|
import torch |
|
import numpy as np |
|
import itertools |
|
from math import gcd |
|
|
|
from comfy import model_management |
|
from comfy.sdxl_clip import SDXLClipModel |
|
|
|
def _grouper(n, iterable): |
|
it = iter(iterable) |
|
while True: |
|
chunk = list(itertools.islice(it, n)) |
|
if not chunk: |
|
return |
|
yield chunk |
|
|
|
def _norm_mag(w, n): |
|
d = w - 1 |
|
return 1 + np.sign(d) * np.sqrt(np.abs(d)**2 / n) |
|
|
|
|
|
def divide_length(word_ids, weights): |
|
sums = dict(zip(*np.unique(word_ids, return_counts=True))) |
|
sums[0] = 1 |
|
weights = [[_norm_mag(w, sums[id]) if id != 0 else 1.0 |
|
for w, id in zip(x, y)] for x, y in zip(weights, word_ids)] |
|
return weights |
|
|
|
def shift_mean_weight(word_ids, weights): |
|
delta = 1 - np.mean([w for x, y in zip(weights, word_ids) for w, id in zip(x,y) if id != 0]) |
|
weights = [[w if id == 0 else w+delta |
|
for w, id in zip(x, y)] for x, y in zip(weights, word_ids)] |
|
return weights |
|
|
|
def scale_to_norm(weights, word_ids, w_max): |
|
top = np.max(weights) |
|
w_max = min(top, w_max) |
|
weights = [[w_max if id == 0 else (w/top) * w_max |
|
for w, id in zip(x, y)] for x, y in zip(weights, word_ids)] |
|
return weights |
|
|
|
def from_zero(weights, base_emb): |
|
weight_tensor = torch.tensor(weights, dtype=base_emb.dtype, device=base_emb.device) |
|
weight_tensor = weight_tensor.reshape(1,-1,1).expand(base_emb.shape) |
|
return base_emb * weight_tensor |
|
|
|
def mask_word_id(tokens, word_ids, target_id, mask_token): |
|
new_tokens = [[mask_token if wid == target_id else t |
|
for t, wid in zip(x,y)] for x,y in zip(tokens, word_ids)] |
|
mask = np.array(word_ids) == target_id |
|
return (new_tokens, mask) |
|
|
|
def batched_clip_encode(tokens, length, encode_func, num_chunks): |
|
embs = [] |
|
for e in _grouper(32, tokens): |
|
enc, pooled = encode_func(e) |
|
enc = enc.reshape((len(e), length, -1)) |
|
embs.append(enc) |
|
embs = torch.cat(embs) |
|
embs = embs.reshape((len(tokens) // num_chunks, length * num_chunks, -1)) |
|
return embs |
|
|
|
def from_masked(tokens, weights, word_ids, base_emb, length, encode_func, m_token=266): |
|
pooled_base = base_emb[0,length-1:length,:] |
|
wids, inds = np.unique(np.array(word_ids).reshape(-1), return_index=True) |
|
weight_dict = dict((id,w) |
|
for id,w in zip(wids ,np.array(weights).reshape(-1)[inds]) |
|
if w != 1.0) |
|
|
|
if len(weight_dict) == 0: |
|
return torch.zeros_like(base_emb), base_emb[0,length-1:length,:] |
|
|
|
weight_tensor = torch.tensor(weights, dtype=base_emb.dtype, device=base_emb.device) |
|
weight_tensor = weight_tensor.reshape(1,-1,1).expand(base_emb.shape) |
|
|
|
|
|
|
|
m_token = (m_token, 1.0) |
|
|
|
ws = [] |
|
masked_tokens = [] |
|
masks = [] |
|
|
|
|
|
for id, w in weight_dict.items(): |
|
masked, m = mask_word_id(tokens, word_ids, id, m_token) |
|
masked_tokens.extend(masked) |
|
|
|
m = torch.tensor(m, dtype=base_emb.dtype, device=base_emb.device) |
|
m = m.reshape(1,-1,1).expand(base_emb.shape) |
|
masks.append(m) |
|
|
|
ws.append(w) |
|
|
|
|
|
embs = batched_clip_encode(masked_tokens, length, encode_func, len(tokens)) |
|
masks = torch.cat(masks) |
|
|
|
embs = (base_emb.expand(embs.shape) - embs) |
|
pooled = embs[0,length-1:length,:] |
|
|
|
embs *= masks |
|
embs = embs.sum(axis=0, keepdim=True) |
|
|
|
pooled_start = pooled_base.expand(len(ws), -1) |
|
ws = torch.tensor(ws).reshape(-1,1).expand(pooled_start.shape) |
|
pooled = (pooled - pooled_start) * (ws - 1) |
|
pooled = pooled.mean(axis=0, keepdim=True) |
