mirror of https://github.com/vladmandic/automatic
383 lines
17 KiB
Python
383 lines
17 KiB
Python
from typing import List, Union
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import os
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import time
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from collections import namedtuple
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import torch
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import safetensors.torch
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from PIL import Image
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from modules import shared, devices, sd_models, errors
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from modules.textual_inversion.image_embedding import embedding_from_b64, extract_image_data_embed
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from modules.files_cache import directory_files, directory_mtime, extension_filter
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debug = shared.log.trace if os.environ.get('SD_TI_DEBUG', None) is not None else lambda *args, **kwargs: None
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debug('Trace: TEXTUAL INVERSION')
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TokenToAdd = namedtuple("TokenToAdd", ["clip_l", "clip_g"])
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TextualInversionTemplate = namedtuple("TextualInversionTemplate", ["name", "path"])
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textual_inversion_templates = {}
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def list_textual_inversion_templates():
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textual_inversion_templates.clear()
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for root, _dirs, fns in os.walk(shared.opts.embeddings_templates_dir):
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for fn in fns:
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path = os.path.join(root, fn)
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textual_inversion_templates[fn] = TextualInversionTemplate(fn, path)
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return textual_inversion_templates
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def list_embeddings(*dirs):
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is_ext = extension_filter(['.SAFETENSORS', '.PT' ] + ( ['.PNG', '.WEBP', '.JXL', '.AVIF', '.BIN' ] if shared.backend != shared.Backend.DIFFUSERS else [] ))
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is_not_preview = lambda fp: not next(iter(os.path.splitext(fp))).upper().endswith('.PREVIEW') # pylint: disable=unnecessary-lambda-assignment
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return list(filter(lambda fp: is_ext(fp) and is_not_preview(fp) and os.stat(fp).st_size > 0, directory_files(*dirs)))
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class Embedding:
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def __init__(self, vec, name, filename=None, step=None):
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self.vec = vec
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self.name = name
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self.tag = name
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self.step = step
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self.filename = filename
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self.basename = os.path.relpath(filename, shared.opts.embeddings_dir) if filename is not None else None
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self.shape = None
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self.vectors = 0
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self.cached_checksum = None
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self.sd_checkpoint = None
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self.sd_checkpoint_name = None
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self.optimizer_state_dict = None
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def save(self, filename):
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embedding_data = {
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"string_to_token": {"*": 265},
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"string_to_param": {"*": self.vec},
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"name": self.name,
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"step": self.step,
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"sd_checkpoint": self.sd_checkpoint,
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"sd_checkpoint_name": self.sd_checkpoint_name,
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}
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torch.save(embedding_data, filename)
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if shared.opts.save_optimizer_state and self.optimizer_state_dict is not None:
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optimizer_saved_dict = {
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'hash': self.checksum(),
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'optimizer_state_dict': self.optimizer_state_dict,
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}
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torch.save(optimizer_saved_dict, f"{filename}.optim")
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def checksum(self):
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if self.cached_checksum is not None:
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return self.cached_checksum
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def const_hash(a):
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r = 0
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for v in a:
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r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
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return r
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self.cached_checksum = f'{const_hash(self.vec.reshape(-1) * 100) & 0xffff:04x}'
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return self.cached_checksum
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class DirWithTextualInversionEmbeddings:
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def __init__(self, path):
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self.path = path
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self.mtime = None
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def has_changed(self):
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if not os.path.isdir(self.path):
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return False
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return directory_mtime(self.path) != self.mtime
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def update(self):
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if not os.path.isdir(self.path):
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return
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self.mtime = directory_mtime(self.path)
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def convert_embedding(tensor, text_encoder, text_encoder_2):
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with torch.no_grad():
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vectors = []
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clip_l_embeds = text_encoder.get_input_embeddings().weight.data.clone().to(device=devices.device)
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tensor = tensor.to(device=devices.device)
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for vec in tensor:
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values, indices = torch.max(torch.nan_to_num(torch.cosine_similarity(vec.unsqueeze(0), clip_l_embeds)), 0)
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if values < 0.707: # Arbitrary similarity to cutoff, here 45 degrees
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indices *= 0 # Use SDXL padding vector 0
