Tgohblio

Models by this creator

AI model preview image

instant-id-multicontrolnet

tgohblio

Total Score

107

The instant-id-multicontrolnet model is an extension of the popular InstantID model, developed by the Replicate creator tgohblio. This model leverages the power of ControlNets to provide advanced image generation capabilities, allowing users to create realistic images of people with customizable features and settings. The model builds upon the foundation of the InstantID model, which is known for its ability to generate highly realistic images of real people. The instant-id-multicontrolnet model adds additional capabilities, such as the ability to control various aspects of the generated image through the use of ControlNets. This includes features like pose, canny edges, depth maps, and more. Model inputs and outputs The instant-id-multicontrolnet model accepts a variety of inputs, including an image of a face, a reference pose image, and a text prompt. The model then generates a new image based on these inputs, adhering to the specified parameters and settings. Inputs face_image_path: The path to an input image of a face pose_image_path: The path to a reference pose image prompt: The text prompt describing the desired image negative_prompt: The text prompt describing the aspects to be avoided in the generated image model: The SDXL image model to be used enable_fast_mode: A toggle to enable or disable SDXL-Lightning fast inference lightning_steps: The number of denoising steps to use for SDXL-Lightning scheduler: The scheduler algorithm to be used width: The width of the output image height: The height of the output image adapter_strength_ratio: The scale for the IP adapter identitynet_strength_ratio: The scale for the ControlNet conditioning pose: A toggle to enable or disable the use of the ControlNet pose model pose_strength: The scale for pose conditioning canny: A toggle to enable or disable the use of the ControlNet canny edge model canny_strength: The scale for canny edge conditioning depth_map: A toggle to enable or disable the use of the ControlNet depth model depth_strength: The scale for depth map conditioning num_steps: The number of denoising steps guidance_scale: The scale for classifier-free guidance seed: The RNG seed number safety_checker: A toggle to enable or disable the NSFW filter Outputs The generated image, represented as a URI. Capabilities The instant-id-multicontrolnet model is capable of generating highly realistic images of people, with the added ability to control various aspects of the image through the use of ControlNets. This allows users to create images that closely match their desired specifications, such as a specific pose, facial features, or environmental context. What can I use it for? The instant-id-multicontrolnet model can be used for a variety of applications, such as: Content creation**: Generating realistic images of people for use in various media, such as social media, advertising, or film/TV productions. Character design**: Creating custom character designs for use in video games, animations, or other creative projects. Virtual photography**: Capturing unique and compelling images of virtual subjects for artistic or commercial purposes. Personalization**: Generating personalized images based on user preferences and inputs, such as profile pictures or avatars. Things to try One interesting aspect of the instant-id-multicontrolnet model is its ability to blend multiple ControlNet modalities, such as pose, canny edges, and depth maps, to create more complex and nuanced images. By experimenting with different combinations of these inputs, users can discover unique and unexpected visual outcomes. Another interesting feature is the model's "fast mode" option, which enables SDXL-Lightning for faster inference times. This can be useful for rapid prototyping or real-time applications, although it may come at the cost of some image quality. Comparing the results of the fast mode to the standard mode can provide insights into the trade-offs between speed and fidelity.

Read more

Updated 5/30/2024

AI model preview image

instant-id-albedobase-xl

tgohblio

Total Score

33

instant-id-albedobase-xl is a state-of-the-art AI model for zero-shot identity-preserving generation. Developed by the InstantX Team at Xiaohongshu Inc., it uses the AlbedoBase-XL v2.0 as its base model and incorporates proprietary techniques like LCM-LoRA acceleration and multi-ControlNets to achieve fast, high-quality results. This model is similar to other InstantID variants like instant-id-multicontrolnet, instant-id-photorealistic, and instant-id-artistic. It also shares some similarities with the latent-consistency-model in terms of speed and control. Model inputs and outputs instant-id-albedobase-xl takes in an input image, prompt, and various settings to control the generation process. It outputs a new image that preserves the identity of the input face while stylizing it based on the given prompt. Inputs Image**: The input face image to use as a reference for identity preservation. Prompt**: The text prompt describing the desired style and attributes for the generated image. Negative Prompt**: The text prompt describing what should be avoided in the generated image. Width/Height**: The desired dimensions of the output image. Guidance Scale**: The scale for classifier-free guidance, with an optimum range of 0-5 when using LCM-LoRA. Safety Checker**: A flag to enable or disable the built-in safety checker. IP Adapter Scale**: The scale for the Identity Preserving Adapter, which controls the balance between identity preservation and style. Num Inference Steps**: The number of denoising steps, with an optimum range of 6-8 when using LCM-LoRA. Controlnet Conditioning Scale**: The scale for the ControlNet conditioning, which affects the balance between the input face and the generated style. Outputs Output Image**: The generated image that preserves the identity of the input face while matching the desired style and attributes. Capabilities instant-id-albedobase-xl is capable of generating high-quality, identity-preserving images in a matter of seconds. It can handle a wide range of styles and attributes, from photorealistic to artistic. The model's ability to balance identity preservation and style integration sets it apart from previous state-of-the-art techniques. What can I use it for? This model can be useful for various applications, such as: Portrait Generation**: Create stylized portraits of real people for use in art, design, or entertainment projects. Character Design**: Generate custom character designs with a consistent identity, but diverse styles. Content Creation**: Quickly produce visually striking images for blogs, social media, or other online content. Personalized Marketing**: Create unique, identity-based visuals for personalized advertising or branding campaigns. Things to try One key advantage of instant-id-albedobase-xl is its compatibility with LCM-LoRA, which allows for significantly faster inference times without sacrificing quality. By adjusting the guidance scale and number of inference steps, you can find the sweet spot between speed and fidelity for your specific use case. Additionally, experiment with different base models and ControlNet configurations to achieve unique styles and better integration between the face and background. The maintainer's Hugging Face profile can be a useful resource for exploring these options.

Read more

Updated 5/30/2024