90 lines
2.9 KiB
Python
90 lines
2.9 KiB
Python
import os
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import folder_paths
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from ..utils.hash import calc_hash
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from ..utils.embedding import get_embedding_file_path
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from comfy.sd1_clip import escape_important, token_weights, unescape_important
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from comfy.sd1_clip import SD1Tokenizer
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from comfy.text_encoders.sd2_clip import SD2Tokenizer
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from comfy.sdxl_clip import SDXLTokenizer
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cache_model_hash = {}
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def calc_model_hash(model_name, input_data):
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filename = folder_paths.get_full_path("checkpoints", model_name)
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return calc_hash(filename)
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def calc_vae_hash(model_name, input_data):
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filename = folder_paths.get_full_path("vae", model_name)
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return calc_hash(filename)
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def calc_lora_hash(model_name, input_data):
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filename = folder_paths.get_full_path("loras", model_name)
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return calc_hash(filename)
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def convert_skip_clip(stop_at_clip_layer, input_data):
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return stop_at_clip_layer * -1
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def get_scaled_width(scaled_by, input_data):
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samples = input_data["samples"][0]["samples"]
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return round(samples.shape[3] * scaled_by * 8)
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def get_scaled_height(scaled_by, input_data):
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samples = input_data["samples"][0]["samples"]
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return round(samples.shape[2] * scaled_by * 8)
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def extract_embedding_names(text, input_data):
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embedding_names, _ = _extract_embedding_names(text, input_data)
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return [os.path.basename(embedding_name) for embedding_name in embedding_names]
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def extract_embedding_hashes(text, input_data):
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embedding_names, tokenizer = _extract_embedding_names(text, input_data)
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embedding_hashes = []
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for embedding_name in embedding_names:
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embedding_file_path = get_embedding_file_path(embedding_name, tokenizer)
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embedding_hashes.append(calc_hash(embedding_file_path))
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return embedding_hashes
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def _extract_embedding_names(text, input_data):
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clip = input_data["clip"][0]
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tokenizer = clip.tokenizer
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if isinstance(tokenizer, SD1Tokenizer):
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tokenizer = tokenizer.clip_l
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elif isinstance(tokenizer, SD2Tokenizer):
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tokenizer = tokenizer.clip_h
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elif isinstance(tokenizer, SDXLTokenizer):
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tokenizer = tokenizer.clip_l
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text = escape_important(text)
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parsed_weights = token_weights(text, 1.0)
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# tokenize words
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embedding_names = []
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for weighted_segment, weight in parsed_weights:
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to_tokenize = unescape_important(weighted_segment).replace("\n", " ").split(" ")
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to_tokenize = [x for x in to_tokenize if x != ""]
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for word in to_tokenize:
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# if we find an embedding, deal with the embedding
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if (
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hasattr(tokenizer, "embedding_identifier")
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and word.startswith(tokenizer.embedding_identifier)
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and tokenizer.embedding_directory is not None
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):
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embedding_name = word[len(tokenizer.embedding_identifier) :].strip("\n")
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embedding_names.append(embedding_name)
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return embedding_names, tokenizer
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