910 lines
48 KiB
Python
910 lines
48 KiB
Python
import torch
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import os
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import folder_paths
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import comfy.utils
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import logging
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import re
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from comfy.sd1_clip import token_weights, escape_important, unescape_important
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from .nodes_debug import PromptEmbeddingFixer, InspectEmbeddingForClip
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def tokenize_preserving_weights(clip, text):
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"""
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Custom tokenizer helper that bypasses 'disable_weights=True' enforced by some
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tokenizers (e.g., Flux/Mistral/Qwen) by manually implementing the tokenization loop
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and weight parsing, ensuring weights are always respected.
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"""
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tokenizer = clip.tokenizer
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out = {}
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potential_parts = [
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'clip_l', 'clip_g', 'clip_h',
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't5xxl', 't5xl', 't5base', 'pile_t5xl', 'umt5xxl',
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'qwen25_7b', 'qwen25_3b', 'qwen3_4b', 'qwen3_8b', 'qwen3_2b', 'qwen3_06b',
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'mistral3_24b',
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'gemma2_2b', 'gemma3_4b', 'gemma3_12b',
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'jina_clip_2', 'gemma', 'jina', 'byt5_small',
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'llama'
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]
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# Dynamic discovery for SD1Tokenizer subclasses (like Flux2, Klein)
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# which store the sub-tokenizer attribute name in 'self.clip'
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if hasattr(tokenizer, "clip") and isinstance(tokenizer.clip, str):
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if tokenizer.clip not in potential_parts:
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potential_parts.append(tokenizer.clip)
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found_any = False
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# Try standard tokenization first to get a baseline for all parts
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# This ensures we don't lose parts that we can't manually tokenize
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try:
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# Some tokenizers might not support llama_template, so we try with and without
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try:
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out = clip.tokenize(text, llama_template="{}")
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except TypeError:
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out = clip.tokenize(text)
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except Exception:
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out = {}
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for attr in potential_parts:
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if hasattr(tokenizer, attr):
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sub_tok = getattr(tokenizer, attr)
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# Check for essential attributes to perform manual tokenization
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if hasattr(sub_tok, "tokenizer") and hasattr(sub_tok, "tokens_start"):
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try:
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# Manual parsing to ensure weights are respected
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# We skip the 'disable_weights' check entirely by implementing the logic ourselves
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# 1. Parse weights
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text_processed = escape_important(text)
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parsed_weights = token_weights(text_processed, 1.0)
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# 2. Tokenize segments
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tokens = []
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embedding_identifier = getattr(sub_tok, "embedding_identifier", "embedding:")
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embedding_directory = getattr(sub_tok, "embedding_directory", None)
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for weighted_segment, weight in parsed_weights:
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to_tokenize = unescape_important(weighted_segment)
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# Handle embeddings (copied from SDTokenizer logic)
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split = re.split(' {0}|\n{0}'.format(embedding_identifier), to_tokenize)
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to_tokenize_list = [split[0]]
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for i in range(1, len(split)):
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to_tokenize_list.append("{}{}".format(embedding_identifier, split[i]))
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to_tokenize_list = [x for x in to_tokenize_list if x != ""]
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for word in to_tokenize_list:
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# Embedding check
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if word.startswith(embedding_identifier) and embedding_directory is not None:
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if hasattr(sub_tok, "_try_get_embedding"):
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embedding_name = word[len(embedding_identifier):].strip('\n')
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embed, leftover = sub_tok._try_get_embedding(embedding_name)
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if embed is None:
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logging.warning(f"EmbeddingToolkit: warning, embedding:{embedding_name} does not exist, ignoring")
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else:
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if len(embed.shape) == 1:
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tokens.append([(embed, weight)])
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else:
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tokens.append([(embed[x], weight) for x in range(embed.shape[0])])
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if leftover != "":
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word = leftover
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else:
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continue
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# Tokenize
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end = 999999999999
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if getattr(sub_tok, "tokenizer_adds_end_token", False):
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end = -1
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start = getattr(sub_tok, "tokens_start", 0)
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# HF Tokenizer call
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hf_tokens = sub_tok.tokenizer(word)["input_ids"]
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# Slice
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sliced = hf_tokens[start:end] if end != 999999999999 else hf_tokens[start:]
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tokens.append([(t, weight) for t in sliced])
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# 3. Batching (replicating SDTokenizer logic)
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batched_tokens = []
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batch = []
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start_token = getattr(sub_tok, "start_token", None)
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end_token = getattr(sub_tok, "end_token", None)
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pad_token = getattr(sub_tok, "pad_token", 0)
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max_length = getattr(sub_tok, "max_length", 77)
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max_word_length = getattr(sub_tok, "max_word_length", 8)
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pad_to_max_length = getattr(sub_tok, "pad_to_max_length", True)
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min_length = getattr(sub_tok, "min_length", None)
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min_padding = getattr(sub_tok, "min_padding", None)
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pad_left = getattr(sub_tok, "pad_left", False) # Mistral uses left padding!
