workflow-templates-broken-zap-2026-06-08/site/knowledge/nodes/custom/LatentOperationTonemapReinhard.md

LatentOperationTonemapReinhard

Category: latent/advanced/operations

Also known as: hdr latent

Description

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

The LatentOperationTonemapReinhard node applies Reinhard tonemapping to latent vectors. This technique normalizes the latent vectors and adjusts their magnitude using a statistical approach based on mean and standard deviation, with the intensity controlled by a multiplier parameter.

Inputs

Parameter	Type	Default	Description
multiplier	Float	1.0

Outputs

Output	Type
),
],
)

@classmethod
def execute(cls, multiplier) -> io.NodeOutput:
    def tonemap_reinhard(latent, **kwargs):
        latent_vector_magnitude = (torch.linalg.vector_norm(latent, dim=(1)) + 0.0000000001)[:,None]
        normalized_latent = latent / latent_vector_magnitude

        mean = torch.mean(latent_vector_magnitude, dim=(1,2,3), keepdim=True)
        std = torch.std(latent_vector_magnitude, dim=(1,2,3), keepdim=True)

        top = (std * 5 + mean) * multiplier

        #reinhard
        latent_vector_magnitude *= (1.0 / top)
        new_magnitude = latent_vector_magnitude / (latent_vector_magnitude + 1.0)
        new_magnitude *= top

        return normalized_latent * new_magnitude
    return io.NodeOutput(tonemap_reinhard | LatentOperation |

Source: comfy_extras/nodes_latent.py

Used in 3 template(s)