mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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475 lines
21 KiB
Python
475 lines
21 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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import csv
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from PIL import Image, PngImagePlugin
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from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers
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import modules.textual_inversion.dataset
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from modules.textual_inversion.learn_schedule import LearnRateScheduler
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from modules.textual_inversion.image_embedding import (embedding_to_b64, embedding_from_b64,
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insert_image_data_embed, extract_image_data_embed,
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caption_image_overlay)
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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 = []
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if os.path.splitext(filename.upper())[-1] in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
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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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name = data.get('name', name)
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else:
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data = extract_image_data_embed(embed_image)
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name = data.get('name', name)
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else:
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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('sd_checkpoint', 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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print("Embeddings:", ', '.join(self.word_embeddings.keys()))
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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, overwrite_old, 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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with devices.autocast():
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cond_model([""]) # will send cond model to GPU if lowvram/medvram is active
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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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# Remove illegal characters from name.
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name = "".join( x for x in name if (x.isalnum() or x in "._- "))
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fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
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if not overwrite_old:
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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 write_loss(log_directory, filename, step, epoch_len, values):
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if shared.opts.training_write_csv_every == 0:
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return
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if step % shared.opts.training_write_csv_every != 0:
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return
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write_csv_header = False if os.path.exists(os.path.join(log_directory, filename)) else True
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with open(os.path.join(log_directory, filename), "a+", newline='') as fout:
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csv_writer = csv.DictWriter(fout, fieldnames=["step", "epoch", "epoch_step", *(values.keys())])
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if write_csv_header:
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csv_writer.writeheader()
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epoch = (step - 1) // epoch_len
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epoch_step = (step - 1) % epoch_len
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csv_writer.writerow({
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"step": step,
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"epoch": epoch,
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"epoch_step": epoch_step,
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**values,
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})
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def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"):
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assert model_name, f"{name} not selected"
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assert learn_rate, "Learning rate is empty or 0"
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assert isinstance(batch_size, int), "Batch size must be integer"
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assert batch_size > 0, "Batch size must be positive"
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assert isinstance(gradient_step, int), "Gradient accumulation step must be integer"
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assert gradient_step > 0, "Gradient accumulation step must be positive"
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assert data_root, "Dataset directory is empty"
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assert os.path.isdir(data_root), "Dataset directory doesn't exist"
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assert os.listdir(data_root), "Dataset directory is empty"
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assert template_file, "Prompt template file is empty"
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assert os.path.isfile(template_file), "Prompt template file doesn't exist"
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assert steps, "Max steps is empty or 0"
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assert isinstance(steps, int), "Max steps must be integer"
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assert steps > 0 , "Max steps must be positive"
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assert isinstance(save_model_every, int), "Save {name} must be integer"
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assert save_model_every >= 0 , "Save {name} must be positive or 0"
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assert isinstance(create_image_every, int), "Create image must be integer"
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assert create_image_every >= 0 , "Create image must be positive or 0"
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if save_model_every or create_image_every:
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assert log_directory, "Log directory is empty"
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def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
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save_embedding_every = save_embedding_every or 0
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create_image_every = create_image_every or 0
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validate_train_inputs(embedding_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
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shared.state.textinfo = "Initializing textual inversion training..."
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shared.state.job_count = steps
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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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unload = shared.opts.unload_models_when_training
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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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if create_image_every > 0 and save_image_with_stored_embedding:
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images_embeds_dir = os.path.join(log_directory, "image_embeddings")
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os.makedirs(images_embeds_dir, exist_ok=True)
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else:
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images_embeds_dir = None
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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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checkpoint = sd_models.select_checkpoint()
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initial_step = embedding.step or 0
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if initial_step >= steps:
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shared.state.textinfo = f"Model has already been trained beyond specified max steps"
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return embedding, filename
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scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
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# dataset loading may take a while, so input validations and early returns should be done before this
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shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
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pin_memory = shared.opts.pin_memory
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ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)
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latent_sampling_method = ds.latent_sampling_method
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dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)
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if unload:
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shared.sd_model.first_stage_model.to(devices.cpu)
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embedding.vec.requires_grad = True
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optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate, weight_decay=0.0)
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scaler = torch.cuda.amp.GradScaler()
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batch_size = ds.batch_size
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gradient_step = ds.gradient_step
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# n steps = batch_size * gradient_step * n image processed
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steps_per_epoch = len(ds) // batch_size // gradient_step
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max_steps_per_epoch = len(ds) // batch_size - (len(ds) // batch_size) % gradient_step
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loss_step = 0
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_loss_step = 0 #internal
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last_saved_file = "<none>"
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last_saved_image = "<none>"
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forced_filename = "<none>"
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embedding_yet_to_be_embedded = False
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pbar = tqdm.tqdm(total=steps - initial_step)
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try:
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for i in range((steps-initial_step) * gradient_step):
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if scheduler.finished:
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break
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if shared.state.interrupted:
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break
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for j, batch in enumerate(dl):
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# works as a drop_last=True for gradient accumulation
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if j == max_steps_per_epoch:
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break
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scheduler.apply(optimizer, embedding.step)
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if scheduler.finished:
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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 = stack_conds(batch.cond).to(devices.device)
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# mask = torch.tensor(batch.emb_index).to(devices.device, non_blocking=pin_memory)
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# print(mask)
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# c[:, 1:1+embedding.vec.shape[0]] = embedding.vec.to(devices.device, non_blocking=pin_memory)
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x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
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c = shared.sd_model.cond_stage_model(batch.cond_text)
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loss = shared.sd_model(x, c)[0] / gradient_step
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del x
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_loss_step += loss.item()
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scaler.scale(loss).backward()
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# go back until we reach gradient accumulation steps
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if (j + 1) % gradient_step != 0:
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continue
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scaler.step(optimizer)
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scaler.update()
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embedding.step += 1
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pbar.update()
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optimizer.zero_grad(set_to_none=True)
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loss_step = _loss_step
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_loss_step = 0
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steps_done = embedding.step + 1
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epoch_num = embedding.step // steps_per_epoch
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epoch_step = embedding.step % steps_per_epoch
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pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}")
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if embedding_dir is not None and steps_done % save_embedding_every == 0:
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# Before saving, change name to match current checkpoint.
