241 lines
12 KiB
Python
241 lines
12 KiB
Python
from einops import repeat, rearrange
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from typing import Callable, Optional, Union
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from .attention_processor import Attention
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# from t2v_enhanced.model.diffusers_conditional.controldiffusers.models.attention import Attention
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from diffusers.utils.import_utils import is_xformers_available
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from ....pl_module_params_controlnet import AttentionMaskParams
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import torch
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import torch.nn.functional as F
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if is_xformers_available():
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import xformers
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import xformers.ops
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else:
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xformers = None
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def set_use_memory_efficient_attention_xformers(
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model, num_frame_conditioning: int, num_frames: int, attention_mask_params: AttentionMaskParams, valid: bool = True, attention_op: Optional[Callable] = None
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) -> None:
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# Recursively walk through all the children.
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# Any children which exposes the set_use_memory_efficient_attention_xformers method
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# gets the message
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def fn_recursive_set_mem_eff(module: torch.nn.Module):
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if hasattr(module, "set_processor"):
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module.set_processor(XFormersAttnProcessor(attention_op=attention_op,
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num_frame_conditioning=num_frame_conditioning,
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num_frames=num_frames,
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attention_mask_params=attention_mask_params,)
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)
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for child in module.children():
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fn_recursive_set_mem_eff(child)
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for module in model.children():
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if isinstance(module, torch.nn.Module):
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fn_recursive_set_mem_eff(module)
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class XFormersAttnProcessor:
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def __init__(self,
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attention_mask_params: AttentionMaskParams,
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attention_op: Optional[Callable] = None,
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num_frame_conditioning: int = None,
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num_frames: int = None,
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use_image_embedding: bool = False,
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):
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self.attention_op = attention_op
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self.num_frame_conditioning = num_frame_conditioning
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self.num_frames = num_frames
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self.temp_attend_on_neighborhood_of_condition_frames = attention_mask_params.temp_attend_on_neighborhood_of_condition_frames
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self.spatial_attend_on_condition_frames = attention_mask_params.spatial_attend_on_condition_frames
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self.use_image_embedding = use_image_embedding
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def __call__(self, attn: Attention, hidden_states, hidden_state_height=None, hidden_state_width=None, encoder_hidden_states=None, attention_mask=None):
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batch_size, sequence_length, _ = (
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hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
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)
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key_img = None
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value_img = None
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hidden_states_img = None
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if attention_mask is not None:
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attention_mask = repeat(
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attention_mask, "1 F D -> B F D", B=batch_size)
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attention_mask = attn.prepare_attention_mask(
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attention_mask, sequence_length, batch_size)
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query = attn.to_q(hidden_states)
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is_cross_attention = encoder_hidden_states is not None
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if encoder_hidden_states is None:
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encoder_hidden_states = hidden_states
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elif attn.norm_cross:
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encoder_hidden_states = attn.norm_encoder_hidden_states(
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encoder_hidden_states)
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default_attention = not hasattr(attn, "is_spatial_attention")
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if default_attention:
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assert not self.temp_attend_on_neighborhood_of_condition_frames, "special attention must be implemented with new interface"
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assert not self.spatial_attend_on_condition_frames, "special attention must be implemented with new interface"
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is_spatial_attention = attn.is_spatial_attention if hasattr(
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attn, "is_spatial_attention") else False
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use_image_embedding = attn.use_image_embedding if hasattr(
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attn, "use_image_embedding") else False
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if is_spatial_attention and use_image_embedding and attn.cross_attention_mode:
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assert not self.spatial_attend_on_condition_frames, "Not implemented together with image embedding"
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alpha = attn.alpha
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encoder_hidden_states_txt = encoder_hidden_states[:, :77, :]
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encoder_hidden_states_mixed = attn.conv(encoder_hidden_states)
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encoder_hidden_states_mixed = attn.conv_ln(encoder_hidden_states_mixed)
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encoder_hidden_states = encoder_hidden_states_txt + encoder_hidden_states_mixed * F.silu(alpha)
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key = attn.to_k(encoder_hidden_states)
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value = attn.to_v(encoder_hidden_states)
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else:
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key = attn.to_k(encoder_hidden_states)
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value = attn.to_v(encoder_hidden_states)
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if not default_attention and not is_spatial_attention and self.temp_attend_on_neighborhood_of_condition_frames and not attn.cross_attention_mode:
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# normal attention
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query_condition = query[:, :self.num_frame_conditioning]
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query_condition = attn.head_to_batch_dim(
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query_condition).contiguous()
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key_condition = key
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value_condition = value
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key_condition = attn.head_to_batch_dim(key_condition).contiguous()
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value_condition = attn.head_to_batch_dim(
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value_condition).contiguous()
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hidden_states_condition = xformers.ops.memory_efficient_attention(
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query_condition, key_condition, value_condition, attn_bias=None, op=self.attention_op, scale=attn.scale
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)
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hidden_states_condition = hidden_states_condition.to(query.dtype)
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hidden_states_condition = attn.batch_to_head_dim(
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hidden_states_condition)
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#
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query_uncondition = query[:, self.num_frame_conditioning:]
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key = key[:, :self.num_frame_conditioning]
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value = value[:, :self.num_frame_conditioning]
