instant-id

Maintainer: zsxkib

Total Score

472

Last updated 6/19/2024
AI model preview image
PropertyValue
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv

Create account to get full access

or

If you already have an account, we'll log you in

Model overview

instant-id is a state-of-the-art AI model developed by the InstantX team that can generate realistic images of real people instantly. It utilizes a tuning-free approach to achieve identity-preserving generation with only a single input image. The model is capable of various downstream tasks such as stylized synthesis, where it can blend the facial features and style of the input image. Compared to similar models like AbsoluteReality V1.8.1, Reliberate v3, Stable Diffusion, Photomaker, and Photomaker Style, instant-id achieves better fidelity and retains good text editability, allowing the generated faces and styles to blend more seamlessly.

Model inputs and outputs

instant-id takes a single input image of a face and a text prompt, and generates one or more realistic images that preserve the identity of the input face while incorporating the desired style and content from the text prompt. The model utilizes a novel identity-preserving generation technique that allows it to generate high-quality, identity-preserving images in a matter of seconds.

Inputs

  • Image: The input face image used as a reference for the generated images.
  • Prompt: The text prompt describing the desired style and content of the generated images.
  • Seed (optional): A random seed value to control the randomness of the generated images.
  • Pose Image (optional): A reference image used to guide the pose of the generated images.

Outputs

  • Images: One or more realistic images that preserve the identity of the input face while incorporating the desired style and content from the text prompt.

Capabilities

instant-id is capable of generating highly realistic images of people in a variety of styles and settings, while preserving the identity of the input face. The model can seamlessly blend the facial features and style of the input image, allowing for unique and captivating results. This makes the model a powerful tool for a wide range of applications, from creative content generation to virtual avatars and character design.

What can I use it for?

instant-id can be used for a variety of applications, such as:

  • Creative Content Generation: Quickly generate unique and realistic images for use in art, design, and multimedia projects.
  • Virtual Avatars: Create personalized virtual avatars that can be used in games, social media, or other digital environments.
  • Character Design: Develop realistic and expressive character designs for use in animation, films, or video games.
  • Augmented Reality: Integrate generated images into augmented reality experiences, allowing for the seamless blending of real and virtual elements.

Things to try

With instant-id, you can experiment with a wide range of text prompts and input images to generate unique and captivating results. Try prompts that explore different styles, genres, or themes, and see how the model can blend the facial features and aesthetics in unexpected ways. You can also experiment with different input images, from close-up portraits to more expressive or stylized faces, to see how the model adapts and responds. By pushing the boundaries of what's possible with identity-preserving generation, you can unlock a world of creative possibilities.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Related Models

AI model preview image

flash-face

zsxkib

Total Score

2

flash-face is a powerful AI model developed by zsxkib that can generate highly realistic and personalized human images. It is similar to other models like GFPGAN, Instant-ID, and Stable Diffusion, which are also focused on creating photorealistic images of people. Model Inputs and Outputs The flash-face model takes in a variety of inputs, including positive and negative prompts, reference face images, and various parameters to control the output. The outputs are high-quality images of realistic-looking people, which can be generated in different formats and quality levels. Inputs Positive Prompt**: The text description of the desired image. Negative Prompt**: Text to exclude from the generated image. Reference Face Images**: Up to 4 face images to use as references for the generated image. Face Bounding Box**: The coordinates of the face region in the generated image. Text Control Scale**: The strength of the text guidance during image generation. Face Guidance**: The strength of the reference face guidance during image generation. Lamda Feature**: The strength of the reference feature guidance during image generation. Steps**: The number of steps to run the image generation process. Num Sample**: The number of images to generate. Seed**: The random seed to use for image generation. Output Format**: The format of the generated images (e.g., WEBP). Output Quality**: The quality level of the generated images (from 1 to 100). Outputs Generated Images**: An array of high-quality, realistic-looking images of people. Capabilities The flash-face model excels at generating personalized human images with high-fidelity identity preservation. It can create images that closely resemble real people, while still maintaining a sense of artistic creativity and uniqueness. The model's ability to blend reference face images with text-based prompts makes it a powerful tool for a wide range of applications, from art and design to entertainment and marketing. What Can I Use It For? The flash-face model can be used for a variety of applications, including: Creative Art and Design**: Generate unique, personalized portraits and character designs for use in illustration, animation, and other creative projects. Entertainment and Media**: Create realistic-looking avatars or virtual characters for use in video games, movies, and other media. Marketing and Advertising**: Generate personalized, high-quality images for use in marketing campaigns, product packaging, and other promotional materials. Education and Research**: Use the model to create diverse, representative datasets for training and testing computer vision and image processing algorithms. Things to Try One interesting aspect of the flash-face model is its ability to blend multiple reference face images together to create a unique, composite image. You could try experimenting with different combinations of reference faces and prompts to see how the model responds and what kind of unique results it can produce. Additionally, you could explore the model's ability to generate images with specific emotional expressions or poses by carefully crafting your prompts and reference images.

