Ким и Канье пишут про диффузию

really cool to see they got back together to write papers on diffusion models 1 INTRODUCTION In image synthesis, diffusion models have advanced to achieving high quality and much wider coverage since the introduction of the DDPM (Ho et al., 2020). They disrupt images by adding noise and generate samples by progressive denoising through multiple steps. Since their deterministic version provides nearly the same result, they are suitable for image editing, where images. However, simply editing the latent variables (i.e., image-graded results (Kim & Ye, 2021)). Instead, they require computation in the reverse process of conditioning models for an attribute. Figure 1(a-c) briefly illustrates the existing approaches. Image of the guiding image with unconditional latent variables (C Meng et al., 2021). Though it provides some control, it is a reflect among the ones in the guide and the unconditional result, and it lacks intuitive control for the magnitude of change. Classifier guidance manipulates images by imposing gradients of a classifier on the latent variables in the reverse process that target class (Dhariwal & Nichol, 2021; Abraham et al., 2022; Liu et al., 2021). It requires training an extra classifier for the latent variables, i.e., noisy images. Furthermore, computing gradients through the classifier during sampling is costly. Finetuning the whole model can steer the resulting images to the target attribute without the above problems (Kim & Ye, 2021). Still, it requires multiple models to reflect multiple descriptions. On the other hand, generative adversarial networks (Goodfellow et al., 2020) inherently provide straightforward image editing in their latent space. Given a latent vector for an original image, we can find their direction in the latent space that maximizes the similarity of the resulting image with a target description in CLIP embedding (Patashnik et al., 2021). The latent direction found on one * corresponding author

На скриншоте твита говорится, что Ким Кардашьян и Канье Уэст снова сошлись, чтобы писать научные статьи о диффузионных моделях. Их фотография вставлена в изображение первой страницы академической статьи, создавая абсурдную шутку о том, что бывшая пара стала соавторами исследований по ИИ. Мем отправляют как реакцию на неожиданное воссоединение или нелепое объяснение совместной работы. EN: A tweet says that Kim Kardashian and Kanye West got back together to write research papers about diffusion models. Their photo is inserted into the first page of an academic paper, creating an absurd joke that the former couple became coauthors of AI research. The meme is sent as a reaction to an unexpected reunion or a ridiculous explanation for working together.

• @pokupkabitka / 42413 2024-07-03