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embedding_name_every = f'{embedding_name}-{steps_done}'
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last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt')
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#if shared.opts.save_optimizer_state:
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#embedding.optimizer_state_dict = optimizer.state_dict()
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save_embedding(embedding, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
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embedding_yet_to_be_embedded = True
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write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, steps_per_epoch, {
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"loss": f"{loss_step:.7f}",
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"learn_rate": scheduler.learn_rate
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})
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if images_dir is not None and steps_done % create_image_every == 0:
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forced_filename = f'{embedding_name}-{steps_done}'
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last_saved_image = os.path.join(images_dir, forced_filename)
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shared.sd_model.first_stage_model.to(devices.device)
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p = processing.StableDiffusionProcessingTxt2Img(
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sd_model=shared.sd_model,
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do_not_save_grid=True,
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do_not_save_samples=True,
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do_not_reload_embeddings=True,
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)
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if preview_from_txt2img:
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p.prompt = preview_prompt
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p.negative_prompt = preview_negative_prompt
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p.steps = preview_steps
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p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
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p.cfg_scale = preview_cfg_scale
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p.seed = preview_seed
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p.width = preview_width
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p.height = preview_height
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else:
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p.prompt = batch.cond_text[0]
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p.steps = 20
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p.width = training_width
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p.height = training_height
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preview_text = p.prompt
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processed = processing.process_images(p)
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image = processed.images[0] if len(processed.images) > 0 else None
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if unload:
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shared.sd_model.first_stage_model.to(devices.cpu)
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if image is not None:
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shared.state.current_image = image
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last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
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last_saved_image += f", prompt: {preview_text}"
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if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
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last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
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info = PngImagePlugin.PngInfo()
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data = torch.load(last_saved_file)
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info.add_text("sd-ti-embedding", embedding_to_b64(data))
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title = "<{}>".format(data.get('name', '???'))
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try:
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vectorSize = list(data['string_to_param'].values())[0].shape[0]
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except Exception as e:
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vectorSize = '?'
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checkpoint = sd_models.select_checkpoint()
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footer_left = checkpoint.model_name
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footer_mid = '[{}]'.format(checkpoint.hash)
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footer_right = '{}v {}s'.format(vectorSize, steps_done)
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captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
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captioned_image = insert_image_data_embed(captioned_image, data)
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captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
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embedding_yet_to_be_embedded = False
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last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
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last_saved_image += f", prompt: {preview_text}"
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shared.state.job_no = embedding.step
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|
|
|
shared.state.textinfo = f"""
|
|
<p>
|
|
Loss: {loss_step:.7f}<br/>
|
|
Step: {steps_done}<br/>
|
|
Last prompt: {html.escape(batch.cond_text[0])}<br/>
|
|
Last saved embedding: {html.escape(last_saved_file)}<br/>
|
|
Last saved image: {html.escape(last_saved_image)}<br/>
|
|
</p>
|
|
"""
|
|
filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
|
|
save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
|
|
except Exception:
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
pass
|
|
finally:
|
|
pbar.leave = False
|
|
pbar.close()
|
|
shared.sd_model.first_stage_model.to(devices.device)
|
|
|
|
return embedding, filename
|
|
|
|
def save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True):
|
|
old_embedding_name = embedding.name
|
|
old_sd_checkpoint = embedding.sd_checkpoint if hasattr(embedding, "sd_checkpoint") else None
|
|
old_sd_checkpoint_name = embedding.sd_checkpoint_name if hasattr(embedding, "sd_checkpoint_name") else None
|
|
old_cached_checksum = embedding.cached_checksum if hasattr(embedding, "cached_checksum") else None
|
|
try:
|
|
embedding.sd_checkpoint = checkpoint.hash
|
|
embedding.sd_checkpoint_name = checkpoint.model_name
|
|
if remove_cached_checksum:
|
|
embedding.cached_checksum = None
|
|
embedding.name = embedding_name
|
|
embedding.save(filename)
|
|
except:
|
|
embedding.sd_checkpoint = old_sd_checkpoint
|
|
embedding.sd_checkpoint_name = old_sd_checkpoint_name
|
|
embedding.name = old_embedding_name
|
|
embedding.cached_checksum = old_cached_checksum
|
|
raise
|