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  • Input types
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  1. Core Nodes
  2. Conditioning
  3. upscale-diffusion

SD_4X Upscale Conditioning

Previousupscale-diffusionNextvideo-models

Last updated 5 months ago

Documentation

  • Class name: SD_4XUpscale_Conditioning

  • Category: conditioning/upscale_diffusion

  • Output node: False

This node specializes in enhancing the resolution of images through a 4x upscale process, incorporating conditioning elements to refine the output. It leverages diffusion techniques to upscale images while allowing for the adjustment of scale ratio and noise augmentation to fine-tune the enhancement process.

Input types

Parameter
Comfy dtype
Description

images

IMAGE

The input images to be upscaled. This parameter is crucial as it directly influences the quality and resolution of the output images.

positive

CONDITIONING

Positive conditioning elements that guide the upscale process towards desired attributes or features in the output images.

negative

CONDITIONING

Negative conditioning elements that the upscale process should avoid, helping to steer the output away from undesired attributes or features.

scale_ratio

FLOAT

Determines the factor by which the image resolution is increased. A higher scale ratio results in a larger output image, allowing for greater detail and clarity.

noise_augmentation

FLOAT

Controls the level of noise augmentation applied during the upscale process. This can be used to introduce variability and improve the robustness of the output images.

Output types

Parameter
Comfy dtype
Description

positive

CONDITIONING

The refined positive conditioning elements resulting from the upscale process.

negative

CONDITIONING

The refined negative conditioning elements resulting from the upscale process.

latent

LATENT

A latent representation generated during the upscale process, which can be utilized in further processing or model training.