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vectors.append(indices)
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vectors = torch.stack(vectors)
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output = text_encoder_2.get_input_embeddings().weight.data[vectors]
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return output
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class EmbeddingDatabase:
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def __init__(self):
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self.ids_lookup = {}
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self.word_embeddings = {}
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self.skipped_embeddings = {}
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self.expected_shape = -1
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self.embedding_dirs = {}
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self.previously_displayed_embeddings = ()
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self.embeddings_used = []
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def add_embedding_dir(self, path):
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self.embedding_dirs[path] = DirWithTextualInversionEmbeddings(path)
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def clear_embedding_dirs(self):
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self.embedding_dirs.clear()
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def register_embedding(self, embedding, model):
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self.word_embeddings[embedding.name] = embedding
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if hasattr(model, 'cond_stage_model'):
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ids = model.cond_stage_model.tokenize([embedding.name])[0]
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elif hasattr(model, 'tokenizer'):
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ids = model.tokenizer.convert_tokens_to_ids(embedding.name)
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if type(ids) != list:
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ids = [ids]
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first_id = ids[0]
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if first_id not in self.ids_lookup:
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self.ids_lookup[first_id] = []
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self.ids_lookup[first_id] = sorted(self.ids_lookup[first_id] + [(ids, embedding)], key=lambda x: len(x[0]), reverse=True)
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return embedding
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def get_expected_shape(self):
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if shared.backend == shared.Backend.DIFFUSERS:
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return 0
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if not shared.sd_loaded:
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shared.log.error('Model not loaded')
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return 0
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vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1)
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return vec.shape[1]
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def load_diffusers_embedding(self, filename: Union[str, List[str]]):
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_loaded_pre = len(self.word_embeddings)
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embeddings_to_load = []
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loaded_embeddings = {}
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skipped_embeddings = []
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if not shared.sd_loaded:
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return 0
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tokenizer = getattr(shared.sd_model, 'tokenizer', None)
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tokenizer_2 = getattr(shared.sd_model, 'tokenizer_2', None)
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clip_l = getattr(shared.sd_model, 'text_encoder', None)
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clip_g = getattr(shared.sd_model, 'text_encoder_2', None)
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if clip_g and tokenizer_2:
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model_type = 'SDXL'
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elif clip_l and tokenizer:
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model_type = 'SD'
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else:
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return 0
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filenames = list(filename)
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exts = [".SAFETENSORS", '.BIN', '.PT', '.PNG', '.WEBP', '.JXL', '.AVIF']
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for _filename in filenames:
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# debug(f'Embedding check: {filename}')
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fullname = _filename
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_filename = os.path.basename(fullname)
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fn, ext = os.path.splitext(_filename)
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name = os.path.basename(fn)
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embedding = Embedding(vec=None, name=name, filename=fullname)
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tokenizer_vocab = tokenizer.get_vocab()
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try:
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if ext.upper() not in exts:
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raise ValueError(f'extension `{ext}` is invalid, expected one of: {exts}')
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if name in tokenizer.get_vocab() or f"{name}_1" in tokenizer.get_vocab():
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loaded_embeddings[name] = embedding
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debug(f'Embedding already loaded: {name}')
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embeddings_to_load.append(embedding)
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except Exception as e:
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skipped_embeddings.append(embedding)
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debug(f'Embedding skipped: "{name}" {e}')
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continue
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embeddings_to_load = sorted(embeddings_to_load, key=lambda e: exts.index(os.path.splitext(e.filename)[1].upper()))
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tokens_to_add = {}
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for embedding in embeddings_to_load:
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try:
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if embedding.name in tokens_to_add or embedding.name in loaded_embeddings:
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raise ValueError('duplicate token')
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embeddings_dict = {}
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_, ext = os.path.splitext(embedding.filename)
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if ext.upper() in ['.SAFETENSORS']:
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with safetensors.torch.safe_open(embedding.filename, framework="pt") as f: # type: ignore
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for k in f.keys():
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embeddings_dict[k] = f.get_tensor(k)
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else: # fallback for sd1.5 pt embeddings
