import functools import torch from torch.cuda.amp import autocast from models.diffusion.gaussian_diffusion import get_named_beta_schedule from models.diffusion.resample import create_named_schedule_sampler, LossAwareSampler, DeterministicSampler, LossSecondMomentResampler from models.diffusion.respace import space_timesteps, SpacedDiffusion from trainer.inject import Injector from utils.util import opt_get def masked_channel_balancer(inp, proportion=1): with torch.no_grad(): only_channels = inp.mean(dim=(0,2)) # Only currently works for audio tensors. Could be retrofitted for 2d (or 3D!) modalities. dist = only_channels / only_channels.sum() dist_mult = only_channels.shape[0] * proportion dist = (dist * dist_mult).clamp(0, 1) mask = torch.bernoulli(dist) return inp * mask.view(1,inp.shape[1],1) def channel_restriction(inp, low, high): assert low > 0 and low < inp.shape[1] and high <= inp.shape[1] m = torch.zeros_like(inp) m[:,low:high] = 1 return inp * m # Injects a gaussian diffusion loss as described by OpenAIs "Improved Denoising Diffusion Probabilistic Models" paper. # Largely uses OpenAI's own code to do so (all code from models.diffusion.*) class GaussianDiffusionInjector(Injector): def __init__(self, opt, env): super().__init__(opt, env) self.generator = opt['generator'] self.output_variational_bounds_key = opt['out_key_vb_loss'] self.output_x_start_key = opt['out_key_x_start'] opt['diffusion_args']['betas'] = get_named_beta_schedule(**opt['beta_schedule']) opt['diffusion_args']['use_timesteps'] = space_timesteps(opt['beta_schedule']['num_diffusion_timesteps'], [opt['beta_schedule']['num_diffusion_timesteps']]) self.diffusion = SpacedDiffusion(**opt['diffusion_args']) self.schedule_sampler = create_named_schedule_sampler(opt['sampler_type'], self.diffusion) self.model_input_keys = opt_get(opt, ['model_input_keys'], []) self.extra_model_output_keys = opt_get(opt, ['extra_model_output_keys'], []) self.deterministic_timesteps_every = opt_get(opt, ['deterministic_timesteps_every'], 0) self.deterministic_sampler = DeterministicSampler(self.diffusion, opt_get(opt, ['deterministic_sampler_expected_batch_size'], 2048), env) k = 0 if 'channel_balancer_proportion' in opt.keys(): self.channel_balancing_fn = functools.partial(masked_channel_balancer, proportion=opt['channel_balancer_proportion']) k += 1 if 'channel_restriction_low' in opt.keys(): self.channel_balancing_fn = functools.partial(channel_restriction, low=opt['channel_restriction_low'], high=opt['channel_restriction_high']) k += 1 if not hasattr(self, 'channel_balancing_fn'): self.channel_balancing_fn = None assert k <= 1, 'Only one channel filtering function can be applied.' self.num_timesteps = opt['beta_schedule']['num_diffusion_timesteps'] self.latest_mse_by_batch = torch.tensor([0]) self.latest_timesteps = torch.tensor([0]) def extra_metrics(self): uqt = self.latest_timesteps > self.num_timesteps * 3 / 4 uql = (self.latest_mse_by_batch * uqt).sum() / uqt.sum() if uqt.sum() != 0 else 0 muqt = (self.latest_timesteps > self.num_timesteps / 2) * (self.latest_timesteps < self.num_timesteps * 3 / 4) muql = (self.latest_mse_by_batch * muqt).sum() / muqt.sum() if muqt.sum() != 0 else 0 d = { 'upper_quantile_mse_loss': uql, 'mid_upper_quantile_mse_loss': muql, } if hasattr(self, 'schedule_sampler') and isinstance(self.schedule_sampler, LossSecondMomentResampler): d['sampler_warmed_up'] = torch.tensor(float(self.schedule_sampler._warmed_up())) return d def forward(self, state): gen = self.env['generators'][self.opt['generator']] hq = state[self.input] assert hq.max() < 1.5 or hq.min() > -1.5, "Attempting to train gaussian diffusion on un-normalized inputs. This won't work, silly!" with autocast(enabled=self.env['opt']['fp16']): if not gen.training or (self.deterministic_timesteps_every != 0 and self.env['step'] % self.deterministic_timesteps_every == 0): sampler = self.deterministic_sampler else: sampler = self.schedule_sampler self.deterministic_sampler.reset() # Keep this