Read more

Updated Invalid Date

AI model preview image

sdxl-lightning-4step

bytedance

Total Score

127.0K

sdxl-lightning-4step is a fast text-to-image model developed by ByteDance that can generate high-quality images in just 4 steps. It is similar to other fast diffusion models like AnimateDiff-Lightning and Instant-ID MultiControlNet, which also aim to speed up the image generation process. Unlike the original Stable Diffusion model, these fast models sacrifice some flexibility and control to achieve faster generation times. Model inputs and outputs The sdxl-lightning-4step model takes in a text prompt and various parameters to control the output image, such as the width, height, number of images, and guidance scale. The model can output up to 4 images at a time, with a recommended image size of 1024x1024 or 1280x1280 pixels. Inputs Prompt**: The text prompt describing the desired image Negative prompt**: A prompt that describes what the model should not generate Width**: The width of the output image Height**: The height of the output image Num outputs**: The number of images to generate (up to 4) Scheduler**: The algorithm used to sample the latent space Guidance scale**: The scale for classifier-free guidance, which controls the trade-off between fidelity to the prompt and sample diversity Num inference steps**: The number of denoising steps, with 4 recommended for best results Seed**: A random seed to control the output image Outputs Image(s)**: One or more images generated based on the input prompt and parameters Capabilities The sdxl-lightning-4step model is capable of generating a wide variety of images based on text prompts, from realistic scenes to imaginative and creative compositions. The model's 4-step generation process allows it to produce high-quality results quickly, making it suitable for applications that require fast image generation. What can I use it for? The sdxl-lightning-4step model could be useful for applications that need to generate images in real-time, such as video game asset generation, interactive storytelling, or augmented reality experiences. Businesses could also use the model to quickly generate product visualization, marketing imagery, or custom artwork based on client prompts. Creatives may find the model helpful for ideation, concept development, or rapid prototyping. Things to try One interesting thing to try with the sdxl-lightning-4step model is to experiment with the guidance scale parameter. By adjusting the guidance scale, you can control the balance between fidelity to the prompt and diversity of the output. Lower guidance scales may result in more unexpected and imaginative images, while higher scales will produce outputs that are closer to the specified prompt.

