import os import sys import traceback import torch import tqdm import html import datetime from PIL import Image,PngImagePlugin from ..images import captionImageOverlay import numpy as np import base64 import json from modules import shared, devices, sd_hijack, processing, sd_models import modules.textual_inversion.dataset class EmbeddingEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, torch.Tensor): return {'TORCHTENSOR':obj.cpu().detach().numpy().tolist()} return json.JSONEncoder.default(self, o) class EmbeddingDecoder(json.JSONDecoder): def __init__(self, *args, **kwargs): json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs) def object_hook(self, d): if 'TORCHTENSOR' in d: return torch.from_numpy(np.array(d['TORCHTENSOR'])) return d def embeddingToB64(data): d = json.dumps(data,cls=EmbeddingEncoder) return base64.b64encode(d.encode()) def embeddingFromB64(data): d = base64.b64decode(data) return json.loads(d,cls=EmbeddingDecoder) 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 = [] if filename.upper().endswith('.PNG'): embed_image = Image.open(path) if 'sd-ti-embedding' in embed_image.text: data = embeddingFromB64(embed_image.text['sd-ti-embedding']) name = data.get('name',name) else: 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_width, training_height, 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 if create_image_every > 0 and save_image_with_stored_embedding: images_embeds_dir = os.path.join(log_directory, "image_embeddings") os.makedirs(images_embeds_dir, exist_ok=True) else: images_embeds_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, width=training_width, height=training_height, 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 = embedding.step // epoch_len epoch_step = embedding.step - (epoch_num * epoch_len) + 1 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_height, width=training_width, do_not_save_grid=True, do_not_save_samples=True, ) processed = processing.process_images(p) image = processed.images[0] shared.state.current_image = image if save_image_with_stored_embedding and os.path.exists(last_saved_file): last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png') info = PngImagePlugin.PngInfo() data = torch.load(last_saved_file) info.add_text("sd-ti-embedding", embeddingToB64(data)) title = "<{}>".format(data.get('name','???')) checkpoint = sd_models.select_checkpoint() footer_left = checkpoint.model_name footer_mid = '[{}]'.format(checkpoint.hash) footer_right = '{}'.format(embedding.step) captioned_image = captionImageOverlay(image,title,footer_left,footer_mid,footer_right) captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info) 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