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Anything inside the path will be included after the clipping path is applied; anything outside the path will be omitted from the output. Applying the clipping path results in a hard (aliased) or soft (anti-aliased) edge, depending on the image editor's capabilities. Clipping path. By convention, the inside of the path is defined by its direction.
Diagram of the latent diffusion architecture used by Stable Diffusion The denoising process used by Stable Diffusion. The model generates images by iteratively denoising random noise until a configured number of steps have been reached, guided by the CLIP text encoder pretrained on concepts along with the attention mechanism, resulting in the desired image depicting a representation of the ...
Next, one side of the clip polygon is extended infinitely in both directions, and the path of the subject polygon is traversed. Vertices from the input list are inserted into an output list if they lie on the visible side of the extended clip polygon line, and new vertices are added to the output list where the subject polygon path crosses the ...
Similarly, an image model prompted with the text "a photo of a CEO" might disproportionately generate images of white male CEOs, [112] if trained on a racially biased data set. A number of methods for mitigating bias have been attempted, such as altering input prompts [ 113 ] and reweighting training data.
An image conditioned on the prompt "an astronaut riding a horse, by Hiroshige", generated by Stable Diffusion 3.5, a large-scale text-to-image model first released in 2022. A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.
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