  Good weight initialization serves as an effective measure to reduce the
training cost of a deep neural network (DNN) model. The choice of how to
initialize parameters is challenging and may require manual tuning, which can
be time-consuming and prone to human error. To overcome such limitations, this
work takes a novel step towards building a weight generator to synthesize the
neural weights for initialization. We use the image-to-image translation task
with generative adversarial networks (GANs) as an example due to the ease of
collecting model weights spanning a wide range. Specifically, we first collect
a dataset with various image editing concepts and their corresponding trained
weights, which are later used for the training of the weight generator. To
address the different characteristics among layers and the substantial number
of weights to be predicted, we divide the weights into equal-sized blocks and
assign each block an index. Subsequently, a diffusion model is trained with
such a dataset using both text conditions of the concept and the block indexes.
By initializing the image translation model with the denoised weights predicted
by our diffusion model, the training requires only 43.3 seconds. Compared to
training from scratch (i.e., Pix2pix), we achieve a 15x training time
acceleration for a new concept while obtaining even better image generation
quality.
