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fixed perlin noise generation for mps (macos) - fix for cpu fallback

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Jakub Kolčář 2022-10-07 14:22:38 +02:00 committed by Lincoln Stein
parent 9c9cb71544
commit 70bb7f4a61
2 changed files with 9 additions and 5 deletions
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4
ldm/dream/generator/base.py
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@ -124,8 +124,8 @@ class Generator():
raise NotImplementedError("get_noise() must be implemented in a descendent class")
def get_perlin_noise(self,width,height):
return torch.stack([rand_perlin_2d((height, width), (8, 8)).to(self.model.device) for _ in range(self.latent_channels)], dim=0)
fixdevice = 'cpu' if (self.model.device.type == 'mps') else self.model.device
return torch.stack([rand_perlin_2d((height, width), (8, 8), device = self.model.device).to(fixdevice) for _ in range(self.latent_channels)], dim=0).to(self.model.device)
def new_seed(self):
self.seed = random.randrange(0, np.iinfo(np.uint32).max)

10
ldm/util.py
View File

@ -214,15 +214,19 @@ def parallel_data_prefetch(
else:
return gather_res
def rand_perlin_2d(shape, res, fade = lambda t: 6*t**5 - 15*t**4 + 10*t**3):
def rand_perlin_2d(shape, res, device, 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]), indexing='ij'), dim = -1) % 1
angles = 2*math.pi*torch.rand(res[0]+1, res[1]+1)
grid = torch.stack(torch.meshgrid(torch.arange(0, res[0], delta[0]), torch.arange(0, res[1], delta[1]), indexing='ij'), dim = -1).to(device) % 1
rand_val = torch.rand(res[0]+1, res[1]+1)
angles = 2*math.pi*rand_val
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])