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.""" from modules.shared import opts if 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