781 lines
30 KiB
Python
781 lines
30 KiB
Python
#taken from: https://github.com/lllyasviel/ControlNet
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#and modified
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#and then taken from comfy/cldm/cldm.py and modified again
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from abc import ABC, abstractmethod
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import math
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from typing import Iterable, Union
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import torch
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import torch as th
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import torch.nn as nn
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from torch import Tensor
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from einops import rearrange, repeat
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from comfy.ldm.modules.diffusionmodules.util import (
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zero_module,
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timestep_embedding,
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)
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from comfy.cldm.cldm import ControlNet as ControlNetCLDM
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from comfy.ldm.modules.attention import SpatialTransformer
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from comfy.ldm.modules.diffusionmodules.openaimodel import TimestepEmbedSequential, ResBlock, Downsample
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from comfy.ldm.util import exists
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from comfy.ldm.modules.attention import default, optimized_attention
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from comfy.ldm.modules.attention import FeedForward, SpatialTransformer
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from comfy.controlnet import broadcast_image_to
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from comfy.utils import repeat_to_batch_size
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import comfy.ops
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from .utils import TimestepKeyframeGroup, disable_weight_init_clean_groupnorm, prepare_mask_batch
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class SparseControlNet(ControlNetCLDM):
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def __init__(self, *args,**kwargs):
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super().__init__(*args, **kwargs)
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hint_channels = kwargs.get("hint_channels")
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operations: disable_weight_init_clean_groupnorm = kwargs.get("operations", disable_weight_init_clean_groupnorm)
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device = kwargs.get("device", None)
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self.use_simplified_conditioning_embedding = kwargs.get("use_simplified_conditioning_embedding", False)
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if self.use_simplified_conditioning_embedding:
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self.input_hint_block = TimestepEmbedSequential(
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zero_module(operations.conv_nd(self.dims, hint_channels, self.model_channels, 3, padding=1, dtype=self.dtype, device=device)),
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#zero_module(operations.conv_nd(self.dims, hint_channels, self.model_channels, 3, padding=1, dtype=self.dtype, device=device)),
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)
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self.motion_holder: MotionWrapperHolder = None
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def set_actual_length(self, actual_length: int, full_length: int):
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if self.motion_holder is not None:
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self.motion_holder.motion_wrapper.set_video_length(video_length=actual_length, full_length=full_length)
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def forward(self, x: Tensor, hint: Tensor, timesteps, context, y=None, **kwargs):
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t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
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emb = self.time_embed(t_emb)
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# SparseCtrl sets noisy input to zeros
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x = torch.zeros_like(x)
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guided_hint = self.input_hint_block(hint, emb, context)
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outs = []
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hs = []
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if self.num_classes is not None:
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assert y.shape[0] == x.shape[0]
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emb = emb + self.label_emb(y)
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h = x
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for module, zero_conv in zip(self.input_blocks, self.zero_convs):
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if guided_hint is not None:
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h = module(h, emb, context)
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h += guided_hint
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guided_hint = None
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else:
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h = module(h, emb, context)
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outs.append(zero_conv(h, emb, context))
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h = self.middle_block(h, emb, context)
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outs.append(self.middle_block_out(h, emb, context))
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return outs
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class SparseSettings:
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def __init__(self, sparse_method: 'SparseMethod'):
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self.sparse_method = sparse_method
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@classmethod
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def default(cls):
