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latents.py
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latents.py
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import comfy.samplers
import comfy.sample
import comfy.sampler_helpers
import torch
import math
from .noise_classes import *
def initialize_or_scale(tensor, value, steps):
if tensor is None:
return torch.full((steps,), value)
else:
return value * tensor
def latent_normalize_channels(x):
mean = x.mean(dim=(2, 3), keepdim=True)
std = x.std (dim=(2, 3), keepdim=True)
return (x - mean) / std
def latent_stdize_channels(x):
std = x.std (dim=(2, 3), keepdim=True)
return x / std
def latent_meancenter_channels(x):
mean = x.mean(dim=(2, 3), keepdim=True)
return x - mean
class set_precision:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"latent_image": ("LATENT", ),
"precision": (["16", "32", "64"], ),
},
}
RETURN_TYPES = ("LATENT",)
RETURN_NAMES = ("passthrough",)
CATEGORY = "sampling/custom_sampling/"
FUNCTION = "main"
def main(self, precision="32", latent_image=None):
match precision:
case "16":
torch.set_default_dtype(torch.float16)
x = latent_image["samples"].to(torch.float16)
case "32":
torch.set_default_dtype(torch.float32)
x = latent_image["samples"].to(torch.float32)
case "64":
torch.set_default_dtype(torch.float64)
x = latent_image["samples"].to(torch.float64)
return ({"samples": x}, )
class latent_to_cuda:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"latent": ("LATENT", ),
"to_cuda": ("BOOLEAN", {"default": True}),
},
}
RETURN_TYPES = ("LATENT",)
RETURN_NAMES = ("passthrough",)
CATEGORY = "sampling/custom_sampling/"
FUNCTION = "main"
def main(self, latent, to_cuda):
match to_cuda:
case "True":
latent = latent.to('cuda')
case "False":
latent = latent.to('cpu')
return (latent,)
class latent_batch:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"latent": ("LATENT", ),
"batch_size": ("INT", {"default": 0, "min": -10000, "max": 10000}),
},
}
RETURN_TYPES = ("LATENT",)
RETURN_NAMES = ("latent_batch",)
CATEGORY = "sampling/custom_sampling/"
FUNCTION = "main"
def main(self, latent, batch_size):
latent = latent["samples"]
b, c, h, w = latent.shape
batch_latents = torch.zeros([batch_size, 4, h, w], device=latent.device)
for i in range(batch_size):
batch_latents[i] = latent
return ({"samples": batch_latents}, )
class LatentPhaseMagnitude:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"latent_0_batch": ("LATENT",),
"latent_1_batch": ("LATENT",),
"phase_mix_power": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"magnitude_mix_power": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"phase_luminosity": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"phase_cyan_red": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"phase_lime_purple": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"phase_pattern_structure": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"magnitude_luminosity": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"magnitude_cyan_red": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"magnitude_lime_purple": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"magnitude_pattern_structure": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"latent_0_normal": ("BOOLEAN", {"default": True}),
"latent_1_normal": ("BOOLEAN", {"default": True}),
"latent_out_normal": ("BOOLEAN", {"default": True}),
"latent_0_stdize": ("BOOLEAN", {"default": True}),
"latent_1_stdize": ("BOOLEAN", {"default": True}),
"latent_out_stdize": ("BOOLEAN", {"default": True}),
"latent_0_meancenter": ("BOOLEAN", {"default": True}),
"latent_1_meancenter": ("BOOLEAN", {"default": True}),
"latent_out_meancenter": ("BOOLEAN", {"default": True}),
},
"optional": {
"phase_mix_powers": ("SIGMAS", ),
"magnitude_mix_powers": ("SIGMAS", ),
"phase_luminositys": ("SIGMAS", ),
"phase_cyan_reds": ("SIGMAS", ),
"phase_lime_purples": ("SIGMAS", ),
"phase_pattern_structures": ("SIGMAS", ),
"magnitude_luminositys": ("SIGMAS", ),
"magnitude_cyan_reds": ("SIGMAS", ),
"magnitude_lime_purples": ("SIGMAS", ),
"magnitude_pattern_structures": ("SIGMAS", ),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "main"
CATEGORY = "sampling/custom_sampling/samplers"
@staticmethod
def latent_repeat(latent, batch_size):
b, c, h, w = latent.shape
batch_latents = torch.zeros((batch_size, c, h, w), dtype=latent.dtype, layout=latent.layout, device=latent.device)
for i in range(batch_size):
batch_latents[i] = latent
return batch_latents
@staticmethod
def mix_latent_phase_magnitude(latent_0, latent_1, power_phase, power_magnitude,
phase_luminosity, phase_cyan_red, phase_lime_purple, phase_pattern_structure,
magnitude_luminosity, magnitude_cyan_red, magnitude_lime_purple, magnitude_pattern_structure
):
dtype = torch.promote_types(latent_0.dtype, latent_1.dtype)
# big accuracy problems with fp32 FFT! let's avoid that
latent_0 = latent_0.double()
latent_1 = latent_1.double()
latent_0_fft = torch.fft.fft2(latent_0)
latent_1_fft = torch.fft.fft2(latent_1)
latent_0_phase = torch.angle(latent_0_fft)
latent_1_phase = torch.angle(latent_1_fft)
latent_0_magnitude = torch.abs(latent_0_fft)
latent_1_magnitude = torch.abs(latent_1_fft)