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if start_token is not None:
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batch.append((start_token, 1.0, 0))
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for i, t_group in enumerate(tokens):
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is_large = len(t_group) >= max_word_length
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has_end_token = 1 if end_token is not None else 0
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while len(t_group) > 0:
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if len(t_group) + len(batch) > max_length - has_end_token:
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remaining_length = max_length - len(batch) - has_end_token
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if is_large:
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batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]])
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if end_token is not None:
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batch.append((end_token, 1.0, 0))
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t_group = t_group[remaining_length:]
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else:
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if end_token is not None:
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batch.append((end_token, 1.0, 0))
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# Padding
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if pad_to_max_length:
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pad_amt = max_length - len(batch)
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if pad_left:
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for _ in range(pad_amt): batch.insert(0, (pad_token, 1.0, 0))
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else:
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batch.extend([(pad_token, 1.0, 0)] * pad_amt)
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batched_tokens.append(batch)
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batch = []
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if start_token is not None:
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batch.append((start_token, 1.0, 0))
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else:
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batch.extend([(t,w,i+1) for t,w in t_group])
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t_group = []
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# Last batch
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if end_token is not None:
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batch.append((end_token, 1.0, 0))
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# Min padding
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if min_padding is not None:
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pad_amt = min_padding
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if pad_left:
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for _ in range(pad_amt): batch.insert(0, (pad_token, 1.0, 0))
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else:
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batch.extend([(pad_token, 1.0, 0)] * pad_amt)
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# Pad to max
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if pad_to_max_length and len(batch) < max_length:
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pad_amt = max_length - len(batch)
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if pad_left:
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for _ in range(pad_amt): batch.insert(0, (pad_token, 1.0, 0))
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else:
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batch.extend([(pad_token, 1.0, 0)] * pad_amt)
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# Min length
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if min_length is not None and len(batch) < min_length:
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pad_amt = min_length - len(batch)
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if pad_left:
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for _ in range(pad_amt): batch.insert(0, (pad_token, 1.0, 0))
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else:
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batch.extend([(pad_token, 1.0, 0)] * pad_amt)
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batched_tokens.append(batch)
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# Map key
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key = attr
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if attr.startswith("clip_") and len(attr) == 6:
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key = attr[5:]
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out[key] = batched_tokens
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found_any = True
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except Exception as e:
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print(f"EmbeddingToolkit: Error manually tokenizing for '{attr}': {e}")
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import traceback
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traceback.print_exc()
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# If manual tokenization was attempted and succeeded for at least one part,
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# 'out' now contains a mix of standard tokenization (for skipped parts)
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# and manual tokenization (for parts that needed weight preservation).
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# If no manual tokenization happened, we rely entirely on the initial clip.tokenize result.
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if not found_any and not out:
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# If both failed (e.g. clip.tokenize failed earlier and no manual parts found)
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# Try one last time or return empty
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return clip.tokenize(text, llama_template="{}")
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return out
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class SaveTokenEmbeddings:
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def __init__(self):
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self.output_dir_list = folder_paths.get_folder_paths("embeddings")
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if not self.output_dir_list:
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print("SaveTokenEmbeddings: Warning: 'embeddings' folder type not found. Falling back to main output directory.")
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self.output_dir_list = [folder_paths.get_output_directory()]
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"clip": ("CLIP",),
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"text": ("STRING", {"multiline": True, "dynamicPrompts": True}),
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"slice_bos_eos": ("BOOLEAN", {"default": False}),
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"filename_prefix": ("STRING", {"default": "token_embeds"}),
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}
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}
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RETURN_TYPES = ()
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FUNCTION = "save_token_embeddings"
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OUTPUT_NODE = True
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CATEGORY = "EmbeddingToolkit"
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def save_token_embeddings(self, clip, text, slice_bos_eos, filename_prefix):
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if clip is None:
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raise RuntimeError("CLIP input is None.")
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if clip.cond_stage_model is None:
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print("SaveTokenEmbeddings: Error: clip.cond_stage_model is None.")
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return {"ui": {"text": ["Error: CLIP's cond_stage_model is None."]}}
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tokenized_text_with_weights = clip.tokenize(text, llama_template="{}")
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actual_clip_model_wrapper = clip.cond_stage_model
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sd_clip_instances = {}
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potential_clip_parts = {
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'l': 'clip_l', 'g': 'clip_g', 'h': 'clip_h',
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'pile_t5xl': 'pile_t5xl', 't5xl': 't5xl', 't5xxl': 't5xxl', 'umt5xxl': 'umt5xxl', 't5base': 't5base',
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'qwen25_7b': 'qwen25_7b', 'qwen25_3b': 'qwen25_3b', 'qwen3_4b': 'qwen3_4b', 'qwen3_8b': 'qwen3_8b', 'qwen3_2b': 'qwen3_2b', 'qwen3_06b': 'qwen3_06b',
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'mistral3_24b': 'mistral3_24b',
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'gemma2_2b': 'gemma2_2b', 'gemma3_4b': 'gemma3_4b', 'gemma3_12b': 'gemma3_12b',
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'jina_clip_2': 'jina_clip_2', 'gemma': 'gemma', 'jina': 'jina', 'byt5': 'byt5_small',
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'llama': 'llama',
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}
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for key_suffix, attr_name in potential_clip_parts.items():
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if hasattr(actual_clip_model_wrapper, attr_name):
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model_instance = getattr(actual_clip_model_wrapper, attr_name)
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if model_instance is not None and hasattr(model_instance, "process_tokens") and hasattr(model_instance, "transformer"):
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sd_clip_instances[key_suffix] = model_instance
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else:
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if model_instance is None:
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print(f"SaveTokenEmbeddings: Warning: Attribute '{attr_name}' is None. Skipping '{key_suffix}'.")
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else:
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print(f"SaveTokenEmbeddings: Warning: Attribute '{attr_name}' (type {type(model_instance)}) is not a valid SDClipModel. Skipping '{key_suffix}'.")
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if not sd_clip_instances and hasattr(actual_clip_model_wrapper, "process_tokens") and hasattr(actual_clip_model_wrapper, "transformer"):
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print(f"SaveTokenEmbeddings: Warning: No standard attributes. Fallback: cond_stage_model as 'l'.")
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sd_clip_instances['l'] = actual_clip_model_wrapper
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if not sd_clip_instances:
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print(f"SaveTokenEmbeddings: Error: No valid SDClipModel instances found.")
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return {"ui": {"text": ["Error: No valid SDClipModel instances found."]}}
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tensors_to_save = {}
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for key_suffix, sd_clip_model_inst in sd_clip_instances.items():
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current_token_segments_with_weights = None
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if isinstance(tokenized_text_with_weights, dict):
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current_token_segments_with_weights = tokenized_text_with_weights.get(key_suffix)
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elif key_suffix == 'l' and not isinstance(tokenized_text_with_weights, dict):
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current_token_segments_with_weights = tokenized_text_with_weights
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if not current_token_segments_with_weights:
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print(f"SaveTokenEmbeddings: Warning: No token data for '{key_suffix}'. Skipping.")
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continue
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if not hasattr(sd_clip_model_inst, 'transformer') or sd_clip_model_inst.transformer is None:
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print(f"SaveTokenEmbeddings: Error: SDClipModel for '{key_suffix}' has invalid 'transformer'. Skipping.")
