we term weights2weights. We demonstrate three
immediate applications of this space -- sampling, editing, and inversion.
First, as each point in the space corresponds to an identity, sampling a set of
weights from it results in a model encoding a novel identity. Next, we find
linear directions in this space corresponding to semantic edits of the identity
(e.g., adding a beard). These edits persist in appearance across generated
samples. Finally, we show that inverting a single image into this space
reconstructs a realistic identity, even if the input image is out of
distribution (e.g., a painting). Our results indicate that the weight space of
fine-tuned diffusion models behaves as an interpretable latent space of
identities.

通过创建超过 60,000 个基础模型，我们调查了一系列定制扩散模型的权重空间。这些模型是通过微调基本模型以插入不同人的视觉特征而创建的。我们将这些权重的潜在流形建模为一个子空间，我们称之为权重到权重。通过权重空间，我们展示了三个直接的应用 —— 抽样、编辑和反转。首先，由于空间中的每个点对应一个身份，从中抽取一组权重会得到一个编码新身份的模型。接下来，我们发现该空间中的线性方向对应于身份的语义编辑（例如，添加胡须）。这些编辑在生成的样本中保持外观一致。最后，我们证明将单个图像反转到该空间中会重构出一个逼真的身份，即使输入图像不符合分布（例如，一幅绘画）。我们的结果表明，经过微调的扩散模型的权重空间行为类似于可解释的身份潜空间。