Add Perlin and swirl latent noise nodes
This commit is contained in:
Ken Simpson
@@ -32,6 +32,14 @@ Separates a latent into low-pass structure and high-frequency detail bands using
|
||||
|
||||
Adds seeded Gaussian noise scaled by the input latent's standard deviation. Useful whenever a latent needs a controlled bump in noise without touching the schedule.
|
||||
|
||||
### Latent Perlin Fractal Noise
|
||||
|
||||
Generates smooth octave-based Perlin noise and adds it to the latent. Tune the base frequency, octaves, persistence, and lacunarity to decide whether you want broad undulations or fine stippling, and choose between shared or per-channel fields for subtle colour-channel offsets.
|
||||
|
||||
### Latent Swirl Noise
|
||||
|
||||
Warps the latent around a seeded vortex using `grid_sample`. Control the maximum rotation (in radians), the falloff radius, centre jitter, and blend strength to inject painterly whirlpools or gentle twisting motion into the underlying features.
|
||||
|
||||
### Conditioning (Add Noise)
|
||||
|
||||
Adds seeded Gaussian noise to the CLIP conditioning embeddings (and pooled output when present). Great for introducing gentle prompt variation without rewriting text.
|
||||
@@ -74,6 +82,7 @@ Image exploration rarely means chasing a single "correct" answer. These nodes ar
|
||||
- Follow the noise with `Conditioning (Gaussian Blur)` to soften abrupt emphasis changes or to merge multi-prompt blends into a single vibe. Blurring after noise often produces dreamlike, painterly shifts instead of chaotic drift.
|
||||
- Use `Conditioning (Scale)` as a volume knob while iterating: dial the factor down to 0.3–0.5 when you want the image to respond mostly to the recovered latent, then crank it above 1.5 when the textual guidance should take the lead.
|
||||
- Pair `Latent Gaussian Blur` with `Conditioning (Gaussian Blur)` for holistic smoothing—latent blur calms texture while conditioning blur calms prompt pacing. Reintroduce energy by sprinkling in `Add Latent Noise` or ramping the conditioning scale afterwards.
|
||||
- Sculpt structured detail by layering `Latent Perlin Fractal Noise` (shared mode for broad waves, per-channel for colour shifts) before blending back with `Latent Swirl Noise` to twist the resulting features into fluid patterns.
|
||||
- Split a latent with `Latent Frequency Split`, sharpen or noise the high band while blurring the low band, then recombine via `Latent Mixer`/`Add` nodes to weave sharp detail onto soft composition.
|
||||
- Carve prompts with `Conditioning (Frequency Split)` and return them through `Conditioning (Frequency Merge)`: keep low-band text steady for the story while remapping the high band through noise, blur, or scale to emphasize only certain fragments.
|
||||
- Swap seeds between the inverter, forward diffusion, and noise nodes as you iterate. Small tweaks here can shift the interplay between latent detail and prompt guidance, rewarding playful experimentation.