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continue
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device = sd_clip_model_inst.transformer.get_input_embeddings().weight.device
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tokens_for_process_tokens_method = []
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for segment_with_weights in current_token_segments_with_weights:
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tokens_for_process_tokens_method.append([item[0] for item in segment_with_weights])
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if not tokens_for_process_tokens_method:
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print(f"SaveTokenEmbeddings: Warning: No token ID segments for '{key_suffix}'. Skipping.")
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continue
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embeds_out_full, attention_mask_full, num_tokens, embeds_info = sd_clip_model_inst.process_tokens(
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tokens_for_process_tokens_method, device
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)
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print(f"Returned values: num_tokens={num_tokens}, embeds_info={embeds_info}")
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all_actual_embeddings_for_this_clip_part = []
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num_segments = embeds_out_full.shape[0]
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for s in range(num_segments):
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current_segment_embeds = embeds_out_full[s]
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current_segment_mask = attention_mask_full[s]
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actual_embeddings_this_segment = current_segment_embeds[current_segment_mask == 1]
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if actual_embeddings_this_segment.shape[0] > 0:
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all_actual_embeddings_for_this_clip_part.append(actual_embeddings_this_segment)
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if not all_actual_embeddings_for_this_clip_part:
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print(f"SaveTokenEmbeddings: Warning: No actual tokens for '{key_suffix}'. Skipping.")
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continue
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if slice_bos_eos:
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final_embeddings_for_this_clip_part = torch.cat(all_actual_embeddings_for_this_clip_part, dim=0)
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num_tokens_in_final = final_embeddings_for_this_clip_part.shape[0]
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if key_suffix == 'l' or key_suffix == 'g':
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if num_tokens_in_final >= 2:
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final_embeddings_for_this_clip_part = final_embeddings_for_this_clip_part[1:-1]
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elif num_tokens_in_final > 0:
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final_embeddings_for_this_clip_part = final_embeddings_for_this_clip_part[0:0]
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elif key_suffix.startswith('t5') or key_suffix.startswith('umt5') or key_suffix.startswith('pile'):
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if num_tokens_in_final >= 1:
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final_embeddings_for_this_clip_part = final_embeddings_for_this_clip_part[:-1]
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else:
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final_embeddings_for_this_clip_part = torch.cat(all_actual_embeddings_for_this_clip_part, dim=0)
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if final_embeddings_for_this_clip_part.shape[0] == 0:
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print(f"SaveTokenEmbeddings: Warning: No embeddings left for '{key_suffix}' after slicing. Skipping.")
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continue
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if key_suffix.startswith('t5') or key_suffix.startswith('umt5') or key_suffix.startswith('pile') or key_suffix.startswith('qwen') or key_suffix.startswith('mistral') or key_suffix.startswith('gemma') or key_suffix.startswith('jina') or key_suffix.startswith('byt5') or key_suffix.startswith('llama'):
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save_key_in_file = key_suffix
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else:
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save_key_in_file = f"clip_{key_suffix}"
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tensors_to_save[save_key_in_file] = final_embeddings_for_this_clip_part.cpu()
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print(f"SaveTokenEmbeddings: Processed unweighted for '{save_key_in_file}': shape {final_embeddings_for_this_clip_part.shape}")
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if not tensors_to_save:
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print("SaveTokenEmbeddings: Error: No unweighted embeddings generated.")
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return {"ui": {"text": ["Error: No unweighted embeddings generated."]}}
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subfolder_in_prefix, filename_base = os.path.split(filename_prefix)
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primary_output_dir = self.output_dir_list[0]
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full_output_folder = os.path.join(primary_output_dir, subfolder_in_prefix)
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if subfolder_in_prefix:
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os.makedirs(full_output_folder, exist_ok=True)
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metadata = {}
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counter = 1
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max_counter = 99999
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while True:
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save_filename = f"{filename_base}_{counter:05}.safetensors"
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save_path = os.path.join(full_output_folder, save_filename)
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if not os.path.exists(save_path):
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break
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counter += 1
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if counter > max_counter:
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print(f"SaveTokenEmbeddings: Warning: Max file attempts for {filename_base}.")
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return {"ui": {"text": [f"Error: Max files for {filename_base}"]}}
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print(f"SaveTokenEmbeddings: Saving unweighted to: {save_path}")
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comfy.utils.save_torch_file(tensors_to_save, save_path, metadata=metadata)
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return {"ui": {"text": [f"Saved unweighted to {save_path}"]}}
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class SaveWeightedEmbeddings:
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def __init__(self):
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self.output_dir_list = folder_paths.get_folder_paths("embeddings")
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if not self.output_dir_list:
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print("SaveWeightedEmbeddings: Warning: 'embeddings' folder type not found. Falling back to main output directory.")
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self.output_dir_list = [folder_paths.get_output_directory()]
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"clip": ("CLIP",),
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"text": ("STRING", {"multiline": True, "dynamicPrompts": True}),
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"slice_bos_eos": ("BOOLEAN", {"default": False}),
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"filename_prefix": ("STRING", {"default": "weighted_embed"}),
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}
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}
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RETURN_TYPES = ()
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FUNCTION = "save_weighted_embeddings"
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OUTPUT_NODE = True
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CATEGORY = "EmbeddingToolkit"
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def save_weighted_embeddings(self, clip, text, slice_bos_eos, filename_prefix):
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if clip is None:
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raise RuntimeError("CLIP input is None.")
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if clip.cond_stage_model is None:
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print("SaveWeightedEmbeddings: Error: clip.cond_stage_model is None.")
