Text-To-Image captcha generator.
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Updated
May 22, 2024 - Python
Text-To-Image captcha generator.
This CNN model will convert the given text to images using stable diffusion
A fashion AI-based model capable of generating images from textual descriptions. The model should take natural language text as input and generate images that visually represent the given text. This text-to-image generation system bridges the gap between textual descriptions and visual content.
Can generate image, Variations of images, Code and answer the user query.
Code in this repo will assist you in creating image file/s of any text you provide
This Python code with Stable Diffusion GenAI model lets you create images from your text descriptions, basically a text-to-image model.
A simple bash script that utilizes the Hugging Face API to generate images based on textual descriptions. This script sends a POST request to the Hugging Face inference API for the facebook/bart-large-cnn model with a given input text, and retrieves the generated image data.
A GUI for getting prediction from https://github.com/taoxugit/AttnGAN with single caption input.
Twitter bot utilising cron jobs to fetch user-generated replies to a main tweet. Leveraged the Stable Diffusion AI model to generate artistic responses
An AI text to image converter with openAI API
This application leverages Next.js, TypeScript, Tailwind CSS, Shadcn UI, Convex, and Clerk to create a platform for generating podcasts. It utilizes OpenAI's Text-to-Speech and Text-to-Image APIs to produce high-quality audio and visual content.
A console based application which uses hugging face inference api to create images out of text using AI.
Generate prompt from a image
Sora can create videos of up to 60 seconds featuring highly detailed scenes, complex camera motion, and multiple characters with vibrant emotions.
The code material for my course about Mastering OpenAI APIs, and building systems based on it.
This is an AI Tool check for more
This website utilizes the Hugging Face API to generate image descriptions based on user-provided text input. The application is built with HTML, CSS, and JavaScript, and it leverages the facebook/bart-large-cnn model for generating textual summaries.
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