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embeddings_dict["clip_l"] = self.load_from_file(embedding.filename, embedding.filename)
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if 'emb_params' in embeddings_dict and 'clip_l' not in embeddings_dict:
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embeddings_dict["clip_l"] = embeddings_dict["emb_params"]
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if 'clip_l' not in embeddings_dict:
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raise ValueError('Invalid Embedding, dict missing required key `clip_l`')
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if 'clip_g' not in embeddings_dict and model_type == "SDXL" and shared.opts.diffusers_convert_embed:
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embeddings_dict["clip_g"] = convert_embedding(embeddings_dict["clip_l"], clip_l, clip_g)
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if 'clip_g' in embeddings_dict:
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embedding_type = 'SDXL'
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else:
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embedding_type = 'SD'
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if embedding_type != model_type:
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raise ValueError(f'Unable to load {embedding_type} Embedding "{embedding.name}" into {model_type} Model')
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_tokens_to_add = {}
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for i in range(len(embeddings_dict["clip_l"])):
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if len(clip_l.get_input_embeddings().weight.data[0]) == len(embeddings_dict["clip_l"][i]):
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token = embedding.name if i == 0 else f"{embedding.name}_{i}"
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if token in tokenizer_vocab:
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raise RuntimeError(f'Multi-Vector Embedding would add pre-existing Token in Vocabulary: {token}')
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if token in tokens_to_add:
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raise RuntimeError(f'Multi-Vector Embedding would add duplicate Token to Add: {token}')
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_tokens_to_add[token] = TokenToAdd(
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embeddings_dict["clip_l"][i],
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embeddings_dict["clip_g"][i] if 'clip_g' in embeddings_dict else None
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)
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if not _tokens_to_add:
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raise ValueError('no valid tokens to add')
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tokens_to_add.update(_tokens_to_add)
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loaded_embeddings[embedding.name] = embedding
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except Exception as e:
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debug(f"Embedding loading: {embedding.filename} {e}")
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continue
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if len(tokens_to_add) > 0:
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tokenizer.add_tokens(list(tokens_to_add.keys()))
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clip_l.resize_token_embeddings(len(tokenizer))
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if model_type == 'SDXL':
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tokenizer_2.add_tokens(list(tokens_to_add.keys())) # type: ignore
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clip_g.resize_token_embeddings(len(tokenizer_2)) # type: ignore
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unk_token_id = tokenizer.convert_tokens_to_ids(tokenizer.unk_token)
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for token, data in tokens_to_add.items():
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token_id = tokenizer.convert_tokens_to_ids(token)
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if token_id > unk_token_id:
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clip_l.get_input_embeddings().weight.data[token_id] = data.clip_l
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if model_type == 'SDXL':
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clip_g.get_input_embeddings().weight.data[token_id] = data.clip_g # type: ignore
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for embedding in loaded_embeddings.values():
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if not embedding:
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continue
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self.register_embedding(embedding, shared.sd_model)
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if embedding in embeddings_to_load:
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embeddings_to_load.remove(embedding)
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skipped_embeddings.extend(embeddings_to_load)
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for embedding in skipped_embeddings:
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if loaded_embeddings.get(embedding.name, None) == embedding:
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continue
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self.skipped_embeddings[embedding.name] = embedding
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try:
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if model_type == 'SD':
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debug(f"Embeddings loaded: text-encoder={shared.sd_model.text_encoder.get_input_embeddings().weight.data.shape[0]}")
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if model_type == 'SDXL':
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debug(f"Embeddings loaded: text-encoder-1={shared.sd_model.text_encoder.get_input_embeddings().weight.data.shape[0]} text-encoder-2={shared.sd_model.text_encoder_2.get_input_embeddings().weight.data.shape[0]}")
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except Exception:
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pass
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return len(self.word_embeddings) - _loaded_pre
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def load_from_file(self, path, filename):
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name, ext = os.path.splitext(filename)
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ext = ext.upper()
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if ext in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
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if '.preview' in filename.lower():
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return
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embed_image = Image.open(path)
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if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text:
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data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
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else:
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data = extract_image_data_embed(embed_image)
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if not data: # if data is None, means this is not an embeding, just a preview image
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return
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elif ext in ['.BIN', '.PT']:
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data = torch.load(path, map_location="cpu")
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elif ext in ['.SAFETENSORS']:
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data = safetensors.torch.load_file(path, device="cpu")
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else:
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return None