|
||||
|
||||
+448
@@ -7,6 +7,230 @@ import torch
|
||||
import torch.nn.functional as F
|
||||
import torchvision.transforms.functional as TF
|
||||
|
||||
|
||||
def _fade(t):
|
||||
return t * t * t * (t * (t * 6 - 15) + 10)
|
||||
|
||||
|
||||
def _lerp(a, b, t):
|
||||
return a + t * (b - a)
|
||||
|
||||
|
||||
def _generate_permutation(seed, device):
|
||||
generator = torch.Generator(device="cpu").manual_seed(seed)
|
||||
perm = torch.randperm(256, generator=generator)
|
||||
return torch.cat([perm, perm]).to(device)
|
||||
|
||||
|
||||
def _coordinate_grid(size, device, dtype):
|
||||
axes = [torch.linspace(0.0, 1.0, steps=s, device=device, dtype=dtype) for s in size]
|
||||
return torch.meshgrid(*axes, indexing="ij")
|
||||
|
||||
|
||||
def _perlin_2d(x, y, perm, dtype):
|
||||
gradients = torch.tensor(
|
||||
[[1, 1], [-1, 1], [1, -1], [-1, -1], [1, 0], [-1, 0], [0, 1], [0, -1]],
|
||||
dtype=dtype,
|
||||
device=x.device,
|
||||
)
|
||||
|
||||
x0 = torch.floor(x)
|
||||
y0 = torch.floor(y)
|
||||
|
||||
xi = torch.remainder(x0.to(torch.int64), 256)
|
||||
yi = torch.remainder(y0.to(torch.int64), 256)
|
||||
|
||||
xf = x - x0
|
||||
yf = y - y0
|
||||
|
||||
xi1 = torch.remainder(xi + 1, 256)
|
||||
yi1 = torch.remainder(yi + 1, 256)
|
||||
|
||||
perm_xi = perm[xi]
|
||||
perm_xi1 = perm[xi1]
|
||||
|
||||
aa = perm[perm_xi + yi]
|
||||
ab = perm[perm_xi + yi1]
|
||||
ba = perm[perm_xi1 + yi]
|
||||
bb = perm[perm_xi1 + yi1]
|
||||
|
||||
grad_count = gradients.shape[0]
|
||||
|
||||
g_aa = gradients[torch.remainder(aa, grad_count)]
|
||||
g_ab = gradients[torch.remainder(ab, grad_count)]
|
||||
g_ba = gradients[torch.remainder(ba, grad_count)]
|
||||
g_bb = gradients[torch.remainder(bb, grad_count)]
|
||||
|
||||
xf_1 = xf - 1.0
|
||||
yf_1 = yf - 1.0
|
||||
|
||||
dot_aa = g_aa[..., 0] * xf + g_aa[..., 1] * yf
|
||||
dot_ba = g_ba[..., 0] * xf_1 + g_ba[..., 1] * yf
|
||||
dot_ab = g_ab[..., 0] * xf + g_ab[..., 1] * yf_1
|
||||
dot_bb = g_bb[..., 0] * xf_1 + g_bb[..., 1] * yf_1
|
||||
|
||||
u = _fade(xf)
|
||||
v = _fade(yf)
|
||||
|
||||
x1 = _lerp(dot_aa, dot_ba, u)
|
||||
x2 = _lerp(dot_ab, dot_bb, u)
|
||||
return _lerp(x1, x2, v)
|
||||
|
||||
|
||||
def _perlin_3d(x, y, z, perm, dtype):
|
||||
gradients = torch.tensor(
|
||||
[
|
||||
[1, 1, 0],
|
||||
[-1, 1, 0],
|
||||
[1, -1, 0],
|
||||
[-1, -1, 0],
|
||||
[1, 0, 1],
|
||||
[-1, 0, 1],
|
||||
[1, 0, -1],
|
||||
[-1, 0, -1],
|
||||
[0, 1, 1],
|
||||
[0, -1, 1],
|
||||
[0, 1, -1],
|
||||
[0, -1, -1],
|
||||
],
|
||||
dtype=dtype,
|
||||
device=x.device,
|
||||
)
|
||||
|
||||
x0 = torch.floor(x)
|
||||
y0 = torch.floor(y)
|
||||
z0 = torch.floor(z)
|
||||
|
||||
xi = torch.remainder(x0.to(torch.int64), 256)
|
||||
yi = torch.remainder(y0.to(torch.int64), 256)
|
||||
zi = torch.remainder(z0.to(torch.int64), 256)
|
||||
|
||||
xf = x - x0
|
||||
yf = y - y0
|
||||
zf = z - z0
|
||||
|
||||
xi1 = torch.remainder(xi + 1, 256)
|
||||
