mirror of
https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-15 15:13:45 +03:00
281 lines
9.9 KiB
Python
281 lines
9.9 KiB
Python
import os
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import sys
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import traceback
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import torch
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import tqdm
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import html
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import datetime
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from modules import shared, devices, sd_hijack, processing, sd_models
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import modules.textual_inversion.dataset
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class Embedding:
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def __init__(self, vec, name, step=None):
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self.vec = vec
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self.name = name
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self.step = step
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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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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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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 EmbeddingDatabase:
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def __init__(self, embeddings_dir):
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self.ids_lookup = {}
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self.word_embeddings = {}
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self.dir_mtime = None
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self.embeddings_dir = embeddings_dir
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def register_embedding(self, embedding, model):
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self.word_embeddings[embedding.name] = embedding
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ids = model.cond_stage_model.tokenizer([embedding.name], add_special_tokens=False)['input_ids'][0]
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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 load_textual_inversion_embeddings(self):
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mt = os.path.getmtime(self.embeddings_dir)
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if self.dir_mtime is not None and mt <= self.dir_mtime:
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return
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self.dir_mtime = mt
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self.ids_lookup.clear()
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self.word_embeddings.clear()
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def process_file(path, filename):
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name = os.path.splitext(filename)[0]
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data = torch.load(path, map_location="cpu")
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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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if hasattr(param_dict, '_parameters'):
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param_dict = getattr(param_dict, '_parameters') # 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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assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
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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 Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")
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vec = emb.detach().to(devices.device, dtype=torch.float32)
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embedding = Embedding(vec, name)
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embedding.step = data.get('step', None)
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embedding.sd_checkpoint = data.get('hash', None)
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embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
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self.register_embedding(embedding, shared.sd_model)
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for fn in os.listdir(self.embeddings_dir):
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try:
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fullfn = os.path.join(self.embeddings_dir, fn)
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if os.stat(fullfn).st_size == 0:
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continue
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process_file(fullfn, fn)
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except Exception:
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print(f"Error loading emedding {fn}:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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continue
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print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
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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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def create_embedding(name, num_vectors_per_token, init_text='*'):
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cond_model = shared.sd_model.cond_stage_model
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embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
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ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"]
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embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
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vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
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for i in range(num_vectors_per_token):
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vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
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fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
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assert not os.path.exists(fn), f"file {fn} already exists"
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embedding = Embedding(vec, name)
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embedding.step = 0
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embedding.save(fn)
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return fn
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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):
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assert embedding_name, 'embedding not selected'
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shared.state.textinfo = "Initializing textual inversion training..."
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shared.state.job_count = steps
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extns = [".jpg",".jpeg",".png"]
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filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
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log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), embedding_name)
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if save_embedding_every > 0:
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embedding_dir = os.path.join(log_directory, "embeddings")
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os.makedirs(embedding_dir, exist_ok=True)
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else:
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embedding_dir = None
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if create_image_every > 0:
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images_dir = os.path.join(log_directory, "images")
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os.makedirs(images_dir, exist_ok=True)
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else:
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images_dir = None
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cond_model = shared.sd_model.cond_stage_model
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shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
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with torch.autocast("cuda"):
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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)
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hijack = sd_hijack.model_hijack
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embedding = hijack.embedding_db.word_embeddings[embedding_name]
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embedding.vec.requires_grad = True
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optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
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losses = torch.zeros((32,))
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last_saved_file = "<none>"
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last_saved_image = "<none>"
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ititial_step = embedding.step or 0
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if ititial_step > steps:
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return embedding, filename
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tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root) if os.path.splitext(file_path.casefold())[1] in extns])
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epoch_len = (tr_img_len * num_repeats) + tr_img_len
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pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
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for i, (x, text) in pbar:
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embedding.step = i + ititial_step
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if embedding.step > steps:
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break
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if shared.state.interrupted:
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break
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with torch.autocast("cuda"):
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c = cond_model([text])
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x = x.to(devices.device)
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loss = shared.sd_model(x.unsqueeze(0), c)[0]
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del x
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losses[embedding.step % losses.shape[0]] = loss.item()
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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epoch_num = embedding.step // epoch_len
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epoch_step = embedding.step - (epoch_num * epoch_len) + 1
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pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
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if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
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last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
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embedding.save(last_saved_file)
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if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
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last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
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p = processing.StableDiffusionProcessingTxt2Img(
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sd_model=shared.sd_model,
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prompt=text,
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steps=20,
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height=training_height,
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width=training_width,
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do_not_save_grid=True,
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do_not_save_samples=True,
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)
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processed = processing.process_images(p)
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image = processed.images[0]
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shared.state.current_image = image
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image.save(last_saved_image)
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last_saved_image += f", prompt: {text}"
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shared.state.job_no = embedding.step
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shared.state.textinfo = f"""
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<p>
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Loss: {losses.mean():.7f}<br/>
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Step: {embedding.step}<br/>
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Last prompt: {html.escape(text)}<br/>
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Last saved embedding: {html.escape(last_saved_file)}<br/>
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Last saved image: {html.escape(last_saved_image)}<br/>
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</p>
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"""
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checkpoint = sd_models.select_checkpoint()
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embedding.sd_checkpoint = checkpoint.hash
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embedding.sd_checkpoint_name = checkpoint.model_name
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embedding.cached_checksum = None
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embedding.save(filename)
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return embedding, filename
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