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import torch
class TonemapNoiseWithRescaleCFG:
@classmethod
def INPUT_TYPES(s):
return {"required": {"model": ("MODEL",),
"tonemap_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
"rescale_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "custom_node_experiments"
def patch(self, model, tonemap_multiplier, rescale_multiplier):
def tonemap_noise_rescale_cfg(args):
cond = args["cond"]
uncond = args["uncond"]
cond_scale = args["cond_scale"]
# Tonemap
noise_pred = (cond - uncond)
noise_pred_vector_magnitude = (torch.linalg.vector_norm(noise_pred, dim=(1)) + 0.0000000001)[:, None]
noise_pred /= noise_pred_vector_magnitude
mean = torch.mean(noise_pred_vector_magnitude, dim=(1, 2, 3), keepdim=True)
std = torch.std(noise_pred_vector_magnitude, dim=(1, 2, 3), keepdim=True)
top = (std * 3 + mean) * tonemap_multiplier
# Reinhard
noise_pred_vector_magnitude *= (1.0 / top)
new_magnitude = noise_pred_vector_magnitude / (noise_pred_vector_magnitude + 1.0)
new_magnitude *= top
# Rescale CFG
x_cfg = uncond + (noise_pred * new_magnitude * cond_scale)
ro_pos = torch.std(cond, dim=(1, 2, 3), keepdim=True)
ro_cfg = torch.std(x_cfg, dim=(1, 2, 3), keepdim=True)
x_rescaled = x_cfg * (ro_pos / ro_cfg)
x_final = rescale_multiplier * x_rescaled + (1.0 - rescale_multiplier) * x_cfg
return x_final
m = model.clone()
m.set_model_sampler_cfg_function(tonemap_noise_rescale_cfg)
return (m, )
NODE_CLASS_MAPPINGS = {
"TonemapNoiseWithRescaleCFG": TonemapNoiseWithRescaleCFG,
}
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