import os import sys import traceback import torch import tqdm import html import datetime import math from modules import shared, devices, sd_hijack, processing, sd_models import modules.textual_inversion.dataset class Embedding: def __init__(self, vec, name, step=None): self.vec = vec self.name = name self.step = step self.cached_checksum = None self.sd_checkpoint = None self.sd_checkpoint_name = None def save(self, filename): embedding_data = { "string_to_token": {"*": 265}, "string_to_param": {"*": self.vec}, "name": self.name, "step": self.step, "sd_checkpoint": self.sd_checkpoint, "sd_checkpoint_name": self.sd_checkpoint_name, } torch.save(embedding_data, filename) def checksum(self): if self.cached_checksum is not None: return self.cached_checksum def const_hash(a): r = 0 for v in a: r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF return r self.cached_checksum = f'{const_hash(self.vec.reshape(-1) * 100) & 0xffff:04x}' return self.cached_checksum class EmbeddingDatabase: def __init__(self, embeddings_dir): self.ids_lookup = {} self.word_embeddings = {} self.dir_mtime = None self.embeddings_dir = embeddings_dir def register_embedding(self, embedding, model): self.word_embeddings[embedding.name] = embedding ids = model.cond_stage_model.tokenizer([embedding.name], add_special_tokens=False)['input_ids'][0] first_id = ids[0] if first_id not in self.ids_lookup: self.ids_lookup[first_id] = [] self.ids_lookup[first_id] = sorted(self.ids_lookup[first_id] + [(ids, embedding)], key=lambda x: len(x[0]), reverse=True) return embedding def load_textual_inversion_embeddings(self): mt = os.path.getmtime(self.embeddings_dir) if self.dir_mtime is not None and mt <= self.dir_mtime: return self.dir_mtime = mt self.ids_lookup.clear() self.word_embeddings.clear() def process_file(path, filename): name = os.path.splitext(filename)[0] data = torch.load(path, map_location="cpu") # textual inversion embeddings if 'string_to_param' in data: param_dict = data['string_to_param'] if hasattr(param_dict, '_parameters'): param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11 assert len(param_dict) == 1, 'embedding file has multiple terms in it' emb = next(iter(param_dict.items()))[1] # diffuser concepts elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: assert len(data.keys()) == 1, 'embedding file has multiple terms in it' emb = next(iter(data.values())) if len(emb.shape) == 1: emb = emb.unsqueeze(0) else: raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.") vec = emb.detach().to(devices.device, dtype=torch.float32) embedding = Embedding(vec, name) embedding.step = data.get('step', None) embedding.sd_checkpoint = data.get('hash', None) embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None) self.register_embedding(embedding, shared.sd_model) for fn in os.listdir(self.embeddings_dir): try: fullfn = os.path.join(self.embeddings_dir, fn) if os.stat(fullfn).st_size == 0: continue process_file(fullfn, fn) except Exception: print(f"Error loading emedding {fn}:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) continue print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.") def find_embedding_at_position(self, tokens, offset): token = tokens[offset] possible_matches = self.ids_lookup.get(token, None) if possible_matches is None: return None, None for ids, embedding in possible_matches: if tokens[offset:offset + len(ids)] == ids: return embedding, len(ids) return None, None def create_embedding(name, num_vectors_per_token, init_text='*'): cond_model = shared.sd_model.cond_stage_model embedding_layer = cond_model.wrapped.transformer.text_model.embeddings ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"] embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0) vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device) for i in range(num_vectors_per_token): vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token] fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt") assert not os.path.exists(fn), f"file {fn} already exists" embedding = Embedding(vec, name) embedding.step = 0 embedding.save(fn) return fn def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_size, steps, num_repeats, create_image_every, save_embedding_every, template_file): assert embedding_name, 'embedding not selected' shared.state.textinfo = "Initializing textual inversion training..." shared.state.job_count = steps filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt') log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), embedding_name) if save_embedding_every > 0: embedding_dir = os.path.join(log_directory, "embeddings") os.makedirs(embedding_dir, exist_ok=True) else: embedding_dir = None if create_image_every > 0: images_dir = os.path.join(log_directory, "images") os.makedirs(images_dir, exist_ok=True) else: images_dir = None cond_model = shared.sd_model.cond_stage_model shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=training_size, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file) hijack = sd_hijack.model_hijack embedding = hijack.embedding_db.word_embeddings[embedding_name] embedding.vec.requires_grad = True optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate) losses = torch.zeros((32,)) last_saved_file = "" last_saved_image = "" ititial_step = embedding.step or 0 if ititial_step > steps: return embedding, filename tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]) epoch_len = (tr_img_len * num_repeats) + tr_img_len pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) for i, (x, text) in pbar: embedding.step = i + ititial_step if embedding.step > steps: break if shared.state.interrupted: break with torch.autocast("cuda"): c = cond_model([text]) x = x.to(devices.device) loss = shared.sd_model(x.unsqueeze(0), c)[0] del x losses[embedding.step % losses.shape[0]] = loss.item() optimizer.zero_grad() loss.backward() optimizer.step() epoch_num = math.floor(embedding.step / epoch_len) epoch_step = embedding.step - (epoch_num * epoch_len) pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}") if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0: last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt') embedding.save(last_saved_file) if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0: last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png') p = processing.StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, prompt=text, steps=20, height=training_size, width=training_size, do_not_save_grid=True, do_not_save_samples=True, ) processed = processing.process_images(p) image = processed.images[0] shared.state.current_image = image image.save(last_saved_image) last_saved_image += f", prompt: {text}" shared.state.job_no = embedding.step shared.state.textinfo = f"""

Loss: {losses.mean():.7f}
Step: {embedding.step}
Last prompt: {html.escape(text)}
Last saved embedding: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

""" checkpoint = sd_models.select_checkpoint() embedding.sd_checkpoint = checkpoint.hash embedding.sd_checkpoint_name = checkpoint.model_name embedding.cached_checksum = None embedding.save(filename) return embedding, filename