# DC corruption...? handle separately??
#dc_index = (0, 0)
#dc_0 = latent_0_fft[:, :, dc_index[0], dc_index[1]]
#dc_1 = latent_1_fft[:, :, dc_index[0], dc_index[1]]
#mixed_dc = dc_0 * 0.5 + dc_1 * 0.5
#mixed_dc = dc_0 * (1 - phase_weight) + dc_1 * phase_weight
# create complex FFT using a weighted mix of phases
chan_weights_phase = [w for w in [phase_luminosity, phase_cyan_red, phase_lime_purple, phase_pattern_structure ]]
chan_weights_magnitude = [w for w in [magnitude_luminosity, magnitude_cyan_red, magnitude_lime_purple, magnitude_pattern_structure]]
mixed_phase = torch.zeros_like(latent_0, dtype=latent_0.dtype, layout=latent_0.layout, device=latent_0.device)
mixed_magnitude = torch.zeros_like(latent_0, dtype=latent_0.dtype, layout=latent_0.layout, device=latent_0.device)
for i in range(4):
mixed_phase[:, i] = ( (latent_0_phase[:,i] * (1-chan_weights_phase[i])) ** power_phase + (latent_1_phase[:,i] * chan_weights_phase[i]) ** power_phase) ** (1/power_phase)
mixed_magnitude[:, i] = ( (latent_0_magnitude[:,i] * (1-chan_weights_magnitude[i])) ** power_magnitude + (latent_1_magnitude[:,i] * chan_weights_magnitude[i]) ** power_magnitude) ** (1/power_magnitude)
new_fft = mixed_magnitude * torch.exp(1j * mixed_phase)
#new_fft[:, :, dc_index[0], dc_index[1]] = mixed_dc
# inverse FFT to convert back to spatial domain
mixed_phase_magnitude = torch.fft.ifft2(new_fft).real
return mixed_phase_magnitude.to(dtype)
def main(self, #batch_size, latent_1_repeat,
latent_0_batch, latent_1_batch, latent_0_normal, latent_1_normal, latent_out_normal,
latent_0_stdize, latent_1_stdize, latent_out_stdize,
latent_0_meancenter, latent_1_meancenter, latent_out_meancenter,
phase_mix_power, magnitude_mix_power,
phase_luminosity, phase_cyan_red, phase_lime_purple, phase_pattern_structure,
magnitude_luminosity, magnitude_cyan_red, magnitude_lime_purple, magnitude_pattern_structure,
phase_mix_powers=None, magnitude_mix_powers=None,
phase_luminositys=None, phase_cyan_reds=None, phase_lime_purples=None, phase_pattern_structures=None,
magnitude_luminositys=None, magnitude_cyan_reds=None, magnitude_lime_purples=None, magnitude_pattern_structures=None
):
latent_0_batch = latent_0_batch["samples"].double()
latent_1_batch = latent_1_batch["samples"].double().to(latent_0_batch.device)
#if batch_size == 0:
batch_size = latent_0_batch.shape[0]
if latent_1_batch.shape[0] == 1:
latent_1_batch = self.latent_repeat(latent_1_batch, batch_size)
magnitude_mix_powers = initialize_or_scale(magnitude_mix_powers, magnitude_mix_power, batch_size)
phase_mix_powers = initialize_or_scale(phase_mix_powers, phase_mix_power, batch_size)
phase_luminositys = initialize_or_scale(phase_luminositys, phase_luminosity, batch_size)
phase_cyan_reds = initialize_or_scale(phase_cyan_reds, phase_cyan_red, batch_size)
phase_lime_purples = initialize_or_scale(phase_lime_purples, phase_lime_purple, batch_size)
phase_pattern_structures = initialize_or_scale(phase_pattern_structures, phase_pattern_structure, batch_size)
magnitude_luminositys = initialize_or_scale(magnitude_luminositys, magnitude_luminosity, batch_size)
magnitude_cyan_reds = initialize_or_scale(magnitude_cyan_reds, magnitude_cyan_red, batch_size)
magnitude_lime_purples = initialize_or_scale(magnitude_lime_purples, magnitude_lime_purple, batch_size)
magnitude_pattern_structures = initialize_or_scale(magnitude_pattern_structures, magnitude_pattern_structure, batch_size)
mixed_phase_magnitude_batch = torch.zeros(latent_0_batch.shape, device=latent_0_batch.device)
if latent_0_normal == True:
latent_0_batch = latent_normalize_channels(latent_0_batch)
if latent_1_normal == True:
latent_1_batch = latent_normalize_channels(latent_1_batch)
if latent_0_meancenter == True:
latent_0_batch = latent_meancenter_channels(latent_0_batch)
if latent_1_meancenter == True:
latent_1_batch = latent_meancenter_channels(latent_1_batch)
if latent_0_stdize == True:
latent_0_batch = latent_stdize_channels(latent_0_batch)
if latent_1_stdize == True:
latent_1_batch = latent_stdize_channels(latent_1_batch)
for i in range(batch_size):
mixed_phase_magnitude = self.mix_latent_phase_magnitude(latent_0_batch[i:i+1], latent_1_batch[i:i+1], phase_mix_powers[i].item(), magnitude_mix_powers[i].item(),
phase_luminositys[i].item(), phase_cyan_reds[i].item(),phase_lime_purples[i].item(),phase_pattern_structures[i].item(),
magnitude_luminositys[i].item(), magnitude_cyan_reds[i].item(),magnitude_lime_purples[i].item(),magnitude_pattern_structures[i].item()
)
if latent_out_normal == True:
mixed_phase_magnitude = latent_normalize_channels(mixed_phase_magnitude)