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return cls(sparse_method=SparseSpreadMethod())
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class SparseMethod(ABC):
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SPREAD = "spread"
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INDEX = "index"
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def __init__(self, method: str):
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self.method = method
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@abstractmethod
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def get_indeces(self, hint_length: int, full_length: int) -> list[int]:
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pass
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class SparseSpreadMethod(SparseMethod):
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def __init__(self, from_start=True):
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super().__init__(self.SPREAD)
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self.from_start = from_start
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def get_indeces(self, hint_length: int, full_length: int) -> list[int]:
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# handle special case of 1 hint image
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if hint_length == 1:
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return [0] if self.from_start else [full_length-1]
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# handle special case of equal or more hint images than full length
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if hint_length >= full_length:
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return list(range(min(hint_length, full_length)))
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if hint_length == 2:
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return [0, full_length-1]
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# TODO: other cases/modes
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class SparseIndexMethod(SparseMethod):
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def __init__(self, idxs: list[int]):
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super().__init__(self.INDEX)
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self.idxs = idxs
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def get_indeces(self, hint_length: int, full_length: int) -> list[int]:
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new_idxs = []
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for idx in self.idxs:
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if idx < 0:
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new_idxs.append(full_length+idx)
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else:
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new_idxs.append(idx)
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return new_idxs
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#########################################
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# motion-related portion of controlnet
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class BlockType:
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UP = "up"
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DOWN = "down"
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MID = "mid"
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def get_down_block_max(mm_state_dict: dict[str, Tensor]) -> int:
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return get_block_max(mm_state_dict, "down_blocks")
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def get_up_block_max(mm_state_dict: dict[str, Tensor]) -> int:
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return get_block_max(mm_state_dict, "up_blocks")
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def get_block_max(mm_state_dict: dict[str, Tensor], block_name: str) -> int:
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# keep track of biggest down_block count in module
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biggest_block = -1
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for key in mm_state_dict.keys():
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if block_name in key:
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try:
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block_int = key.split(".")[1]
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block_num = int(block_int)
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if block_num > biggest_block:
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biggest_block = block_num
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except ValueError:
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pass
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return biggest_block
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def has_mid_block(mm_state_dict: dict[str, Tensor]):
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# check if keys contain mid_block
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for key in mm_state_dict.keys():
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if key.startswith("mid_block."):
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return True
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return False
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def get_position_encoding_max_len(mm_state_dict: dict[str, Tensor], mm_name: str=None) -> int:
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# use pos_encoder.pe entries to determine max length - [1, {max_length}, {320|640|1280}]
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for key in mm_state_dict.keys():
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if key.endswith("pos_encoder.pe"):
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return mm_state_dict[key].size(1) # get middle dim
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raise ValueError(f"No pos_encoder.pe found in SparseCtrl state_dict - {mm_name} is not a valid SparseCtrl model!")
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class MotionWrapperHolder:
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def __init__(self, motion_wrapper: 'SparseCtrlMotionWrapper'):
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self.motion_wrapper = motion_wrapper
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class SparseCtrlMotionWrapper(nn.Module):
