307 lines
12 KiB
Python
307 lines
12 KiB
Python
import torch
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import numpy as np
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import itertools
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from comfy import model_management
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from comfy.sdxl_clip import SDXLClipModel, SDXLRefinerClipModel, SDXLClipG
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from nodes import NODE_CLASS_MAPPINGS
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def _grouper(n, iterable):
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it = iter(iterable)
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while True:
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chunk = list(itertools.islice(it, n))
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if not chunk:
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return
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yield chunk
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def _norm_mag(w, n):
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d = w - 1
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return 1 + np.sign(d) * np.sqrt(np.abs(d) ** 2 / n)
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# return np.sign(w) * np.sqrt(np.abs(w)**2 / n)
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def divide_length(word_ids, weights):
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sums = dict(zip(*np.unique(word_ids, return_counts=True)))
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sums[0] = 1
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weights = [[_norm_mag(w, sums[id]) if id != 0 else 1.0
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for w, id in zip(x, y)] for x, y in zip(weights, word_ids)]
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return weights
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def shift_mean_weight(word_ids, weights):
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delta = 1 - np.mean([w for x, y in zip(weights, word_ids) for w, id in zip(x, y) if id != 0])
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weights = [[w if id == 0 else w + delta
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for w, id in zip(x, y)] for x, y in zip(weights, word_ids)]
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return weights
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def scale_to_norm(weights, word_ids, w_max):
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top = np.max(weights)
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w_max = min(top, w_max)
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weights = [[w_max if id == 0 else (w / top) * w_max
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for w, id in zip(x, y)] for x, y in zip(weights, word_ids)]
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return weights
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def from_zero(weights, base_emb):
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weight_tensor = torch.tensor(weights, dtype=base_emb.dtype, device=base_emb.device)
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weight_tensor = weight_tensor.reshape(1, -1, 1).expand(base_emb.shape)
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return base_emb * weight_tensor
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def mask_word_id(tokens, word_ids, target_id, mask_token):
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new_tokens = [[mask_token if wid == target_id else t
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for t, wid in zip(x, y)] for x, y in zip(tokens, word_ids)]
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mask = np.array(word_ids) == target_id
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return (new_tokens, mask)
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def batched_clip_encode(tokens, length, encode_func, num_chunks):
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embs = []
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for e in _grouper(32, tokens):
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enc, pooled = encode_func(e)
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enc = enc.reshape((len(e), length, -1))
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embs.append(enc)
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embs = torch.cat(embs)
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embs = embs.reshape((len(tokens) // num_chunks, length * num_chunks, -1))
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return embs
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def from_masked(tokens, weights, word_ids, base_emb, length, encode_func, m_token=266):
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pooled_base = base_emb[0, length - 1:length, :]
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wids, inds = np.unique(np.array(word_ids).reshape(-1), return_index=True)
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weight_dict = dict((id, w)
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for id, w in zip(wids, np.array(weights).reshape(-1)[inds])
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if w != 1.0)
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if len(weight_dict) == 0:
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return torch.zeros_like(base_emb), base_emb[0, length - 1:length, :]
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weight_tensor = torch.tensor(weights, dtype=base_emb.dtype, device=base_emb.device)
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weight_tensor = weight_tensor.reshape(1, -1, 1).expand(base_emb.shape)
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# m_token = (clip.tokenizer.end_token, 1.0) if clip.tokenizer.pad_with_end else (0,1.0)
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# TODO: find most suitable masking token here
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m_token = (m_token, 1.0)
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ws = []
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masked_tokens = []
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masks = []
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# create prompts
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for id, w in weight_dict.items():
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masked, m = mask_word_id(tokens, word_ids, id, m_token)
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masked_tokens.extend(masked)
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m = torch.tensor(m, dtype=base_emb.dtype, device=base_emb.device)
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m = m.reshape(1, -1, 1).expand(base_emb.shape)
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masks.append(m)
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ws.append(w)
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# batch process prompts
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embs = batched_clip_encode(masked_tokens, length, encode_func, len(tokens))
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masks = torch.cat(masks)
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embs = (base_emb.expand(embs.shape) - embs)