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# textual inversion embeddings
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if 'string_to_param' in data:
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param_dict = data['string_to_param']
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param_dict = getattr(param_dict, '_parameters', param_dict) # fix for torch 1.12.1 loading saved file from torch 1.11
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assert len(param_dict) == 1, 'embedding file has multiple terms in it'
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emb = next(iter(param_dict.items()))[1]
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# diffuser concepts
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elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
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if len(data.keys()) != 1:
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self.skipped_embeddings[name] = Embedding(None, name=name, filename=path)
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return
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emb = next(iter(data.values()))
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if len(emb.shape) == 1:
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emb = emb.unsqueeze(0)
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else:
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raise RuntimeError(f"Couldn't identify {filename} as textual inversion embedding")
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if shared.backend == shared.Backend.DIFFUSERS:
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return emb
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vec = emb.detach().to(devices.device, dtype=torch.float32)
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# name = data.get('name', name)
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embedding = Embedding(vec=vec, name=name, filename=path)
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embedding.tag = data.get('name', None)
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embedding.step = data.get('step', None)
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embedding.sd_checkpoint = data.get('sd_checkpoint', None)
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embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
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embedding.vectors = vec.shape[0]
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embedding.shape = vec.shape[-1]
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if self.expected_shape == -1 or self.expected_shape == embedding.shape:
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self.register_embedding(embedding, shared.sd_model)
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else:
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self.skipped_embeddings[name] = embedding
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def load_from_dir(self, embdir):
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if sd_models.model_data.sd_model is None:
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shared.log.info('Skipping embeddings load: model not loaded')
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return
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if not os.path.isdir(embdir.path):
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return
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file_paths = list_embeddings(embdir.path)
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if shared.backend == shared.Backend.DIFFUSERS:
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self.load_diffusers_embedding(file_paths)
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else:
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for file_path in file_paths:
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try:
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fn = os.path.basename(file_path)
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self.load_from_file(file_path, fn)
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except Exception as e:
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errors.display(e, f'Load embeding={fn}')
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continue
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def load_textual_inversion_embeddings(self, force_reload=False):
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if not shared.sd_loaded:
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return
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t0 = time.time()
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if not force_reload:
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need_reload = False
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for embdir in self.embedding_dirs.values():
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if embdir.has_changed():
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need_reload = True
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break
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if not need_reload:
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return
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self.ids_lookup.clear()
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self.word_embeddings.clear()
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self.skipped_embeddings.clear()
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self.embeddings_used.clear()
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self.expected_shape = self.get_expected_shape()
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for embdir in self.embedding_dirs.values():
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self.load_from_dir(embdir)
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embdir.update()
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# re-sort word_embeddings because load_from_dir may not load in alphabetic order.
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# using a temporary copy so we don't reinitialize self.word_embeddings in case other objects have a reference to it.
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sorted_word_embeddings = {e.name: e for e in sorted(self.word_embeddings.values(), key=lambda e: e.name.lower())}
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self.word_embeddings.clear()
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self.word_embeddings.update(sorted_word_embeddings)
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displayed_embeddings = (tuple(self.word_embeddings.keys()), tuple(self.skipped_embeddings.keys()))
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if self.previously_displayed_embeddings != displayed_embeddings:
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self.previously_displayed_embeddings = displayed_embeddings
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t1 = time.time()
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shared.log.info(f"Load embeddings: loaded={len(self.word_embeddings)} skipped={len(self.skipped_embeddings)} time={t1-t0:.2f}")
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def find_embedding_at_position(self, tokens, offset):
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token = tokens[offset]
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possible_matches = self.ids_lookup.get(token, None)
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if possible_matches is None:
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return None, None
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for ids, embedding in possible_matches:
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if tokens[offset:offset + len(ids)] == ids:
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return embedding, len(ids)
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return None, None
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