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def __init__(self, mm_state_dict: dict[str, Tensor]):
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super().__init__()
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self.down_blocks: Iterable[MotionModule] = None
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self.up_blocks: Iterable[MotionModule] = None
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self.mid_block: MotionModule = None
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self.encoding_max_len = get_position_encoding_max_len(mm_state_dict, "")
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layer_channels = (320, 640, 1280, 1280)
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if get_down_block_max(mm_state_dict) > -1:
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self.down_blocks = nn.ModuleList([])
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for c in layer_channels:
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self.down_blocks.append(MotionModule(c, temporal_position_encoding_max_len=self.encoding_max_len, block_type=BlockType.DOWN))
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if get_up_block_max(mm_state_dict) > -1:
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self.up_blocks = nn.ModuleList([])
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for c in reversed(layer_channels):
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self.up_blocks.append(MotionModule(c, temporal_position_encoding_max_len=self.encoding_max_len, block_type=BlockType.UP))
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if has_mid_block(mm_state_dict):
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self.mid_block = MotionModule(1280, temporal_position_encoding_max_len=self.encoding_max_len, block_type=BlockType.MID)
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def inject(self, unet: SparseControlNet):
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# inject input (down) blocks
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self._inject(unet.input_blocks, self.down_blocks)
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# inject mid block, if present
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if self.mid_block is not None:
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self._inject([unet.middle_block], [self.mid_block])
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unet.motion_holder = MotionWrapperHolder(self)
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def _inject(self, unet_blocks: nn.ModuleList, mm_blocks: nn.ModuleList):
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# Rules for injection:
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# For each component list in a unet block:
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# if SpatialTransformer exists in list, place next block after last occurrence
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# elif ResBlock exists in list, place next block after first occurrence
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# else don't place block
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injection_count = 0
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unet_idx = 0
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# details about blocks passed in
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per_block = len(mm_blocks[0].motion_modules)
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injection_goal = len(mm_blocks) * per_block
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# only stop injecting when modules exhausted
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while injection_count < injection_goal:
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# figure out which VanillaTemporalModule from mm to inject
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mm_blk_idx, mm_vtm_idx = injection_count // per_block, injection_count % per_block
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# figure out layout of unet block components
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st_idx = -1 # SpatialTransformer index
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res_idx = -1 # first ResBlock index
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# first, figure out indeces of relevant blocks
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for idx, component in enumerate(unet_blocks[unet_idx]):
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if type(component) == SpatialTransformer:
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st_idx = idx
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elif type(component).__name__ == "ResBlock" and res_idx < 0:
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res_idx = idx
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# if SpatialTransformer exists, inject right after
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if st_idx >= 0:
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unet_blocks[unet_idx].insert(st_idx+1, mm_blocks[mm_blk_idx].motion_modules[mm_vtm_idx])
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injection_count += 1
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# otherwise, if only ResBlock exists, inject right after
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elif res_idx >= 0:
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unet_blocks[unet_idx].insert(res_idx+1, mm_blocks[mm_blk_idx].motion_modules[mm_vtm_idx])
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injection_count += 1
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# increment unet_idx
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unet_idx += 1
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def eject(self, unet: SparseControlNet):
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# remove from input blocks (downblocks)
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self._eject(unet.input_blocks)
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# remove from middle block (encapsulate in list to make compatible)
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self._eject([unet.middle_block])
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del unet.motion_holder
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unet.motion_holder = None
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def _eject(self, unet_blocks: nn.ModuleList):