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pooled = embs[0, length - 1:length, :]
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embs *= masks
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embs = embs.sum(axis=0, keepdim=True)
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pooled_start = pooled_base.expand(len(ws), -1)
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ws = torch.tensor(ws).reshape(-1, 1).expand(pooled_start.shape)
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pooled = (pooled - pooled_start) * (ws - 1)
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pooled = pooled.mean(axis=0, keepdim=True)
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return ((weight_tensor - 1) * embs), pooled_base + pooled
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def mask_inds(tokens, inds, mask_token):
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clip_len = len(tokens[0])
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inds_set = set(inds)
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new_tokens = [[mask_token if i * clip_len + j in inds_set else t
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for j, t in enumerate(x)] for i, x in enumerate(tokens)]
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return new_tokens
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def down_weight(tokens, weights, word_ids, base_emb, length, encode_func, m_token=266):
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w, w_inv = np.unique(weights, return_inverse=True)
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if np.sum(w < 1) == 0:
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return base_emb, tokens, base_emb[0, length - 1:length, :]
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# m_token = (clip.tokenizer.end_token, 1.0) if clip.tokenizer.pad_with_end else (0,1.0)
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# using the comma token as a masking token seems to work better than aos tokens for SD 1.x
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m_token = (m_token, 1.0)
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masked_tokens = []
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masked_current = tokens
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for i in range(len(w)):
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if w[i] >= 1:
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continue
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masked_current = mask_inds(masked_current, np.where(w_inv == i)[0], m_token)
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masked_tokens.extend(masked_current)
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embs = batched_clip_encode(masked_tokens, length, encode_func, len(tokens))
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embs = torch.cat([base_emb, embs])
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w = w[w <= 1.0]
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w_mix = np.diff([0] + w.tolist())
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w_mix = torch.tensor(w_mix, dtype=embs.dtype, device=embs.device).reshape((-1, 1, 1))
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weighted_emb = (w_mix * embs).sum(axis=0, keepdim=True)
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return weighted_emb, masked_current, weighted_emb[0, length - 1:length, :]
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def scale_emb_to_mag(base_emb, weighted_emb):
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norm_base = torch.linalg.norm(base_emb)
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norm_weighted = torch.linalg.norm(weighted_emb)
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embeddings_final = (norm_base / norm_weighted) * weighted_emb
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return embeddings_final
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def recover_dist(base_emb, weighted_emb):
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fixed_std = (base_emb.std() / weighted_emb.std()) * (weighted_emb - weighted_emb.mean())
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embeddings_final = fixed_std + (base_emb.mean() - fixed_std.mean())
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return embeddings_final
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def A1111_renorm(base_emb, weighted_emb):
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embeddings_final = (base_emb.mean() / weighted_emb.mean()) * weighted_emb
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return embeddings_final
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def advanced_encode_from_tokens(tokenized, token_normalization, weight_interpretation, encode_func, m_token=266,
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length=77, w_max=1.0, return_pooled=False, apply_to_pooled=False):
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tokens = [[t for t, _, _ in x] for x in tokenized]
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weights = [[w for _, w, _ in x] for x in tokenized]
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word_ids = [[wid for _, _, wid in x] for x in tokenized]
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# weight normalization
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# ====================
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# distribute down/up weights over word lengths
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if token_normalization.startswith("length"):
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weights = divide_length(word_ids, weights)
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# make mean of word tokens 1
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if token_normalization.endswith("mean"):
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weights = shift_mean_weight(word_ids, weights)
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# weight interpretation
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# =====================
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pooled = None
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if weight_interpretation == "comfy":
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weighted_tokens = [[(t, w) for t, w in zip(x, y)] for x, y in zip(tokens, weights)]
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weighted_emb, pooled_base = encode_func(weighted_tokens)
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pooled = pooled_base
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else:
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unweighted_tokens = [[(t, 1.0) for t, _, _ in x] for x in tokenized]
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base_emb, pooled_base = encode_func(unweighted_tokens)
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if weight_interpretation == "A1111":
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weighted_emb = from_zero(weights, base_emb)
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weighted_emb = A1111_renorm(base_emb, weighted_emb)
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pooled = pooled_base
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if weight_interpretation == "compel":
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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)]