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return {"ui": {"text": ["Error: CLIP's cond_stage_model is None."]}}
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# Use custom tokenizer to ensure we get weights even for Flux/Mistral/Qwen
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tokenized_text_with_weights = tokenize_preserving_weights(clip, text)
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actual_clip_model_wrapper = clip.cond_stage_model
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sd_clip_instances = {}
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potential_clip_parts = {
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'l': 'clip_l', 'g': 'clip_g', 'h': 'clip_h',
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'pile_t5xl': 'pile_t5xl', 't5xl': 't5xl', 't5xxl': 't5xxl', 'umt5xxl': 'umt5xxl', 't5base': 't5base',
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'qwen25_7b': 'qwen25_7b', 'qwen25_3b': 'qwen25_3b', 'qwen3_4b': 'qwen3_4b', 'qwen3_8b': 'qwen3_8b', 'qwen3_2b': 'qwen3_2b', 'qwen3_06b': 'qwen3_06b',
|
|
'mistral3_24b': 'mistral3_24b',
|
|
'gemma2_2b': 'gemma2_2b', 'gemma3_4b': 'gemma3_4b', 'gemma3_12b': 'gemma3_12b',
|
|
'jina_clip_2': 'jina_clip_2', 'gemma': 'gemma', 'jina': 'jina', 'byt5': 'byt5_small',
|
|
'llama': 'llama',
|
|
}
|
|
|
|
for key_suffix, attr_name in potential_clip_parts.items():
|
|
if hasattr(actual_clip_model_wrapper, attr_name):
|
|
model_instance = getattr(actual_clip_model_wrapper, attr_name)
|
|
if model_instance is not None and hasattr(model_instance, "process_tokens") and hasattr(model_instance, "transformer"):
|
|
sd_clip_instances[key_suffix] = model_instance
|
|
else:
|
|
if model_instance is None:
|
|
print(f"SaveWeightedEmbeddings: Warning: Attribute '{attr_name}' is None. Skipping '{key_suffix}'.")
|
|
else:
|
|
print(f"SaveWeightedEmbeddings: Warning: Attribute '{attr_name}' (type {type(model_instance)}) is not a valid SDClipModel. Skipping '{key_suffix}'.")
|
|
|
|
if not sd_clip_instances and hasattr(actual_clip_model_wrapper, "process_tokens") and hasattr(actual_clip_model_wrapper, "transformer"):
|
|
print(f"SaveWeightedEmbeddings: Warning: No standard attributes. Fallback: cond_stage_model as 'l'.")
|
|
sd_clip_instances['l'] = actual_clip_model_wrapper
|
|
|
|
if not sd_clip_instances:
|
|
print(f"SaveWeightedEmbeddings: Error: No valid SDClipModel instances found.")
|
|
return {"ui": {"text": ["Error: No valid SDClipModel instances found."]}}
|
|
|
|
tensors_to_save = {}
|
|
for key_suffix, sd_clip_model_inst in sd_clip_instances.items():
|
|
current_prompt_segments_with_weights = None
|
|
if isinstance(tokenized_text_with_weights, dict):
|
|
current_prompt_segments_with_weights = tokenized_text_with_weights.get(key_suffix)
|
|
elif key_suffix == 'l' and not isinstance(tokenized_text_with_weights, dict):
|
|
current_prompt_segments_with_weights = tokenized_text_with_weights
|
|
|
|
if not current_prompt_segments_with_weights:
|
|
print(f"SaveWeightedEmbeddings: Warning: No token data for '{key_suffix}'. Skipping.")
|
|
continue
|
|
|
|
if not hasattr(sd_clip_model_inst, 'transformer') or sd_clip_model_inst.transformer is None:
|
|
print(f"SaveWeightedEmbeddings: Error: SDClipModel for '{key_suffix}' has invalid 'transformer'. Skipping.")
|
|
continue
|
|
device = sd_clip_model_inst.transformer.get_input_embeddings().weight.device
|
|
|
|
prompt_token_id_segments = []
|
|
max_segment_len = 0
|
|
for segment_w_weights in current_prompt_segments_with_weights:
|
|
prompt_token_id_segments.append([item[0] for item in segment_w_weights])
|
|
max_segment_len = max(max_segment_len, len(segment_w_weights))
|
|
|
|
if not prompt_token_id_segments:
|
|
print(f"SaveWeightedEmbeddings: Warning: No token ID segments for '{key_suffix}'. Skipping.")
|
|
continue
|
|
|
|
empty_token_sequence = []
|
|
if hasattr(sd_clip_model_inst, "gen_empty_tokens") and hasattr(sd_clip_model_inst, "special_tokens"):
|
|
empty_token_sequence = sd_clip_model_inst.gen_empty_tokens(sd_clip_model_inst.special_tokens, max_segment_len)
|
|
else:
|
|
pad_token_id = None
|
|
if hasattr(sd_clip_model_inst, "special_tokens") and "pad" in sd_clip_model_inst.special_tokens:
|
|
pad_token_id = sd_clip_model_inst.special_tokens["pad"]
|
|
|
|
if pad_token_id is not None:
|
|
print(f"SaveWeightedEmbeddings: Warning: '{key_suffix}' SDClipModel instance missing 'gen_empty_tokens' or 'special_tokens'. Using simple pad token sequence for empty.")
|
|
empty_token_sequence = [pad_token_id] * max_segment_len
|
|
else:
|
|
print(f"SaveWeightedEmbeddings: Error: Cannot determine pad token for '{key_suffix}'. Skipping weighting for this part.")