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# eject all VanillaTemporalModule objects from all blocks
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for block in unet_blocks:
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idx_to_pop = []
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for idx, component in enumerate(block):
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if type(component) == VanillaTemporalModule:
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idx_to_pop.append(idx)
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# pop in backwards order, as to not disturb what the indeces refer to
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for idx in sorted(idx_to_pop, reverse=True):
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block.pop(idx)
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def set_video_length(self, video_length: int, full_length: int):
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self.AD_video_length = video_length
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if self.down_blocks is not None:
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for block in self.down_blocks:
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block.set_video_length(video_length, full_length)
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if self.up_blocks is not None:
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for block in self.up_blocks:
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block.set_video_length(video_length, full_length)
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if self.mid_block is not None:
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self.mid_block.set_video_length(video_length, full_length)
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def set_scale_multiplier(self, multiplier: Union[float, None]):
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for block in self.down_blocks:
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block.set_scale_multiplier(multiplier)
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for block in self.up_blocks:
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block.set_scale_multiplier(multiplier)
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if self.mid_block is not None:
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self.mid_block.set_scale_multiplier(multiplier)
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def reset_temp_vars(self):
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for block in self.down_blocks:
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block.reset_temp_vars()
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for block in self.up_blocks:
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block.reset_temp_vars()
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if self.mid_block is not None:
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self.mid_block.reset_temp_vars()
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def reset_scale_multiplier(self):
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self.set_scale_multiplier(None)
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def reset(self):
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self.reset_scale_multiplier()
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self.reset_temp_vars()
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class MotionModule(nn.Module):
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def __init__(self, in_channels, temporal_position_encoding_max_len=24, block_type: str=BlockType.DOWN):
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super().__init__()
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if block_type == BlockType.MID:
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# mid blocks contain only a single VanillaTemporalModule
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self.motion_modules: Iterable[VanillaTemporalModule] = nn.ModuleList([get_motion_module(in_channels, temporal_position_encoding_max_len)])
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else:
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# down blocks contain two VanillaTemporalModules
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self.motion_modules: Iterable[VanillaTemporalModule] = nn.ModuleList(
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[
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get_motion_module(in_channels, temporal_position_encoding_max_len),
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get_motion_module(in_channels, temporal_position_encoding_max_len)
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]
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)
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# up blocks contain one additional VanillaTemporalModule
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if block_type == BlockType.UP:
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self.motion_modules.append(get_motion_module(in_channels, temporal_position_encoding_max_len))
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def set_video_length(self, video_length: int, full_length: int):
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for motion_module in self.motion_modules:
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motion_module.set_video_length(video_length, full_length)
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def set_scale_multiplier(self, multiplier: Union[float, None]):
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for motion_module in self.motion_modules:
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motion_module.set_scale_multiplier(multiplier)
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def set_masks(self, masks: Tensor, min_val: float, max_val: float):
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for motion_module in self.motion_modules:
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motion_module.set_masks(masks, min_val, max_val)
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def set_sub_idxs(self, sub_idxs: list[int]):
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for motion_module in self.motion_modules:
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motion_module.set_sub_idxs(sub_idxs)
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def reset_temp_vars(self):
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for motion_module in self.motion_modules:
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motion_module.reset_temp_vars()
