forked from cubiq/ComfyUI_InstantID
-
Notifications
You must be signed in to change notification settings - Fork 0
/
CrossAttentionPatch.py
184 lines (162 loc) · 9.74 KB
/
CrossAttentionPatch.py
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
import torch
import math
import torch.nn.functional as F
from comfy.ldm.modules.attention import optimized_attention
from .utils import tensor_to_size
class CrossAttentionPatch:
# forward for patching
def __init__(self, ipadapter=None, number=0, weight=1.0, cond=None, cond_alt=None, uncond=None, weight_type="linear", mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False, embeds_scaling='V only'):
self.weights = [weight]
self.ipadapters = [ipadapter]
self.conds = [cond]
self.conds_alt = [cond_alt]
self.unconds = [uncond]
self.weight_types = [weight_type]
self.masks = [mask]
self.sigma_starts = [sigma_start]
self.sigma_ends = [sigma_end]
self.unfold_batch = [unfold_batch]
self.embeds_scaling = [embeds_scaling]
self.number = number
self.layers = 11 if '101_to_k_ip' in ipadapter.ip_layers.to_kvs else 15 # TODO: check if this is a valid condition to detect all models
self.k_key = str(self.number*2+1) + "_to_k_ip"
self.v_key = str(self.number*2+1) + "_to_v_ip"
def set_new_condition(self, ipadapter=None, number=0, weight=1.0, cond=None, cond_alt=None, uncond=None, weight_type="linear", mask=None, sigma_start=0.0, sigma_end=1.0, unfold_batch=False, embeds_scaling='V only'):
self.weights.append(weight)
self.ipadapters.append(ipadapter)
self.conds.append(cond)
self.conds_alt.append(cond_alt)
self.unconds.append(uncond)
self.weight_types.append(weight_type)
self.masks.append(mask)
self.sigma_starts.append(sigma_start)
self.sigma_ends.append(sigma_end)
self.unfold_batch.append(unfold_batch)
self.embeds_scaling.append(embeds_scaling)
def __call__(self, q, k, v, extra_options):
dtype = q.dtype
cond_or_uncond = extra_options["cond_or_uncond"]
sigma = extra_options["sigmas"].detach().cpu()[0].item() if 'sigmas' in extra_options else 999999999.9
block_type = extra_options["block"][0]
#block_id = extra_options["block"][1]
t_idx = extra_options["transformer_index"]
# extra options for AnimateDiff
ad_params = extra_options['ad_params'] if "ad_params" in extra_options else None
b = q.shape[0]
seq_len = q.shape[1]
batch_prompt = b // len(cond_or_uncond)
out = optimized_attention(q, k, v, extra_options["n_heads"])
_, _, oh, ow = extra_options["original_shape"]
for weight, cond, cond_alt, uncond, ipadapter, mask, weight_type, sigma_start, sigma_end, unfold_batch, embeds_scaling in zip(self.weights, self.conds, self.conds_alt, self.unconds, self.ipadapters, self.masks, self.weight_types, self.sigma_starts, self.sigma_ends, self.unfold_batch, self.embeds_scaling):
if sigma <= sigma_start and sigma >= sigma_end:
if weight_type == 'ease in':
weight = weight * (0.05 + 0.95 * (1 - t_idx / self.layers))
elif weight_type == 'ease out':
weight = weight * (0.05 + 0.95 * (t_idx / self.layers))
elif weight_type == 'ease in-out':
weight = weight * (0.05 + 0.95 * (1 - abs(t_idx - (self.layers/2)) / (self.layers/2)))
elif weight_type == 'reverse in-out':
weight = weight * (0.05 + 0.95 * (abs(t_idx - (self.layers/2)) / (self.layers/2)))
elif weight_type == 'weak input' and block_type == 'input':
weight = weight * 0.2
elif weight_type == 'weak middle' and block_type == 'middle':
weight = weight * 0.2
elif weight_type == 'weak output' and block_type == 'output':
weight = weight * 0.2
elif weight_type == 'strong middle' and (block_type == 'input' or block_type == 'output'):
weight = weight * 0.2
elif weight_type.startswith('style transfer'):
if t_idx != 6:
continue
elif weight_type.startswith('composition'):
if t_idx != 3:
continue
elif isinstance(weight, dict):