yi1 = torch.remainder(yi + 1, 256)
|
||||
zi1 = torch.remainder(zi + 1, 256)
|
||||
|
||||
perm_xi = perm[xi]
|
||||
perm_xi1 = perm[xi1]
|
||||
|
||||
perm_xy = perm[perm_xi + yi]
|
||||
perm_xy1 = perm[perm_xi + yi1]
|
||||
perm_x1y = perm[perm_xi1 + yi]
|
||||
perm_x1y1 = perm[perm_xi1 + yi1]
|
||||
|
||||
aaa = perm[perm_xy + zi]
|
||||
aab = perm[perm_xy + zi1]
|
||||
aba = perm[perm_xy1 + zi]
|
||||
abb = perm[perm_xy1 + zi1]
|
||||
baa = perm[perm_x1y + zi]
|
||||
bab = perm[perm_x1y + zi1]
|
||||
bba = perm[perm_x1y1 + zi]
|
||||
bbb = perm[perm_x1y1 + zi1]
|
||||
|
||||
grad_count = gradients.shape[0]
|
||||
|
||||
g_aaa = gradients[torch.remainder(aaa, grad_count)]
|
||||
g_aab = gradients[torch.remainder(aab, grad_count)]
|
||||
g_aba = gradients[torch.remainder(aba, grad_count)]
|
||||
g_abb = gradients[torch.remainder(abb, grad_count)]
|
||||
g_baa = gradients[torch.remainder(baa, grad_count)]
|
||||
g_bab = gradients[torch.remainder(bab, grad_count)]
|
||||
g_bba = gradients[torch.remainder(bba, grad_count)]
|
||||
g_bbb = gradients[torch.remainder(bbb, grad_count)]
|
||||
|
||||
xf_1 = xf - 1.0
|
||||
yf_1 = yf - 1.0
|
||||
zf_1 = zf - 1.0
|
||||
|
||||
dot_aaa = g_aaa[..., 0] * xf + g_aaa[..., 1] * yf + g_aaa[..., 2] * zf
|
||||
dot_baa = g_baa[..., 0] * xf_1 + g_baa[..., 1] * yf + g_baa[..., 2] * zf
|
||||
dot_aba = g_aba[..., 0] * xf + g_aba[..., 1] * yf_1 + g_aba[..., 2] * zf
|
||||
dot_bba = g_bba[..., 0] * xf_1 + g_bba[..., 1] * yf_1 + g_bba[..., 2] * zf
|
||||
dot_aab = g_aab[..., 0] * xf + g_aab[..., 1] * yf + g_aab[..., 2] * zf_1
|
||||
dot_bab = g_bab[..., 0] * xf_1 + g_bab[..., 1] * yf + g_bab[..., 2] * zf_1
|
||||
dot_abb = g_abb[..., 0] * xf + g_abb[..., 1] * yf_1 + g_abb[..., 2] * zf_1
|
||||
dot_bbb = g_bbb[..., 0] * xf_1 + g_bbb[..., 1] * yf_1 + g_bbb[..., 2] * zf_1
|
||||
|
||||
u = _fade(xf)
|
||||
v = _fade(yf)
|
||||
w = _fade(zf)
|
||||
|
||||
x1 = _lerp(dot_aaa, dot_baa, u)
|
||||
x2 = _lerp(dot_aba, dot_bba, u)
|
||||
y1 = _lerp(x1, x2, v)
|
||||
|
||||
x3 = _lerp(dot_aab, dot_bab, u)
|
||||
x4 = _lerp(dot_abb, dot_bbb, u)
|
||||
y2 = _lerp(x3, x4, v)
|
||||
|
||||
return _lerp(y1, y2, w)
|
||||
|
||||
|
||||
def _fractal_perlin(size, seed, frequency, octaves, persistence, lacunarity, device):
|
||||
if len(size) not in (2, 3):
|
||||
raise ValueError("Perlin noise supports 2D or 3D shapes")
|
||||
|
||||
noise_dtype = torch.float32
|
||||
coords = _coordinate_grid(size, device, noise_dtype)
|
||||
|
||||
total = torch.zeros(size, dtype=noise_dtype, device=device)
|
||||
amplitude = 1.0
|
||||
max_amplitude = 0.0
|
||||
freq = frequency
|
||||
|
||||
for octave in range(octaves):
|
||||
perm = _generate_permutation(seed + octave, device)
|
||||
|
||||
scaled_coords = [coord * freq for coord in coords]
|
||||
|
||||
if len(size) == 2:
|
||||
octave_noise = _perlin_2d(scaled_coords[1], scaled_coords[0], perm, noise_dtype)
|
||||
else:
|
||||
octave_noise = _perlin_3d(scaled_coords[2], scaled_coords[1], scaled_coords[0], perm, noise_dtype)
