mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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706 lines
31 KiB
Python
706 lines
31 KiB
Python
import os
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from collections import namedtuple
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from contextlib import closing
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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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import safetensors.torch
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import numpy as np
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from PIL import Image, PngImagePlugin
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from modules import shared, devices, sd_hijack, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes
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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, insert_image_data_embed, extract_image_data_embed, caption_image_overlay
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from modules.textual_inversion.logging import save_settings_to_file
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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, _, fns in os.walk(shared.cmd_opts.textual_inversion_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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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.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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self.filename = None
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self.hash = None
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self.shorthash = 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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def set_hash(self, v):
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self.hash = v
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self.shorthash = self.hash[0:12]
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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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mt = os.path.getmtime(self.path)
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if self.mtime is None or mt > self.mtime:
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return True
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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 = os.path.getmtime(self.path)
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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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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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return self.register_embedding_by_name(embedding, model, embedding.name)
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def register_embedding_by_name(self, embedding, model, name):
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ids = model.cond_stage_model.tokenize([name])[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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if name in self.word_embeddings:
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# remove old one from the lookup list
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lookup = [x for x in self.ids_lookup[first_id] if x[1].name!=name]
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else:
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lookup = self.ids_lookup[first_id]
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if embedding is not None:
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lookup += [(ids, embedding)]
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self.ids_lookup[first_id] = sorted(lookup, key=lambda x: len(x[0]), reverse=True)
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if embedding is None:
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# unregister embedding with specified name
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if name in self.word_embeddings:
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del self.word_embeddings[name]
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if len(self.ids_lookup[first_id])==0:
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del self.ids_lookup[first_id]
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return None
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self.word_embeddings[name] = embedding
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return embedding
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def get_expected_shape(self):
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devices.torch_npu_set_device()
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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_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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_, second_ext = os.path.splitext(name)
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if second_ext.upper() == '.PREVIEW':
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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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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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if data:
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name = data.get('name', name)
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else:
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# if data is None, means this is not an embedding, 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
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embedding = create_embedding_from_data(data, name, filename=filename, filepath=path)
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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 not os.path.isdir(embdir.path):
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return
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for root, _, fns in os.walk(embdir.path, followlinks=True):
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for fn in fns:
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try:
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fullfn = os.path.join(root, fn)
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if os.stat(fullfn).st_size == 0:
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continue
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self.load_from_file(fullfn, fn)
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except Exception:
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errors.report(f"Error loading embedding {fn}", exc_info=True)
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continue
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def load_textual_inversion_embeddings(self, force_reload=False):
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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.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 shared.opts.textual_inversion_print_at_load and self.previously_displayed_embeddings != displayed_embeddings:
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self.previously_displayed_embeddings = displayed_embeddings
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print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
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if self.skipped_embeddings:
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print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_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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with devices.autocast():
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cond_model([""]) # will send cond model to GPU if lowvram/medvram is active
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#cond_model expects at least some text, so we provide '*' as backup.
