Spaces:
Build error
Build error
File size: 2,876 Bytes
dee645c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
import random
import torch
import math
def rotate_axis(x, add_angle=0, axis=1): # TODO Replace with a rotation matrix # But this is more fun
axes = list(range(3))
axes.remove(axis)
ax1, ax2 = axes
angle = torch.atan2(x[..., ax1], x[..., ax2])
if isinstance(add_angle, torch.Tensor):
while add_angle.ndim < angle.ndim:
add_angle = add_angle.unsqueeze(-1)
angle = angle + add_angle
dist = x.norm(dim=-1)
t = []
_, t = zip(*sorted([
(axis, x[..., axis]),
(ax1, torch.sin(angle) * dist),
(ax2, torch.cos(angle) * dist),
]))
return torch.stack(t, dim=-1)
noise_level = 0.5
# stolen from https://gist.github.com/ac1b097753f217c5c11bc2ff396e0a57
# ported from https://github.com/pvigier/perlin-numpy/blob/master/perlin2d.py
def rand_perlin_2d(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(shape, res, octaves=1, persistence=0.5):
noise = torch.zeros(shape)
frequency = 1
amplitude = 1
for _ in range(octaves):
noise += amplitude * rand_perlin_2d(shape, (frequency * res[0], frequency * res[1]))
frequency *= 2
amplitude *= persistence
noise *= random.random() - noise_level # haha
noise += random.random() - noise_level # haha x2
return noise
def load_clip(model_name="ViT-B/16", device="cuda:0" if torch.cuda.is_available() else "cpu"):
import clip
model, preprocess = clip.load(model_name, device=device, jit=False)
if len(preprocess.transforms) > 4:
preprocess.transforms = preprocess.transforms[-1:]
return model, preprocess
|