|
||||
|
||||
total = total + octave_noise * amplitude
|
||||
max_amplitude += amplitude
|
||||
amplitude *= persistence
|
||||
freq *= lacunarity
|
||||
|
||||
if max_amplitude > 0:
|
||||
total = total / max_amplitude
|
||||
|
||||
return total
|
||||
|
||||
|
||||
def _normalize_noise_tensor(noise):
|
||||
mean = torch.mean(noise)
|
||||
std = torch.std(noise)
|
||||
if std > 1e-6:
|
||||
return (noise - mean) / std
|
||||
return noise - mean
|
||||
|
||||
|
||||
def _normalized_meshgrid(height, width, device, dtype):
|
||||
ys = torch.linspace(-1.0, 1.0, steps=height, device=device, dtype=dtype)
|
||||
xs = torch.linspace(-1.0, 1.0, steps=width, device=device, dtype=dtype)
|
||||
return torch.meshgrid(ys, xs, indexing="ij")
|
||||
|
||||
|
||||
def _swirl_grid(base_y, base_x, center_x, center_y, strength, radius, direction):
|
||||
radius = max(radius, 1e-3)
|
||||
dx = base_x - center_x
|
||||
dy = base_y - center_y
|
||||
r = torch.sqrt(dx * dx + dy * dy)
|
||||
falloff = torch.exp(-torch.square(r / radius))
|
||||
angle = direction * strength * falloff
|
||||
sin_a = torch.sin(angle)
|
||||
cos_a = torch.cos(angle)
|
||||
x_new = dx * cos_a - dy * sin_a + center_x
|
||||
y_new = dx * sin_a + dy * cos_a + center_y
|
||||
grid = torch.stack((x_new, y_new), dim=-1)
|
||||
return torch.clamp(grid, -1.0, 1.0)
|
||||
|
||||
|
||||
def slerp(val, low, high):
|
||||
"""
|
||||
Robust, batch-aware Spherical Linear Interpolation for PyTorch tensors.
|
||||
@@ -419,6 +643,226 @@ class LatentAddNoise:
|
||||
|
||||
return (out,)
|
||||
|
||||
|
||||
class LatentPerlinFractalNoise:
|
||||
"""Adds smooth fractal Perlin noise to latent tensors for structured variation."""
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"latent": ("LATENT",),
|
||||
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
||||
"frequency": ("FLOAT", {
|
||||
"default": 2.0,
|
||||
"min": 0.01,
|
||||
"max": 64.0,
|
||||
"step": 0.01,
|
||||
"tooltip": "Base lattice frequency. Higher values produce finer details."
|
||||
}),
|
||||
"octaves": ("INT", {
|
||||
"default": 4,
|
||||
"min": 1,
|
||||
"max": 12,
|
||||
"tooltip": "Number of noise layers to accumulate."
|
||||
}),
|
||||
"persistence": ("FLOAT", {
|
||||
"default": 0.5,
|
||||
"min": 0.0,
|
||||
"max": 1.0,
|
||||
"step": 0.01,
|
||||
"tooltip": "Amplitude multiplier between octaves."
|
||||
}),
|
||||
"lacunarity": ("FLOAT", {
|
||||
"default": 2.0,
|
||||
"min": 1.0,
|
||||
"max": 6.0,
|
||||
"step": 0.1,
|
||||
"tooltip": "Frequency multiplier between octaves."
|
||||
}),
|
||||
"strength": ("FLOAT", {
|
||||
"default": 0.5,
|
||||
"min": 0.0,
|
||||
"max": 5.0,
|
||||
"step": 0.01,
|
||||
"tooltip": "Scales the normalized noise relative to the latent's standard deviation."
|
||||
}),
|
||||
"channel_mode": (["shared", "per_channel"], {
|
||||
"default": "shared",
|
||||
"tooltip": "Shared noise across channels or unique noise per channel."
