| import torch
|
| import numpy as np
|
| import re
|
| import itertools
|
|
|
| from comfy import model_management
|
| from comfy.sdxl_clip import SDXLClipModel, SDXLRefinerClipModel, SDXLClipG
|
| try:
|
| from comfy.text_encoders.sd3_clip import SD3ClipModel, T5XXLModel
|
| except ImportError:
|
| from comfy.sd3_clip import SD3ClipModel, T5XXLModel
|
|
|
| from nodes import NODE_CLASS_MAPPINGS, ConditioningConcat, ConditioningZeroOut, ConditioningSetTimestepRange, ConditioningCombine
|
|
|
| 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, pooled = model.clip_l.encode_token_weights(token_weight_pairs)
|
| return l_out, pooled
|
|
|
| def encode_token_weights_t5(model, token_weight_pairs):
|
| return model.t5xxl.encode_token_weights(token_weight_pairs)
|
|
|
|
|
| def encode_token_weights(model, token_weight_pairs, encode_func):
|
| if model.layer_idx is not None:
|
|
|
|
|
| model.cond_stage_model.set_clip_options({'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 prepareSD3(out, pooled, clip_balance):
|
| lg_w = 1 - max(0, clip_balance - .5) * 2
|
| t5_w = 1 - max(0, .5 - clip_balance) * 2
|
| if out.shape[0] > 1:
|
| return torch.cat([out[0] * lg_w, out[1] * t5_w], dim=-1), pooled
|
| else:
|
| return out, pooled
|
|
|
| def advanced_encode(clip, text, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5,
|
| apply_to_pooled=True, width=1024, height=1024, crop_w=0, crop_h=0, target_width=1024, target_height=1024, a1111_prompt_style=False, steps=1):
|
|
|
|
|
| if a1111_prompt_style:
|
| if "smZ CLIPTextEncode" in NODE_CLASS_MAPPINGS:
|
| cls = NODE_CLASS_MAPPINGS['smZ CLIPTextEncode']
|
| embeddings_final, = cls().encode(clip, text, weight_interpretation, True, True, False, False, 6, 1024, 1024, 0, 0, 1024, 1024, '', '', steps)
|
| return embeddings_final
|
| else:
|
| raise Exception(f"[smzNodes Not Found] you need to install 'ComfyUI-smzNodes'")
|
|
|
| time_start = 0
|
| time_end = 1
|
| match = re.search(r'TIMESTEP.*$', text)
|
| if match:
|
| timestep = match.group()
|
| timestep = timestep.split(' ')
|
| timestep = timestep[0]
|
| text = text.replace(timestep, '')
|
| value = timestep.split(':')
|
| if len(value) >= 3:
|
| time_start = float(value[1])
|
| time_end = float(value[2])
|
| elif len(value) == 2:
|
| time_start = float(value[1])
|
| time_end = 1
|
| elif len(value) == 1:
|
| time_start = 0.1
|
| time_end = 1
|
|
|
| pass3 = [x.strip() for x in text.split("BREAK")]
|
| pass3 = [x for x in pass3 if x != '']
|
|
|
| if len(pass3) == 0:
|
| pass3 = ['']
|
|
|
|
|
|
|
|
|
| conditioning = None
|
|
|
| for text in pass3:
|
| tokenized = clip.tokenize(text, return_word_ids=True)
|
| if SD3ClipModel and isinstance(clip.cond_stage_model, SD3ClipModel):
|
| lg_out = None
|
| pooled = None
|
| out = None
|
|
|
| if len(tokenized['l']) > 0 or len(tokenized['g']) > 0:
|
| if clip.cond_stage_model.clip_l is not None:
|
| lg_out, l_pooled = 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=True,)
|
| else:
|
| l_pooled = torch.zeros((1, 768), device=model_management.intermediate_device())
|
|
|
| if clip.cond_stage_model.clip_g is not None:
|
| g_out, 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)
|
| if lg_out is not None:
|
| lg_out = torch.cat([lg_out, g_out], dim=-1)
|
| else:
|
| lg_out = torch.nn.functional.pad(g_out, (768, 0))
|
| else:
|
| g_out = None
|
| g_pooled = torch.zeros((1, 1280), device=model_management.intermediate_device())
|
|
|
| if lg_out is not None:
|
| lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
|
| out = lg_out
|
| pooled = torch.cat((l_pooled, g_pooled), dim=-1)
|
|
|
|
|
| if 't5xxl' in tokenized:
|
| t5_out, t5_pooled = advanced_encode_from_tokens(tokenized['t5xxl'],
|
| token_normalization,
|
| weight_interpretation,
|
| lambda x: encode_token_weights(clip, x, encode_token_weights_t5),
|
| w_max=w_max, return_pooled=True)
|
| if lg_out is not None:
|
| out = torch.cat([lg_out, t5_out], dim=-2)
|
| else:
|
| out = t5_out
|
|
|
| if out is None:
|
| out = torch.zeros((1, 77, 4096), device=model_management.intermediate_device())
|
|
|
| if pooled is None:
|
| pooled = torch.zeros((1, 768 + 1280), device=model_management.intermediate_device())
|
|
|
| embeddings_final, pooled = prepareSD3(out, pooled, clip_balance)
|
| cond = [[embeddings_final, {"pooled_output": pooled}]]
|
|
|
| elif isinstance(clip.cond_stage_model, (SDXLClipModel, SDXLRefinerClipModel, SDXLClipG)):
|
| 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)
|
|
|
| embeddings_final, pooled = prepareXL(embs_l, embs_g, pooled, clip_balance)
|
|
|
| cond = [[embeddings_final, {"pooled_output": pooled}]]
|
|
|
|
|
|
|
| else:
|
| embeddings_final, pooled = 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=True,)
|
| cond = [[embeddings_final, {"pooled_output": pooled}]]
|
|
|
| if conditioning is not None:
|
| conditioning = ConditioningConcat().concat(conditioning, cond)[0]
|
| else:
|
| conditioning = cond
|
|
|
|
|
| if time_start > 0 or time_end < 1:
|
| conditioning_2, = ConditioningSetTimestepRange().set_range(conditioning, 0, time_start)
|
| conditioning_1, = ConditioningZeroOut().zero_out(conditioning)
|
| conditioning_1, = ConditioningSetTimestepRange().set_range(conditioning_1, time_start, time_end)
|
| conditioning, = ConditioningCombine().combine(conditioning_1, conditioning_2)
|
|
|
| return conditioning
|
|
|
|
|
|
|
|
|