|
|
continue
|
|
|
|
all_token_sequences_for_process_tokens = prompt_token_id_segments + [empty_token_sequence]
|
|
embeds_combined, attention_mask_combined, num_tokens, embeds_info = sd_clip_model_inst.process_tokens(
|
|
all_token_sequences_for_process_tokens, device
|
|
)
|
|
print(f"Returned values: num_tokens={num_tokens}, embeds_info={embeds_info}")
|
|
|
|
num_prompt_segments = len(prompt_token_id_segments)
|
|
prompt_embeds_full_segments = embeds_combined[:num_prompt_segments]
|
|
embeds_empty_segment_full = embeds_combined[num_prompt_segments:]
|
|
prompt_attention_mask_full_segments = attention_mask_combined[:num_prompt_segments]
|
|
|
|
all_weighted_actual_embeddings_for_this_clip_part = []
|
|
for s_idx in range(num_prompt_segments):
|
|
current_segment_raw_embeds = prompt_embeds_full_segments[s_idx]
|
|
segment_data_from_tokenizer = current_prompt_segments_with_weights[s_idx]
|
|
current_segment_mask_from_process_tokens = prompt_attention_mask_full_segments[s_idx]
|
|
weighted_segment_embeds = torch.zeros_like(current_segment_raw_embeds)
|
|
|
|
for token_idx in range(current_segment_raw_embeds.shape[0]):
|
|
is_active_token = current_segment_mask_from_process_tokens[token_idx] == 1
|
|
original_embedding = current_segment_raw_embeds[token_idx]
|
|
embed_empty_for_this_pos = embeds_empty_segment_full[0, token_idx]
|
|
|
|
if is_active_token:
|
|
weight = segment_data_from_tokenizer[token_idx][1] if token_idx < len(segment_data_from_tokenizer) else 1.0
|
|
if weight == 1.0:
|
|
weighted_segment_embeds[token_idx] = original_embedding
|
|
else:
|
|
weighted_segment_embeds[token_idx] = (original_embedding - embed_empty_for_this_pos) * weight + embed_empty_for_this_pos
|
|
else:
|
|
weighted_segment_embeds[token_idx] = original_embedding
|
|
|
|
actual_weighted_embeddings_this_segment = weighted_segment_embeds[current_segment_mask_from_process_tokens == 1]
|
|
if actual_weighted_embeddings_this_segment.shape[0] > 0:
|
|
all_weighted_actual_embeddings_for_this_clip_part.append(actual_weighted_embeddings_this_segment)
|
|
|
|
if not all_weighted_actual_embeddings_for_this_clip_part:
|
|
print(f"SaveWeightedEmbeddings: Warning: No actual weighted tokens for '{key_suffix}'. Skipping.")
|
|
continue
|
|
|
|
if slice_bos_eos:
|
|
final_embeddings_for_this_clip_part = torch.cat(all_weighted_actual_embeddings_for_this_clip_part, dim=0)
|
|
|
|
num_tokens_in_final = final_embeddings_for_this_clip_part.shape[0]
|
|
if key_suffix == 'l' or key_suffix == 'g':
|
|
if num_tokens_in_final >= 2:
|
|
final_embeddings_for_this_clip_part = final_embeddings_for_this_clip_part[1:-1]
|
|
elif num_tokens_in_final > 0:
|
|
final_embeddings_for_this_clip_part = final_embeddings_for_this_clip_part[0:0]
|
|
elif key_suffix.startswith('t5') or key_suffix.startswith('umt5') or key_suffix.startswith('pile'):
|
|
if num_tokens_in_final >= 1:
|
|
final_embeddings_for_this_clip_part = final_embeddings_for_this_clip_part[:-1]
|
|
else:
|
|
final_embeddings_for_this_clip_part = torch.cat(all_weighted_actual_embeddings_for_this_clip_part, dim=0)
|
|
|
|
if final_embeddings_for_this_clip_part.shape[0] == 0:
|
|
print(f"SaveWeightedEmbeddings: Warning: No embeddings left for '{key_suffix}' after slicing. Skipping.")
|
|
continue
|
|
|
|
if key_suffix.startswith('t5') or key_suffix.startswith('umt5') or key_suffix.startswith('pile') or key_suffix.startswith('qwen') or key_suffix.startswith('mistral') or key_suffix.startswith('gemma') or key_suffix.startswith('jina') or key_suffix.startswith('byt5') or key_suffix.startswith('llama'):
|
|
save_key_in_file = key_suffix
|
|
else:
|
|
save_key_in_file = f"clip_{key_suffix}"
|
|
|
|
tensors_to_save[save_key_in_file] = final_embeddings_for_this_clip_part.cpu()
|
|
print(f"SaveWeightedEmbeddings: Processed weighted for '{save_key_in_file}': shape {final_embeddings_for_this_clip_part.shape}")
|
|
|
|
if not tensors_to_save:
|
|
print("SaveWeightedEmbeddings: Error: No weighted embeddings generated.")
|
|
return {"ui": {"text": ["Error: No weighted embeddings generated."]}}
|
|
|
|
subfolder_in_prefix, filename_base = os.path.split(filename_prefix)
|
|
primary_output_dir = self.output_dir_list[0]
|
|
full_output_folder = os.path.join(primary_output_dir, subfolder_in_prefix)
|
|
if subfolder_in_prefix:
|
|
os.makedirs(full_output_folder, exist_ok=True)
|
|
|
|
metadata = {}
|
|
counter = 1
|
|
max_counter = 99999
|
|
while True:
|
|
save_filename = f"{filename_base}_{counter:05}.safetensors"
|
|
save_path = os.path.join(full_output_folder, save_filename)
|
|
if not os.path.exists(save_path):
|
|
break
|
|
counter += 1
|
|
if counter > max_counter:
|
|
print(f"SaveWeightedEmbeddings: Warning: Max file attempts for {filename_base}.")
|
|
return {"ui": {"text": [f"Error: Max files for {filename_base}"]}}
|
|
|
|
print(f"SaveWeightedEmbeddings: Saving weighted to: {save_path}")
|
|
comfy.utils.save_torch_file(tensors_to_save, save_path, metadata=metadata)
|
|
return {"ui": {"text": [f"Saved weighted to {save_path}"]}}
|
|
|
|
class SaveA1111WeightedEmbeddings:
|
|
def __init__(self):
|
|
self.output_dir_list = folder_paths.get_folder_paths("embeddings")
|
|
if not self.output_dir_list:
|
|
print("SaveA1111WeightedEmbeddings: Warning: 'embeddings' folder type not found. Falling back to main output directory.")
|
|
self.output_dir_list = [folder_paths.get_output_directory()]
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"clip": ("CLIP",),
|
|
"text": ("STRING", {"multiline": True, "dynamicPrompts": True}),
|
|
"slice_bos_eos": ("BOOLEAN", {"default": False}),
|
|
"filename_prefix": ("STRING", {"default": "a1111_weighted_embed"}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ()
|
|
FUNCTION = "save_a1111_weighted_embeddings"
|
|
OUTPUT_NODE = True
|
|
CATEGORY = "EmbeddingToolkit"
|
|
|
|
def save_a1111_weighted_embeddings(self, clip, text, slice_bos_eos, filename_prefix):
|
|
if clip is None:
|
|
raise RuntimeError("CLIP input is None.")
