Collage Diffusion is a novel interface for interacting with image generation models. It allows you to specify the composition of an image in a familiar Photoshop-like interface. Our modified version of Stable Diffusion takes the layers in and produces a harmonized image, ensuring that everything from perspectives to lighting are plausible. Unlike text prompting supported by traditional diffusion interfaces, Layered Diffusion allows you to precisely outline how a scene should be composed—from where objects are relative to each other to what they look like.
IP-Adapter, an effective and lightweight adapter to achieve image prompt capability for the pre-trained text-to-image diffusion models. An IP-Adapter with only 22M parameters can achieve comparable or even better performance to a fine-tuned image prompt model. IP-Adapter can be generalized not only to other custom models fine-tuned from the same base model, but also to controllable generation using existing controllable tools. Moreover, the image prompt can also work well with the text prompt to accomplish multimodal image generation.
A collection of GAN. GAN is a new generator architecture for generative adversarial networks that enables unsupervised separation of high-level attributes and stochastic variation in generated images. This generator improves the state-of-the-art in distribution quality metrics and disentangles latent factors of variation. The article also introduces two new automated methods to quantify interpolation quality and disentanglement, and a new dataset of human faces.
Literary analysts have long noticed the hand of another author in Shakespeare’s Henry VIII. Now a neural network has identified the specific scenes in question—and who actually wrote them.
For much of his life, William Shakespeare was the house playwright for an acting company called the King’s Men that performed his plays on the banks of the River Thames in London. When Shakespeare died in 1616, the company needed a replacement and turned to one of the most prolific and famous playwrights of the time, a man named John Fletcher.
Generative Adversarial Networks (GANs) are an exciting tool for artists, allowing them to create unique, unpredictable digital art. The GAN process simulates a game, with a Critic network and a generator network competing to create realistic images. The artist works as a curator, selecting the most interesting images produced by the generator. The author recommends using CycleGAN, a neural network architecture that transforms images from one dataset into the style of another, as it allows for high-resolution images and quick training. The author offers practical advice on using CycleGAN, such as fine-tuning models on smaller datasets and experimenting with different batch sizes. The author also emphasizes the importance of using unique, personal datasets for training. Overall, the author encourages artists to experiment with GANs and to let the unpredictability of the process inspire them to create something special.
Copying an element from a photo and pasting it into a painting is a challenging task. Applying photo compositing techniques in this context yields subpar results that look like a collage --- and existing painterly stylization algorithms, which are global, perform poorly when applied locally. We address these issues with a dedicated algorithm that carefully determines the local statistics to be transferred. We ensure both spatial and inter-scale statistical consistency and demonstrate that both aspects are key to generating quality results. To cope with the diversity of abstraction levels and types of paintings, we introduce a technique to adjust the parameters of the transfer depending on the painting. We show that our algorithm produces significantly better results than photo compositing or global stylization techniques and that it enables creative painterly edits that would be otherwise difficult to achieve.
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