bf811f80c1
- Report variational loss separately - Report model prediction from injector - Log these things - Use respacing like guided diffusion
129 lines
5.1 KiB
Python
129 lines
5.1 KiB
Python
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)
|
|
|
|
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.rescale_timesteps, 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, 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)
|