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embedded = cond_model.encode_embedding_init_text(init_text or '*', num_vectors_per_token)
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vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
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#Only copy if we provided an init_text, otherwise keep vectors as zeros
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if init_text:
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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 create_embedding_from_data(data, name, filename='unknown embedding file', filepath=None):
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if 'string_to_param' in data: # textual inversion embeddings
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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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vec = emb.detach().to(devices.device, dtype=torch.float32)
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shape = vec.shape[-1]
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vectors = vec.shape[0]
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elif type(data) == dict and 'clip_g' in data and 'clip_l' in data: # SDXL embedding
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vec = {k: v.detach().to(devices.device, dtype=torch.float32) for k, v in data.items()}
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shape = data['clip_g'].shape[-1] + data['clip_l'].shape[-1]
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vectors = data['clip_g'].shape[0]
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elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: # diffuser concepts
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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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vec = emb.detach().to(devices.device, dtype=torch.float32)
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shape = vec.shape[-1]
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vectors = vec.shape[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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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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embedding.vectors = vectors
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embedding.shape = shape
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if filepath:
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embedding.filename = filepath
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embedding.set_hash(hashes.sha256(filepath, "textual_inversion/" + name) or '')
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return embedding
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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 tensorboard_setup(log_directory):
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from torch.utils.tensorboard import SummaryWriter
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os.makedirs(os.path.join(log_directory, "tensorboard"), exist_ok=True)
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return SummaryWriter(
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log_dir=os.path.join(log_directory, "tensorboard"),
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flush_secs=shared.opts.training_tensorboard_flush_every)
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def tensorboard_add(tensorboard_writer, loss, global_step, step, learn_rate, epoch_num):
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tensorboard_add_scaler(tensorboard_writer, "Loss/train", loss, global_step)
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tensorboard_add_scaler(tensorboard_writer, f"Loss/train/epoch-{epoch_num}", loss, step)
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tensorboard_add_scaler(tensorboard_writer, "Learn rate/train", learn_rate, global_step)
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tensorboard_add_scaler(tensorboard_writer, f"Learn rate/train/epoch-{epoch_num}", learn_rate, step)
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def tensorboard_add_scaler(tensorboard_writer, tag, value, step):
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tensorboard_writer.add_scalar(tag=tag,
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scalar_value=value, global_step=step)
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def tensorboard_add_image(tensorboard_writer, tag, pil_image, step):
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# Convert a pil image to a torch tensor
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img_tensor = torch.as_tensor(np.array(pil_image, copy=True))
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img_tensor = img_tensor.view(pil_image.size[1], pil_image.size[0],
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len(pil_image.getbands()))
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img_tensor = img_tensor.permute((2, 0, 1))
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tensorboard_writer.add_image(tag, img_tensor, global_step=step)
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def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, 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_filename, "Prompt template file not selected"
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assert template_file, f"Prompt template file {template_filename} not found"
