SUPIRApply - ComfyUI Built-in Node Documentation

Complete documentation for the SUPIRApply node in ComfyUI. Learn its inputs, outputs, parameters and usage.

The SUPIRApply node applies a SUPIR model patch to a diffusion model. It uses the patch to modify the model’s behavior, allowing it to incorporate guidance from an input image during the sampling process. The node also provides controls for adjusting the strength of this guidance over time and includes an optional feature to help maintain fidelity to the original input.

Inputs

Parameter	Description	Data Type	Required	Range
model	The base diffusion model to which the SUPIR patch will be applied.	MODEL	Yes	-
model_patch	The SUPIR model patch containing the weights and configuration for modifying the model.	MODELPATCH	Yes	-
vae	The VAE (Variational Autoencoder) used for encoding the input image into a latent representation.	VAE	Yes	-
image	The input image used to guide the generation process. Only the first three color channels (RGB) are used.	IMAGE	Yes	-
strength_start	Control strength at the start of sampling (high sigma). The influence of the image guidance begins at this value. (default: 1.0)	FLOAT	No	0.0 - 10.0
strength_end	Control strength at the end of sampling (low sigma). Linearly interpolated from start. The influence of the image guidance ends at this value. (default: 1.0)	FLOAT	No	0.0 - 10.0
restore_cfg	Pulls denoised output toward the input latent. Higher = stronger fidelity to input. 0 to disable. (default: 4.0)	FLOAT	No	0.0 - 20.0
restore_cfg_s_tmin	Sigma threshold below which restore_cfg is disabled. (default: 0.05)	FLOAT	No	0.0 - 1.0

Note: The image input is processed to extract only the RGB channels. If an image with an alpha channel is provided, the alpha channel is ignored.

Outputs

Output Name	Description	Data Type
model	The diffusion model with the SUPIR patch applied and any additional post-CFG functions configured.	MODEL

This documentation was AI-generated. If you find any errors or have suggestions for improvement, please feel free to contribute! Edit on GitHub

---

Source fingerprint (SHA-256): 41900f166aa7c0e325da874a3e250c56b94569822292785b1b3165705eb22e0c