|
|
if clip.cond_stage_model is None:
|
|
print("SaveA1111WeightedEmbeddings: Error: clip.cond_stage_model is None.")
|
|
return {"ui": {"text": ["Error: CLIP's cond_stage_model is None."]}}
|
|
|
|
# Use custom tokenizer to ensure we get weights even for Flux/Mistral/Qwen
|
|
tokenized_text_with_weights = tokenize_preserving_weights(clip, text)
|
|
actual_clip_model_wrapper = clip.cond_stage_model
|
|
|
|
sd_clip_instances = {}
|
|
potential_clip_parts = {
|
|
'l': 'clip_l', 'g': 'clip_g', 'h': 'clip_h',
|
|
'pile_t5xl': 'pile_t5xl', 't5xl': 't5xl', 't5xxl': 't5xxl', 'umt5xxl': 'umt5xxl', 't5base': 't5base',
|
|
'qwen25_7b': 'qwen25_7b', 'qwen25_3b': 'qwen25_3b', 'qwen3_4b': 'qwen3_4b', 'qwen3_8b': 'qwen3_8b', 'qwen3_2b': 'qwen3_2b', 'qwen3_06b': 'qwen3_06b',
|
|
'mistral3_24b': 'mistral3_24b',
|
|
'gemma2_2b': 'gemma2_2b', 'gemma3_4b': 'gemma3_4b', 'gemma3_12b': 'gemma3_12b',
|
|
'jina_clip_2': 'jina_clip_2', 'gemma': 'gemma', 'jina': 'jina', 'byt5': 'byt5_small',
|
|
'llama': 'llama',
|
|
}
|
|
|
|
for key_suffix, attr_name in potential_clip_parts.items():
|
|
if hasattr(actual_clip_model_wrapper, attr_name):
|
|
model_instance = getattr(actual_clip_model_wrapper, attr_name)
|
|
if model_instance is not None and hasattr(model_instance, "process_tokens") and hasattr(model_instance, "transformer"):
|
|
sd_clip_instances[key_suffix] = model_instance
|
|
else:
|
|
if model_instance is None:
|
|
print(f"SaveA1111WeightedEmbeddings: Warning: Attribute '{attr_name}' is None. Skipping '{key_suffix}'.")
|
|
else: print(f"SaveA1111WeightedEmbeddings: Warning: Attribute '{attr_name}' (type {type(model_instance)}) is not a valid SDClipModel. Skipping '{key_suffix}'.")
|
|
|
|
if not sd_clip_instances and hasattr(actual_clip_model_wrapper, "process_tokens") and hasattr(actual_clip_model_wrapper, "transformer"):
|
|
print(f"SaveA1111WeightedEmbeddings: Warning: No standard attributes. Fallback: cond_stage_model as 'l'.")
|
|
sd_clip_instances['l'] = actual_clip_model_wrapper
|
|
|
|
if not sd_clip_instances:
|
|
print(f"SaveA1111WeightedEmbeddings: Error: No valid SDClipModel instances found.")
|
|
return {"ui": {"text": ["Error: No valid SDClipModel instances found."]}}
|
|
|
|
tensors_to_save = {}
|
|
for key_suffix, sd_clip_model_inst in sd_clip_instances.items():
|
|
current_prompt_segments_with_weights = None
|
|
if isinstance(tokenized_text_with_weights, dict):
|
|
current_prompt_segments_with_weights = tokenized_text_with_weights.get(key_suffix)
|
|
elif key_suffix == 'l' and not isinstance(tokenized_text_with_weights, dict):
|
|
current_prompt_segments_with_weights = tokenized_text_with_weights
|
|
|
|
if not current_prompt_segments_with_weights:
|
|
print(f"SaveA1111WeightedEmbeddings: Warning: No token data for '{key_suffix}'. Skipping.")
|
|
continue
|
|
|
|
if not hasattr(sd_clip_model_inst, 'transformer') or sd_clip_model_inst.transformer is None:
|
|
print(f"SaveA1111WeightedEmbeddings: Error: SDClipModel for '{key_suffix}' has invalid 'transformer'. Skipping.")
|
|
continue
|
|
device = sd_clip_model_inst.transformer.get_input_embeddings().weight.device
|
|
|
|
prompt_token_id_segments = []
|
|
for segment_w_weights in current_prompt_segments_with_weights:
|
|
prompt_token_id_segments.append([item[0] for item in segment_w_weights])
|
|
|
|
if not prompt_token_id_segments:
|
|
print(f"SaveA1111WeightedEmbeddings: Warning: No token ID segments for '{key_suffix}'. Skipping.")
|
|
continue
|
|
|
|
embeds_out_full, attention_mask_full, num_tokens, embeds_info = sd_clip_model_inst.process_tokens(
|
|
prompt_token_id_segments, device
|
|
)
|
|
print(f"Returned values: num_tokens={num_tokens}, embeds_info={embeds_info}")
|
|
|
|
all_scaled_actual_embeddings_for_this_clip_part = []
|
|
num_prompt_segments = embeds_out_full.shape[0]
|
|
|
|
for s_idx in range(num_prompt_segments):
|
|
current_segment_raw_embeds = embeds_out_full[s_idx]
|
|
segment_data_from_tokenizer = current_prompt_segments_with_weights[s_idx]
|
|
current_segment_mask_from_process_tokens = attention_mask_full[s_idx]
|
|
|
|
scaled_segment_embeds = torch.zeros_like(current_segment_raw_embeds)
|
|
|
|
for token_idx in range(current_segment_raw_embeds.shape[0]):
|
|
is_active_token = current_segment_mask_from_process_tokens[token_idx] == 1
|
|
original_embedding = current_segment_raw_embeds[token_idx]
|
|
|
|
if is_active_token:
|
|
weight = segment_data_from_tokenizer[token_idx][1] if token_idx < len(segment_data_from_tokenizer) else 1.0
|
|
|
|
scaled_segment_embeds[token_idx] = original_embedding * weight
|
|
else:
|
|
scaled_segment_embeds[token_idx] = original_embedding
|
|
|
|
actual_scaled_embeddings_this_segment = scaled_segment_embeds[current_segment_mask_from_process_tokens == 1]
|
|
|
|
if actual_scaled_embeddings_this_segment.shape[0] > 0:
|
|
all_scaled_actual_embeddings_for_this_clip_part.append(actual_scaled_embeddings_this_segment)
|
|
|
|
if not all_scaled_actual_embeddings_for_this_clip_part:
|
|
print(f"SaveA1111WeightedEmbeddings: Warning: No actual scaled tokens found for '{key_suffix}' after processing. Skipping.")
