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Enhances AI-generated image quality with advanced filtering and scaling techniques for improved visual fidelity and detail.
FreeU is a node designed to enhance the performance and quality of AI-generated images by applying advanced filtering and scaling techniques. It leverages the power of Fourier filtering and dynamic scaling based on the model's channel configuration to refine the output. The primary goal of FreeU is to improve the visual fidelity and detail of images produced by AI models, making it a valuable tool for AI artists looking to achieve higher quality results. By dynamically adjusting the hidden states and applying Fourier filters, FreeU ensures that the generated images are not only visually appealing but also maintain a high level of detail and clarity.
This parameter represents the number of channels in the model's configuration. It is used to determine the scaling factors for the hidden states and the Fourier filter. The value of model_channels
directly impacts the scaling dictionary, which in turn affects the intensity and application of the filters. The exact value is derived from the model's UNet configuration and is crucial for the proper functioning of the node.
The scale_dict
parameter is a dictionary that maps the model channels to their respective scaling factors. It contains pairs of values that determine how the hidden states and Fourier filters are scaled. The dictionary is constructed using the model channels and predefined scaling factors, ensuring that the filters are applied correctly based on the model's configuration. This parameter is essential for dynamically adjusting the filters to match the model's architecture.
This parameter is a dictionary that keeps track of devices that do not support certain torch.fft functions. If a device is found to be incompatible, the Fourier filter operations are switched to the CPU. This ensures that the node can function correctly even on devices with limited support for specific operations. The on_cpu_devices
dictionary helps maintain compatibility and stability across different hardware configurations.
The h
parameter represents the modified hidden states after applying the scaling factors. It is an intermediate output that reflects the adjustments made to the hidden states based on the scaling dictionary. The h
parameter is crucial for ensuring that the hidden states are appropriately scaled, leading to improved image quality.
The hsp
parameter represents the modified hidden states after applying the Fourier filter. It is an intermediate output that reflects the adjustments made to the hidden states based on the Fourier filter and the scaling factors. The hsp
parameter is essential for ensuring that the hidden states are appropriately filtered, leading to enhanced image detail and clarity.
model_channels
parameter is derived from the model's UNet configuration.scale_dict
to match the model's architecture and achieve the desired filtering effects.© Copyright 2024 RunComfy. All Rights Reserved.