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import torch
def mix(latent_image, noise_image, mix_percent):
return ((1 - mix_percent) * latent_image + mix_percent *
noise_image) / ((mix_percent**2 + (1-mix_percent)**2) ** 0.5)
class FluxNoiseMixerNode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"latent": ("LATENT", ),
"noise": ("LATENT", ),
"mix_percent": ("FLOAT", {"default": 0.5, "min": 0, "max": 1.0, "step": 0.01}),
"random_noise": ("FLOAT", {"default": 0.0, "min": 0, "max": 100.0, "step": 0.01}),
"mix_type": (['mix', 'add'],),
"random_mix_type": (['mix', 'add'],),
"take_diff": ("BOOLEAN", ),
}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "append"
CATEGORY = "fluxtapoz"
def append(self, latent, noise, mix_percent, random_noise, mix_type, random_mix_type, take_diff):
latent_image = latent.copy()
noise = noise['samples']
latent = latent_image['samples']
random_noise_latent = torch.randn_like(noise)
if random_mix_type == 'mix':
noise = mix(noise, random_noise_latent, random_noise)
# noise = (noise * (1-random_noise) + random_noise_latent * (random_noise))
elif random_mix_type == 'add':
noise += random_noise_latent * random_noise
if mix_type == 'mix':
new_latent = mix(latent, noise, mix_percent)
# new_latent = (latent * (1-mix_percent) + noise * (mix_percent))
elif mix_type == 'add':
new_latent = latent + noise * mix_percent
if take_diff:
new_latent = new_latent - latent * mix_percent
latent_image['samples'] = new_latent
return (latent_image, ) |