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| 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.""" | |
| if generator is not None: | |
| # If generator is not none, we must use another seed to | |
| # avoid global torch.rand to get same noise again. | |
| # Note: removing this will make DDPM sampler broken. | |
| manual_seed((seed + 100000) % 65536) | |
| else: | |
| 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 generator. | |
| 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 generator. | |
| 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 = tuple(map(int, 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], int(self.seed_resize_from_h) // 8, int(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 | |