(iPad) Fixed an issue where the drawing result of the end of a line may be incorrect when Show Effects when using Pencil is turned on in the OS settings.(iPad) Fixed an issue where the “Getting Started” dialog could not be closed on iPad Mini.(iPad) Fixed an issue where unintended lines may be drawn if using the single swipe gesture while double-tapping the canvas with the pen.(Windows / macOS / iPad / Galaxy / Android / Chromebook) Fixed an issue on non-smartphone devices where if Quality is not displayed in the JPEG export settings dialog, the Preview rendering results when exporting settings would not be used.Fixed an issue where scrolling or rotating the canvas would become sluggish after switching to another tool while an object is selected with the Object sub tool.In the text copied with the Version information dialog > Copy Diagnostics, the Settings Path folder was incorrect.Fixed an issue where 3D layers would not be compatible with other versions even when saved in compatibility mode in Clip Studio Paint.In the text copied with the Version information dialog > Copy Diagnostics, text related to the license information has been changed.The following features have been improved. (Windows / macOS / iPad / Galaxy / Android / Chromebook) The Edit Set dialog, which manages auto action sets, can now be resized on non-smartphone devices.Note that the C value should be determined via a hyperparameter sweep using a validation split.Updates in Version 1.13.2 (released January 31, 2023) Main improvements in Version 1.13.2 Improvements # Prepare the inputs image, class_id = cifar100 expanduser( "~/.cache"), download = True, train = False) # Download the dataset cifar100 = CIFAR100( root = os. is_available() else "cpu" model, preprocess = clip. datasets import CIFAR100 # Load the model device = "cuda" if torch. Import os import clip import torch from torchvision. This example takes an image from the CIFAR-100 dataset, and predicts the most likely labels among the 100 textual labels from the dataset. The code below performs zero-shot prediction using CLIP, as shown in Appendix B in the paper. The values are cosine similarities between the corresponding image and text features, times 100. Given a batch of images and a batch of text tokens, returns two Tensors, containing the logit scores corresponding to each image and text input. Given a batch of text tokens, returns the text features encoded by the language portion of the CLIP model. Given a batch of images, returns the image features encoded by the vision portion of the CLIP model. The model returned by clip.load() supports the following methods: model.encode_image(image: Tensor) This can be used as the input to the model Returns a LongTensor containing tokenized sequences of given text input(s). clip.tokenize(text: Union], context_length=77) When jit is False, a non-JIT version of the model will be loaded. The device to run the model can be optionally specified, and the default is to use the first CUDA device if there is any, otherwise the CPU. The name argument can also be a path to a local checkpoint. Returns the model and the TorchVision transform needed by the model, specified by the model name returned by clip.available_models(). Returns the names of the available CLIP models. The CLIP module clip provides the following methods: clip.available_models() Print( "Label probs:", probs) # prints: ] API Logits_per_image, logits_per_text = model( image, text) Import torch import clip from PIL import Image device = "cuda" if torch.
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