|
|
continue
|
|
|
|
if slice_bos_eos:
|
|
final_embeddings_for_this_clip_part = torch.cat(all_scaled_actual_embeddings_for_this_clip_part, dim=0)
|
|
|
|
num_tokens_in_final = final_embeddings_for_this_clip_part.shape[0]
|
|
if key_suffix == 'l' or key_suffix == 'g':
|
|
if num_tokens_in_final >= 2:
|
|
final_embeddings_for_this_clip_part = final_embeddings_for_this_clip_part[1:-1]
|
|
elif num_tokens_in_final > 0:
|
|
final_embeddings_for_this_clip_part = final_embeddings_for_this_clip_part[0:0]
|
|
elif key_suffix.startswith('t5') or key_suffix.startswith('umt5') or key_suffix.startswith('pile'):
|
|
if num_tokens_in_final >= 1:
|
|
final_embeddings_for_this_clip_part = final_embeddings_for_this_clip_part[:-1]
|
|
else:
|
|
final_embeddings_for_this_clip_part = torch.cat(all_scaled_actual_embeddings_for_this_clip_part, dim=0)
|
|
|
|
if final_embeddings_for_this_clip_part.shape[0] == 0:
|
|
print(f"SaveA1111WeightedEmbeddings: Warning: No embeddings left for '{key_suffix}' after slicing. Skipping.")
|
|
continue
|
|
|
|
if key_suffix.startswith('t5') or key_suffix.startswith('umt5') or key_suffix.startswith('pile') or key_suffix.startswith('qwen') or key_suffix.startswith('mistral') or key_suffix.startswith('gemma') or key_suffix.startswith('jina') or key_suffix.startswith('byt5') or key_suffix.startswith('llama'):
|
|
save_key_in_file = key_suffix
|
|
else:
|
|
save_key_in_file = f"clip_{key_suffix}"
|
|
|
|
tensors_to_save[save_key_in_file] = final_embeddings_for_this_clip_part.cpu()
|
|
print(f"SaveA1111WeightedEmbeddings: Processed A1111-style weighted for '{save_key_in_file}': shape {final_embeddings_for_this_clip_part.shape}")
|
|
|
|
if not tensors_to_save:
|
|
print("SaveA1111WeightedEmbeddings: Error: No A1111-style weighted embeddings generated.")
|
|
return {"ui": {"text": ["Error: No A1111-style weighted embeddings generated."]}}
|
|
|
|
subfolder_in_prefix, filename_base = os.path.split(filename_prefix)
|
|
primary_output_dir = self.output_dir_list[0]
|
|
full_output_folder = os.path.join(primary_output_dir, subfolder_in_prefix)
|
|
if subfolder_in_prefix:
|
|
os.makedirs(full_output_folder, exist_ok=True)
|
|
|
|
metadata = {}
|
|
counter = 1
|
|
max_counter = 99999
|
|
while True:
|
|
save_filename = f"{filename_base}_{counter:05}.safetensors"
|
|
save_path = os.path.join(full_output_folder, save_filename)
|
|
if not os.path.exists(save_path):
|
|
break
|
|
counter += 1
|
|
if counter > max_counter:
|
|
print(f"SaveA1111WeightedEmbeddings: Warning: Max file attempts for {filename_base}.")
|
|
return {"ui": {"text": [f"Error: Max files for {filename_base}"]}}
|
|
|
|
print(f"SaveA1111WeightedEmbeddings: Saving A1111-style weighted to: {save_path}")
|
|
comfy.utils.save_torch_file(tensors_to_save, save_path, metadata=metadata)
|
|
return {"ui": {"text": [f"Saved A1111-style weighted to {save_path}"]}}
|
|
|
|
|
|
class SliceExistingEmbedding:
|
|
def __init__(self):
|
|
self.embeddings_dir_paths = folder_paths.get_folder_paths("embeddings")
|
|
if not self.embeddings_dir_paths:
|
|
print("SliceExistingEmbedding: Warning: 'embeddings' folder type not found. Falling back to main output directory.")
|
|
self.embeddings_dir_paths = [folder_paths.get_output_directory()]
|
|
self.primary_embeddings_dir = self.embeddings_dir_paths[0]
|
|
os.makedirs(self.primary_embeddings_dir, exist_ok=True)
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
try:
|
|
embedding_files = [f for f in folder_paths.get_filename_list("embeddings") if f.lower().endswith((".safetensors", ".pt"))]
|
|
except Exception as e:
|
|
print(f"SliceExistingEmbedding: Error listing embedding files: {e}")
|
|
embedding_files = ["None"]
|
|
|
|
if not embedding_files:
|
|
embedding_files = ["None"]
|
|
|
|
return {
|
|
"required": {
|
|
"embedding_file": (embedding_files,),
|
|
"output_filename_prefix": ("STRING", {"default": "sliced_embedding"}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ()
|
|
FUNCTION = "slice_and_save"
|
|
OUTPUT_NODE = True
|
|
CATEGORY = "EmbeddingToolkit/Utils"
|
|
|
|
def slice_and_save(self, embedding_file, output_filename_prefix):
|
|
if embedding_file == "None" or not embedding_file:
|
|
print("SliceExistingEmbedding: No embedding file selected.")
