| import numpy as np |
| import torch as th |
|
|
| from .gaussian_diffusion import GaussianDiffusion |
|
|
|
|
| def space_timesteps(num_timesteps, section_counts): |
| """ |
| Create a list of timesteps to use from an original diffusion process, |
| given the number of timesteps we want to take from equally-sized portions |
| of the original process. |
| |
| For example, if there's 300 timesteps and the section counts are [10,15,20] |
| then the first 100 timesteps are strided to be 10 timesteps, the second 100 |
| are strided to be 15 timesteps, and the final 100 are strided to be 20. |
| |
| If the stride is a string starting with "ddim", then the fixed striding |
| from the DDIM paper is used, and only one section is allowed. |
| |
| :param num_timesteps: the number of diffusion steps in the original |
| process to divide up. |
| :param section_counts: either a list of numbers, or a string containing |
| comma-separated numbers, indicating the step count |
| per section. As a special case, use "ddimN" where N |
| is a number of steps to use the striding from the |
| DDIM paper. |
| :return: a set of diffusion steps from the original process to use. |
| """ |
| if isinstance(section_counts, str): |
| if section_counts.startswith("ddim"): |
| desired_count = int(section_counts[len("ddim") :]) |
| for i in range(1, num_timesteps): |
| if len(range(0, num_timesteps, i)) == desired_count: |
| return set(range(0, num_timesteps, i)) |
| raise ValueError( |
| f"cannot create exactly {num_timesteps} steps with an integer stride" |
| ) |
| section_counts = [int(x) for x in section_counts.split(",")] |
| size_per = num_timesteps // len(section_counts) |
| extra = num_timesteps % len(section_counts) |
| start_idx = 0 |
| all_steps = [] |
| for i, section_count in enumerate(section_counts): |
| size = size_per + (1 if i < extra else 0) |
| if size < section_count: |
| raise ValueError( |
| f"cannot divide section of {size} steps into {section_count}" |
| ) |
| if section_count <= 1: |
| frac_stride = 1 |
| else: |
| frac_stride = (size - 1) / (section_count - 1) |
| cur_idx = 0.0 |
| taken_steps = [] |
| for _ in range(section_count): |
| taken_steps.append(start_idx + round(cur_idx)) |
| cur_idx += frac_stride |
| all_steps += taken_steps |
| start_idx += size |
| return set(all_steps) |
|
|
|
|
| class SpacedDiffusion(GaussianDiffusion): |
| """ |
| A diffusion process which can skip steps in a base diffusion process. |
| |
| :param use_timesteps: a collection (sequence or set) of timesteps from the |
| original diffusion process to retain. |
| :param kwargs: the kwargs to create the base diffusion process. |
| """ |
|
|
| def __init__(self, use_timesteps, **kwargs): |
| self.use_timesteps = set(use_timesteps) |
| self.timestep_map = [] |
| self.original_num_steps = len(kwargs["betas"]) |
|
|
| base_diffusion = GaussianDiffusion(**kwargs) |
| last_alpha_cumprod = 1.0 |
| new_betas = [] |
| for i, alpha_cumprod in enumerate(base_diffusion.alphas_cumprod): |
| if i in self.use_timesteps: |
| new_betas.append(1 - alpha_cumprod / last_alpha_cumprod) |
| last_alpha_cumprod = alpha_cumprod |
| self.timestep_map.append(i) |
| kwargs["betas"] = np.array(new_betas) |
| super().__init__(**kwargs) |
|
|
| def p_mean_variance( |
| self, model, *args, **kwargs |
| ): |
| return super().p_mean_variance(self._wrap_model(model), *args, **kwargs) |
|
|
| def training_losses( |
| self, model, *args, **kwargs |
| ): |
| return super().training_losses(self._wrap_model(model), *args, **kwargs) |
|
|
| def condition_mean(self, cond_fn, *args, **kwargs): |
| return super().condition_mean(self._wrap_model(cond_fn), *args, **kwargs) |
|
|
| def condition_score(self, cond_fn, *args, **kwargs): |
| return super().condition_score(self._wrap_model(cond_fn), *args, **kwargs) |
|
|
| def _wrap_model(self, model): |
| if isinstance(model, _WrappedModel): |
| return model |
| return _WrappedModel( |
| model, self.timestep_map, self.rescale_timesteps, self.original_num_steps |
| ) |
|
|
| def _scale_timesteps(self, t): |
| |
| return t |
|
|
|
|
| class _WrappedModel: |
| def __init__(self, model, timestep_map, rescale_timesteps, original_num_steps): |
| self.model = model |
| self.timestep_map = timestep_map |
| self.rescale_timesteps = rescale_timesteps |
| self.original_num_steps = original_num_steps |
|
|
| def __call__(self, x, ts, **kwargs): |
| map_tensor = th.tensor(self.timestep_map, device=ts.device, dtype=ts.dtype) |
| new_ts = map_tensor[ts] |
| if self.rescale_timesteps: |
| new_ts = new_ts.float() * (1000.0 / self.original_num_steps) |
| return self.model(x, new_ts, **kwargs) |
|
|