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key = rearrange(key, "(B W H) F C -> B W H F C",
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H=hidden_state_height, W=hidden_state_width)
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value = rearrange(value, "(B W H) F C -> B W H F C",
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H=hidden_state_height, W=hidden_state_width)
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keys = []
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values = []
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for shifts_width in [-1, 0, 1]:
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for shifts_height in [-1, 0, 1]:
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keys.append(torch.roll(key, shifts=(
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shifts_width, shifts_height), dims=(1, 2)))
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values.append(torch.roll(value, shifts=(
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shifts_width, shifts_height), dims=(1, 2)))
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key = rearrange(torch.cat(keys, dim=3), "B W H F C -> (B W H) F C")
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value = rearrange(torch.cat(values, dim=3),
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'B W H F C -> (B W H) F C')
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query = attn.head_to_batch_dim(query_uncondition).contiguous()
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key = attn.head_to_batch_dim(key).contiguous()
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value = attn.head_to_batch_dim(value).contiguous()
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hidden_states = xformers.ops.memory_efficient_attention(
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query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
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)
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hidden_states = hidden_states.to(query.dtype)
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hidden_states = attn.batch_to_head_dim(hidden_states)
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hidden_states = torch.cat(
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[hidden_states_condition, hidden_states], dim=1)
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elif not default_attention and is_spatial_attention and self.spatial_attend_on_condition_frames and not attn.cross_attention_mode:
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# (B F) W H C -> B F W H C
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query_condition = rearrange(
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query, "(B F) S C -> B F S C", F=self.num_frames)
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query_condition = query_condition[:, :self.num_frame_conditioning]
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query_condition = rearrange(
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query_condition, "B F S C -> (B F) S C")
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query_condition = attn.head_to_batch_dim(
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query_condition).contiguous()
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key_condition = rearrange(
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key, "(B F) S C -> B F S C", F=self.num_frames)
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key_condition = key_condition[:, :self.num_frame_conditioning]
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key_condition = rearrange(key_condition, "B F S C -> (B F) S C")
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value_condition = rearrange(
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value, "(B F) S C -> B F S C", F=self.num_frames)
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value_condition = value_condition[:, :self.num_frame_conditioning]
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value_condition = rearrange(
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value_condition, "B F S C -> (B F) S C")
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key_condition = attn.head_to_batch_dim(key_condition).contiguous()
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value_condition = attn.head_to_batch_dim(
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value_condition).contiguous()
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hidden_states_condition = xformers.ops.memory_efficient_attention(
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query_condition, key_condition, value_condition, attn_bias=None, op=self.attention_op, scale=attn.scale
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)
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hidden_states_condition = hidden_states_condition.to(query.dtype)
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hidden_states_condition = attn.batch_to_head_dim(
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hidden_states_condition)
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query_uncondition = rearrange(
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query, "(B F) S C -> B F S C", F=self.num_frames)
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query_uncondition = query_uncondition[:,
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self.num_frame_conditioning:]
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key_uncondition = rearrange(
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key, "(B F) S C -> B F S C", F=self.num_frames)
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value_uncondition = rearrange(
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value, "(B F) S C -> B F S C", F=self.num_frames)
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key_uncondition = key_uncondition[:,
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self.num_frame_conditioning-1, None]
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value_uncondition = value_uncondition[:,
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self.num_frame_conditioning-1, None]
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# if self.trainer.training:
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# import pdb
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# pdb.set_trace()
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# print("now")
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query_uncondition = rearrange(
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query_uncondition, "B F S C -> (B F) S C")
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key_uncondition = repeat(rearrange(
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key_uncondition, "B F S C -> B (F S) C"), "B T C -> (B F) T C", F=self.num_frames-self.num_frame_conditioning)
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value_uncondition = repeat(rearrange(
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value_uncondition, "B F S C -> B (F S) C"), "B T C -> (B F) T C", F=self.num_frames-self.num_frame_conditioning)
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query_uncondition = attn.head_to_batch_dim(
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query_uncondition).contiguous()
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key_uncondition = attn.head_to_batch_dim(
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key_uncondition).contiguous()
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value_uncondition = attn.head_to_batch_dim(
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value_uncondition).contiguous()
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hidden_states_uncondition = xformers.ops.memory_efficient_attention(
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query_uncondition, key_uncondition, value_uncondition, attn_bias=None, op=self.attention_op, scale=attn.scale
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)
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hidden_states_uncondition = hidden_states_uncondition.to(
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query.dtype)
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hidden_states_uncondition = attn.batch_to_head_dim(
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hidden_states_uncondition)
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hidden_states = torch.cat([rearrange(hidden_states_condition, "(B F) S C -> B F S C", F=self.num_frame_conditioning), rearrange(
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hidden_states_uncondition, "(B F) S C -> B F S C", F=self.num_frames-self.num_frame_conditioning)], dim=1)
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hidden_states = rearrange(hidden_states, "B F S C -> (B F) S C")
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else:
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query = attn.head_to_batch_dim(query).contiguous()
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key = attn.head_to_batch_dim(key).contiguous()
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value = attn.head_to_batch_dim(value).contiguous()
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hidden_states = xformers.ops.memory_efficient_attention(
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query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
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)
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hidden_states = hidden_states.to(query.dtype)
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hidden_states = attn.batch_to_head_dim(hidden_states)
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# linear proj
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hidden_states = attn.to_out[0](hidden_states)
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# dropout
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hidden_states = attn.to_out[1](hidden_states)
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return hidden_states
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