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CLIP GmP ViT L 14

Developed by zer0int
A fine-tuned model based on OpenAI CLIP ViT-L/14, achieving performance improvements through Geometric Parametrization (GmP), with special optimization for text encoding capabilities
Downloads 6,275
Release Time : 6/15/2024

Model Overview

This is an improved version of the CLIP vision-language model, focusing on enhancing text understanding and image retrieval capabilities, suitable for tasks like text-to-image generation

Model Features

Geometric Parametrization (GmP)
By decomposing weights into radial and angular components, maintains the directionality and magnitude of weight vectors to enhance model performance
High-temperature Training Optimization
Adopts 0.1 high-temperature training + parameter tuning, significantly improving text understanding capabilities
Multi-version Options
Provides TEXT (text-optimized) and SMOOTH (image-optimized) versions to accommodate different needs
High-performance Retrieval
Demonstrates excellent image-text retrieval capabilities on datasets like MSCOCO

Model Capabilities

Text encoding
Image-text matching
Image retrieval
Text understanding
Supports Diffusers/Transformers integration

Use Cases

Text-to-Image Generation
Replacement Text Encoder for SD/SDXL/SD3
Serves as a replacement text encoder for models like Stable Diffusion, offering better prompt-following capabilities
Particularly adept at handling text details
Textless Image Generation
The SMOOTH version presents better details in textless images
Depends on specific prompts
Cross-modal Retrieval
Image-Text Retrieval
Retrieves relevant images based on text queries
Golden Retriever-level retrieval expert
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