From e71f278e7836a183d8ce99cc840a6a1574ea07ad Mon Sep 17 00:00:00 2001 From: wailovet Date: Thu, 23 May 2024 23:42:04 +0800 Subject: [PATCH] 'update' --- ADV_CLIP_emb_encode.py | 294 +++++++++++++++++++++++++++++++++++++++++ __init__.py | 26 ++-- mz_prompt_utils.py | 6 + 3 files changed, 308 insertions(+), 18 deletions(-) create mode 100644 ADV_CLIP_emb_encode.py diff --git a/ADV_CLIP_emb_encode.py b/ADV_CLIP_emb_encode.py new file mode 100644 index 0000000..db7c245 --- /dev/null +++ b/ADV_CLIP_emb_encode.py @@ -0,0 +1,294 @@ +import torch +import numpy as np +import itertools +from math import gcd + +from comfy import model_management +from comfy.sdxl_clip import SDXLClipModel, SDXLRefinerClipModel, SDXLClipG + +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: + if hasattr(model.cond_stage_model, 'set_clip_options'): + model.cond_stage_model.set_clip_options({"layer": model.layer_idx}) + else: + print(f"[ComfyUI_ADV_CLIP_emb] ComfyUI is outdated.") + 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(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) + return prepareXL(embs_l, embs_g, pooled, clip_balance) + else: + return advanced_encode_from_tokens(tokenized['l'], + token_normalization, + weight_interpretation, + lambda x: (clip.encode_from_tokens({'l': 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) diff --git a/__init__.py b/__init__.py index 23ae9af..62554d2 100644 --- a/__init__.py +++ b/__init__.py @@ -172,9 +172,7 @@ class MZ_LLama3CLIPTextEncode: conditionings = None clip = kwargs.get("clip", None) if clip is not None: - tokens = clip.tokenize(text) - cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True,) - conditionings = [[cond, {"pooled_output": pooled}]] + conditionings = Utils.a1111_clip_text_encode(clip, text, ) return {"ui": {"string": [text,]}, "result": (text, conditionings)} @@ -229,9 +227,7 @@ class MZ_Phi3CLIPTextEncode: conditionings = None clip = kwargs.get("clip", None) if clip is not None: - tokens = clip.tokenize(text) - cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True,) - conditionings = [[cond, {"pooled_output": pooled}]] + conditionings = Utils.a1111_clip_text_encode(clip, text, ) return {"ui": {"string": [text,]}, "result": (text, conditionings)} @@ -277,9 +273,7 @@ class MZ_BaseLLamaCPPCLIPTextEncode: conditionings = None clip = kwargs.get("clip", None) if clip is not None: - tokens = clip.tokenize(text) - cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True,) - conditionings = [[cond, {"pooled_output": pooled}]] + conditionings = Utils.a1111_clip_text_encode(clip, text, ) return {"ui": {"string": [text,]}, "result": (text, conditionings)} @@ -345,9 +339,8 @@ class MZ_LLavaImageInterrogator: conditionings = None clip = kwargs.get("clip", None) if clip is not None: - tokens = clip.tokenize(text) - cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True,) - conditionings = [[cond, {"pooled_output": pooled}]] + conditionings = Utils.a1111_clip_text_encode(clip, text, ) + return {"ui": {"string": [text,]}, "result": (text, conditionings)} @@ -388,9 +381,8 @@ class MZ_BaseLLavaImageInterrogator: conditionings = None clip = kwargs.get("clip", None) if clip is not None: - tokens = clip.tokenize(text) - cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True,) - conditionings = [[cond, {"pooled_output": pooled}]] + conditionings = Utils.a1111_clip_text_encode(clip, text, ) + return {"ui": {"string": [text,]}, "result": (text, conditionings)} @@ -475,9 +467,7 @@ class MZ_OpenAIApiCLIPTextEncode: conditionings = None clip = kwargs.get("clip", None) if clip is not None: - tokens = clip.tokenize(text) - cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True,) - conditionings = [[cond, {"pooled_output": pooled}]] + conditionings = Utils.a1111_clip_text_encode(clip, text, ) return {"ui": {"string": [text,]}, "result": (text, conditionings)} diff --git a/mz_prompt_utils.py b/mz_prompt_utils.py index ec573d5..b061291 100644 --- a/mz_prompt_utils.py +++ b/mz_prompt_utils.py @@ -145,6 +145,12 @@ class Utils: cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) return [[cond, {"pooled_output": pooled}]] + def a1111_clip_text_encode(clip, text): + from . import ADV_CLIP_emb_encode + cond, pooled = ADV_CLIP_emb_encode.advanced_encode( + clip, text, "none", "A1111", w_max=1.0, apply_to_pooled=False) + return [[cond, {"pooled_output": pooled}]] + def cache_get(key): return CACHE_POOL.get(key, None)