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| # Modified from OpenAI's diffusion repos | |
| # GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py | |
| # ADM: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion | |
| # IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py | |
| import torch | |
| 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) # pylint: disable=missing-kwoa | |
| 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 | |
| ): # pylint: disable=signature-differs | |
| return super().p_mean_variance(self._wrap_model(model), *args, **kwargs) | |
| # @torch.compile | |
| def training_losses( | |
| self, model, *args, **kwargs | |
| ): # pylint: disable=signature-differs | |
| 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.original_num_steps | |
| ) | |
| def _scale_timesteps(self, t): | |
| # Scaling is done by the wrapped model. | |
| return t | |
| class _WrappedModel: | |
| def __init__(self, model, timestep_map, 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) | |