Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translationarxiv.org/abs/2211.12572 项目网站:https://pnp-diffusion.github.io/。 封面图来自https://www.artstation.com/artwork/Ya4WAb。 摘要 大规模的文本到图像生成(text-to-image generative)模型,是生成式 AI 发展例程中的革命性的突破...
本文:以一种plug-and-play的方式用损失函数来指导diffusion函数——LGD:Loss-guided diffusion 难点:依旧有不易解的分布的期望值运算,还得近似。 方法:采用Monte Carlo积分方法 步骤1:用一个更加平滑的分布来代替delta分布 步骤2:使用融合了MC方法得到的误差来进行近似——LGD-MC ——因为大多数运算依赖于diffusi...
We consider the problem of inferring high-dimensional datain a model that consists of a priorand an auxiliary differentiable constraintongiven some additional information. In this paper, the prior is an independently trained denoising diffusion generative model. The auxiliary constraint is expected to ...
In this paper, we present a new framework that takes text-to-image synthesis to the realm of image-to-image translation -- given a guidance image and a target text prompt, our method harnesses the power of a pre-trained text-to-image diffusion model to generate a new image that complies...
Large diffusion-based Text-to-Image (T2I) models have shown impressive generative powers for text-to-image generation and spatially conditioned image generation. We can train the model end-to-end with paired data for most applications to obtain photorealistic generation quality. However, to add a...
To plug-and-play diffusion features, please follow these steps: Setup Feature extraction Running PnP TI2I Benchmarks Setup Our codebase is built onCompVis/stable-diffusionand has shared dependencies and model architecture. Creating a Conda Environment ...
《BrushNet - BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion | a Hugging Face Space by TencentARC》 http://t.cn/A6TcubOR #机器学习##人工智能# http://t.cn/A6Tc...
"Denoising Diffusion Models for Plug-and-Play Image Restoration", Yuanzhi Zhu, Kai Zhang, Jingyun Liang, Jiezhang Cao, Bihan Wen, Radu Timofte, Luc Van Gool. - yuanzhi-zhu/DiffPIR
Our goal is to have the fine-tuned model generate high-quality images of arbitrary (known) objects in this (previously unknown) style. Our proposed solution, Specialist Diffusion, is a plug-and-play set of fine-tuning techniques that...
Our method does not require any training or fine-tuning, but rather leverages a pre-trained and fixed text-to-image diffusion model [37]. We pose the fundamental question of how structure in- formation is internally encoded in such a model. We dive i...