|
|
|
return ((weight_tensor - 1) * embs), pooled_base + pooled |
|
|
|
def mask_inds(tokens, inds, mask_token): |
|
clip_len = len(tokens[0]) |
|
inds_set = set(inds) |
|
new_tokens = [[mask_token if i*clip_len + j in inds_set else t |
|
for j, t in enumerate(x)] for i, x in enumerate(tokens)] |
|
return new_tokens |
|
|
|
def down_weight(tokens, weights, word_ids, base_emb, length, encode_func, m_token=266): |
|
w, w_inv = np.unique(weights,return_inverse=True) |
|
|
|
if np.sum(w < 1) == 0: |
|
return base_emb, tokens, base_emb[0,length-1:length,:] |
|
|
|
|
|
m_token = (m_token, 1.0) |
|
|
|
masked_tokens = [] |
|
|
|
masked_current = tokens |
|
for i in range(len(w)): |
|
if w[i] >= 1: |
|
continue |
|
masked_current = mask_inds(masked_current, np.where(w_inv == i)[0], m_token) |
|
masked_tokens.extend(masked_current) |
|
|
|
embs = batched_clip_encode(masked_tokens, length, encode_func, len(tokens)) |
|
embs = torch.cat([base_emb, embs]) |
|
w = w[w<=1.0] |
|
w_mix = np.diff([0] + w.tolist()) |
|
w_mix = torch.tensor(w_mix, dtype=embs.dtype, device=embs.device).reshape((-1,1,1)) |
|
|
|
weighted_emb = (w_mix * embs).sum(axis=0, keepdim=True) |
|
return weighted_emb, masked_current, weighted_emb[0,length-1:length,:] |
|
|
|
def scale_emb_to_mag(base_emb, weighted_emb): |
|
norm_base = torch.linalg.norm(base_emb) |
|
norm_weighted = torch.linalg.norm(weighted_emb) |
|
embeddings_final = (norm_base / norm_weighted) * weighted_emb |
|
return embeddings_final |
|
|
|
def recover_dist(base_emb, weighted_emb): |
|
fixed_std = (base_emb.std() / weighted_emb.std()) * (weighted_emb - weighted_emb.mean()) |
|
embeddings_final = fixed_std + (base_emb.mean() - fixed_std.mean()) |
|
return embeddings_final |
|
|
|
def A1111_renorm(base_emb, weighted_emb): |
|
embeddings_final = (base_emb.mean() / weighted_emb.mean()) * weighted_emb |
|
return embeddings_final |
|
|
|
def advanced_encode_from_tokens(tokenized, token_normalization, weight_interpretation, encode_func, m_token=266, length=77, w_max=1.0, return_pooled=False, apply_to_pooled=False): |
|
tokens = [[t for t,_,_ in x] for x in tokenized] |
|
weights = [[w for _,w,_ in x] for x in tokenized] |
|
word_ids = [[wid for _,_,wid in x] for x in tokenized] |
|
|
|
|
|
|
|
|
|
|
|
if token_normalization.startswith("length"): |
|
weights = divide_length(word_ids, weights) |
|
|
|
|
|
if token_normalization.endswith("mean"): |
|
weights = shift_mean_weight(word_ids, weights) |
|
|
|
|
|
|
|
pooled = None |
|
|
|
if weight_interpretation == "comfy": |
|
weighted_tokens = [[(t,w) for t, w in zip(x, y)] for x, y in zip(tokens, weights)] |
|
weighted_emb, pooled_base = encode_func(weighted_tokens) |
|
pooled = pooled_base |
|
else: |
|
unweighted_tokens = [[(t,1.0) for t, _,_ in x] for x in tokenized] |
|
base_emb, pooled_base = encode_func(unweighted_tokens) |
|
|
|
if weight_interpretation == "A1111": |
|
weighted_emb = from_zero(weights, base_emb) |
|
weighted_emb = A1111_renorm(base_emb, weighted_emb) |
|
pooled = pooled_base |
|
|
|
if weight_interpretation == "compel": |
|
pos_tokens = [[(t,w) if w >= 1.0 else (t,1.0) for t, w in zip(x, y)] for x, y in zip(tokens, weights)] |
|
weighted_emb, _ = encode_func(pos_tokens) |
|
weighted_emb, _, pooled = down_weight(pos_tokens, weights, word_ids, weighted_emb, length, encode_func) |
|
|
|
if weight_interpretation == "comfy++": |
|
weighted_emb, tokens_down, _ = down_weight(unweighted_tokens, weights, word_ids, base_emb, length, encode_func) |
|
