Lucataco
Rank:Average Model Cost: $0.0000
Number of Runs: 9,002,477
Models by this creator
faceswap
The Faceswap model is an image-to-image model that swaps the faces of two individuals in an image. It uses deep learning techniques to detect and extract facial features, and then replaces one person's face with the other, resulting in a convincing face swap. The model also includes a face enhancer, which improves the quality and appearance of the swapped face. This model is useful for various applications such as entertainment, generating visual effects, or creating realistic deepfakes.
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8.2M
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illusion-diffusion-hq
illusion-diffusion-hq
The Illusion-Diffusion-HQ is an image-to-image model developed by Monster Labs. It transforms an input image based on a given prompt and guidance scale. The model uses the QRCode ControlNet and SD Realistic Vision v5.1 for image rendering. Inputs parameters include the seed, image URL, width, height, border, quality prompt, output quantity, guidance scale, number of inference steps, and options related to QR code generation. The output is an URL of the modified image. The model is designed to avoid creating images that are ugly, disfigured, of low quality, blurry, or not safe for work.
$-/run
173.6K
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sdxl-controlnet
SDXL ControlNet is a model that applies the Canny edge detection algorithm to images. The Canny algorithm is commonly used in computer vision to detect edges in an image by identifying areas with significant changes in pixel intensity. This model can be used to enhance the features and boundaries of objects in images, providing critical information for various applications such as object detection, image segmentation, and image recognition.
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115.2K
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illusion-diffusion-hq
illusion-diffusion-hq
The Illusion-Diffusion-HQ model is an image-to-image model designed by Monster Labs. The model uses a control net to manipulate input images based on given parameters such as width, height, border, guidance_scale, prompt, seed, etc. The model is capable of creating output images according to the user-defined specs while avoiding specified negative prompts. A QR-code feature is also included, which allows for input of a QR code content with a defined background color. The output of the model is an image URL. A notable use case could be processing an input image and generating a detailed medieval village scene according to the provided specifications.
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114.6K
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animate-diff
Animate-diff is a text-to-image diffusion model that generates animations based on personalized text inputs. It utilizes a diffusion model to translate text into image sequences, enabling users to animate their own custom descriptions. The model's output is a dynamic and visually appealing animation that corresponds to the given text input.
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101.7K
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clip-interrogator
The clip-interrogator model is designed to improve the efficiency of inference in the CLIP model. It takes advantage of the efficient retrieval of the most relevant texts from a large pool and replaces the computation-intensive image forward pass in CLIP with a single forward pass of the target text. This helps reduce the computational cost and speed up the inference process.
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63.8K
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realistic-vision-v5-img2img
The Realistic Vision v5.0 Image-to-Image model is a deep learning model that is used for generating realistic images from input images. It takes an input image and transforms it into an output image that has realistic textures, colors, and details. This model is based on deep convolutional neural networks and has been trained on a large dataset of images to learn the mapping between input and output images. It can be used for a variety of tasks such as image style transfer, image inpainting, and image synthesis.
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50.7K
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gfpgan
GFPGAN is a practical face restoration algorithm that can be used to restore and enhance the details of old photos or AI-generated faces. It uses a generative adversarial network (GAN) framework and incorporates several modules to address the challenges posed by these types of images. The model has been trained on a large dataset and produces high-quality results by effectively capturing and restoring facial features and details.
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48.2K
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realistic-vision-v4.0
The Realistic Vision V4.0 is a text-to-image deep learning model. It generates high-quality and photorealistic images based on textual descriptions provided as input. The model uses advanced convolutional neural networks (CNNs) to understand and interpret the text, and then applies image generation techniques to convert the text into realistic images. The resulting images are visually appealing and match the textual description accurately. This model has been trained on a large dataset of textual descriptions and corresponding images to ensure its ability to generate realistic and accurate images in a wide range of scenarios.
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34.5K
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