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Merge pull request #11 from blepping/fix_code_duplication

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Reuse code for 16 (or other) channel versions
This commit is contained in:
Extraltodeus
2024-08-13 16:19:11 +02:00
committed by GitHub
parent b852cb150b 97d2172de5
commit 2ee8066fa8
1 changed files with 10 additions and 70 deletions
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latent_noisy_perlin.py
+10 -70
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@@ -24,14 +24,14 @@ class NoisyLatentPerlin:
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])
@@ -50,89 +50,29 @@ class NoisyLatentPerlin:
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):
def create_noisy_latents_perlin(self, seed, width, height, batch_size, detail_level, *, channels=4):
torch.manual_seed(seed)
noise = torch.zeros((batch_size, 4, height // 8, width // 8), dtype=torch.float32, device="cpu").cpu()
noise = torch.zeros((batch_size, channels, height // 8, width // 8), dtype=torch.float32, device="cpu").cpu()
for i in range(batch_size):
for j in range(4):
for j in range(channels):
noise_values = self.rand_perlin_2d_octaves((height // 8, width // 8), (1,1), 1, 1)
result = (1+detail_level/10)*torch.erfinv(2 * noise_values - 1) * (2 ** 0.5)
result = torch.clamp(result,-5,5)
noise[i, j, :, :] = result
return ({"samples": noise},)
class NoisyLatentPerlin16ch:
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": 64}),
"detail_level": ("FLOAT", {"default": 0, "min": -1, "max": 1.0, "step": 0.1}),
}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "create_noisy_latents_perlin16ch"
CATEGORY = "latent/noise"
class NoisyLatentPerlin16ch(NoisyLatentPerlin):
def create_noisy_latents_perlin(self, seed, width, height, batch_size, detail_level, *, channels=16):
return super().create_noisy_latents_perlin(seed, width, height, batch_size, detail_level, channels=channels)
# 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_perlin16ch(self, seed, width, height, batch_size, detail_level):
torch.manual_seed(seed)
noise = torch.zeros((batch_size, 16, height // 8, width // 8), dtype=torch.float32, device="cpu").cpu()
for i in range(batch_size):
for j in range(16):
noise_values = self.rand_perlin_2d_octaves((height // 8, width // 8), (1,1), 1, 1)
result = (1+detail_level/10)*torch.erfinv(2 * noise_values - 1) * (2 ** 0.5)
result = torch.clamp(result,-5,5)
noise[i, j, :, :] = result
return ({"samples": noise},)
NODE_CLASS_MAPPINGS = {
"NoisyLatentPerlin": NoisyLatentPerlin,
"NoisyLatentPerlin16ch": NoisyLatentPerlin16ch,