if latent_out_stdize == True:
mixed_phase_magnitude = latent_stdize_channels(mixed_phase_magnitude)
if latent_out_meancenter == True:
mixed_phase_magnitude = latent_meancenter_channels(mixed_phase_magnitude)
mixed_phase_magnitude_batch[i, :, :, :] = mixed_phase_magnitude
return ({"samples": mixed_phase_magnitude_batch}, )
class LatentPhaseMagnitudeMultiply:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"latent_0_batch": ("LATENT",),
"phase_luminosity": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"phase_cyan_red": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"phase_lime_purple": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"phase_pattern_structure": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"magnitude_luminosity": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"magnitude_cyan_red": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"magnitude_lime_purple": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"magnitude_pattern_structure": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"latent_0_normal": ("BOOLEAN", {"default": False}),
"latent_out_normal": ("BOOLEAN", {"default": False}),
},
"optional": {
"phase_luminositys": ("SIGMAS", ),
"phase_cyan_reds": ("SIGMAS", ),
"phase_lime_purples": ("SIGMAS", ),
"phase_pattern_structures": ("SIGMAS", ),
"magnitude_luminositys": ("SIGMAS", ),
"magnitude_cyan_reds": ("SIGMAS", ),
"magnitude_lime_purples": ("SIGMAS", ),
"magnitude_pattern_structures": ("SIGMAS", ),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "main"
CATEGORY = "sampling/custom_sampling/samplers"
@staticmethod
def latent_repeat(latent, batch_size):
b, c, h, w = latent.shape
batch_latents = torch.zeros((batch_size, c, h, w), dtype=latent.dtype, layout=latent.layout, device=latent.device)
for i in range(batch_size):
batch_latents[i] = latent
return batch_latents
@staticmethod
def mix_latent_phase_magnitude(latent_0,
phase_luminosity, phase_cyan_red, phase_lime_purple, phase_pattern_structure,
magnitude_luminosity, magnitude_cyan_red, magnitude_lime_purple, magnitude_pattern_structure
):
dtype = latent_0.dtype
# avoid big accuracy problems with fp32 FFT!
latent_0 = latent_0.double()
latent_0_fft = torch.fft.fft2(latent_0)
latent_0_phase = torch.angle(latent_0_fft)
latent_0_magnitude = torch.abs(latent_0_fft)
# create new complex FFT using weighted mix of phases
chan_weights_phase = [w for w in [phase_luminosity, phase_cyan_red, phase_lime_purple, phase_pattern_structure ]]
chan_weights_magnitude = [ w for w in [magnitude_luminosity, magnitude_cyan_red, magnitude_lime_purple, magnitude_pattern_structure]]
mixed_phase = torch.zeros_like(latent_0, dtype=latent_0.dtype, layout=latent_0.layout, device=latent_0.device)
mixed_magnitude = torch.zeros_like(latent_0, dtype=latent_0.dtype, layout=latent_0.layout, device=latent_0.device)
for i in range(4):
mixed_phase[:, i] = latent_0_phase[:,i] * chan_weights_phase[i]
mixed_magnitude[:, i] = latent_0_magnitude[:,i] * chan_weights_magnitude[i]
new_fft = mixed_magnitude * torch.exp(1j * mixed_phase)
# inverse FFT to convert back to spatial domain
mixed_phase_magnitude = torch.fft.ifft2(new_fft).real
return mixed_phase_magnitude.to(dtype)
def main(self,
latent_0_batch, latent_0_normal, latent_out_normal,
phase_luminosity, phase_cyan_red, phase_lime_purple, phase_pattern_structure,
magnitude_luminosity, magnitude_cyan_red, magnitude_lime_purple, magnitude_pattern_structure,
phase_luminositys=None, phase_cyan_reds=None, phase_lime_purples=None, phase_pattern_structures=None,
magnitude_luminositys=None, magnitude_cyan_reds=None, magnitude_lime_purples=None, magnitude_pattern_structures=None
):
latent_0_batch = latent_0_batch["samples"].double()
batch_size = latent_0_batch.shape[0]
phase_luminositys = initialize_or_scale(phase_luminositys, phase_luminosity, batch_size)
phase_cyan_reds = initialize_or_scale(phase_cyan_reds, phase_cyan_red, batch_size)
phase_lime_purples = initialize_or_scale(phase_lime_purples, phase_lime_purple, batch_size)
phase_pattern_structures = initialize_or_scale(phase_pattern_structures, phase_pattern_structure, batch_size)
magnitude_luminositys = initialize_or_scale(magnitude_luminositys, magnitude_luminosity, batch_size)
magnitude_cyan_reds = initialize_or_scale(magnitude_cyan_reds, magnitude_cyan_red, batch_size)
magnitude_lime_purples = initialize_or_scale(magnitude_lime_purples, magnitude_lime_purple, batch_size)
magnitude_pattern_structures = initialize_or_scale(magnitude_pattern_structures, magnitude_pattern_structure, batch_size)