|
||||
}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("LATENT",)
|
||||
FUNCTION = "add_perlin_noise"
|
||||
CATEGORY = "Latent/Noise"
|
||||
|
||||
def add_perlin_noise(self, latent, seed, frequency, octaves, persistence, lacunarity, strength, channel_mode):
|
||||
if strength == 0.0:
|
||||
return (latent,)
|
||||
|
||||
device = comfy.model_management.get_torch_device()
|
||||
latent_tensor = latent["samples"].clone().to(device)
|
||||
is_video = latent_tensor.dim() == 5
|
||||
|
||||
if is_video:
|
||||
dims = (latent_tensor.shape[2], latent_tensor.shape[3], latent_tensor.shape[4])
|
||||
else:
|
||||
dims = (latent_tensor.shape[-2], latent_tensor.shape[-1])
|
||||
|
||||
batch = latent_tensor.shape[0]
|
||||
channels = latent_tensor.shape[1]
|
||||
|
||||
output = latent_tensor.clone()
|
||||
|
||||
for batch_index in range(batch):
|
||||
sample = latent_tensor[batch_index]
|
||||
sample_std = torch.std(sample.float())
|
||||
|
||||
if channel_mode == "shared":
|
||||
noise = _fractal_perlin(dims, seed + batch_index, frequency, octaves, persistence, lacunarity, device)
|
||||
noise = _normalize_noise_tensor(noise)
|
||||
|
||||
if is_video:
|
||||
noise = noise.unsqueeze(0).expand(channels, dims[0], dims[1], dims[2])
|
||||
else:
|
||||
noise = noise.unsqueeze(0).expand(channels, dims[-2], dims[-1])
|
||||
else:
|
||||
if is_video:
|
||||
noise = torch.zeros((channels, dims[0], dims[1], dims[2]), dtype=torch.float32, device=device)
|
||||
else:
|
||||
noise = torch.zeros((channels, dims[-2], dims[-1]), dtype=torch.float32, device=device)
|
||||
|
||||
for channel_index in range(channels):
|
||||
channel_seed = seed + batch_index * 997 + channel_index * 1013
|
||||
channel_noise = _fractal_perlin(dims, channel_seed, frequency, octaves, persistence, lacunarity, device)
|
||||
noise[channel_index] = _normalize_noise_tensor(channel_noise)
|
||||
|
||||
scale = sample_std * strength
|
||||
scaled_noise = (noise * scale).to(sample.dtype)
|
||||
output[batch_index] = sample + scaled_noise
|
||||
|
||||
out = latent.copy()
|
||||
out["samples"] = output.cpu()
|
||||
|
||||
return (out,)
|
||||
|
||||
|
||||
class LatentSwirlNoise:
|
||||
"""Swirls latent pixels around randomized centers for vortex-like perturbations."""
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"latent": ("LATENT",),
|
||||
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
||||
"strength": ("FLOAT", {
|
||||
"default": 0.75,
|
||||
"min": 0.0,
|
||||
"max": 6.28,
|
||||
"step": 0.01,
|
||||
"tooltip": "Peak swirl rotation in radians near the vortex center."
|
||||
}),
|
||||
"radius": ("FLOAT", {
|
||||
"default": 0.5,
|
||||
"min": 0.05,
|
||||
"max": 2.0,
|
||||
"step": 0.01,
|
||||
"tooltip": "Normalized radius controlling how far the vortex influence extends."
|
||||
}),
|
||||
"center_spread": ("FLOAT", {
|
||||
"default": 0.25,
|
||||
"min": 0.0,
|
||||
"max": 1.0,
|
||||
"step": 0.01,
|
||||
"tooltip": "How far the vortex origin drifts from the latent center."
|
||||
}),
|
||||
"direction_bias": ("FLOAT", {
|
||||
"default": 0.0,
|
||||
"min": -1.0,
|
||||
"max": 1.0,
|
||||
"step": 0.05,
|
||||
"tooltip": "Bias toward counter-clockwise (1) or clockwise (-1) swirl."
|
||||
}),
|
||||
"mix": ("FLOAT", {
|
||||
"default": 1.0,
|
||||
"min": 0.0,
|
||||
"max": 1.0,
|
||||
"step": 0.01,
|
||||
"tooltip": "Blend between original latent (0) and fully swirled result (1)."
