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Download as PDF; Printable version; In other projects ... it is usually called the guidance scale. ... Overview of classifier guidance and classifier-free guidance ...
Another configurable option, the classifier-free guidance scale value, allows the user to adjust how closely the output image adheres to the prompt. [29] More experimentative use cases may opt for a lower scale value, while use cases aiming for more specific outputs may use a higher value. [34]
SD 1.1 to 1.4 were released by CompVis in August 2022. There is no "version 1.0". SD 1.1 was a LDM trained on the laion2B-en dataset. SD 1.1 was finetuned to 1.2 on more aesthetic images. SD 1.2 was finetuned to 1.3, 1.4 and 1.5, with 10% of text-conditioning dropped, to improve classifier-free guidance.
Analogously, a classifier based on a generative model is a generative classifier, while a classifier based on a discriminative model is a discriminative classifier, though this term also refers to classifiers that are not based on a model. Standard examples of each, all of which are linear classifiers, are: generative classifiers:
LightGBM, short for Light Gradient-Boosting Machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft. [4] [5] It is based on decision tree algorithms and used for ranking, classification and other machine learning tasks. The development focus is on performance and ...
SD-1 was the test set, and it contained digits written by high school students, 58,646 images written by 500 different writers. Each image is accompanied by the identity of its writer. SD-3 was the training set, and it contained digits written by 2000 employees of the United States Census Bureau .
In terms of machine learning and pattern classification, the labels of a set of random observations can be divided into 2 or more classes. Each observation is called an instance and the class it belongs to is the label .
In machine learning, a linear classifier makes a classification decision for each object based on a linear combination of its features.Such classifiers work well for practical problems such as document classification, and more generally for problems with many variables (), reaching accuracy levels comparable to non-linear classifiers while taking less time to train and use.