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def get_motion_module(in_channels, temporal_position_encoding_max_len):
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# unlike normal AD, there is only one attention block expected in SparseCtrl models
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return VanillaTemporalModule(in_channels=in_channels, attention_block_types=("Temporal_Self",), temporal_position_encoding_max_len=temporal_position_encoding_max_len)
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class VanillaTemporalModule(nn.Module):
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def __init__(
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self,
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in_channels,
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num_attention_heads=8,
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num_transformer_block=1,
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attention_block_types=("Temporal_Self", "Temporal_Self"),
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cross_frame_attention_mode=None,
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temporal_position_encoding=True,
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temporal_position_encoding_max_len=24,
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temporal_attention_dim_div=1,
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zero_initialize=True,
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):
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super().__init__()
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self.temporal_transformer = TemporalTransformer3DModel(
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in_channels=in_channels,
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num_attention_heads=num_attention_heads,
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attention_head_dim=in_channels
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// num_attention_heads
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// temporal_attention_dim_div,
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num_layers=num_transformer_block,
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attention_block_types=attention_block_types,
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cross_frame_attention_mode=cross_frame_attention_mode,
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temporal_position_encoding=temporal_position_encoding,
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temporal_position_encoding_max_len=temporal_position_encoding_max_len,
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)
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if zero_initialize:
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self.temporal_transformer.proj_out = zero_module(
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self.temporal_transformer.proj_out
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)
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def set_video_length(self, video_length: int, full_length: int):
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self.temporal_transformer.set_video_length(video_length, full_length)
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def set_scale_multiplier(self, multiplier: Union[float, None]):
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self.temporal_transformer.set_scale_multiplier(multiplier)
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def set_masks(self, masks: Tensor, min_val: float, max_val: float):
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self.temporal_transformer.set_masks(masks, min_val, max_val)
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def set_sub_idxs(self, sub_idxs: list[int]):
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self.temporal_transformer.set_sub_idxs(sub_idxs)
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def reset_temp_vars(self):
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self.temporal_transformer.reset_temp_vars()
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def forward(self, input_tensor, encoder_hidden_states=None, attention_mask=None):
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return self.temporal_transformer(input_tensor, encoder_hidden_states, attention_mask)
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class TemporalTransformer3DModel(nn.Module):
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def __init__(
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self,
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in_channels,
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num_attention_heads,
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attention_head_dim,
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num_layers,
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attention_block_types=(
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"Temporal_Self",
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"Temporal_Self",
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),
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dropout=0.0,
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norm_num_groups=32,
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cross_attention_dim=768,
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activation_fn="geglu",
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attention_bias=False,
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upcast_attention=False,
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cross_frame_attention_mode=None,
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temporal_position_encoding=False,
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temporal_position_encoding_max_len=24,
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):
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super().__init__()
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self.video_length = 16
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self.full_length = 16
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self.scale_min = 1.0
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self.scale_max = 1.0
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self.raw_scale_mask: Union[Tensor, None] = None
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self.temp_scale_mask: Union[Tensor, None] = None
