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from typing import Callable, Optional
import numpy as np
from .motion_utils import NoiseType
class ContextType:
UNIFORM_WINDOW = "uniform window"
class ContextOptions:
CONTEXT_TYPE = None
def __init__(self):
pass
class UniformContextOptions(ContextOptions):
CONTEXT_TYPE = ContextType.UNIFORM_WINDOW
def __init__(self, context_length: int, context_stride: int, context_overlap: int, context_schedule: int, closed_loop: bool):
self.context_length = context_length
self.context_stride = context_stride
self.context_overlap = context_overlap
self.context_schedule = context_schedule
self.closed_loop = closed_loop
self.sync_context_to_pe = False
self.noise_type = NoiseType.DEFAULT
def set_sync_context_to_pe(self, sync_context_to_pe: bool):
self.sync_context_to_pe = sync_context_to_pe
def set_noise_type(self, noise_type: str):
self.noise_type = noise_type
class ContextSchedules:
UNIFORM = "uniform"
UNIFORM_CONSTANT = "uniform_constant"
UNIFORM_V2 = "uniform v2"
CONTEXT_SCHEDULE_LIST = [UNIFORM] # only include somewhat functional contexts here
# Returns fraction that has denominator that is a power of 2
def ordered_halving(val, print_final=False):
# get binary value, padded with 0s for 64 bits
bin_str = f"{val:064b}"
# flip binary value, padding included
bin_flip = bin_str[::-1]
# convert binary to int
as_int = int(bin_flip, 2)
# divide by 1 << 64, equivalent to 2**64, or 18446744073709551616,
# or b10000000000000000000000000000000000000000000000000000000000000000 (1 with 64 zero's)
final = as_int / (1 << 64)
if print_final:
print(f"$$$$ final: {final}")
return final
# Generator that returns lists of latent indeces to diffuse on
def uniform(
step: int = ...,
num_steps: Optional[int] = None,
num_frames: int = ...,
context_size: Optional[int] = None,
context_stride: int = 3,
context_overlap: int = 4,
closed_loop: bool = True,
print_final: bool = False,
):
if num_frames <= context_size:
yield list(range(num_frames))
return
context_stride = min(context_stride, int(np.ceil(np.log2(num_frames / context_size))) + 1)
for context_step in 1 << np.arange(context_stride):
pad = int(round(num_frames * ordered_halving(step, print_final)))
for j in range(
int(ordered_halving(step) * context_step) + pad,
num_frames + pad + (0 if closed_loop else -context_overlap),
(context_size * context_step - context_overlap),
):
yield [e % num_frames for e in range(j, j + context_size * context_step, context_step)]
def uniform_v2(
step: int = ...,
num_steps: Optional[int] = None,
num_frames: int = ...,
context_size: Optional[int] = None,
context_stride: int = 3,
context_overlap: int = 4,
closed_loop: bool = True,
print_final: bool = False,
):
if num_frames <= context_size:
yield list(range(num_frames))
return
context_stride = min(context_stride, int(np.ceil(np.log2(num_frames / context_size))) + 1)
pad = int(round(num_frames * ordered_halving(step, print_final)))
for context_step in 1 << np.arange(context_stride):
j_initial = int(ordered_halving(step) * context_step) + pad
for j in range(
j_initial,
num_frames + pad - context_overlap,
(context_size * context_step - context_overlap),
):
if context_size * context_step > num_frames:
# On the final context_step,
# ensure no frame appears in the window twice
yield [e % num_frames for e in range(j, j + num_frames, context_step)]
continue
j = j % num_frames
if j > (j + context_size * context_step) % num_frames and not closed_loop:
yield [e for e in range(j, num_frames, context_step)]
j_stop = (j + context_size * context_step) % num_frames
# When ((num_frames % (context_size - context_overlap)+context_overlap) % context_size != 0,
# This can cause 'superflous' runs where all frames in
# a context window have already been processed during
# the first context window of this stride and step.
# While the following commented if should prevent this,
# I believe leaving it in is more correct as it maintains
# the total conditional passes per frame over a large total steps
# if j_stop > context_overlap:
yield [e for e in range(0, j_stop, context_step)]
continue
yield [e % num_frames for e in range(j, j + context_size * context_step, context_step)]
def uniform_constant(
step: int = ...,
num_steps: Optional[int] = None,
num_frames: int = ...,
context_size: Optional[int] = None,
context_stride: int = 3,
context_overlap: int = 4,
closed_loop: bool = True,
print_final: bool = False,
):
if num_frames <= context_size:
yield list(range(num_frames))
return
context_stride = min(context_stride, int(np.ceil(np.log2(num_frames / context_size))) + 1)
# want to avoid loops that connect end to beginning
for context_step in 1 << np.arange(context_stride):
pad = int(round(num_frames * ordered_halving(step, print_final)))
for j in range(
int(ordered_halving(step) * context_step) + pad,
num_frames + pad + (0 if closed_loop else -context_overlap),
(context_size * context_step - context_overlap),
):
skip_this_window = False
prev_val = -1
to_yield = []
for e in range(j, j + context_size * context_step, context_step):
e = e % num_frames
# if not a closed loop and loops back on itself, should be skipped
if not closed_loop and e < prev_val:
skip_this_window = True
break
to_yield.append(e)
prev_val = e
if skip_this_window:
continue
# yield if not skipped
yield to_yield
# This needs to stay here below the context functions
UNIFORM_CONTEXT_MAPPING = {
ContextSchedules.UNIFORM: uniform,
ContextSchedules.UNIFORM_CONSTANT: uniform_constant,
ContextSchedules.UNIFORM_V2: uniform_v2,
}
# TODO: expand to support other context window types (future feature)
def get_context_scheduler(name: str) -> Callable:
context_func = UNIFORM_CONTEXT_MAPPING.get(name, None)
if not context_func:
raise ValueError(f"Unknown context_overlap policy {name}")
return context_func
def get_total_steps(
scheduler,
timesteps: list[int],
num_steps: Optional[int] = None,
num_frames: int = ...,
context_size: Optional[int] = None,
context_stride: int = 3,
context_overlap: int = 4,
closed_loop: bool = True,
):
return sum(
len(
list(
scheduler(
i,
num_steps,
num_frames,
context_size,
context_stride,
context_overlap,
)
)
)
for i in range(len(timesteps))
)
def get_total_steps_fixed(
scheduler,
timesteps: list[int],
num_steps: Optional[int] = None,
num_frames: int = ...,
context_size: Optional[int] = None,
context_stride: int = 3,
context_overlap: int = 4,
closed_loop: bool = True,
):
total_loops = 0
for i, t in enumerate(timesteps):
for context in scheduler(i, num_steps, num_frames, context_size, context_stride, context_overlap, closed_loop=closed_loop):
total_loops += 1
return total_loops
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