Details

Autor(en) / Beteiligte

• Choi, Jooyoung
• Kim, Sungwon
• Jeong, Yonghyun
• Gwon, Youngjune
• Yoon, Sungroh

Titel

ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models

Ist Teil von

• 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021, p.14347-14356

Ort / Verlag

IEEE

Erscheinungsjahr

2021

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• Denoising diffusion probabilistic models (DDPM) have shown remarkable performance in unconditional image generation. However, due to the stochasticity of the generative process in DDPM, it is challenging to generate images with the desired semantics. In this work, we propose Iterative Latent Variable Refinement (ILVR), a method to guide the generative process in DDPM to generate high-quality images based on a given reference image. Here, the refinement of the generative process in DDPM enables a single DDPM to sample images from various sets directed by the reference image. The proposed ILVR method generates high-quality images while controlling the generation. The controllability of our method allows adaptation of a single DDPM without any additional learning in various image generation tasks, such as generation from various downsampling factors, multi-domain image translation, paint-to-image, and editing with scribbles.