|
|
return {"ui": {"text": ["No embedding file selected."]}}
|
|
|
|
full_file_path = folder_paths.get_full_path("embeddings", embedding_file)
|
|
|
|
if full_file_path is None or not os.path.exists(full_file_path):
|
|
potential_path = os.path.join(self.primary_embeddings_dir, embedding_file)
|
|
if os.path.exists(potential_path):
|
|
full_file_path = potential_path
|
|
else:
|
|
print(f"SliceExistingEmbedding: Error: File not found: {embedding_file}")
|
|
return {"ui": {"text": [f"Error: File not found: {embedding_file}"]}}
|
|
|
|
try:
|
|
loaded_data = comfy.utils.load_torch_file(full_file_path)
|
|
metadata_key = "__metadata__"
|
|
file_metadata = {}
|
|
if isinstance(loaded_data, dict) and metadata_key in loaded_data:
|
|
file_metadata = loaded_data.pop(metadata_key)
|
|
|
|
except Exception as e:
|
|
print(f"SliceExistingEmbedding: Error loading {embedding_file}: {e}")
|
|
return {"ui": {"text": [f"Error loading {embedding_file}: {e}"]}}
|
|
|
|
if not isinstance(loaded_data, dict):
|
|
print(f"SliceExistingEmbedding: Error: Loaded data from {embedding_file} is not a dictionary (unexpected format).")
|
|
return {"ui": {"text": [f"Error: Unexpected format in {embedding_file}."]}}
|
|
|
|
sliced_tensors = {}
|
|
any_sliced = False
|
|
|
|
for key, tensor in loaded_data.items():
|
|
if not isinstance(tensor, torch.Tensor) or tensor.ndim == 0:
|
|
sliced_tensors[key] = tensor
|
|
continue
|
|
|
|
original_shape = tensor.shape
|
|
num_tokens = original_shape[0] if tensor.ndim > 0 else 0
|
|
processed_tensor = tensor
|
|
|
|
if num_tokens == 0:
|
|
print(f"SliceExistingEmbedding: Tensor for key '{key}' is already empty (shape {original_shape}). Keeping as is.")
|
|
elif key.startswith("clip_l") or key.startswith("clip_g"):
|
|
print(f"SliceExistingEmbedding: Applying CLIP-style slicing (BOS & EOS) to key '{key}' (shape {original_shape})")
|
|
if num_tokens >= 2:
|
|
processed_tensor = tensor[1:-1]
|
|
any_sliced = True
|
|
elif num_tokens > 0 :
|
|
processed_tensor = tensor[0:0]
|
|
any_sliced = True
|
|
print(f" Resulting shape: {processed_tensor.shape}")
|
|
elif key.startswith("t5") or key.startswith("umt5") or key.startswith("pile"):
|
|
print(f"SliceExistingEmbedding: Applying T5-style slicing (EOS) to key '{key}' (shape {original_shape})")
|
|
if num_tokens >= 1:
|
|
processed_tensor = tensor[:-1]
|
|
any_sliced = True
|
|
print(f" Resulting shape: {processed_tensor.shape}")
|
|
else:
|
|
print(f"SliceExistingEmbedding: Info: Key '{key}' does not match known slicing patterns. Tensor will not be sliced.")
|
|
|
|
sliced_tensors[key] = processed_tensor
|
|
|
|
if not any_sliced:
|
|
msg = f"No changes made to {embedding_file} (no applicable slicing rules or tensors already short/empty)."
|
|
print(f"SliceExistingEmbedding: {msg}")
|
|
return {"ui": {"text": [msg]}}
|
|
|
|
subfolder_in_prefix, filename_base_from_prefix = os.path.split(output_filename_prefix)
|
|
|
|
output_target_folder = os.path.join(self.primary_embeddings_dir, subfolder_in_prefix)
|
|
os.makedirs(output_target_folder, exist_ok=True)
|
|
|
|
_, input_ext = os.path.splitext(embedding_file)
|
|
output_ext = input_ext if input_ext.lower() in [".safetensors", ".pt"] else ".safetensors"
|
|
|
|
counter = 1
|
|
max_counter = 99999
|
|
final_save_path = ""
|
|
while True:
|
|
save_filename_numbered = f"{filename_base_from_prefix}_{counter:05}{output_ext}"
|
|
current_save_path_candidate = os.path.join(output_target_folder, save_filename_numbered)
|
|
|
|
if not os.path.exists(current_save_path_candidate):
|
|
final_save_path = current_save_path_candidate
|
|
break
|
|
|
|
counter += 1
|
|
if counter > max_counter:
|
|
msg = f"Max file attempts for {output_filename_prefix}. Please check your embeddings folder or prefix."
|
|
print(f"SliceExistingEmbedding: {msg}")
|
|
return {"ui": {"text": [f"Error: {msg}"]}}
|
|
|
|
try:
|
|
if file_metadata:
|
|
sliced_tensors[metadata_key] = file_metadata
|
|
comfy.utils.save_torch_file(sliced_tensors, final_save_path)
|
|
msg = f"Saved sliced embedding to {final_save_path}"
|
|
print(f"SliceExistingEmbedding: {msg}")
|
|
return {"ui": {"text": [msg]}}
|
|
except Exception as e:
|
|
msg = f"Error saving to {final_save_path}: {e}"
|
|
print(f"SliceExistingEmbedding: {msg}")
|
|
return {"ui": {"text": [f"Error saving to {final_save_path}: {e}"]}}
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"SaveTokenEmbeddings": SaveTokenEmbeddings,
|
|
"SaveWeightedEmbeddings": SaveWeightedEmbeddings,
|
|
"SaveA1111WeightedEmbeddings": SaveA1111WeightedEmbeddings,
|
|
"SliceExistingEmbedding": SliceExistingEmbedding,
|
|
"PromptEmbeddingFixer": PromptEmbeddingFixer,
|
|
"InspectEmbeddingForClip": InspectEmbeddingForClip,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"SaveTokenEmbeddings": "Save Token Embeddings",
|
|
"SaveWeightedEmbeddings": "Save Weighted Embeddings",
|
|
"SaveA1111WeightedEmbeddings": "Save A1111-style Weighted Embeddings",
|
|
"SliceExistingEmbedding": "Slice Existing Embedding File",
|
|
"PromptEmbeddingFixer": "Prompt Embedding Fixer",
|
|
"InspectEmbeddingForClip": "Inspect Embedding For Clip",
|
|
}
|
|
|
|
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] |