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