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) #return np.sign(w) * np.sqrt(np.abs(w)**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 = (clip.tokenizer.end_token, 1.0) if clip.tokenizer.pad_with_end else (0,1.0) #TODO: find most suitable masking token here m_token = (m_token, 1.0) ws = [] masked_tokens = [] masks = [] #create prompts 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) #batch process prompts 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 = (clip.tokenizer.end_token, 1.0) if clip.tokenizer.pad_with_end else (0,1.0) #using the comma token as a masking token seems to work better than aos tokens for SD 1.x 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] #weight normalization #==================== #distribute down/up weights over word lengths if token_normalization.startswith("length"): weights = divide_length(word_ids, weights) #make mean of word tokens 1 if token_normalization.endswith("mean"): weights = shift_mean_weight(word_ids, weights) #weight interpretation #===================== 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] #unweighted_tokens = [[(t,1.0) for t, _,_ in x] for x in tokens_down] 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)