|
||||
}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("LATENT",)
|
||||
FUNCTION = "add_swirl_noise"
|
||||
CATEGORY = "Latent/Noise"
|
||||
|
||||
def add_swirl_noise(self, latent, seed, strength, radius, center_spread, direction_bias, mix):
|
||||
if strength == 0.0 or mix == 0.0:
|
||||
return (latent,)
|
||||
|
||||
device = comfy.model_management.get_torch_device()
|
||||
latent_tensor = latent["samples"].clone().to(device)
|
||||
is_video = latent_tensor.dim() == 5
|
||||
|
||||
if is_video:
|
||||
batch, channels, frames, height, width = latent_tensor.shape
|
||||
working = latent_tensor.permute(0, 2, 1, 3, 4).reshape(batch * frames, channels, height, width)
|
||||
else:
|
||||
batch, channels, height, width = latent_tensor.shape
|
||||
frames = 1
|
||||
working = latent_tensor
|
||||
|
||||
base_dtype = torch.float32
|
||||
base_y, base_x = _normalized_meshgrid(height, width, device, base_dtype)
|
||||
result = torch.empty_like(working)
|
||||
|
||||
center_spread = max(0.0, min(center_spread, 1.0))
|
||||
mix = max(0.0, min(mix, 1.0))
|
||||
bias_threshold = max(0.0, min(1.0, (direction_bias + 1.0) * 0.5))
|
||||
|
||||
cpu_generator = torch.Generator(device="cpu").manual_seed(seed)
|
||||
|
||||
total = working.shape[0]
|
||||
|
||||
for idx in range(total):
|
||||
rand_vals = torch.rand(3, generator=cpu_generator)
|
||||
offsets = (rand_vals[:2] * 2.0 - 1.0) * center_spread
|
||||
center_y = offsets[0].item()
|
||||
center_x = offsets[1].item()
|
||||
direction = 1.0 if rand_vals[2].item() < bias_threshold else -1.0
|
||||
|
||||
grid = _swirl_grid(base_y, base_x, center_x, center_y, strength, radius, direction).unsqueeze(0)
|
||||
|
||||
sample = working[idx:idx + 1]
|
||||
warped = F.grid_sample(
|
||||
sample.to(base_dtype),
|
||||
grid,
|
||||
mode="bilinear",
|
||||
padding_mode="reflection",
|
||||
align_corners=True,
|
||||
)
|
||||
|
||||
warped = warped.to(sample.dtype)
|
||||
|
||||
if mix >= 1.0:
|
||||
combined = warped
|
||||
else:
|
||||
combined = sample + (warped - sample) * mix
|
||||
|
||||
result[idx] = combined[0]
|
||||
|
||||
if is_video:
|
||||
result = result.reshape(batch, frames, channels, height, width).permute(0, 2, 1, 3, 4)
|
||||
|
||||
out = latent.copy()
|
||||
out["samples"] = result.cpu()
|
||||
|
||||
return (out,)
|
||||
|
||||
class LatentForwardDiffusion:
|
||||
"""
|
||||
Applies the 'natural' forward diffusion process to a clean latent.
|
||||
@@ -829,6 +1273,8 @@ NODE_CLASS_MAPPINGS = {
|
||||
"LatentGaussianBlur": LatentGaussianBlur,
|
||||
"LatentFrequencySplit": LatentFrequencySplit,
|
||||
"LatentAddNoise": LatentAddNoise,
|
||||
"LatentPerlinFractalNoise": LatentPerlinFractalNoise,
|
||||
"LatentSwirlNoise": LatentSwirlNoise,
|
||||
"LatentForwardDiffusion": LatentForwardDiffusion,
|
||||
"LatentHybridInverter": LatentHybridInverter,
|
||||
"ConditioningAddNoise": ConditioningAddNoise,
|
||||
@@ -843,6 +1289,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"LatentGaussianBlur": "Latent Gaussian Blur",
|
||||
"LatentFrequencySplit": "Latent Frequency Split",
|
||||
"LatentAddNoise": "Add Latent Noise (Seeded)",
|
||||
"LatentPerlinFractalNoise": "Latent Perlin Fractal Noise",
|
||||
"LatentSwirlNoise": "Latent Swirl Noise",
|
||||
"LatentForwardDiffusion": "Forward Diffusion (Add Scheduled Noise)",
|
||||
"LatentHybridInverter": "Latent Hybrid Inverter (Qwen)",
|
||||
"ConditioningAddNoise": "Conditioning (Add Noise)",
|
||||
|
||||
Reference in New Issue
Block a user