mixed_phase_magnitude_batch = torch.zeros(latent_0_batch.shape, device=latent_0_batch.device)
if latent_0_normal == True:
latent_0_batch = latent_normalize_channels(latent_0_batch)
for i in range(batch_size):
mixed_phase_magnitude = self.mix_latent_phase_magnitude(latent_0_batch[i:i+1],
phase_luminositys[i].item(), phase_cyan_reds[i].item(),phase_lime_purples[i].item(),phase_pattern_structures[i].item(),
magnitude_luminositys[i].item(), magnitude_cyan_reds[i].item(),magnitude_lime_purples[i].item(),magnitude_pattern_structures[i].item()
)
if latent_out_normal == True:
mixed_phase_magnitude = latent_normalize_channels(mixed_phase_magnitude)
mixed_phase_magnitude_batch[i, :, :, :] = mixed_phase_magnitude
return ({"samples": mixed_phase_magnitude_batch}, )
class LatentPhaseMagnitudeOffset:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"latent_0_batch": ("LATENT",),
"phase_luminosity": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"phase_cyan_red": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"phase_lime_purple": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"phase_pattern_structure": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"magnitude_luminosity": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"magnitude_cyan_red": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"magnitude_lime_purple": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"magnitude_pattern_structure": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"latent_0_normal": ("BOOLEAN", {"default": False}),
"latent_out_normal": ("BOOLEAN", {"default": False}),
},
"optional": {
"phase_luminositys": ("SIGMAS", ),
"phase_cyan_reds": ("SIGMAS", ),
"phase_lime_purples": ("SIGMAS", ),
"phase_pattern_structures": ("SIGMAS", ),
"magnitude_luminositys": ("SIGMAS", ),
"magnitude_cyan_reds": ("SIGMAS", ),
"magnitude_lime_purples": ("SIGMAS", ),
"magnitude_pattern_structures": ("SIGMAS", ),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "main"
CATEGORY = "sampling/custom_sampling/samplers"
@staticmethod
def latent_repeat(latent, batch_size):
b, c, h, w = latent.shape
batch_latents = torch.zeros((batch_size, c, h, w), dtype=latent.dtype, layout=latent.layout, device=latent.device)
for i in range(batch_size):
batch_latents[i] = latent
return batch_latents
@staticmethod
def mix_latent_phase_magnitude(latent_0,
phase_luminosity, phase_cyan_red, phase_lime_purple, phase_pattern_structure,
magnitude_luminosity, magnitude_cyan_red, magnitude_lime_purple, magnitude_pattern_structure
):
dtype = latent_0.dtype
# avoid big accuracy problems with fp32 FFT!
latent_0 = latent_0.double()
latent_0_fft = torch.fft.fft2(latent_0)
latent_0_phase = torch.angle(latent_0_fft)
latent_0_magnitude = torch.abs(latent_0_fft)
# create new complex FFT using a weighted mix of phases
chan_weights_phase = [w for w in [phase_luminosity, phase_cyan_red, phase_lime_purple, phase_pattern_structure ]]
chan_weights_magnitude = [ w for w in [magnitude_luminosity, magnitude_cyan_red, magnitude_lime_purple, magnitude_pattern_structure]]
mixed_phase = torch.zeros_like(latent_0, dtype=latent_0.dtype, layout=latent_0.layout, device=latent_0.device)
mixed_magnitude = torch.zeros_like(latent_0, dtype=latent_0.dtype, layout=latent_0.layout, device=latent_0.device)
for i in range(4):
mixed_phase[:, i] = latent_0_phase[:,i] + chan_weights_phase[i]
mixed_magnitude[:, i] = latent_0_magnitude[:,i] + chan_weights_magnitude[i]
new_fft = mixed_magnitude * torch.exp(1j * mixed_phase)
# inverse FFT to convert back to spatial domain
mixed_phase_magnitude = torch.fft.ifft2(new_fft).real
return mixed_phase_magnitude.to(dtype)
def main(self,
latent_0_batch, latent_0_normal, latent_out_normal,
phase_luminosity, phase_cyan_red, phase_lime_purple, phase_pattern_structure,
magnitude_luminosity, magnitude_cyan_red, magnitude_lime_purple, magnitude_pattern_structure,
phase_luminositys=None, phase_cyan_reds=None, phase_lime_purples=None, phase_pattern_structures=None,
magnitude_luminositys=None, magnitude_cyan_reds=None, magnitude_lime_purples=None, magnitude_pattern_structures=None
):
latent_0_batch = latent_0_batch["samples"].double()
batch_size = latent_0_batch.shape[0]
phase_luminositys = initialize_or_scale(phase_luminositys, phase_luminosity, batch_size)
phase_cyan_reds = initialize_or_scale(phase_cyan_reds, phase_cyan_red, batch_size)
phase_lime_purples = initialize_or_scale(phase_lime_purples, phase_lime_purple, batch_size)
phase_pattern_structures = initialize_or_scale(phase_pattern_structures, phase_pattern_structure, batch_size)
magnitude_luminositys = initialize_or_scale(magnitude_luminositys, magnitude_luminosity, batch_size)
