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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.
4.6 Classifier-free guidance (CFG) 4.7 Samplers. 4.8 Other examples. ... In the context of diffusion models, it is usually called the guidance scale. A high ...
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.
Variables, including classifier-free guidance (CFG) scale, seed, steps, sampler, scheduler, denoise, upscaler, and encoder, are sometimes available for adjustment. Additional influence can be exerted during pre-inference by means of noise manipulation, while traditional post-processing techniques are frequently used post-inference.
Naive Bayes is a simple technique for constructing classifiers: models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set.
A strong learner is a classifier that is arbitrarily well-correlated with the true classification. Robert Schapire answered the question in the affirmative in a paper published in 1990. [ 5 ] This has had significant ramifications in machine learning and statistics , most notably leading to the development of boosting.
A step-wise schematic illustrating a generic Michigan-style learning classifier system learning cycle performing supervised learning. Keeping in mind that LCS is a paradigm for genetic-based machine learning rather than a specific method, the following outlines key elements of a generic, modern (i.e. post-XCS) LCS algorithm.
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.