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| """Mask Mod for Image2Video""" | |
| from math import floor | |
| import torch | |
| from torch import Tensor | |
| from functools import lru_cache | |
| from typing import Optional, List | |
| import torch | |
| from torch.nn.attention.flex_attention import ( | |
| create_block_mask, | |
| ) | |
| def create_block_mask_cached(score_mod, B, H, M, N, device="cuda", _compile=False): | |
| block_mask = create_block_mask(score_mod, B, H, M, N, device=device, _compile=_compile) | |
| return block_mask | |
| def generate_temporal_head_mask_mod(context_length: int = 226, prompt_length: int = 226, num_frames: int = 13, token_per_frame: int = 1350, mul: int = 2): | |
| def round_to_multiple(idx): | |
| return floor(idx / 128) * 128 | |
| real_length = num_frames * token_per_frame + prompt_length | |
| def temporal_mask_mod(b, h, q_idx, kv_idx): | |
| real_mask = (kv_idx < real_length) & (q_idx < real_length) | |
| fake_mask = (kv_idx >= real_length) & (q_idx >= real_length) | |
| two_frame = round_to_multiple(mul * token_per_frame) | |
| temporal_head_mask = (torch.abs(q_idx - kv_idx) < two_frame) | |
| text_column_mask = (num_frames * token_per_frame <= kv_idx) & (kv_idx < real_length) | |
| text_row_mask = (num_frames * token_per_frame <= q_idx) & (q_idx < real_length) | |
| video_mask = temporal_head_mask | text_column_mask | text_row_mask | |
| real_mask = real_mask & video_mask | |
| return real_mask | fake_mask | |
| return temporal_mask_mod | |