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assert os.path.isfile(template_file.path), f"Prompt template file {template_filename} 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(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_name, preview_cfg_scale, preview_seed, preview_width, preview_height):
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from modules import processing
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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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template_file = textual_inversion_templates.get(template_filename, None)
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validate_train_inputs(embedding_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
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template_file = template_file.path
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shared.state.job = "train-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 = "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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clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else \
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torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else \
|
|
None
|
|
if clip_grad:
|
|
clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False)
|
|
# dataset loading may take a while, so input validations and early returns should be done before this
|
|
shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
|
|
old_parallel_processing_allowed = shared.parallel_processing_allowed
|
|
|
|
tensorboard_writer = None
|
|
if shared.opts.training_enable_tensorboard:
|
|
try:
|
|
tensorboard_writer = tensorboard_setup(log_directory)
|
|
except ImportError:
|
|
errors.report("Error initializing tensorboard", exc_info=True)
|
|
|
|
pin_memory = shared.opts.pin_memory
|
|
|
|
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, varsize=varsize, use_weight=use_weight)
|
|
|
|
if shared.opts.save_training_settings_to_txt:
|
|
save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()})
|
|
|
|
latent_sampling_method = ds.latent_sampling_method
|
|
|
|
dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)
|
|
|
|
if unload:
|
|
shared.parallel_processing_allowed = False
|
|
shared.sd_model.first_stage_model.to(devices.cpu)
|
|
|
|
embedding.vec.requires_grad = True
|
|
optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate, weight_decay=0.0)
|
|
if shared.opts.save_optimizer_state:
|
|
optimizer_state_dict = None
|
|
if os.path.exists(f"{filename}.optim"):
|
|
optimizer_saved_dict = torch.load(f"{filename}.optim", map_location='cpu')
|
|
if embedding.checksum() == optimizer_saved_dict.get('hash', None):
|
|
optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
|
|
|
|
if optimizer_state_dict is not None:
|
|
optimizer.load_state_dict(optimizer_state_dict)
|
|
print("Loaded existing optimizer from checkpoint")
|
|
else:
|
|
print("No saved optimizer exists in checkpoint")
|
|
|
|
scaler = torch.cuda.amp.GradScaler()
|
|
|
|
batch_size = ds.batch_size
|
|
gradient_step = ds.gradient_step
|
|
# n steps = batch_size * gradient_step * n image processed
|
|
steps_per_epoch = len(ds) // batch_size // gradient_step
|
|
max_steps_per_epoch = len(ds) // batch_size - (len(ds) // batch_size) % gradient_step
|
|
loss_step = 0
|
|
_loss_step = 0 #internal
|
|
|
|
last_saved_file = "<none>"
|
|
last_saved_image = "<none>"
|
|
forced_filename = "<none>"
|
|
embedding_yet_to_be_embedded = False
|
|
|
|
is_training_inpainting_model = shared.sd_model.model.conditioning_key in {'hybrid', 'concat'}
|
|
img_c = None
|
|
|
|
pbar = tqdm.tqdm(total=steps - initial_step)
|
|
try:
|
|
sd_hijack_checkpoint.add()
|
|
|
|
for _ in range((steps-initial_step) * gradient_step):
|
|
if scheduler.finished:
|
|
break
|
|
if shared.state.interrupted:
|
|
break
|
|
for j, batch in enumerate(dl):
|
|
# works as a drop_last=True for gradient accumulation
|
|
if j == max_steps_per_epoch:
|
|
break
|
|
scheduler.apply(optimizer, embedding.step)
|
|
if scheduler.finished:
|
|
break
|
|
if shared.state.interrupted:
|
|
break
|
|
|
|
if clip_grad:
|
|
clip_grad_sched.step(embedding.step)
|
|
|
|
with devices.autocast():
|
|
x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
|
|
if use_weight:
|
|
w = batch.weight.to(devices.device, non_blocking=pin_memory)
|
|
c = shared.sd_model.cond_stage_model(batch.cond_text)
|
|
|
|
if is_training_inpainting_model:
|
|
if img_c is None:
|
|
img_c = processing.txt2img_image_conditioning(shared.sd_model, c, training_width, training_height)
|
|
|
|
cond = {"c_concat": [img_c], "c_crossattn": [c]}
|
|
else:
|
|
cond = c
|
|
|
|
if use_weight:
|
|
loss = shared.sd_model.weighted_forward(x, cond, w)[0] / gradient_step
|
|
del w
|
|
else:
|
|
loss = shared.sd_model.forward(x, cond)[0] / gradient_step
|
|
del x
|
|
|
|
_loss_step += loss.item()
|
|
scaler.scale(loss).backward()
|
|
|
|
# go back until we reach gradient accumulation steps
|
|
if (j + 1) % gradient_step != 0:
|
|
continue
|
|
|
|
if clip_grad:
|
|
clip_grad(embedding.vec, clip_grad_sched.learn_rate)
|
|
|
|
scaler.step(optimizer)
|
|
scaler.update()
|
|
embedding.step += 1
|
|
pbar.update()
|
|
optimizer.zero_grad(set_to_none=True)
|
|
loss_step = _loss_step
|
|
_loss_step = 0
|
|
|
|
steps_done = embedding.step + 1
|
|
|
|
epoch_num = embedding.step // steps_per_epoch
|
|
epoch_step = embedding.step % steps_per_epoch
|
|
|
|
description = f"Training textual inversion [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}] loss: {loss_step:.7f}"
|
|
pbar.set_description(description)
|
|
if embedding_dir is not None and steps_done % save_embedding_every == 0:
