To achieve this, we present a new diffusion model (ControlStyle) via upgrading a pre-trained text-to-image model with a trainable modulation network enabling more conditions of text prompts and style images.
Nevertheless, there has not been an open-source codebase in support of training and deploying numerous neural network models for cross-modal analytics in a unified and modular fashion.
Despite having impressive vision-language (VL) pretraining with BERT-based encoder for VL understanding, the pretraining of a universal encoder-decoder for both VL understanding and generation remains challenging.
To address this problem, we model a metro system as graphs with various topologies and propose a unified Physical-Virtual Collaboration Graph Network (PVCGN), which can effectively learn the complex ridership patterns from the tailor-designed graphs.
Moreover, the inherently recurrent dependency in RNN prevents parallelization within a sequence during training and therefore limits the computations.
In this paper, we consider a typical image blind denoising problem, which is to remove unknown noise from noisy images.
We further show that applying deep residual learning can boost the convergence speed of our novel deep recurret convolutional networks.
Creating aesthetically pleasing pieces of art, including music, has been a long-term goal for artificial intelligence research.