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fix perlin noise and txt2img2img

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This commit is contained in:
Lincoln Stein 2023-01-16 22:50:13 -05:00
parent 7e8f364d8d
commit ce00c9856f
4 changed files with 12 additions and 7 deletions
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3
ldm/invoke/generator/base.py
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@ -237,7 +237,8 @@ class Generator:
def get_perlin_noise(self,width,height):
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)
noise = 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)
return noise
def new_seed(self):
self.seed = random.randrange(0, np.iinfo(np.uint32).max)

10
ldm/invoke/generator/txt2img2img.py
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@ -90,9 +90,9 @@ class Txt2Img2Img(Generator):
def get_noise_like(self, like: torch.Tensor):
device = like.device
if device.type == 'mps':
x = torch.randn_like(like, device='cpu').to(device)
x = torch.randn_like(like, device='cpu', dtype=self.torch_dtype()).to(device)
else:
x = torch.randn_like(like, device=device)
x = torch.randn_like(like, device=device, dtype=self.torch_dtype())
if self.perlin > 0.0:
shape = like.shape
x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(shape[3], shape[2])
@ -117,10 +117,12 @@ class Txt2Img2Img(Generator):
self.latent_channels,
scaled_height // self.downsampling_factor,
scaled_width // self.downsampling_factor],
device='cpu').to(device)
dtype=self.torch_dtype(),
device='cpu').to(device)
else:
return torch.randn([1,
self.latent_channels,
scaled_height // self.downsampling_factor,
scaled_width // self.downsampling_factor],
device=device)
dtype=self.torch_dtype(),
device=device)

2
ldm/invoke/model_manager.py
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@ -349,7 +349,7 @@ class ModelManager(object):
if self.precision == 'float16':
print(' | Using faster float16 precision')
model.to(torch.float16)
model = model.to(torch.float16)
else:
print(' | Using more accurate float32 precision')

4
ldm/util.py
View File

@ -8,6 +8,7 @@ from threading import Thread
from urllib import request
from tqdm import tqdm
from pathlib import Path
from ldm.invoke.devices import torch_dtype
import numpy as np
import torch
@ -235,7 +236,8 @@ def rand_perlin_2d(shape, res, device, fade = lambda t: 6*t**5 - 15*t**4 + 10*t*
n01 = dot(tile_grads([0, -1],[1, None]), [0, -1]).to(device)
n11 = dot(tile_grads([1, None], [1, None]), [-1,-1]).to(device)
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]).to(device)
noise = math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]).to(device)
return noise.to(dtype=torch_dtype(device))
def ask_user(question: str, answers: list):
from itertools import chain, repeat