if t_idx not in weight:
continue
weight = weight[t_idx]
if cond_alt is not None and t_idx in cond_alt:
cond = cond_alt[t_idx]
del cond_alt
if weight == 0:
continue
if unfold_batch and cond.shape[0] > 1:
# Check AnimateDiff context window
if ad_params is not None and ad_params["sub_idxs"] is not None:
# if image length matches or exceeds full_length get sub_idx images
if cond.shape[0] >= ad_params["full_length"]:
cond = torch.Tensor(cond[ad_params["sub_idxs"]])
uncond = torch.Tensor(uncond[ad_params["sub_idxs"]])
# otherwise get sub_idxs images
else:
cond = tensor_to_size(cond, ad_params["full_length"])
uncond = tensor_to_size(uncond, ad_params["full_length"])
cond = cond[ad_params["sub_idxs"]]
uncond = uncond[ad_params["sub_idxs"]]
cond = tensor_to_size(cond, batch_prompt)
uncond = tensor_to_size(uncond, batch_prompt)
k_cond = ipadapter.ip_layers.to_kvs[self.k_key](cond)
k_uncond = ipadapter.ip_layers.to_kvs[self.k_key](uncond)
v_cond = ipadapter.ip_layers.to_kvs[self.v_key](cond)
v_uncond = ipadapter.ip_layers.to_kvs[self.v_key](uncond)
else:
k_cond = ipadapter.ip_layers.to_kvs[self.k_key](cond).repeat(batch_prompt, 1, 1)
k_uncond = ipadapter.ip_layers.to_kvs[self.k_key](uncond).repeat(batch_prompt, 1, 1)
v_cond = ipadapter.ip_layers.to_kvs[self.v_key](cond).repeat(batch_prompt, 1, 1)
v_uncond = ipadapter.ip_layers.to_kvs[self.v_key](uncond).repeat(batch_prompt, 1, 1)
ip_k = torch.cat([(k_cond, k_uncond)[i] for i in cond_or_uncond], dim=0)
ip_v = torch.cat([(v_cond, v_uncond)[i] for i in cond_or_uncond], dim=0)
if embeds_scaling == 'K+mean(V) w/ C penalty':
scaling = float(ip_k.shape[2]) / 1280.0
weight = weight * scaling
ip_k = ip_k * weight
ip_v_mean = torch.mean(ip_v, dim=1, keepdim=True)
ip_v = (ip_v - ip_v_mean) + ip_v_mean * weight
out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
del ip_v_mean
elif embeds_scaling == 'K+V w/ C penalty':
scaling = float(ip_k.shape[2]) / 1280.0
weight = weight * scaling
ip_k = ip_k * weight
ip_v = ip_v * weight
out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
elif embeds_scaling == 'K+V':
ip_k = ip_k * weight
ip_v = ip_v * weight
out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
else:
#ip_v = ip_v * weight
out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
out_ip = out_ip * weight # I'm doing this to get the same results as before
if mask is not None:
mask_h = oh / math.sqrt(oh * ow / seq_len)
mask_h = int(mask_h) + int((seq_len % int(mask_h)) != 0)
mask_w = seq_len // mask_h
# check if using AnimateDiff and sliding context window
if (mask.shape[0] > 1 and ad_params is not None and ad_params["sub_idxs"] is not None):
# if mask length matches or exceeds full_length, get sub_idx masks
if mask.shape[0] >= ad_params["full_length"]:
mask = torch.Tensor(mask[ad_params["sub_idxs"]])
mask = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bilinear").squeeze(1)
else:
mask = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bilinear").squeeze(1)
mask = tensor_to_size(mask, ad_params["full_length"])
mask = mask[ad_params["sub_idxs"]]
else:
mask = F.interpolate(mask.unsqueeze(1), size=(mask_h, mask_w), mode="bilinear").squeeze(1)
mask = tensor_to_size(mask, batch_prompt)
mask = mask.repeat(len(cond_or_uncond), 1, 1)
mask = mask.view(mask.shape[0], -1, 1).repeat(1, 1, out.shape[2])
# covers cases where extreme aspect ratios can cause the mask to have a wrong size
mask_len = mask_h * mask_w
if mask_len < seq_len:
pad_len = seq_len - mask_len
pad1 = pad_len // 2
pad2 = pad_len - pad1
mask = F.pad(mask, (0, 0, pad1, pad2), value=0.0)
elif mask_len > seq_len:
crop_start = (mask_len - seq_len) // 2
mask = mask[:, crop_start:crop_start+seq_len, :]
out_ip = out_ip * mask
out = out + out_ip
return out.to(dtype=dtype)