init

library/custom_train_functions.py

@@ -373,3 +373,54 @@ def apply_noise_offset(latents, noise, noise_offset, adaptive_noise_scale):
 
     noise = noise + noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
     return noise
+
+
+
+##########################################
+# Perlin Noise
+def rand_perlin_2d(device, 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],device=device), torch.arange(0, res[1], delta[1],device=device)), dim=-1) % 1
+    angles = 2 * torch.pi * torch.rand(res[0] + 1, res[1] + 1,device=device)
+    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 1.414 * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1])
+
+def rand_perlin_2d_octaves(device, shape, res, octaves=1, persistence=0.5):
+    noise = torch.zeros(shape,device=device)
+    frequency = 1
+    amplitude = 1
+    for _ in range(octaves):
+        noise += amplitude * rand_perlin_2d(device, shape, (frequency*res[0], frequency*res[1]))
+        frequency *= 2
+        amplitude *= persistence
+    return noise
+
+def perlin_noise(noise, device):
+    b, c, w, h = noise.shape()
+    perlin = lambda : rand_perlin_2d_octaves(device,(w,h),(4,4),1)
+    noise_perlin_r = torch.rand(noise.shape, device=device) + perlin()
+    noise_perlin_g = torch.rand(noise.shape, device=device) + perlin()
+    noise_perlin_b = torch.rand(noise.shape, device=device) + perlin()
+    noise_perlin = torch.cat(
+        (noise_perlin_r,
+         noise_perlin_g,
+         noise_perlin_b),
+        2)
+    return noise_perlin
+
+