Read more

Updated Invalid Date

AI model preview image

instant-id-photorealistic

grandlineai

Total Score

22

instant-id-photorealistic is a state-of-the-art AI model developed by grandlineai that can generate photorealistic images of individuals in a matter of seconds. This model builds upon the powerful Juggernaut-XL v8 base model and aims to preserve the identity and characteristics of the input face while allowing for flexible control over the generated image's style and composition. The instant-id-photorealistic model is closely related to other advanced face generation and editing models like instant-id, gfpgan, juggernaut-xl-v9, instant-id-multicontrolnet, and real-esrgan. These models offer a range of capabilities, from face restoration and enhancement to highly customizable image generation. Model inputs and outputs The instant-id-photorealistic model takes in a single input image and a text prompt, and generates a photorealistic image that preserves the identity of the input face while allowing for flexible control over the style and composition of the output image. Inputs Image**: The input image containing the face to be used as the reference for identity preservation. Prompt**: The text prompt that describes the desired style, composition, and other attributes of the generated image. Negative Prompt**: An optional text prompt that specifies undesired attributes to be avoided in the generated image. Width/Height**: The desired width and height of the output image. Guidance Scale**: The scale factor for the classifier-free guidance, which controls the influence of the text prompt on the generated image. IP Adapter Scale**: The scale factor for the IP adapter, which controls the influence of the input face on the generated image. Controlnet Conditioning Scale**: The scale factor for the ControlNet conditioning, which controls the influence of the input face's pose and features on the generated image. Num Inference Steps**: The number of denoising steps used during the image generation process. Outputs Generated Image**: The photorealistic image that preserves the identity of the input face while reflecting the desired style and composition specified in the text prompt. Capabilities The instant-id-photorealistic model excels at generating photorealistic images that maintain the identity of the input face, while allowing for a high degree of control over the final style and composition of the output. This makes it a powerful tool for a variety of applications, such as portrait editing, character design, and creative image generation. What can I use it for? The instant-id-photorealistic model can be used for a wide range of applications, including: Portrait Editing**: Easily create photorealistic portraits of individuals with customizable styles and compositions, without the need for extensive editing or retouching. Character Design**: Generate highly detailed and photorealistic character designs for use in various creative projects, such as films, games, or illustrations. Creative Image Generation**: Explore and experiment with different artistic styles and compositions while preserving the identity of the input face, opening up new possibilities for creative expression. Things to try One interesting aspect of the instant-id-photorealistic model is its ability to seamlessly blend the input face with the desired style and composition specified in the text prompt. This allows for the creation of unique and visually striking images that maintain a strong sense of realism, while incorporating elements of fantasy, surrealism, or other artistic styles. For example, you could try generating a portrait of a character in a film noir style, with dramatic lighting, moody shadows, and a sense of mystery. Alternatively, you could experiment with blending the input face with more abstract or experimental art styles, such as cubism or expressionism, to create truly one-of-a-kind artworks. The model's versatility and high degree of control also make it a valuable tool for tasks like character design, where you can quickly generate a range of photorealistic character concepts with varying styles and attributes, all while preserving the core identity of the input face.

Read more

Updated Invalid Date

AI model preview image

gfpgan

tencentarc

Total Score

76.0K

gfpgan is a practical face restoration algorithm developed by the Tencent ARC team. It leverages the rich and diverse priors encapsulated in a pre-trained face GAN (such as StyleGAN2) to perform blind face restoration on old photos or AI-generated faces. This approach contrasts with similar models like Real-ESRGAN, which focuses on general image restoration, or PyTorch-AnimeGAN, which specializes in anime-style photo animation. Model inputs and outputs gfpgan takes an input image and rescales it by a specified factor, typically 2x. The model can handle a variety of face images, from low-quality old photos to high-quality AI-generated faces. Inputs Img**: The input image to be restored Scale**: The factor by which to rescale the output image (default is 2) Version**: The gfpgan model version to use (v1.3 for better quality, v1.4 for more details and better identity) Outputs Output**: The restored face image Capabilities gfpgan can effectively restore a wide range of face images, from old, low-quality photos to high-quality AI-generated faces. It is able to recover fine details, fix blemishes, and enhance the overall appearance of the face while preserving the original identity. What can I use it for? You can use gfpgan to restore old family photos, enhance AI-generated portraits, or breathe new life into low-quality images of faces. The model's capabilities make it a valuable tool for photographers, digital artists, and anyone looking to improve the quality of their facial images. Additionally, the maintainer tencentarc offers an online demo on Replicate, allowing you to try the model without setting up the local environment. Things to try Experiment with different input images, varying the scale and version parameters, to see how gfpgan can transform low-quality or damaged face images into high-quality, detailed portraits. You can also try combining gfpgan with other models like Real-ESRGAN to enhance the background and non-facial regions of the image.

Read more

Updated Invalid Date