context.py

import numpy as np
from typing import Callable, Optional, List


def ordered_halving(val):
    bin_str = f"{val:064b}"
    bin_flip = bin_str[::-1]
    as_int = int(bin_flip, 2)

    return as_int / (1 << 64)

def does_window_roll_over(window: list[int], num_frames: int) -> tuple[bool, int]:
    prev_val = -1
    for i, val in enumerate(window):
        val = val % num_frames
        if val < prev_val:
            return True, i
        prev_val = val
    return False, -1

def shift_window_to_start(window: list[int], num_frames: int):
    start_val = window[0]
    for i in range(len(window)):
        # 1) subtract each element by start_val to move vals relative to the start of all frames
        # 2) add num_frames and take modulus to get adjusted vals
        window[i] = ((window[i] - start_val) + num_frames) % num_frames

def shift_window_to_end(window: list[int], num_frames: int):
    # 1) shift window to start
    shift_window_to_start(window, num_frames)
    end_val = window[-1]
    end_delta = num_frames - end_val - 1
    for i in range(len(window)):
        # 2) add end_delta to each val to slide windows to end
        window[i] = window[i] + end_delta

def get_missing_indexes(windows: list[list[int]], num_frames: int) -> list[int]:
    all_indexes = list(range(num_frames))
    for w in windows:
        for val in w:
            try:
                all_indexes.remove(val)
            except ValueError:
                pass
    return all_indexes

def uniform_looped(
    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,
):
    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)))
        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)]

#from AnimateDiff-Evolved by Kosinkadink (https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved)
def uniform_standard(
    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,
):
    windows = []
    if num_frames <= context_size:
        windows.append(list(range(num_frames)))
        return windows

    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)))
        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),
        ):
            windows.append([e % num_frames for e in range(j, j + context_size * context_step, context_step)])

    # now that windows are created, shift any windows that loop, and delete duplicate windows
    delete_idxs = []
    win_i = 0
    while win_i < len(windows):
        # if window is rolls over itself, need to shift it
        is_roll, roll_idx = does_window_roll_over(windows[win_i], num_frames)
        if is_roll:
            roll_val = windows[win_i][roll_idx]  # roll_val might not be 0 for windows of higher strides
            shift_window_to_end(windows[win_i], num_frames=num_frames)
            # check if next window (cyclical) is missing roll_val
            if roll_val not in windows[(win_i+1) % len(windows)]:
                # need to insert new window here - just insert window starting at roll_val
                windows.insert(win_i+1, list(range(roll_val, roll_val + context_size)))
        # delete window if it's not unique
        for pre_i in range(0, win_i):
            if windows[win_i] == windows[pre_i]:
                delete_idxs.append(win_i)
                break
        win_i += 1

    # reverse delete_idxs so that they will be deleted in an order that doesn't break idx correlation
    delete_idxs.reverse()
    for i in delete_idxs:
        windows.pop(i)
    return windows

def static_standard(
    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,
):
    windows = []
    if num_frames <= context_size:
        windows.append(list(range(num_frames)))
        return windows
    # always return the same set of windows
    delta = context_size - context_overlap
    for start_idx in range(0, num_frames, delta):
        # if past the end of frames, move start_idx back to allow same context_length
        ending = start_idx + context_size
        if ending >= num_frames:
            final_delta = ending - num_frames
            final_start_idx = start_idx - final_delta
            windows.append(list(range(final_start_idx, final_start_idx + context_size)))
            break
        windows.append(list(range(start_idx, start_idx + context_size)))
    return windows

def get_context_scheduler(name: str) -> Callable:
    if name == "uniform_looped":
        return uniform_looped
    elif name == "uniform_standard":
        return uniform_standard
    elif name == "static_standard":
        return static_standard
    else:
        raise ValueError(f"Unknown context_overlap policy {name}")


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))
    )