reset whenever it is not being used, so it is ready to use automatically. model_inputs = {k: state[v] if isinstance(v, str) else v for k, v in self.model_input_keys.items()} t, weights = sampler.sample(hq.shape[0], hq.device) diffusion_outputs = self.diffusion.training_losses(gen, hq, t, model_kwargs=model_inputs, channel_balancing_fn=self.channel_balancing_fn) if isinstance(sampler, LossAwareSampler): sampler.update_with_local_losses(t, diffusion_outputs['loss']) if len(self.extra_model_output_keys) > 0: assert(len(self.extra_model_output_keys) == len(diffusion_outputs['extra_outputs'])) out = {k: v for k, v in zip(self.extra_model_output_keys, diffusion_outputs['extra_outputs'])} else: out = {} out.update({self.output: diffusion_outputs['mse'], self.output_variational_bounds_key: diffusion_outputs['vb'], self.output_x_start_key: diffusion_outputs['x_start_predicted']}) self.latest_mse_by_batch = diffusion_outputs['mse_by_batch'].detach().clone() self.latest_timesteps = t.clone() return out def closest_multiple(inp, multiple): div = inp // multiple mod = inp % multiple if mod == 0: return inp else: return int((div+1)*multiple) # Performs inference using a network trained to predict a reverse diffusion process, which nets a image. class GaussianDiffusionInferenceInjector(Injector): def __init__(self, opt, env): super().__init__(opt, env) use_ddim = opt_get(opt, ['use_ddim'], False) self.generator = opt['generator'] self.output_batch_size = opt['output_batch_size'] self.output_scale_factor = opt['output_scale_factor'] self.undo_n1_to_1 = opt_get(opt, ['undo_n1_to_1'], False) # Explanation: when specified, will shift the output of this injector from [-1,1] to [0,1] opt['diffusion_args']['betas'] = get_named_beta_schedule(**opt['beta_schedule']) if use_ddim: spacing = "ddim" + str(opt['respaced_timestep_spacing']) else: spacing = [opt_get(opt, ['respaced_timestep_spacing'], opt['beta_schedule']['num_diffusion_timesteps'])] opt['diffusion_args']['use_timesteps'] = space_timesteps(opt['beta_schedule']['num_diffusion_timesteps'], spacing) self.diffusion = SpacedDiffusion(**opt['diffusion_args']) self.sampling_fn = self.diffusion.ddim_sample_loop if use_ddim else self.diffusion.p_sample_loop self.model_input_keys = opt_get(opt, ['model_input_keys'], []) self.use_ema_model = opt_get(opt, ['use_ema'], False) self.noise_style = opt_get(opt, ['noise_type'], 'random') # 'zero', 'fixed' or 'random' self.multiple_requirement = opt_get(opt, ['multiple_requirement'], 4096) def forward(self, state): if self.use_ema_model: gen = self.env['emas'][self.opt['generator']] else: gen = self.env['generators'][self.opt['generator']] model_inputs = {k: state[v][:self.output_batch_size] for k, v in self.model_input_keys.items()} gen.eval() with torch.no_grad(): if 'low_res' in model_inputs.keys(): output_shape = (self.output_batch_size, 3, model_inputs['low_res'].shape[-2] * self.output_scale_factor, model_inputs['low_res'].shape[-1] * self.output_scale_factor) dev = model_inputs['low_res'].device elif 'spectrogram' in model_inputs.keys(): output_shape = (self.output_batch_size, 1, closest_multiple(model_inputs['spectrogram'].shape[-1] * self.output_scale_factor, self.multiple_requirement)) dev = model_inputs['spectrogram'].device elif 'discrete_spectrogram' in model_inputs.keys(): output_shape = (self.output_batch_size, 1, closest_multiple(model_inputs['discrete_spectrogram'].shape[-1]*1024, self.multiple_requirement)) dev = model_inputs['discrete_spectrogram'].device else: raise NotImplementedError noise = None if self.noise_style == 'zero': noise = torch.zeros(output_shape, device=dev) elif self.noise_style == 'fixed': if not hasattr(self, 'fixed_noise') or self.fixed_noise.shape != output_shape: self.fixed_noise = torch.randn(output_shape, device=dev) noise = self.fixed_noise gen = self.sampling_fn(gen, output_shape, noise=noise, model_kwargs=model_inputs, progress=True, device=dev) if self.undo_n1_to_1: gen = (gen + 1) / 2 return {self.output: gen}