Average Model Cost: $0.0091
Number of Runs: 17,087,173
Models by this creator
clip-vit-large-patch14 is a transformer-based model that combines the CLIP (Contrastive Language-Image Pretraining) architecture with a Vision Transformer (ViT) backbone. It can understand and generate natural language descriptions for images, allowing it to perform a wide range of tasks, such as visual question answering, image captioning, and visual search. The model achieves state-of-the-art performance on numerous image-text benchmark datasets and can be fine-tuned for specific downstream tasks.
Rembg is a deep learning model and implementation that aims to remove the background from images. The model is trained on a large dataset of images with known foreground and background regions, and it learns to segment and remove the background accurately. The implementation allows users to easily utilize the model and remove the background from images, which can be useful for various applications such as object recognition and image editing.
ZoeDepth is an image-to-image model that combines relative and metric depth information. It takes an input image and generates a corresponding depth map, which provides distance information for each pixel in the image. The model uses a combination of relative depth estimation, which estimates the depth differences between objects, and metric depth estimation, which estimates the absolute depth values. This approach allows the model to accurately estimate the depth of objects in the image and provide a more comprehensive depth map.
Anything-v4.0 is a text-to-image model that generates anime-style images with high quality and fine details. It uses Stable Diffusion, a technique that helps propagate information and retain coherence during the image generation process. This model is specifically designed to produce visually appealing and realistic anime-style artwork.
Waifu Diffusion is a text-to-image model trained on a dataset from the Danbooru image board. It uses a technique called stable diffusion to generate high-quality images from text prompts. The model is able to effectively capture complex visual details and produce realistic images based on the given text input.
Real-ESRGAN is a deep learning model that performs image super-resolution, specifically targeting real-world scenarios. It is designed to enhance the clarity of low-resolution images, making them look more detailed and sharp. This model is capable of producing high-quality results even when the input image is noisy or contains artifacts, making it useful in various real-world applications such as medical imaging, surveillance footage, and satellite imagery.
rudalle-sr is a real-ESRGAN super-resolution model that enhances the quality and resolution of images. It is based on the ruDALL-E model and uses advanced deep learning techniques to produce sharper and more detailed images. This model can be particularly useful for tasks such as upscaling low-resolution images or improving the quality of compressed images.
The anything-v3.0 model is a highly advanced text-to-image artificial intelligence model. It is designed to generate high-quality, highly detailed anime-style images from text inputs. The model uses stable diffusion techniques to create stable and realistic outputs. It is capable of transforming text descriptions into visually appealing and aesthetically pleasing anime-style images.