magnitude_cyan_reds = initialize_or_scale(magnitude_cyan_reds, magnitude_cyan_red, batch_size)
magnitude_lime_purples = initialize_or_scale(magnitude_lime_purples, magnitude_lime_purple, batch_size)
magnitude_pattern_structures = initialize_or_scale(magnitude_pattern_structures, magnitude_pattern_structure, batch_size)
mixed_phase_magnitude_batch = torch.zeros(latent_0_batch.shape, device=latent_0_batch.device)
if latent_0_normal == True:
latent_0_batch = latent_normalize_channels(latent_0_batch)
for i in range(batch_size):
mixed_phase_magnitude = self.mix_latent_phase_magnitude(latent_0_batch[i:i+1],
phase_luminositys[i].item(), phase_cyan_reds[i].item(),phase_lime_purples[i].item(),phase_pattern_structures[i].item(),
magnitude_luminositys[i].item(), magnitude_cyan_reds[i].item(),magnitude_lime_purples[i].item(),magnitude_pattern_structures[i].item()
)
if latent_out_normal == True:
mixed_phase_magnitude = latent_normalize_channels(mixed_phase_magnitude)
mixed_phase_magnitude_batch[i, :, :, :] = mixed_phase_magnitude
return ({"samples": mixed_phase_magnitude_batch}, )
class LatentPhaseMagnitudePower:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"latent_0_batch": ("LATENT",),
"phase_luminosity": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"phase_cyan_red": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"phase_lime_purple": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"phase_pattern_structure": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"magnitude_luminosity": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"magnitude_cyan_red": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"magnitude_lime_purple": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"magnitude_pattern_structure": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"latent_0_normal": ("BOOLEAN", {"default": False}),
"latent_out_normal": ("BOOLEAN", {"default": False}),
},
"optional": {
"phase_luminositys": ("SIGMAS", ),
"phase_cyan_reds": ("SIGMAS", ),
"phase_lime_purples": ("SIGMAS", ),
"phase_pattern_structures": ("SIGMAS", ),
"magnitude_luminositys": ("SIGMAS", ),
"magnitude_cyan_reds": ("SIGMAS", ),
"magnitude_lime_purples": ("SIGMAS", ),
"magnitude_pattern_structures": ("SIGMAS", ),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "main"
CATEGORY = "sampling/custom_sampling/samplers"
@staticmethod
def latent_repeat(latent, batch_size):
b, c, h, w = latent.shape
batch_latents = torch.zeros((batch_size, c, h, w), dtype=latent.dtype, layout=latent.layout, device=latent.device)
for i in range(batch_size):
batch_latents[i] = latent
return batch_latents
@staticmethod
def mix_latent_phase_magnitude(latent_0,
phase_luminosity, phase_cyan_red, phase_lime_purple, phase_pattern_structure,
magnitude_luminosity, magnitude_cyan_red, magnitude_lime_purple, magnitude_pattern_structure
):
dtype = latent_0.dtype
# avoid big accuracy problems with fp32 FFT!
latent_0 = latent_0.double()
latent_0_fft = torch.fft.fft2(latent_0)
latent_0_phase = torch.angle(latent_0_fft)
latent_0_magnitude = torch.abs(latent_0_fft)
# create new complex FFT using a weighted mix of phases
chan_weights_phase = [w for w in [phase_luminosity, phase_cyan_red, phase_lime_purple, phase_pattern_structure ]]
chan_weights_magnitude = [ w for w in [magnitude_luminosity, magnitude_cyan_red, magnitude_lime_purple, magnitude_pattern_structure]]
mixed_phase = torch.zeros_like(latent_0, dtype=latent_0.dtype, layout=latent_0.layout, device=latent_0.device)
mixed_magnitude = torch.zeros_like(latent_0, dtype=latent_0.dtype, layout=latent_0.layout, device=latent_0.device)
for i in range(4):
mixed_phase[:, i] = latent_0_phase[:,i] ** chan_weights_phase[i]
mixed_magnitude[:, i] = latent_0_magnitude[:,i] ** chan_weights_magnitude[i]
new_fft = mixed_magnitude * torch.exp(1j * mixed_phase)
# inverse FFT to convert back to spatial domain
mixed_phase_magnitude = torch.fft.ifft2(new_fft).real
return mixed_phase_magnitude.to(dtype)
def main(self,
latent_0_batch, latent_0_normal, latent_out_normal,
phase_luminosity, phase_cyan_red, phase_lime_purple, phase_pattern_structure,
magnitude_luminosity, magnitude_cyan_red, magnitude_lime_purple, magnitude_pattern_structure,
phase_luminositys=None, phase_cyan_reds=None, phase_lime_purples=None, phase_pattern_structures=None,
magnitude_luminositys=None, magnitude_cyan_reds=None, magnitude_lime_purples=None, magnitude_pattern_structures=None
):
latent_0_batch = latent_0_batch["samples"].double()
batch_size = latent_0_batch.shape[0]
