Efficient Personalization of Quantized Diffusion Model without
  Backpropagation
Authors: name: Hoigi Seo, name: Wongi Jeong, name: Kyungryeol Lee, name: Se Young Chun

Diffusion models have shown remarkable performance in image synthesis, but
they demand extensive computational and memory resources for training,
fine-tuning and inference. Although advanced quantization techniques have
successfully minimized memory usage for inference, training and fine-tuning
these quantized models still require large memory possibly due to
dequantization for accurate computation of gradients and/or backpropagation for
gradient-based algorithms. However, memory-efficient fine-tuning is
particularly desirable for applications such as personalization that often must
be run on edge devices like mobile phones with private data. In this work, we
address this challenge by quantizing a diffusion model with personalization via
Textual Inversion and by leveraging a zeroth-order optimization on
personalization tokens without dequantization so that it does not require
gradient and activation storage for backpropagation that consumes considerable
memory. Since a gradient estimation using zeroth-order optimization is quite
noisy for a single or a few images in personalization, we propose to denoise
the estimated gradient by projecting it onto a subspace that is constructed
with the past history of the tokens, dubbed Subspace Gradient. In addition, we
investigated the influence of text embedding in image generation, leading to
our proposed time steps sampling, dubbed Partial Uniform Timestep Sampling for
sampling with effective diffusion timesteps. Our method achieves comparable
performance to prior methods in image and text alignment scores for
personalizing Stable Diffusion with only forward passes while reducing training
memory demand up to 8.2times.