mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-06-07 21:20:49 +00:00
171 lines
6.4 KiB
Python
171 lines
6.4 KiB
Python
import torch
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from modules import devices, rng_philox, shared
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def randn(seed, shape, generator=None):
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"""Generate a tensor with random numbers from a normal distribution using seed.
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Uses the seed parameter to set the global torch seed; to generate more with that seed, use randn_like/randn_without_seed."""
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manual_seed(seed)
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if shared.opts.randn_source == "NV":
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return torch.asarray((generator or nv_rng).randn(shape), device=devices.device)
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if shared.opts.randn_source == "CPU" or devices.device.type == 'mps':
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return torch.randn(shape, device=devices.cpu, generator=generator).to(devices.device)
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return torch.randn(shape, device=devices.device, generator=generator)
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def randn_local(seed, shape):
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"""Generate a tensor with random numbers from a normal distribution using seed.
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Does not change the global random number generator. You can only generate the seed's first tensor using this function."""
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if shared.opts.randn_source == "NV":
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rng = rng_philox.Generator(seed)
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return torch.asarray(rng.randn(shape), device=devices.device)
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local_device = devices.cpu if shared.opts.randn_source == "CPU" or devices.device.type == 'mps' else devices.device
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local_generator = torch.Generator(local_device).manual_seed(int(seed))
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return torch.randn(shape, device=local_device, generator=local_generator).to(devices.device)
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def randn_like(x):
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"""Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
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Use either randn() or manual_seed() to initialize the generator."""
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if shared.opts.randn_source == "NV":
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return torch.asarray(nv_rng.randn(x.shape), device=x.device, dtype=x.dtype)
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if shared.opts.randn_source == "CPU" or x.device.type == 'mps':
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return torch.randn_like(x, device=devices.cpu).to(x.device)
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return torch.randn_like(x)
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def randn_without_seed(shape, generator=None):
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"""Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
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Use either randn() or manual_seed() to initialize the generator."""
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if shared.opts.randn_source == "NV":
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return torch.asarray((generator or nv_rng).randn(shape), device=devices.device)
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if shared.opts.randn_source == "CPU" or devices.device.type == 'mps':
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return torch.randn(shape, device=devices.cpu, generator=generator).to(devices.device)
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return torch.randn(shape, device=devices.device, generator=generator)
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def manual_seed(seed):
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"""Set up a global random number generator using the specified seed."""
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if shared.opts.randn_source == "NV":
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global nv_rng
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nv_rng = rng_philox.Generator(seed)
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return
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torch.manual_seed(seed)
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def create_generator(seed):
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if shared.opts.randn_source == "NV":
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return rng_philox.Generator(seed)
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device = devices.cpu if shared.opts.randn_source == "CPU" or devices.device.type == 'mps' else devices.device
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generator = torch.Generator(device).manual_seed(int(seed))
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return generator
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# from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
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def slerp(val, low, high):
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low_norm = low/torch.norm(low, dim=1, keepdim=True)
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high_norm = high/torch.norm(high, dim=1, keepdim=True)
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dot = (low_norm*high_norm).sum(1)
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if dot.mean() > 0.9995:
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return low * val + high * (1 - val)
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omega = torch.acos(dot)
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so = torch.sin(omega)
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res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
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return res
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class ImageRNG:
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def __init__(self, shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0):
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self.shape = tuple(map(int, shape))
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self.seeds = seeds
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self.subseeds = subseeds
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self.subseed_strength = subseed_strength
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self.seed_resize_from_h = seed_resize_from_h
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self.seed_resize_from_w = seed_resize_from_w
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self.generators = [create_generator(seed) for seed in seeds]
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self.is_first = True
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def first(self):
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noise_shape = self.shape if self.seed_resize_from_h <= 0 or self.seed_resize_from_w <= 0 else (self.shape[0], int(self.seed_resize_from_h) // 8, int(self.seed_resize_from_w // 8))
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xs = []
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for i, (seed, generator) in enumerate(zip(self.seeds, self.generators)):
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subnoise = None
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if self.subseeds is not None and self.subseed_strength != 0:
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subseed = 0 if i >= len(self.subseeds) else self.subseeds[i]
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subnoise = randn(subseed, noise_shape)
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if noise_shape != self.shape:
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noise = randn(seed, noise_shape)
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else:
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noise = randn(seed, self.shape, generator=generator)
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if subnoise is not None:
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noise = slerp(self.subseed_strength, noise, subnoise)
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if noise_shape != self.shape:
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x = randn(seed, self.shape, generator=generator)
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dx = (self.shape[2] - noise_shape[2]) // 2
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dy = (self.shape[1] - noise_shape[1]) // 2
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w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx
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h = noise_shape[1] if dy >= 0 else noise_shape[1] + 2 * dy
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tx = 0 if dx < 0 else dx
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ty = 0 if dy < 0 else dy
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dx = max(-dx, 0)
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dy = max(-dy, 0)
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x[:, ty:ty + h, tx:tx + w] = noise[:, dy:dy + h, dx:dx + w]
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noise = x
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xs.append(noise)
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eta_noise_seed_delta = shared.opts.eta_noise_seed_delta or 0
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if eta_noise_seed_delta:
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self.generators = [create_generator(seed + eta_noise_seed_delta) for seed in self.seeds]
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return torch.stack(xs).to(shared.device)
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def next(self):
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if self.is_first:
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self.is_first = False
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return self.first()
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xs = []
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for generator in self.generators:
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x = randn_without_seed(self.shape, generator=generator)
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xs.append(x)
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return torch.stack(xs).to(shared.device)
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devices.randn = randn
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devices.randn_local = randn_local
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devices.randn_like = randn_like
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devices.randn_without_seed = randn_without_seed
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devices.manual_seed = manual_seed
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