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self.sub_idxs: Union[list[int], None] = None
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self.prev_hidden_states_batch = 0
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inner_dim = num_attention_heads * attention_head_dim
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self.norm = disable_weight_init_clean_groupnorm.GroupNorm(
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num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True
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)
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self.proj_in = nn.Linear(in_channels, inner_dim)
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self.transformer_blocks: Iterable[TemporalTransformerBlock] = nn.ModuleList(
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[
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TemporalTransformerBlock(
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dim=inner_dim,
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num_attention_heads=num_attention_heads,
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attention_head_dim=attention_head_dim,
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attention_block_types=attention_block_types,
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dropout=dropout,
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norm_num_groups=norm_num_groups,
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cross_attention_dim=cross_attention_dim,
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activation_fn=activation_fn,
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attention_bias=attention_bias,
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upcast_attention=upcast_attention,
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cross_frame_attention_mode=cross_frame_attention_mode,
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temporal_position_encoding=temporal_position_encoding,
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temporal_position_encoding_max_len=temporal_position_encoding_max_len,
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)
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for d in range(num_layers)
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]
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)
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self.proj_out = nn.Linear(inner_dim, in_channels)
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def set_video_length(self, video_length: int, full_length: int):
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self.video_length = video_length
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self.full_length = full_length
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def set_scale_multiplier(self, multiplier: Union[float, None]):
|
|
for block in self.transformer_blocks:
|
|
block.set_scale_multiplier(multiplier)
|
|
|
|
def set_masks(self, masks: Tensor, min_val: float, max_val: float):
|
|
self.scale_min = min_val
|
|
self.scale_max = max_val
|
|
self.raw_scale_mask = masks
|
|
|
|
def set_sub_idxs(self, sub_idxs: list[int]):
|
|
self.sub_idxs = sub_idxs
|
|
for block in self.transformer_blocks:
|
|
block.set_sub_idxs(sub_idxs)
|
|
|
|
def reset_temp_vars(self):
|
|
del self.temp_scale_mask
|
|
self.temp_scale_mask = None
|
|
self.prev_hidden_states_batch = 0
|
|
|
|
def get_scale_mask(self, hidden_states: Tensor) -> Union[Tensor, None]:
|
|
# if no raw mask, return None
|
|
if self.raw_scale_mask is None:
|
|
return None
|
|
shape = hidden_states.shape
|
|
batch, channel, height, width = shape
|
|
# if temp mask already calculated, return it
|
|
if self.temp_scale_mask != None:
|
|
# check if hidden_states batch matches
|
|
if batch == self.prev_hidden_states_batch:
|
|
if self.sub_idxs is not None:
|
|
return self.temp_scale_mask[:, self.sub_idxs, :]
|
|
return self.temp_scale_mask
|
|
# if does not match, reset cached temp_scale_mask and recalculate it
|
|
del self.temp_scale_mask
|
|
self.temp_scale_mask = None
|
|
# otherwise, calculate temp mask
|
|
self.prev_hidden_states_batch = batch
|
|
mask = prepare_mask_batch(self.raw_scale_mask, shape=(self.full_length, 1, height, width))
|
|
mask = repeat_to_batch_size(mask, self.full_length)
|
|
# if mask not the same amount length as full length, make it match
|
|
if self.full_length != mask.shape[0]:
|
|
mask = broadcast_image_to(mask, self.full_length, 1)
|
|
# reshape mask to attention K shape (h*w, latent_count, 1)
|
|
batch, channel, height, width = mask.shape
|
|
# first, perform same operations as on hidden_states,
|
|
# turning (b, c, h, w) -> (b, h*w, c)
|
|
mask = mask.permute(0, 2, 3, 1).reshape(batch, height*width, channel)
|
|
# then, make it the same shape as attention's k, (h*w, b, c)
|
|
mask = mask.permute(1, 0, 2)
|
|
# make masks match the expected length of h*w
|
|
batched_number = shape[0] // self.video_length
|
|
if batched_number > 1:
|
|
mask = torch.cat([mask] * batched_number, dim=0)
|
|
# cache mask and set to proper device
|
|
self.temp_scale_mask = mask
|
|
# move temp_scale_mask to proper dtype + device
|
|
self.temp_scale_mask = self.temp_scale_mask.to(dtype=hidden_states.dtype, device=hidden_states.device)
|
|
# return subset of masks, if needed
|
|
if self.sub_idxs is not None:
|
|
return self.temp_scale_mask[:, self.sub_idxs, :]
|
|
return self.temp_scale_mask
|
|
|
|
def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None):
|
|
batch, channel, height, width = hidden_states.shape
|
|
residual = hidden_states
|
|
scale_mask = self.get_scale_mask(hidden_states)
|
|
# add some casts for fp8 purposes - does not affect speed otherwise
|
|
hidden_states = self.norm(hidden_states).to(hidden_states.dtype)
|
|
inner_dim = hidden_states.shape[1]
|
|
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(
|
|
batch, height * width, inner_dim
|
|
)
|
|
hidden_states = self.proj_in(hidden_states).to(hidden_states.dtype)
|
|
|
|
# Transformer Blocks
|
|
for block in self.transformer_blocks:
|
|
hidden_states = block(
|
|
hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
attention_mask=attention_mask,
|
|
video_length=self.video_length,
|
|
scale_mask=scale_mask
|
|
)
|
|
|
|
# output
|
|
hidden_states = self.proj_out(hidden_states)
|
|
hidden_states = (
|
|
hidden_states.reshape(batch, height, width, inner_dim)
|
|
.permute(0, 3, 1, 2)
|
|
.contiguous()
|
|
)
|
|
|
|
output = hidden_states + residual
|
|
|
|
return output
|
|
|
|
|
|