phase_luminositys = initialize_or_scale(phase_luminositys, phase_luminosity, batch_size)
phase_cyan_reds = initialize_or_scale(phase_cyan_reds, phase_cyan_red, batch_size)
phase_lime_purples = initialize_or_scale(phase_lime_purples, phase_lime_purple, batch_size)
phase_pattern_structures = initialize_or_scale(phase_pattern_structures, phase_pattern_structure, batch_size)
magnitude_luminositys = initialize_or_scale(magnitude_luminositys, magnitude_luminosity, batch_size)
magnitude_cyan_reds = initialize_or_scale(magnitude_cyan_reds, magnitude_cyan_red, batch_size)
magnitude_lime_purples = initialize_or_scale(magnitude_lime_purples, magnitude_lime_purple, batch_size)
magnitude_pattern_structures = initialize_or_scale(magnitude_pattern_structures, magnitude_pattern_structure, batch_size)
mixed_phase_magnitude_batch = torch.zeros(latent_0_batch.shape, device=latent_0_batch.device)
if latent_0_normal == True:
latent_0_batch = latent_normalize_channels(latent_0_batch)
for i in range(batch_size):
mixed_phase_magnitude = self.mix_latent_phase_magnitude(latent_0_batch[i:i+1],
phase_luminositys[i].item(), phase_cyan_reds[i].item(),phase_lime_purples[i].item(),phase_pattern_structures[i].item(),
magnitude_luminositys[i].item(), magnitude_cyan_reds[i].item(),magnitude_lime_purples[i].item(),magnitude_pattern_structures[i].item()
)
if latent_out_normal == True:
mixed_phase_magnitude = latent_normalize_channels(mixed_phase_magnitude)
mixed_phase_magnitude_batch[i, :, :, :] = mixed_phase_magnitude
return ({"samples": mixed_phase_magnitude_batch}, )
class EmptyLatentImage64:
def __init__(self):
self.device = comfy.model_management.intermediate_device()
@classmethod
def INPUT_TYPES(s):
return {"required": { "width": ("INT", {"default": 1024, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 1024, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "generate"
CATEGORY = "latent"
def generate(self, width, height, batch_size=1):
latent = torch.zeros([batch_size, 4, height // 8, width // 8], dtype=torch.float64, device=self.device)
return ({"samples":latent}, )
"""class CheckpointLoader32:
@classmethod
def INPUT_TYPES(s):
return {"required": { "config_name": (folder_paths.get_filename_list("configs"), ),
"ckpt_name": (folder_paths.get_filename_list("checkpoints"), )}}
RETURN_TYPES = ("MODEL", "CLIP", "VAE")
FUNCTION = "load_checkpoint"
CATEGORY = "advanced/loaders"
def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True):
#torch.set_default_dtype(torch.float64)
config_path = folder_paths.get_full_path("configs", config_name)
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))"""
MAX_RESOLUTION=8192
class LatentNoiseBatch_perlin:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {"required": {
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"width": ("INT", {"default": 1024, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
"height": ("INT", {"default": 1024, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 256}),
"detail_level": ("FLOAT", {"default": 0, "min": -1, "max": 1.0, "step": 0.1}),
},
"optional": {
"details": ("SIGMAS", ),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "create_noisy_latents_perlin"
CATEGORY = "latent/noise"
# found at https://gist.github.com/vadimkantorov/ac1b097753f217c5c11bc2ff396e0a57
# which was ported from https://github.com/pvigier/perlin-numpy/blob/master/perlin2d.py
def rand_perlin_2d(self, shape, res, fade = lambda t: 6*t**5 - 15*t**4 + 10*t**3):
delta = (res[0] / shape[0], res[1] / shape[1])
d = (shape[0] // res[0], shape[1] // res[1])
grid = torch.stack(torch.meshgrid(torch.arange(0, res[0], delta[0]), torch.arange(0, res[1], delta[1])), dim = -1) % 1
angles = 2*math.pi*torch.rand(res[0]+1, res[1]+1)
gradients = torch.stack((torch.cos(angles), torch.sin(angles)), dim = -1)
tile_grads = lambda slice1, slice2: gradients[slice1[0]:slice1[1], slice2[0]:slice2[1]].repeat_interleave(d[0], 0).repeat_interleave(d[1], 1)
dot = lambda grad, shift: (torch.stack((grid[:shape[0],:shape[1],0] + shift[0], grid[:shape[0],:shape[1], 1] + shift[1] ), dim = -1) * grad[:shape[0], :shape[1]]).sum(dim = -1)
n00 = dot(tile_grads([0, -1], [0, -1]), [0, 0])
n10 = dot(tile_grads([1, None], [0, -1]), [-1, 0])
n01 = dot(tile_grads([0, -1],[1, None]), [0, -1])
n11 = dot(tile_grads([1, None], [1, None]), [-1,-1])
t = fade(grid[:shape[0], :shape[1]])
return math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1])
def rand_perlin_2d_octaves(self, shape, res, octaves=1, persistence=0.5):
noise = torch.zeros(shape)
frequency = 1
amplitude = 1
for _ in range(octaves):