|
|
# Before saving, change name to match current checkpoint.
|
|
embedding_name_every = f'{embedding_name}-{steps_done}'
|
|
last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt')
|
|
save_embedding(embedding, optimizer, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
|
|
embedding_yet_to_be_embedded = True
|
|
|
|
write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, steps_per_epoch, {
|
|
"loss": f"{loss_step:.7f}",
|
|
"learn_rate": scheduler.learn_rate
|
|
})
|
|
|
|
if images_dir is not None and steps_done % create_image_every == 0:
|
|
forced_filename = f'{embedding_name}-{steps_done}'
|
|
last_saved_image = os.path.join(images_dir, forced_filename)
|
|
|
|
shared.sd_model.first_stage_model.to(devices.device)
|
|
|
|
p = processing.StableDiffusionProcessingTxt2Img(
|
|
sd_model=shared.sd_model,
|
|
do_not_save_grid=True,
|
|
do_not_save_samples=True,
|
|
do_not_reload_embeddings=True,
|
|
)
|
|
|
|
if preview_from_txt2img:
|
|
p.prompt = preview_prompt
|
|
p.negative_prompt = preview_negative_prompt
|
|
p.steps = preview_steps
|
|
p.sampler_name = sd_samplers.samplers_map[preview_sampler_name.lower()]
|
|
p.cfg_scale = preview_cfg_scale
|
|
p.seed = preview_seed
|
|
p.width = preview_width
|
|
p.height = preview_height
|
|
else:
|
|
p.prompt = batch.cond_text[0]
|
|
p.steps = 20
|
|
p.width = training_width
|
|
p.height = training_height
|
|
|
|
preview_text = p.prompt
|
|
|
|
with closing(p):
|
|
processed = processing.process_images(p)
|
|
image = processed.images[0] if len(processed.images) > 0 else None
|
|
|
|
if unload:
|
|
shared.sd_model.first_stage_model.to(devices.cpu)
|
|
|
|
if image is not None:
|
|
shared.state.assign_current_image(image)
|
|
|
|
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)
|
|
last_saved_image += f", prompt: {preview_text}"
|
|
|
|
if tensorboard_writer and shared.opts.training_tensorboard_save_images:
|
|
tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}", image, embedding.step)
|
|
|
|
if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
|
|
|
|
last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
|
|
|
|
info = PngImagePlugin.PngInfo()
|
|
data = torch.load(last_saved_file)
|
|
info.add_text("sd-ti-embedding", embedding_to_b64(data))
|
|
|
|
title = f"<{data.get('name', '???')}>"
|
|
|
|
try:
|
|
vectorSize = list(data['string_to_param'].values())[0].shape[0]
|
|
except Exception:
|
|
vectorSize = '?'
|
|
|
|
checkpoint = sd_models.select_checkpoint()
|
|
footer_left = checkpoint.model_name
|
|
footer_mid = f'[{checkpoint.shorthash}]'
|
|
footer_right = f'{vectorSize}v {steps_done}s'
|
|
|
|
captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
|
|
captioned_image = insert_image_data_embed(captioned_image, data)
|
|
|
|
captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
|
|
embedding_yet_to_be_embedded = False
|
|
|
|
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)
|
|
last_saved_image += f", prompt: {preview_text}"
|
|
|
|
shared.state.job_no = embedding.step
|
|
|
|
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, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True)
|
|
except Exception:
|
|
errors.report("Error training embedding", exc_info=True)
|
|
finally:
|
|
pbar.leave = False
|
|
pbar.close()
|
|
shared.sd_model.first_stage_model.to(devices.device)
|
|
shared.parallel_processing_allowed = old_parallel_processing_allowed
|
|
sd_hijack_checkpoint.remove()
|
|
|
|
return embedding, filename
|
|
|
|
|
|
def save_embedding(embedding, optimizer, 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.shorthash
|
|
embedding.sd_checkpoint_name = checkpoint.model_name
|
|
if remove_cached_checksum:
|
|
embedding.cached_checksum = None
|
|
embedding.name = embedding_name
|
|
embedding.optimizer_state_dict = optimizer.state_dict()
|
|
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
|