  Denoising Diffusion Probabilistic Models (DDPMs) have recently achieved
remarkable results in conditional and unconditional image generation. The
pre-trained models can be adapted without further training to different
downstream tasks, by guiding their iterative denoising process at inference
time to satisfy additional constraints. For the specific task of image
inpainting, the current guiding mechanism relies on copying-and-pasting the
known regions from the input image at each denoising step. However, diffusion
models are strongly conditioned by the initial random noise, and therefore
struggle to harmonize predictions inside the inpainting mask with the real
parts of the input image, often producing results with unnatural artifacts.
  Our method, dubbed GradPaint, steers the generation towards a globally
coherent image. At each step in the denoising process, we leverage the model's
"denoised image estimation" by calculating a custom loss measuring its
coherence with the masked input image. Our guiding mechanism uses the gradient
obtained from backpropagating this loss through the diffusion model itself.
GradPaint generalizes well to diffusion models trained on various datasets,
improving upon current state-of-the-art supervised and unsupervised methods.
