Table of Contents
What is a Stable Diffusion prompt?
A prompt is a set of instructions fed to a deep-learning algorithm to produce a definite output. In AI art generation, prompts are fed to the Stable Diffusion model to generate images based on the specific prompts. Here’s a good example of a Stable Diffusion prompt: “Generate a picture of a black cat on a kitchen top.” A negative prompt is an argument that instructs the Stable Diffusion model to not include certain things in the generated image. This powerful feature allows users to remove any object, styles, or abnormalities from the original generated image. Response prompts act on the learner response to evoke the correct response. There are three major forms: Verbal Instructions (oral and nonvocal), Modeling, and Physical Guidance Prompt fading: There is a risk of prompt dependence when using prompts (Grow & LeBlanc, 2013). As a brief recap, Stable Diffusion, an AI image synthesis model, gained its ability to generate images by learning from a large dataset of images scraped from the Internet without consulting any rights holders for permission. As a brief recap, Stable Diffusion, an AI image synthesis model, gained its ability to generate images by learning from a large dataset of images scraped from the Internet without consulting any rights holders for permission. Verbal prompts will range from saying the entire word or phrase that you are trying to elicit from the learner to providing only the first sound or syllable to cue the learner. Gestural Prompts include pointing to, looking at, motioning, or nodding to indicate a correct response.
What is Stable Diffusion weights in prompt?
Stable Diffusion supports weighting of prompt keywords. In other words, you can tell it that it really needs to pay attention to a specific keyword (or keywords) and pay less attention to others. It is handy if you’re getting results that are kinda what you’re looking for, but not quite there. Dream Studio, the official online implementation of Stable Diffusion by Stability AI, also got an update to go along with this release. We’re happy to release Stable Diffusion, Version 2.1! Stable Diffusion is a deep learning, text-to-image model released in 2022. Appendix A: Stable Diffusion Prompt Guide Avoid verbs, as Stable Diffusion has a hard time interpreting them correctly. Style cues can be anything you want to condition the image on. I wouldn’t add too many, maybe only 1 to 3.
What is Stable Diffusion?
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. This model card gives an overview of all available model checkpoints. Speed Boost: Diffusion on Compressed (Latent) Data Instead of the Pixel Image. To speed up the image generation process, the Stable Diffusion paper runs the diffusion process not on the pixel images themselves, but on a compressed version of the image. By default, Stable Diffusion generates images in sizes 512 to 512 pixels. You will get the most consistent result when you use this size. It’s very cheap to train a Stable Diffusion model on GCP or AWS. Prepare to spend $5-10 of your own money to fully set up the training environment and to train a model.
How does Stable Diffusion work?
Stable Diffusion is a latent diffusion model. Instead of operating in the high-dimensional image space, it first compresses the image into the latent space. The latent space is 48 times smaller so it reaps the benefit of crunching a lot fewer numbers. That’s why its a lot faster. Stable Diffusion isn’t just an image model, though, it’s also a natural language model. It has two latent spaces: the image representation space learned by the encoder used during training, and the prompt latent space which is learned using a combination of pretraining and training-time fine-tuning. The Stable Diffusion model is created by a collaboration between engineers and researchers from CompVis, Stability AI, and LAION and released under a Creative ML OpenRAIL-M license, which means that it can be used for commercial and non-commercial purposes. The best alternatives to Stable Diffusion – DreamStudio are Insense , Playground AI and Pixelixe. If these 3 options don’t work for you, we’ve listed over 10 alternatives below. Currently, the default sampler of stable-diffusion is PNDM, which needs 50 steps to generate high-quality samples. However, DPM-Solver can generate high-quality samples within only 20-25 steps, and for some samples even within 10-15 steps.
Why is Stable Diffusion so good?
Stable Diffusion splits up the runtime “image generation” process into a diffusion process which starts with noise. Then gradually improves the quality of the image until there is no more noise and the result is closer to the description of the presented text. At a high level, Diffusion models work by destroying training data by adding noise and then learn to recover the data by reversing this noising process. In Other words, Diffusion models can generate coherent images from noise. Diffusion models train by adding noise to images, which the model then learns how to remove. Stable Diffusion 2.0 is now live on Playground AI, and it’s completely free to use. Playground AI is one of my favorite AI image-generator web apps for its feature-rich tools and fast generation. Each user is limited to 1000 images per day, but you can choose to pay for credits if you want more. Embedding is using Textual inversion, which is a process for creating a custom model of Stable Diffusion. By providing your own images and text descriptions for them, the algorithm learns to create new images that match the text description and the style of the images.