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c881b77 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 | import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers.models.attention_processor import Attention
from typing import Optional
from .layers import MIFusion, MIFusionPrototype
from .utils import get_masks, get_sigmoid
class AttnProcessor2_0(nn.Module):
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
"""
def __init__(self, hidden_size=None, cross_attention_dim=None):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
super().__init__()
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
temb: Optional[torch.Tensor] = None,
# Useless
bboxes=[],
obboxes=[],
embeds_pooler=None,
height=512,
width=512,
prototypes=None,
ref_features=None,
guidance_masks=None,
supplement_mask=None,
sigmoid_values=None,
in_box=None,
do_classifier_free_guidance=False,
# End Useless
*args,
**kwargs,
) -> torch.Tensor:
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states
class MaskedProcessor2_0(nn.Module):
def __init__(self, hidden_size, cross_attention_dim=None,
use_ea_attn=False, **kwargs):
super().__init__()
self.hidden_size = hidden_size
self.cross_attention_dim = cross_attention_dim
self.use_ea_attn = use_ea_attn
self.prototype_mode = use_ea_attn and (kwargs['phase'] == 'novel') and (kwargs.get('attn_prototype_switch') is True)
if self.prototype_mode:
self.fusion = MIFusionPrototype(hidden_size, context_dim=cross_attention_dim)
elif use_ea_attn:
self.fusion = MIFusion(hidden_size, context_dim=cross_attention_dim)
# Train ; Test (do_classifier_free_guidance)
# Train // Train2(self.use_ea_attn) ; Test // Test2(self.use_ea_attn)
def __call__(
self,
attn: Attention,
# Output the same size as hidden_states, encoder_hidden_states as Key and Value to inject information to hidden_states
# shape[-2] 4096 as 64x64, 64 as 8x8; shape[-1] as hidden_size
hidden_states, # [1, 4096, 320] // [1, 64, 1280] ; [2, 4096, 320] // [2, 64, 1280] && torch.all(hidden_states[0] == hidden_states[1]) is Ture
encoder_hidden_states=None, # [16, 77, 768] ; [17, 77, 768]
attention_mask=None,
bboxes=[],
obboxes=[],
embeds_pooler=None,
height=512,
width=512,
prototypes=None,
ref_features=None,
guidance_masks=None,
supplement_mask=None,
sigmoid_values=None,
in_box=None,
do_classifier_free_guidance=False,
):
instance_num = len(obboxes[0]) # 15
if not self.use_ea_attn:
# [1, 77, 768]; [2, 77, 768]
encoder_hidden_states = encoder_hidden_states[:2, ...] if do_classifier_free_guidance else encoder_hidden_states[:1, ...]
if self.use_ea_attn:
if do_classifier_free_guidance:
hidden_states = torch.cat([hidden_states[0:1], hidden_states[1:2].repeat(instance_num + 1, 1, 1)]) # ;//[17, 64, 1280]
image_token = hidden_states[1:]
else:
hidden_states = hidden_states.repeat(instance_num + 1, 1, 1) # //[16, 64, 1280]
image_token = hidden_states
batch_size, sequence_length, _ = hidden_states.shape # _ is hidden_size
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
query = attn.to_q(hidden_states) # [1, 4096, 320] // [16, 64, 1280] ; [2, 4096, 320]->[2, 4096, 320] // [17, 64, 1280]->[17, 64, 1280]
key = attn.to_k(encoder_hidden_states) # [1, 77, 320] // [16, 77, 1280] ; [2, 77, 768]->[2, 77, 320] // [17, 77, 768 (cross_attention_dim)] -> [17, 77, 1280 (self.inner_kv_dim)]
value = attn.to_v(encoder_hidden_states) # Same with key
inner_dim = key.shape[-1] # 320 // 1280
head_dim = inner_dim // attn.heads # 40 // 160
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) # [1, 8, 4096, 40] // [16, 8, 64, 160]
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) # [1, 8, 77, 40] // [16, 8, 77, 160]
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) # [1, 8, 77, 40] // [16, 8, 77, 160]
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
) # [1, 8, 4096, 40] // [16, 8, 64, 1280]
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) # [1, 4096, 320] // [16, 64, 1280]
hidden_states = hidden_states.to(query.dtype)
hidden_states = attn.to_out[0](hidden_states) # [1, 4096, 320] // [16, 64, 1280] ; [2, 4096, 320] // [17, 64, 1280] # Linear
hidden_states = attn.to_out[1](hidden_states) # [1, 4096, 320] // [16, 64, 1280] ; [2, 4096, 320] // [17, 64, 1280] # Dropout
if not self.use_ea_attn:
return hidden_states
assert self.use_ea_attn
if do_classifier_free_guidance:
hidden_states_uncond, hidden_states = hidden_states[0:1], hidden_states[1:] # torch.Size([1, HW, C])
other_info = {}
other_info['image_token'] = image_token.unsqueeze(0) # [1, 16, 64, 1280]
other_info['context'] = encoder_hidden_states[1:, ...] if do_classifier_free_guidance else encoder_hidden_states # [16, 77, 768]
other_info['box'] = in_box # [1, 15, 8]
other_info['context_pooler'] = embeds_pooler # [16, 1, 768]
other_info['supplement_mask'] = supplement_mask # [1, 1, 64, 64]
other_info['height'] = height # 512
other_info['width'] = width # 512
other_info['ref_features'] = ref_features # [15, 16, 768], [1, 16, 768]
other_info['sigmoid_values'] = sigmoid_values # [1, 16, 768]
other_info['guidance_masks'] = guidance_masks # [1, 15, 64, 64]
other_info['instance_num'] = instance_num
other_info['prototypes'] = prototypes
hidden_states = self.fusion(hidden_states.unsqueeze(0), # [1, 16, 64, 1280]
other_info=other_info)
# hidden_states_cond.shape [1, 64, 1280]
if do_classifier_free_guidance:
hidden_states = torch.cat([hidden_states_uncond, hidden_states])
return hidden_states
def set_processors(unet, **kwargs):
attn_processors = {}
for name, _ in unet.attn_processors.items():
use_ea_attn = False
kwargs['attn_prototype_switch'] = False
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = unet.config.block_out_channels[-1] # unet.config.block_out_channels [320, 640, 1280, 1280]
use_ea_attn = True
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
attention_id = int(name[len("up_blocks.2.attentions.")])
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
if block_id == 1:
use_ea_attn = True
elif (block_id != 1) and (kwargs['phase'] == 'novel'): # run-4
use_ea_attn = True
kwargs['attn_prototype_switch'] = True
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = unet.config.block_out_channels[block_id]
if cross_attention_dim is not None:
attn_processors[name] = MaskedProcessor2_0(hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
use_ea_attn=use_ea_attn,
**kwargs)
else:
attn_processors[name] = AttnProcessor2_0()
unet.set_attn_processor(attn_processors) |