class TemporalTransformerBlock(nn.Module):
|
|
def __init__(
|
|
self,
|
|
dim,
|
|
num_attention_heads,
|
|
attention_head_dim,
|
|
attention_block_types=(
|
|
"Temporal_Self",
|
|
"Temporal_Self",
|
|
),
|
|
dropout=0.0,
|
|
norm_num_groups=32,
|
|
cross_attention_dim=768,
|
|
activation_fn="geglu",
|
|
attention_bias=False,
|
|
upcast_attention=False,
|
|
cross_frame_attention_mode=None,
|
|
temporal_position_encoding=False,
|
|
temporal_position_encoding_max_len=24,
|
|
):
|
|
super().__init__()
|
|
|
|
attention_blocks = []
|
|
norms = []
|
|
|
|
for block_name in attention_block_types:
|
|
attention_blocks.append(
|
|
VersatileAttention(
|
|
attention_mode=block_name.split("_")[0],
|
|
context_dim=cross_attention_dim # called context_dim for ComfyUI impl
|
|
if block_name.endswith("_Cross")
|
|
else None,
|
|
query_dim=dim,
|
|
heads=num_attention_heads,
|
|
dim_head=attention_head_dim,
|
|
dropout=dropout,
|
|
#bias=attention_bias, # remove for Comfy CrossAttention
|
|
#upcast_attention=upcast_attention, # remove for Comfy CrossAttention
|
|
cross_frame_attention_mode=cross_frame_attention_mode,
|
|
temporal_position_encoding=temporal_position_encoding,
|
|
temporal_position_encoding_max_len=temporal_position_encoding_max_len,
|
|
)
|
|
)
|
|
norms.append(nn.LayerNorm(dim))
|
|
|
|
self.attention_blocks: Iterable[VersatileAttention] = nn.ModuleList(attention_blocks)
|
|
self.norms = nn.ModuleList(norms)
|
|
|
|
self.ff = FeedForward(dim, dropout=dropout, glu=(activation_fn == "geglu"))
|
|
self.ff_norm = nn.LayerNorm(dim)
|
|
|
|
def set_scale_multiplier(self, multiplier: Union[float, None]):
|
|
for block in self.attention_blocks:
|
|
block.set_scale_multiplier(multiplier)
|
|
|
|
def set_sub_idxs(self, sub_idxs: list[int]):
|
|
for block in self.attention_blocks:
|
|
block.set_sub_idxs(sub_idxs)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
encoder_hidden_states=None,
|
|
attention_mask=None,
|
|
video_length=None,
|
|
scale_mask=None
|
|
):
|
|
for attention_block, norm in zip(self.attention_blocks, self.norms):
|
|
norm_hidden_states = norm(hidden_states).to(hidden_states.dtype)
|
|
hidden_states = (
|
|
attention_block(
|
|
norm_hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states
|
|
if attention_block.is_cross_attention
|
|
else None,
|
|
attention_mask=attention_mask,
|
|
video_length=video_length,
|
|
scale_mask=scale_mask
|
|
)
|
|
+ hidden_states
|
|
)
|
|
|
|
hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states
|
|
|
|
output = hidden_states
|
|
return output
|
|
|
|
|
|
class PositionalEncoding(nn.Module):
|
|
def __init__(self, d_model, dropout=0.0, max_len=24):
|
|
super().__init__()
|
|
self.dropout = nn.Dropout(p=dropout)
|
|
position = torch.arange(max_len).unsqueeze(1)
|
|
div_term = torch.exp(
|
|
torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)
|
|
)
|
|
pe = torch.zeros(1, max_len, d_model)
|
|
pe[0, :, 0::2] = torch.sin(position * div_term)
|
|
pe[0, :, 1::2] = torch.cos(position * div_term)
|
|
self.register_buffer("pe", pe)
|
|
self.sub_idxs = None
|
|
|
|
def set_sub_idxs(self, sub_idxs: list[int]):
|
|
self.sub_idxs = sub_idxs
|
|
|
|
def forward(self, x):
|
|
#if self.sub_idxs is not None:
|
|
# x = x + self.pe[:, self.sub_idxs]
|
|
#else:
|
|
x = x + self.pe[:, : x.size(1)]
|
|
return self.dropout(x)
|
|
|
|
|
|
class CrossAttentionMM(nn.Module):
|
|
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None,
|
|
operations=comfy.ops.disable_weight_init):
|
|
super().__init__()
|
|
inner_dim = dim_head * heads
|
|
context_dim = default(context_dim, query_dim)
|
|
|
|
self.heads = heads
|
|
self.dim_head = dim_head
|
|
self.scale = None
|
|
|
|
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
|
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
|
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
|
|
|
self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
|
|
|
|
def forward(self, x, context=None, value=None, mask=None, scale_mask=None):
|
|
q = self.to_q(x)
|
|
context = default(context, x)
|
|
k: Tensor = self.to_k(context)
|
|
if value is not None:
|
|
v = self.to_v(value)
|
|
del value
|
|
else:
|
|
v = self.to_v(context)
|
|
|
|
# apply custom scale by multiplying k by scale factor
|
|
if self.scale is not None:
|
|
k *= self.scale
|
|
|
|
# apply scale mask, if present
|
|
if scale_mask is not None:
|
|
k *= scale_mask
|
|
|
|
out = optimized_attention(q, k, v, self.heads, mask)
|
|
return self.to_out(out)
|
|
|
|
|
|
class VersatileAttention(CrossAttentionMM):
|
|
def __init__(
|
|
self,
|
|
attention_mode=None,
|
|
cross_frame_attention_mode=None,
|
|
temporal_position_encoding=False,
|
|
temporal_position_encoding_max_len=24,
|
|
*args,
|
|
**kwargs,
|
|
):
|
|
super().__init__(*args, **kwargs)
|
|
assert attention_mode == "Temporal"
|
|
|
|
self.attention_mode = attention_mode
|
|
self.is_cross_attention = kwargs["context_dim"] is not None
|
|
|
|
self.pos_encoder = (
|
|
PositionalEncoding(
|
|
kwargs["query_dim"],
|
|
dropout=0.0,
|
|
max_len=temporal_position_encoding_max_len,
|
|
)
|
|
if (temporal_position_encoding and attention_mode == "Temporal")
|
|
else None
|
|
)
|
|
|
|
def extra_repr(self):
|
|
return f"(Module Info) Attention_Mode: {self.attention_mode}, Is_Cross_Attention: {self.is_cross_attention}"
|
|
|
|
def set_scale_multiplier(self, multiplier: Union[float, None]):
|
|
if multiplier is None or math.isclose(multiplier, 1.0):
|
|
self.scale = None
|
|
else:
|
|
self.scale = multiplier
|
|
|
|
def set_sub_idxs(self, sub_idxs: list[int]):
|
|
if self.pos_encoder != None:
|
|
self.pos_encoder.set_sub_idxs(sub_idxs)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: Tensor,
|
|
encoder_hidden_states=None,
|
|
attention_mask=None,
|
|
video_length=None,
|
|
scale_mask=None,
|
|
):
|
|
if self.attention_mode != "Temporal":
|
|
raise NotImplementedError
|
|
|
|
d = hidden_states.shape[1]
|
|
hidden_states = rearrange(
|
|
hidden_states, "(b f) d c -> (b d) f c", f=video_length
|
|
)
|
|
|
|
if self.pos_encoder is not None:
|
|
hidden_states = self.pos_encoder(hidden_states).to(hidden_states.dtype)
|
|
|
|
encoder_hidden_states = (
|
|
repeat(encoder_hidden_states, "b n c -> (b d) n c", d=d)
|
|
if encoder_hidden_states is not None
|
|
else encoder_hidden_states
|
|
)
|
|
|
|
hidden_states = super().forward(
|
|
hidden_states,
|
|
encoder_hidden_states,
|
|
value=None,
|
|
mask=attention_mask,
|
|
scale_mask=scale_mask,
|
|
)
|
|
|
|
hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
|
|
|
|
return hidden_states
|