Average Model Cost: $0.0115
Number of Runs: 534,460
Models by this creator
The Text-to-Image model called "erlich" is designed to generate logo images based on text input. This model takes in textual input and produces an image that represents a logo corresponding to the given text. It uses a deep learning architecture to understand the meaning and aesthetics of the input and create high-quality and visually appealing logo designs.
The ongo model is a text-to-image model that can generate paintings based on written descriptions. It uses a deep learning architecture to translate textual input into visual output. The model has been trained on a large dataset of paintings and text descriptions, which allows it to learn the relationship between words and images. This enables it to generate realistic and visually appealing paintings based on a given text prompt. The model can be useful in various applications, such as generating illustrations for books, creating visual representations of concepts, or assisting artists in the creative process.
laionide-v2 is a text-to-image model that has been finetuned on approximately 30 million additional samples compared to the base GLIDE model from OpenAI. It is an improved version of the model and can be used to generate images based on textual descriptions.
The Retro Videogame Text-to-Image model is a deep learning model that generates retro videogame-style artwork based on text descriptions. Given a textual input, the model is able to generate accompanying images that resemble the visuals of classic videogames. This model uses a combination of natural language processing and image generation techniques to create pixelated, 8-bit style graphics that evoke nostalgia for the golden age of videogames. It can be used in applications such as game development, art creation, and generating visual content for retro gaming websites or social media.
Deep Image Diffusion Prior is a model that converts text into images by visualizing CLIP (Contrastive Language-Image Pretraining) features. CLIP is a neural network model that has been trained on a large dataset of images and their accompanying captions. By leveraging the robust visual understanding capabilities of CLIP, Deep Image Diffusion Prior is able to generate images based on textual descriptions.