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We offer a simple model interface to instantiate both pre-trained and untrained models. To compute billions of embeddings efficiently, you can use clip-retrieval which has openclip support. softmax ( dim =- 1 ) print ( "Label probs:", text_probs ) # prints: ] norm ( dim =- 1, keepdim = True ) text_probs = ( 100.0 * image_features text_features. norm ( dim =- 1, keepdim = True ) text_features /= text_features. encode_text ( text ) image_features /= image_features. encode_image ( image ) text_features = model. unsqueeze ( 0 ) text = tokenizer () with torch. ![]() get_tokenizer ( 'ViT-B-32' ) image = preprocess ( Image. create_model_and_transforms ( 'ViT-B-32', pretrained = 'laion2b_s34b_b79k' ) tokenizer = open_clip. Import torch from PIL import Image import open_clip model, _, preprocess = open_clip. Note that portions of src/open_clip/ modelling and tokenizer code are adaptations of OpenAI's official repository. 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Welcome to an open source implementation of OpenAI's CLIP (Contrastive Language-Image Pre-training). ![]() |
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