forked from mrq/DL-Art-School
113 lines
6.6 KiB
Python
113 lines
6.6 KiB
Python
import torch
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from models.diffusion.gaussian_diffusion import GaussianDiffusion, get_named_beta_schedule
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from models.diffusion.resample import create_named_schedule_sampler, LossAwareSampler
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from models.diffusion.respace import space_timesteps, SpacedDiffusion
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from trainer.inject import Injector
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from utils.util import opt_get
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# Injects a gaussian diffusion loss as described by OpenAIs "Improved Denoising Diffusion Probabilistic Models" paper.
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# Largely uses OpenAI's own code to do so (all code from models.diffusion.*)
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class GaussianDiffusionInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.generator = opt['generator']
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self.output_variational_bounds_key = opt['out_key_vb_loss']
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self.output_x_start_key = opt['out_key_x_start']
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opt['diffusion_args']['betas'] = get_named_beta_schedule(**opt['beta_schedule'])
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opt['diffusion_args']['use_timesteps'] = space_timesteps(opt['beta_schedule']['num_diffusion_timesteps'],
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[opt['beta_schedule']['num_diffusion_timesteps']])
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self.diffusion = SpacedDiffusion(**opt['diffusion_args'])
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self.schedule_sampler = create_named_schedule_sampler(opt['sampler_type'], self.diffusion)
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self.model_input_keys = opt_get(opt, ['model_input_keys'], [])
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def forward(self, state):
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gen = self.env['generators'][self.opt['generator']]
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hq = state[self.input]
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model_inputs = {k: state[v] for k, v in self.model_input_keys.items()}
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t, weights = self.schedule_sampler.sample(hq.shape[0], hq.device)
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diffusion_outputs = self.diffusion.training_losses(gen, hq, t, model_kwargs=model_inputs)
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if isinstance(self.schedule_sampler, LossAwareSampler):
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self.schedule_sampler.update_with_local_losses(t, diffusion_outputs['losses'])
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return {self.output: diffusion_outputs['mse'],
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self.output_variational_bounds_key: diffusion_outputs['vb'],
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self.output_x_start_key: diffusion_outputs['x_start_predicted']}
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class AutoregressiveGaussianDiffusionInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.generator = opt['generator']
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self.output_variational_bounds_key = opt['out_key_vb_loss']
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self.output_x_start_key = opt['out_key_x_start']
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opt['diffusion_args']['betas'] = get_named_beta_schedule(**opt['beta_schedule'])
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opt['diffusion_args']['use_timesteps'] = space_timesteps(opt['beta_schedule']['num_diffusion_timesteps'],
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[opt['beta_schedule']['num_diffusion_timesteps']])
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self.diffusion = SpacedDiffusion(**opt['diffusion_args'])
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self.schedule_sampler = create_named_schedule_sampler(opt['sampler_type'], self.diffusion)
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self.model_input_keys = opt_get(opt, ['model_input_keys'], [])
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self.model_output_keys = opt['model_output_keys']
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self.model_eps_pred_key = opt['prediction_key']
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def forward(self, state):
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gen = self.env['generators'][self.opt['generator']]
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hq = state[self.input]
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model_inputs = {k: state[v] for k, v in self.model_input_keys.items()}
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t, weights = self.schedule_sampler.sample(hq.shape[0], hq.device)
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diffusion_outputs = self.diffusion.autoregressive_training_losses(gen, hq, t, self.model_output_keys,
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self.model_eps_pred_key,
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model_kwargs=model_inputs)
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if isinstance(self.schedule_sampler, LossAwareSampler):
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self.schedule_sampler.update_with_local_losses(t, diffusion_outputs['losses'])
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outputs = {k: diffusion_outputs[k] for k in self.model_output_keys}
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outputs.update({self.output: diffusion_outputs['mse'],
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self.output_variational_bounds_key: diffusion_outputs['vb'],
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self.output_x_start_key: diffusion_outputs['x_start_predicted']})
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return outputs
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# Performs inference using a network trained to predict a reverse diffusion process, which nets a image.
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class GaussianDiffusionInferenceInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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use_ddim = opt_get(opt, ['use_ddim'], False)
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self.generator = opt['generator']
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self.output_batch_size = opt['output_batch_size']
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self.output_scale_factor = opt['output_scale_factor']
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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]
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opt['diffusion_args']['betas'] = get_named_beta_schedule(**opt['beta_schedule'])
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if use_ddim:
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spacing = "ddim" + str(opt['respaced_timestep_spacing'])
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else:
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spacing = [opt_get(opt, ['respaced_timestep_spacing'], opt['beta_schedule']['num_diffusion_timesteps'])]
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opt['diffusion_args']['use_timesteps'] = space_timesteps(opt['beta_schedule']['num_diffusion_timesteps'], spacing)
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self.diffusion = SpacedDiffusion(**opt['diffusion_args'])
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self.sampling_fn = self.diffusion.ddim_sample_loop if use_ddim else self.diffusion.p_sample_loop
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self.model_input_keys = opt_get(opt, ['model_input_keys'], [])
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self.use_ema_model = opt_get(opt, ['use_ema'], False)
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self.noise_style = opt_get(opt, ['noise_type'], 'random') # 'zero', 'fixed' or 'random'
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def forward(self, state):
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if self.use_ema_model:
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gen = self.env['emas'][self.opt['generator']]
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else:
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gen = self.env['generators'][self.opt['generator']]
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model_inputs = {k: state[v][:self.output_batch_size] for k, v in self.model_input_keys.items()}
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gen.eval()
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with torch.no_grad():
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output_shape = (self.output_batch_size, 3, model_inputs['low_res'].shape[-2] * self.output_scale_factor,
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model_inputs['low_res'].shape[-1] * self.output_scale_factor)
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noise = None
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if self.noise_style == 'zero':
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noise = torch.zeros(output_shape, device=model_inputs['low_res'].device)
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elif self.noise_style == 'fixed':
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if not hasattr(self, 'fixed_noise') or self.fixed_noise.shape != output_shape:
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self.fixed_noise = torch.randn(output_shape, device=model_inputs['low_res'].device)
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noise = self.fixed_noise
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gen = self.sampling_fn(gen, output_shape, noise=noise, model_kwargs=model_inputs, progress=True)
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if self.undo_n1_to_1:
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gen = (gen + 1) / 2
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return {self.output: gen}
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