noise += amplitude * self.rand_perlin_2d(shape, (frequency*res[0], frequency*res[1]))
frequency *= 2
amplitude *= persistence
noise = torch.remainder(torch.abs(noise)*1000000,11)/11
# noise = (torch.sin(torch.remainder(noise*1000000,83))+1)/2
return noise
def scale_tensor(self, x):
min_value = x.min()
max_value = x.max()
x = (x - min_value) / (max_value - min_value)
return x
def create_noisy_latents_perlin(self, seed, width, height, batch_size, detail_level, details=None):
if details is None:
details = torch.full((10000,), detail_level)
else:
details = detail_level * details
torch.manual_seed(seed)
noise = torch.zeros((batch_size, 4, height // 8, width // 8), dtype=torch.float32, device="cpu").cpu()
for i in range(batch_size):
for j in range(4):
noise_values = self.rand_perlin_2d_octaves((height // 8, width // 8), (1,1), 1, 1)
result = (1+details[i]/10)*torch.erfinv(2 * noise_values - 1) * (2 ** 0.5)
result = torch.clamp(result,-5,5)
noise[i, j, :, :] = result
return ({"samples": noise},)
class LatentNoiseBatch_gaussian_channels:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"latent": ("LATENT",),
"mean": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"mean_luminosity": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"mean_cyan_red": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"mean_lime_purple": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"mean_pattern_structure": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"std": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"steps": ("INT", {"default": 0, "min": -10000, "max": 10000}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
},
"optional": {
"means": ("SIGMAS", ),
"mean_luminositys": ("SIGMAS", ),
"mean_cyan_reds": ("SIGMAS", ),
"mean_lime_purples": ("SIGMAS", ),
"mean_pattern_structures": ("SIGMAS", ),
"stds": ("SIGMAS", ),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "main"
CATEGORY = "sampling/custom_sampling/samplers"
""" @staticmethod
def gaussian_noise_channels_like(x, mean=0.0, mean_luminosity = -0.1, mean_cyan_red = 0.0, mean_lime_purple=0.0, mean_pattern_structure=0.0, std_dev=1.0, seed=42):
x = x.squeeze(0)
noise = torch.randn_like(x) * std_dev + mean
luminosity = noise[0:1] + mean_luminosity
cyan_red = noise[1:2] + mean_cyan_red
lime_purple = noise[2:3] + mean_lime_purple
pattern_structure = noise[3:4] + mean_pattern_structure
noise = torch.unsqueeze(torch.cat([luminosity, cyan_red, lime_purple, pattern_structure]), 0)
return noise.to(x.device)"""
@staticmethod
def gaussian_noise_channels(x, mean_luminosity = -0.1, mean_cyan_red = 0.0, mean_lime_purple=0.0, mean_pattern_structure=0.0):
x = x.squeeze(0)
luminosity = x[0:1] + mean_luminosity
cyan_red = x[1:2] + mean_cyan_red
lime_purple = x[2:3] + mean_lime_purple
pattern_structure = x[3:4] + mean_pattern_structure
x = torch.unsqueeze(torch.cat([luminosity, cyan_red, lime_purple, pattern_structure]), 0)
return x
def main(self, latent, steps, seed,
mean, mean_luminosity, mean_cyan_red, mean_lime_purple, mean_pattern_structure, std,
means=None, mean_luminositys=None, mean_cyan_reds=None, mean_lime_purples=None, mean_pattern_structures=None, stds=None):
if steps == 0:
steps = len(means)
x = latent["samples"]
b, c, h, w = x.shape
# x_noised = torch.zeros([steps, 4, h, w], device=x.device)
noise_latents = torch.zeros([steps, 4, h, w], dtype=x.dtype, layout=x.layout, device=x.device)
noise_sampler = NOISE_GENERATOR_CLASSES.get('gaussian')(x=x, seed = seed)
means = initialize_or_scale(means, mean, steps)
mean_luminositys = initialize_or_scale(mean_luminositys, mean_luminosity, steps)
mean_cyan_reds = initialize_or_scale(mean_cyan_reds, mean_cyan_red, steps)
mean_lime_purples = initialize_or_scale(mean_lime_purples, mean_lime_purple, steps)
mean_pattern_structures = initialize_or_scale(mean_pattern_structures, mean_pattern_structure, steps)
stds = initialize_or_scale(stds, std, steps)
for i in range(steps):
noise = noise_sampler(mean=means[i].item(), std=stds[i].item())
noise = self.gaussian_noise_channels(noise, mean_luminositys[i].item(), mean_cyan_reds[i].item(), mean_lime_purples[i].item(), mean_pattern_structures[i].item())
noise_latents[i] = x + noise
return ({"samples": noise_latents}, )
class LatentNoiseBatch_gaussian:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"latent": ("LATENT",),
"mean": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"std": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"steps": ("INT", {"default": 0, "min": -10000, "max": 10000}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
},
"optional": {
"means": ("SIGMAS", ),
