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Efficiently search and connect VAEs in ComfyUI for streamlined image generation.
The Runware VAE Search node is designed to streamline the process of finding and connecting Variational Autoencoders (VAEs) to Runware Inference Nodes within the ComfyUI environment. This node allows you to efficiently search for specific VAEs by name or Civit AIR code, making it easier to integrate the desired VAE into your workflow. By providing a direct search capability, it enhances the user experience by reducing the time and effort needed to locate and connect the appropriate VAE, thus facilitating smoother and more effective image generation processes. The node is particularly beneficial for AI artists who wish to experiment with different VAEs to achieve varied artistic effects in their generated images.
This parameter allows you to search for a specific VAE by its name or Civit AIR code. It is a string input where you can type the name of the VAE you are looking for, such as "ClearVAE". This search functionality helps in quickly locating the desired VAE from a potentially large list, ensuring that you can efficiently connect the right VAE to your inference node. There are no minimum or maximum values, as it is a text-based search.
This parameter provides a list of options to filter VAEs based on their model architecture. The available options include "All", "FLUX.1-Schnell", "FLUX.1-Dev", "Pony", "SD 1.5", "SD 1.5 Hyper", "SD 1.5 LCM", "SD 3", "SDXL 1.0", "SDXL 1.0 LCM", "SDXL Distilled", "SDXL Hyper", "SDXL Lightning", and "SDXL Turbo". By selecting a specific architecture, you can narrow down the search results to only those VAEs that match the chosen architecture, making it easier to find the most suitable VAE for your project. The default value is "SD 1.5".
The output of this node is a RUNWAREVAE
, which represents the selected VAE that has been searched and connected to the Runware Inference Node. This output is crucial as it determines the VAE that will be used in the image generation process, influencing the style and quality of the generated images. By providing a direct connection to the chosen VAE, this output ensures that the inference node can utilize the VAE's capabilities to enhance the image generation results.
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