67 lines
3.9 KiB
Python
67 lines
3.9 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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# 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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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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opt['diffusion_args']['use_timesteps'] = space_timesteps(opt['beta_schedule']['num_diffusion_timesteps'],
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[opt_get(opt, ['respaced_timestep_spacing'], opt['beta_schedule']['num_diffusion_timesteps'])])
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self.diffusion = SpacedDiffusion(**opt['diffusion_args'])
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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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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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gen = self.diffusion.p_sample_loop(gen, output_shape, model_kwargs=model_inputs)
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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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