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weighted_emb, _ = encode_func(pos_tokens)
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weighted_emb, _, pooled = down_weight(pos_tokens, weights, word_ids, weighted_emb, length, encode_func)
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if weight_interpretation == "comfy++":
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weighted_emb, tokens_down, _ = down_weight(unweighted_tokens, weights, word_ids, base_emb, length, encode_func)
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weights = [[w if w > 1.0 else 1.0 for w in x] for x in weights]
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# unweighted_tokens = [[(t,1.0) for t, _,_ in x] for x in tokens_down]
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embs, pooled = from_masked(unweighted_tokens, weights, word_ids, base_emb, length, encode_func)
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weighted_emb += embs
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if weight_interpretation == "down_weight":
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weights = scale_to_norm(weights, word_ids, w_max)
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weighted_emb, _, pooled = down_weight(unweighted_tokens, weights, word_ids, base_emb, length, encode_func)
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if return_pooled:
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if apply_to_pooled:
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return weighted_emb, pooled
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else:
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return weighted_emb, pooled_base
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return weighted_emb, None
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def encode_token_weights_g(model, token_weight_pairs):
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return model.clip_g.encode_token_weights(token_weight_pairs)
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def encode_token_weights_l(model, token_weight_pairs):
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l_out, _ = model.clip_l.encode_token_weights(token_weight_pairs)
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return l_out, None
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def encode_token_weights(model, token_weight_pairs, encode_func):
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if model.layer_idx is not None:
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model.cond_stage_model.clip_layer(model.layer_idx)
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model_management.load_model_gpu(model.patcher)
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return encode_func(model.cond_stage_model, token_weight_pairs)
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def prepareXL(embs_l, embs_g, pooled, clip_balance):
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l_w = 1 - max(0, clip_balance - .5) * 2
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g_w = 1 - max(0, .5 - clip_balance) * 2
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if embs_l is not None:
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return torch.cat([embs_l * l_w, embs_g * g_w], dim=-1), pooled
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else:
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return embs_g, pooled
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def advanced_encode(clip, text, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5,
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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):
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# Use clip text encode by smzNodes like same as a1111, when if you need installed the smzNodes
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if a1111_prompt_style:
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if "smZ CLIPTextEncode" in NODE_CLASS_MAPPINGS:
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cls = NODE_CLASS_MAPPINGS['smZ CLIPTextEncode']
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embeddings_final, = cls().encode(clip, text,weight_interpretation, True, True, False, False, 6, 1024, 1024, 0, 0, 1024, 1024, '', '', steps)
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return embeddings_final
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else:
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raise Exception(f"[smzNodes Not Found] you need to install 'ComfyUI-smzNodes'")
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tokenized = clip.tokenize(text, return_word_ids=True)
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if isinstance(clip.cond_stage_model, (SDXLClipModel, SDXLRefinerClipModel, SDXLClipG)):
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embs_l = None
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embs_g = None
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pooled = None
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if 'l' in tokenized and isinstance(clip.cond_stage_model, SDXLClipModel):
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embs_l, _ = advanced_encode_from_tokens(tokenized['l'],
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token_normalization,
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weight_interpretation,
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lambda x: encode_token_weights(clip, x, encode_token_weights_l),
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w_max=w_max,
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return_pooled=False)
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if 'g' in tokenized:
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embs_g, pooled = advanced_encode_from_tokens(tokenized['g'],
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token_normalization,
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weight_interpretation,
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lambda x: encode_token_weights(clip, x,
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encode_token_weights_g),
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w_max=w_max,
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return_pooled=True,
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apply_to_pooled=apply_to_pooled)
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embeddings_final, pooled = prepareXL(embs_l, embs_g, pooled, clip_balance)
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return [[embeddings_final, {"pooled_output": pooled, "width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]]
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else:
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embeddings_final, pooled = advanced_encode_from_tokens(tokenized['l'],
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token_normalization,
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weight_interpretation,
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lambda x: (clip.encode_from_tokens({'l': x}), None),
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w_max=w_max)
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return [[embeddings_final, {"pooled_output": pooled}]]
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