"stds": ("SIGMAS", ),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "main"
CATEGORY = "sampling/custom_sampling/samplers"
def main(self, latent, mean, std, steps, seed, means=None, stds=None):
if steps == 0:
steps = len(means)
means = initialize_or_scale(means, mean, steps)
stds = initialize_or_scale(stds, std, steps)
latent_samples = latent["samples"]
b, c, h, w = latent_samples.shape
#noise_latents = torch.zeros([steps, 4, h, w], device=latent_samples.device)
#for i in range(steps):
# noise = self.adjustable_gaussian_noise_like(latent_samples, means[i].item(), stds[i].item(), seed+i)
# noise_latents[i] = latent_samples + noise
noise_latents = torch.zeros([steps, 4, h, w], dtype=latent_samples.dtype, layout=latent_samples.layout, device=latent_samples.device)
noise_sampler = NOISE_GENERATOR_CLASSES.get('gaussian')(x=latent_samples, seed = seed)
for i in range(steps):
noise = noise_sampler(mean=means[i].item(), std=stds[i].item())
noise_latents[i] = latent_samples + noise
return ({"samples": noise_latents}, )
class LatentNoiseBatch_fractal:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"latent": ("LATENT",),
"alpha": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"k_flip": ("BOOLEAN", {"default": False}),
"steps": ("INT", {"default": 0, "min": -10000, "max": 10000}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
},
"optional": {
"alphas": ("SIGMAS", ),
"ks": ("SIGMAS", ),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "main"
CATEGORY = "sampling/custom_sampling/samplers"
def main(self, latent, alpha, k_flip, steps, seed=42, alphas=None, ks=None):
if steps == 0:
steps = len(alphas)
alphas = initialize_or_scale(alphas, alpha, steps)
k_flip = -1 if k_flip else 1
ks = initialize_or_scale(ks, k_flip, steps)
latent_samples = latent["samples"]
b, c, h, w = latent_samples.shape
noise_latents = torch.zeros([steps, 4, h, w], dtype=latent_samples.dtype, layout=latent_samples.layout, device=latent_samples.device)
noise_sampler = NOISE_GENERATOR_CLASSES.get('fractal')(x=latent_samples, seed = seed)
for i in range(steps):
noise = noise_sampler(alpha=alphas[i].item(), k=ks[i].item(), scale=0.1)
noise_latents[i] = latent_samples + noise
return ({"samples": noise_latents}, )
class LatentNoiseList:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"latent": ("LATENT",),
"alpha": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.001}),
"k_flip": ("BOOLEAN", {"default": False}),
"steps": ("INT", {"default": 0, "min": -10000, "max": 10000}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
},
"optional": {
"alphas": ("SIGMAS", ),
"ks": ("SIGMAS", ),
}
}
RETURN_TYPES = ("LATENT",)
OUTPUT_IS_LIST = (True,)
FUNCTION = "main"
CATEGORY = "sampling/custom_sampling/samplers"
def main(self, seed, latent, alpha, k_flip, steps, alphas=None, ks=None):
alphas = initialize_or_scale(alphas, alpha, steps)
k_flip = -1 if k_flip else 1
ks = initialize_or_scale(ks, k_flip, steps)
latent_samples = latent["samples"]
latents = []
size = latent_samples.shape
steps = len(alphas) if steps == 0 else steps
noise_sampler = NOISE_GENERATOR_CLASSES.get('fractal')(x=latent_samples, seed=seed)
for i in range(steps):
noise = noise_sampler(alpha=alphas[i].item(), k=ks[i].item(), scale=0.1)
noisy_latent = latent_samples + noise
new_latent = {"samples": noisy_latent}
latents.append(new_latent)
return (latents, )
class LatentBatch_channels:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"latent": ("LATENT",),
"mode": (["offset", "multiply", "power"],),
"luminosity": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
"cyan_red": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
"lime_purple": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
"pattern_structure": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
},
"optional": {
"luminositys": ("SIGMAS", ),
"cyan_reds": ("SIGMAS", ),
"lime_purples": ("SIGMAS", ),
"pattern_structures": ("SIGMAS", ),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "main"
CATEGORY = "sampling/custom_sampling/samplers"
@staticmethod
def latent_channels_multiply(x, luminosity = -0.1, cyan_red = 0.0, lime_purple=0.0, pattern_structure=0.0):
luminosity = x[0:1] * luminosity
cyan_red = x[1:2] * cyan_red
lime_purple = x[2:3] * lime_purple
pattern_structure = x[3:4] * pattern_structure
x = torch.unsqueeze(torch.cat([luminosity, cyan_red, lime_purple, pattern_structure]), 0)
return x
@staticmethod
def latent_channels_offset(x, luminosity = -0.1, cyan_red = 0.0, lime_purple=0.0, pattern_structure=0.0):
luminosity = x[0:1] + luminosity
cyan_red = x[1:2] + cyan_red
lime_purple = x[2:3] + lime_purple