mirror of
https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-15 07:03:06 +03:00
171 lines
6.3 KiB
Python
171 lines
6.3 KiB
Python
import torch
|
|
|
|
from modules import devices, rng_philox, shared
|
|
|
|
|
|
def randn(seed, shape, generator=None):
|
|
"""Generate a tensor with random numbers from a normal distribution using seed.
|
|
|
|
Uses the seed parameter to set the global torch seed; to generate more with that seed, use randn_like/randn_without_seed."""
|
|
|
|
manual_seed(seed)
|
|
|
|
if shared.opts.randn_source == "NV":
|
|
return torch.asarray((generator or nv_rng).randn(shape), device=devices.device)
|
|
|
|
if shared.opts.randn_source == "CPU" or devices.device.type == 'mps':
|
|
return torch.randn(shape, device=devices.cpu, generator=generator).to(devices.device)
|
|
|
|
return torch.randn(shape, device=devices.device, generator=generator)
|
|
|
|
|
|
def randn_local(seed, shape):
|
|
"""Generate a tensor with random numbers from a normal distribution using seed.
|
|
|
|
Does not change the global random number generator. You can only generate the seed's first tensor using this function."""
|
|
|
|
if shared.opts.randn_source == "NV":
|
|
rng = rng_philox.Generator(seed)
|
|
return torch.asarray(rng.randn(shape), device=devices.device)
|
|
|
|
local_device = devices.cpu if shared.opts.randn_source == "CPU" or devices.device.type == 'mps' else devices.device
|
|
local_generator = torch.Generator(local_device).manual_seed(int(seed))
|
|
return torch.randn(shape, device=local_device, generator=local_generator).to(devices.device)
|
|
|
|
|
|
def randn_like(x):
|
|
"""Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
|
|
|
|
Use either randn() or manual_seed() to initialize the generator."""
|
|
|
|
if shared.opts.randn_source == "NV":
|
|
return torch.asarray(nv_rng.randn(x.shape), device=x.device, dtype=x.dtype)
|
|
|
|
if shared.opts.randn_source == "CPU" or x.device.type == 'mps':
|
|
return torch.randn_like(x, device=devices.cpu).to(x.device)
|
|
|
|
return torch.randn_like(x)
|
|
|
|
|
|
def randn_without_seed(shape, generator=None):
|
|
"""Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
|
|
|
|
Use either randn() or manual_seed() to initialize the generator."""
|
|
|
|
if shared.opts.randn_source == "NV":
|
|
return torch.asarray((generator or nv_rng).randn(shape), device=devices.device)
|
|
|
|
if shared.opts.randn_source == "CPU" or devices.device.type == 'mps':
|
|
return torch.randn(shape, device=devices.cpu, generator=generator).to(devices.device)
|
|
|
|
return torch.randn(shape, device=devices.device, generator=generator)
|
|
|
|
|
|
def manual_seed(seed):
|
|
"""Set up a global random number generator using the specified seed."""
|
|
|
|
if shared.opts.randn_source == "NV":
|
|
global nv_rng
|
|
nv_rng = rng_philox.Generator(seed)
|
|
return
|
|
|
|
torch.manual_seed(seed)
|
|
|
|
|
|
def create_generator(seed):
|
|
if shared.opts.randn_source == "NV":
|
|
return rng_philox.Generator(seed)
|
|
|
|
device = devices.cpu if shared.opts.randn_source == "CPU" or devices.device.type == 'mps' else devices.device
|
|
generator = torch.Generator(device).manual_seed(int(seed))
|
|
return generator
|
|
|
|
|
|
# from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
|
|
def slerp(val, low, high):
|
|
low_norm = low/torch.norm(low, dim=1, keepdim=True)
|
|
high_norm = high/torch.norm(high, dim=1, keepdim=True)
|
|
dot = (low_norm*high_norm).sum(1)
|
|
|
|
if dot.mean() > 0.9995:
|
|
return low * val + high * (1 - val)
|
|
|
|
omega = torch.acos(dot)
|
|
so = torch.sin(omega)
|
|
res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
|
|
return res
|
|
|
|
|
|
class ImageRNG:
|
|
def __init__(self, shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0):
|
|
self.shape = shape
|
|
self.seeds = seeds
|
|
self.subseeds = subseeds
|
|
self.subseed_strength = subseed_strength
|
|
self.seed_resize_from_h = seed_resize_from_h
|
|
self.seed_resize_from_w = seed_resize_from_w
|
|
|
|
self.generators = [create_generator(seed) for seed in seeds]
|
|
|
|
self.is_first = True
|
|
|
|
def first(self):
|
|
noise_shape = self.shape if self.seed_resize_from_h <= 0 or self.seed_resize_from_w <= 0 else (self.shape[0], self.seed_resize_from_h // 8, self.seed_resize_from_w // 8)
|
|
|
|
xs = []
|
|
|
|
for i, (seed, generator) in enumerate(zip(self.seeds, self.generators)):
|
|
subnoise = None
|
|
if self.subseeds is not None and self.subseed_strength != 0:
|
|
subseed = 0 if i >= len(self.subseeds) else self.subseeds[i]
|
|
subnoise = randn(subseed, noise_shape)
|
|
|
|
if noise_shape != self.shape:
|
|
noise = randn(seed, noise_shape)
|
|
else:
|
|
noise = randn(seed, self.shape, generator=generator)
|
|
|
|
if subnoise is not None:
|
|
noise = slerp(self.subseed_strength, noise, subnoise)
|
|
|
|
if noise_shape != self.shape:
|
|
x = randn(seed, self.shape, generator=generator)
|
|
dx = (self.shape[2] - noise_shape[2]) // 2
|
|
dy = (self.shape[1] - noise_shape[1]) // 2
|
|
w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx
|
|
h = noise_shape[1] if dy >= 0 else noise_shape[1] + 2 * dy
|
|
tx = 0 if dx < 0 else dx
|
|
ty = 0 if dy < 0 else dy
|
|
dx = max(-dx, 0)
|
|
dy = max(-dy, 0)
|
|
|
|
x[:, ty:ty + h, tx:tx + w] = noise[:, dy:dy + h, dx:dx + w]
|
|
noise = x
|
|
|
|
xs.append(noise)
|
|
|
|
eta_noise_seed_delta = shared.opts.eta_noise_seed_delta or 0
|
|
if eta_noise_seed_delta:
|
|
self.generators = [create_generator(seed + eta_noise_seed_delta) for seed in self.seeds]
|
|
|
|
return torch.stack(xs).to(shared.device)
|
|
|
|
def next(self):
|
|
if self.is_first:
|
|
self.is_first = False
|
|
return self.first()
|
|
|
|
xs = []
|
|
for generator in self.generators:
|
|
x = randn_without_seed(self.shape, generator=generator)
|
|
xs.append(x)
|
|
|
|
return torch.stack(xs).to(shared.device)
|
|
|
|
|
|
devices.randn = randn
|
|
devices.randn_local = randn_local
|
|
devices.randn_like = randn_like
|
|
devices.randn_without_seed = randn_without_seed
|
|
devices.manual_seed = manual_seed
|