krthr/clip-embeddings

Replicate Explore Tools

Summary

A CLIP-based embedding model hosted on Replicate that generates 768-dimensional embeddings for both images and text using the clip-vit-large-patch14 architecture, costing ~$0.00022 per run.

krthr / clip-embeddings
Original Article
View Cached Full Text

Cached at: 05/09/26, 06:25 AM

# CLIP Embeddings for Images and Text on Replicate Source: [https://replicate.com/krthr/clip-embeddings](https://replicate.com/krthr/clip-embeddings) ## Run time and cost This model costs approximately $0\.00022 to run on Replicate, or 4545 runs per $1, but this varies depending on your inputs\. It is also open source and you can[run it on your own computer with Docker](https://replicate.com/krthr/clip-embeddings/api)\. This model runs on[Nvidia T4 GPU hardware](https://replicate.com/docs/billing)\. Predictions typically complete within 1 seconds\. ## Readme Get text & image embeddings using CLIP\. ### Details - Model used:`clip\-vit\-large\-patch14` - Length of the embeddings:`768` ### Response ``` { "embedding": [0.1, 0.2, ..., 0.5] } ``` Model createdover 1 year ago

Similar Articles

andreasjansson/clip-features

Replicate Explore

A model on Replicate that outputs CLIP ViT-L/14 features for text and images, allowing similarity computation between inputs.

zsxkib/jina-clip-v2

Replicate Explore

Jina CLIP v2 is an improved multimodal embedding model supporting 89 languages, high-resolution images, and flexible embedding dimensions, with 3% better performance than v1 and state-of-the-art results on multilingual benchmarks.

beautyyuyanli/multilingual-e5-large

Replicate Explore

Multilingual E5-large embedding model is now available on Replicate, costing ~$0.00098 per run and completing in ~1 second on Nvidia L40S.

CLIP: Connecting text and images

OpenAI Blog

CLIP is OpenAI's vision-language model that learns from text-image pairs from the internet, enabling zero-shot visual classification without task-specific training data. It addresses major limitations in traditional computer vision by reducing dependence on expensive labeled datasets and improving real-world generalization.

zsxkib/embedding-gemma-300m

Replicate Explore

zsxkib/embedding-gemma-300m is a Replicate deployment of Google's EmbeddingGemma-300M model for generating 768-dimensional text embeddings, supporting flexible output dimensions via Matryoshka representation learning.