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import torch
class RescaleClassifierFreeGuidance:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"multiplier": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "custom_node_experiments"
def patch(self, model, multiplier):
def rescale_cfg(args):
cond = args["cond"]
uncond = args["uncond"]
cond_scale = args["cond_scale"]
x_cfg = uncond + cond_scale * (cond - uncond)
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 = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg
return x_final
m = model.clone()
m.set_model_sampler_cfg_function(rescale_cfg)
return (m, )
NODE_CLASS_MAPPINGS = {
"RescaleClassifierFreeGuidanceTest": RescaleClassifierFreeGuidance,
}
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