A beginner's guide to the Clip-Embeddings model by Krthr on Replicate

This is a simplified guide to an AI model called Clip-Embeddings maintained by Krthr. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter. Model overview The clip-embeddings model, developed by krthr, generates CLIP text and image embeddings using the clip-vit-large-patch14 model. CLIP (Contrastive Language-Image Pre-Training) is a computer vision model developed by researchers at OpenAI to learn about robustness and generalization in zero-shot image classification tasks. The clip-embeddings model allows users to generate CLIP embeddings for both text and image inputs, which can be useful for tasks like image-text similarity matching, retrieval, and multimodal analysis. This model is similar to other CLIP-based models like clip-vit-large-patch14, clip-vit-base-patch16, clip-vit-base-patch32, and clip-interrogator, all of which use different CLIP model variants and configurations. Model inputs and outputs The clip-embeddings model takes two inputs: text and image. The text input is a string of text, while the image input is a URI pointing to an image. The model outputs a single object with an "embedding" field, which is an array of numbers representing the CLIP embedding for the input text and image. Inputs text: Input text as a string image: Input image as a URI Outputs embedding: An array of numbers representing the CLIP embedding for the input text and image Capabilities The clip-embeddings model can be use... Click here to read the full guide to Clip-Embeddings

Apr 9, 2025 - 13:08

A beginner's guide to the Clip-Embeddings model by Krthr on Replicate

This is a simplified guide to an AI model called Clip-Embeddings maintained by Krthr. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.

Model overview

The clip-embeddings model, developed by krthr, generates CLIP text and image embeddings using the clip-vit-large-patch14 model. CLIP (Contrastive Language-Image Pre-Training) is a computer vision model developed by researchers at OpenAI to learn about robustness and generalization in zero-shot image classification tasks. The clip-embeddings model allows users to generate CLIP embeddings for both text and image inputs, which can be useful for tasks like image-text similarity matching, retrieval, and multimodal analysis.

This model is similar to other CLIP-based models like clip-vit-large-patch14, clip-vit-base-patch16, clip-vit-base-patch32, and clip-interrogator, all of which use different CLIP model variants and configurations.

Model inputs and outputs

The clip-embeddings model takes two inputs: text and image. The text input is a string of text, while the image input is a URI pointing to an image. The model outputs a single object with an "embedding" field, which is an array of numbers representing the CLIP embedding for the input text and image.

Inputs

text: Input text as a string
image: Input image as a URI

Outputs

embedding: An array of numbers representing the CLIP embedding for the input text and image

Capabilities

The clip-embeddings model can be use...

Click here to read the full guide to Clip-Embeddings