File size: 3,498 Bytes
baa8e90 |
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 |
import torch
import copy
from comfy.sd1_clip import gen_empty_tokens
class Negpip:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL", ),
"clip": ("CLIP", ),
},
}
RETURN_TYPES = ("MODEL", "CLIP")
FUNCTION = "apply"
CATEGORY = "loaders"
def apply(self, model, clip):
new_clip = copy.copy(clip)
if hasattr(new_clip.cond_stage_model, "clip_g"):
new_clip.cond_stage_model.clip_g.encode_token_weights = hook_clip_encode_token_weights(new_clip.cond_stage_model.clip_g)
if hasattr(new_clip.cond_stage_model, "clip_h"):
new_clip.cond_stage_model.clip_h.encode_token_weights = hook_clip_encode_token_weights(new_clip.cond_stage_model.clip_h)
if hasattr(new_clip.cond_stage_model, "clip_l"):
new_clip.cond_stage_model.clip_l.encode_token_weights = hook_clip_encode_token_weights(new_clip.cond_stage_model.clip_l)
new_model = model.clone()
def negpip_apply(q, k, v, extra_options):
new_k = k[:, 0::2]
new_v = v[:, 1::2]
return q, new_k, new_v
new_model.set_model_attn2_patch(negpip_apply)
return new_model, new_clip
# prompt weightingの計算で無理やりk,vを計算
# k,vはattn2_patchで分離する
# weightがマイナスのときはvだけマイナスにする
def hook_clip_encode_token_weights(self):
def encode_token_weights(token_weight_pairs):
to_encode = list()
max_token_len = 0
has_weights = False
for x in token_weight_pairs:
tokens = list(map(lambda a: a[0], x))
max_token_len = max(len(tokens), max_token_len)
has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x))
to_encode.append(tokens)
sections = len(to_encode)
if has_weights or sections == 0:
to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len))
out, pooled = self.encode(to_encode)
if pooled is not None:
first_pooled = pooled[0:1].cpu()
else:
first_pooled = pooled
output = []
for k in range(0, sections):
zk = out[k:k+1].clone()
zv = out[k:k+1].clone()
if has_weights:
z_empty = out[-1]
for i in range(len(zk)):
for j in range(len(zk[i])):
weight = token_weight_pairs[k][j][1]
if weight < 0:
weight = -weight
sign = -1
else:
sign = 1
zk[i][j] = (zk[i][j] - z_empty[0][j]) * weight + z_empty[0][j]
zv[i][j] = sign * ((zv[i][j] - z_empty[0][j]) * weight + z_empty[0][j])
z = torch.zeros_like(zk).repeat(1, 2, 1)
for i in range(zk.shape[1]): # 頭悪いのでfor文
z[:, 2*i, :] += zk[:, i, :]
z[:, 2*i+1, :] += zv[:, i, :]
output.append(z)
if (len(output) == 0):
return out[-1:].cpu(), first_pooled
return torch.cat(output, dim=-2).cpu(), first_pooled
return encode_token_weights
NODE_CLASS_MAPPINGS = {
"Negpip": Negpip,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"Negpip": "Apply Negpip",
}
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
|