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