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Introduction:
Are you tired of struggling to come up with creative text prompts for your images?
Introducing img2prompt, the ultimate solution for image-based text prompt generation.
With img2prompt, you can effortlessly generate approximate text prompts that perfectly match your images.
Optimized for stable-diffusion using clip ViT-L/14, this powerful tool leverages the OpenAI CLIP models to match your images to various artists, mediums, and styles.
By combining the results with BLIP captions, img2prompt generates text prompts that enable you to create stunning images similar to the original.
Whether you prefer using the API or exploring the GitHub repository, img2prompt offers a seamless experience.
Experience lightning-fast predictions, with completion times as quick as 24 seconds, thanks to Nvidia T4 GPU hardware.
Unleash your creativity with img2prompt and unlock a world of possibilities for your AI projects.
Overview:
img2prompt is an AI tool developed by Methexis-Inc that generates approximate text prompts based on an input image. It is specifically optimized for stable-diffusion using the clip ViT-L/14 model. The tool utilizes the open-source CLIP Interrogator notebook created by @pharmapsychotic and leverages the power of OpenAI CLIP models to match images with various artists, mediums, and styles. By combining the comparison results with BLIP captions, img2prompt generates a text prompt that can be used to create similar images to the original. The tool can be accessed through an API or by visiting the GitHub repository, which also provides detailed information about the tool and its license. With predictions typically completing within 24 seconds, img2prompt runs efficiently on Nvidia T4 GPU hardware.
Benefits:
- Generates approximate text prompts that match an image
- Optimized for stable-diffusion (clip ViT-L/14)
- Utilizes the OpenAI CLIP models to match an image to various artists, mediums, and styles
- Combines results with BLIP captions to generate a text prompt
- Can be run via an API or accessed through the GitHub repository for more information
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