weights = [[w if w > 1.0 else 1.0 for w in x] for x in weights] |
|
|
|
embs, pooled = from_masked(unweighted_tokens, weights, word_ids, base_emb, length, encode_func) |
|
weighted_emb += embs |
|
|
|
if weight_interpretation == "down_weight": |
|
weights = scale_to_norm(weights, word_ids, w_max) |
|
weighted_emb, _, pooled = down_weight(unweighted_tokens, weights, word_ids, base_emb, length, encode_func) |
|
|
|
if return_pooled: |
|
if apply_to_pooled: |
|
return weighted_emb, pooled |
|
else: |
|
return weighted_emb, pooled_base |
|
return weighted_emb, None |
|
|
|
def encode_token_weights_g(model, token_weight_pairs): |
|
return model.clip_g.encode_token_weights(token_weight_pairs) |
|
|
|
def encode_token_weights_l(model, token_weight_pairs): |
|
l_out, _ = model.clip_l.encode_token_weights(token_weight_pairs) |
|
return l_out, None |
|
|
|
def encode_token_weights(model, token_weight_pairs, encode_func): |
|
if model.layer_idx is not None: |
|
model.cond_stage_model.clip_layer(model.layer_idx) |
|
|
|
model_management.load_model_gpu(model.patcher) |
|
return encode_func(model.cond_stage_model, token_weight_pairs) |
|
|
|
def prepareXL(embs_l, embs_g, pooled, clip_balance): |
|
l_w = 1 - max(0, clip_balance - .5) * 2 |
|
g_w = 1 - max(0, .5 - clip_balance) * 2 |
|
if embs_l is not None: |
|
return torch.cat([embs_l * l_w, embs_g * g_w], dim=-1), pooled |
|
else: |
|
return embs_g, pooled |
|
|
|
def advanced_encode(clip, text, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5, apply_to_pooled=True): |
|
tokenized = clip.tokenize(text, return_word_ids=True) |
|
if isinstance(tokenized, dict): |
|
embs_l = None |
|
embs_g = None |
|
pooled = None |
|
if 'l' in tokenized and isinstance(clip.cond_stage_model, SDXLClipModel): |
|
embs_l, _ = advanced_encode_from_tokens(tokenized['l'], |
|
token_normalization, |
|
weight_interpretation, |
|
lambda x: encode_token_weights(clip, x, encode_token_weights_l), |
|
w_max=w_max, |
|
return_pooled=False) |
|
if 'g' in tokenized: |
|
embs_g, pooled = advanced_encode_from_tokens(tokenized['g'], |
|
token_normalization, |
|
weight_interpretation, |
|
lambda x: encode_token_weights(clip, x, encode_token_weights_g), |
|
w_max=w_max, |
|
return_pooled=True, |
|
apply_to_pooled=apply_to_pooled) |
|
return prepareXL(embs_l, embs_g, pooled, clip_balance) |
|
else: |
|
return advanced_encode_from_tokens(tokenized, |
|
token_normalization, |
|
weight_interpretation, |
|
lambda x: (clip.encode_from_tokens(x), None), |
|
w_max=w_max) |
|
def advanced_encode_XL(clip, text1, text2, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5, apply_to_pooled=True): |
|
tokenized1 = clip.tokenize(text1, return_word_ids=True) |
|
tokenized2 = clip.tokenize(text2, return_word_ids=True) |
|
|
|
embs_l, _ = advanced_encode_from_tokens(tokenized1['l'], |
|
token_normalization, |
|
weight_interpretation, |
|
lambda x: encode_token_weights(clip, x, encode_token_weights_l), |
|
w_max=w_max, |
|
return_pooled=False) |
|
|
|
embs_g, pooled = advanced_encode_from_tokens(tokenized2['g'], |
|
token_normalization, |
|
weight_interpretation, |
|
lambda x: encode_token_weights(clip, x, encode_token_weights_g), |
|
w_max=w_max, |
|
return_pooled=True, |
|
apply_to_pooled=apply_to_pooled) |
|
|
|
gcd_num = gcd(embs_l.shape[1], embs_g.shape[1]) |
|
repeat_l = int((embs_g.shape[1] / gcd_num) * embs_l.shape[1]) |
|
repeat_g = int((embs_l.shape[1] / gcd_num) * embs_g.shape[1]) |
|
|
|
return prepareXL(embs_l.expand((-1,repeat_l,-1)), embs_g.expand((-1,repeat_g,-1)), pooled, clip_balance) |
|
|