864 lines
31 KiB
Python
864 lines
31 KiB
Python
import collections
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import os.path
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import sys
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import threading
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import torch
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import re
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import safetensors.torch
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from omegaconf import OmegaConf, ListConfig
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from os import mkdir
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from urllib import request
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import ldm.modules.midas as midas
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from ldm.util import instantiate_from_config
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from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack, patches
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from modules.timer import Timer
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import tomesd
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import numpy as np
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model_dir = "Stable-diffusion"
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model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
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checkpoints_list = {}
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checkpoint_aliases = {}
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checkpoint_alisases = checkpoint_aliases # for compatibility with old name
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checkpoints_loaded = collections.OrderedDict()
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def replace_key(d, key, new_key, value):
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keys = list(d.keys())
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d[new_key] = value
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if key not in keys:
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return d
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index = keys.index(key)
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keys[index] = new_key
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new_d = {k: d[k] for k in keys}
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d.clear()
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d.update(new_d)
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return d
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class CheckpointInfo:
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def __init__(self, filename):
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self.filename = filename
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abspath = os.path.abspath(filename)
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abs_ckpt_dir = os.path.abspath(shared.cmd_opts.ckpt_dir) if shared.cmd_opts.ckpt_dir is not None else None
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self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
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if abs_ckpt_dir and abspath.startswith(abs_ckpt_dir):
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name = abspath.replace(abs_ckpt_dir, '')
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elif abspath.startswith(model_path):
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name = abspath.replace(model_path, '')
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else:
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name = os.path.basename(filename)
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if name.startswith("\\") or name.startswith("/"):
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name = name[1:]
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def read_metadata():
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metadata = read_metadata_from_safetensors(filename)
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self.modelspec_thumbnail = metadata.pop('modelspec.thumbnail', None)
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return metadata
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self.metadata = {}
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if self.is_safetensors:
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try:
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self.metadata = cache.cached_data_for_file('safetensors-metadata', "checkpoint/" + name, filename, read_metadata)
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except Exception as e:
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errors.display(e, f"reading metadata for {filename}")
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self.name = name
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self.name_for_extra = os.path.splitext(os.path.basename(filename))[0]
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self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
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self.hash = model_hash(filename)
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self.sha256 = hashes.sha256_from_cache(self.filename, f"checkpoint/{name}")
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self.shorthash = self.sha256[0:10] if self.sha256 else None
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self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
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self.short_title = self.name_for_extra if self.shorthash is None else f'{self.name_for_extra} [{self.shorthash}]'
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self.ids = [self.hash, self.model_name, self.title, name, self.name_for_extra, f'{name} [{self.hash}]']
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if self.shorthash:
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self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]', f'{self.name_for_extra} [{self.shorthash}]']
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def register(self):
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checkpoints_list[self.title] = self
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for id in self.ids:
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checkpoint_aliases[id] = self
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def calculate_shorthash(self):
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self.sha256 = hashes.sha256(self.filename, f"checkpoint/{self.name}")
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if self.sha256 is None:
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return
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shorthash = self.sha256[0:10]
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if self.shorthash == self.sha256[0:10]:
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return self.shorthash
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self.shorthash = shorthash
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if self.shorthash not in self.ids:
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self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]', f'{self.name_for_extra} [{self.shorthash}]']
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old_title = self.title
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self.title = f'{self.name} [{self.shorthash}]'
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self.short_title = f'{self.name_for_extra} [{self.shorthash}]'
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replace_key(checkpoints_list, old_title, self.title, self)
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self.register()
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return self.shorthash
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try:
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# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
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from transformers import logging, CLIPModel # noqa: F401
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logging.set_verbosity_error()
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except Exception:
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pass
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def setup_model():
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"""called once at startup to do various one-time tasks related to SD models"""
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os.makedirs(model_path, exist_ok=True)
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enable_midas_autodownload()
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patch_given_betas()
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def checkpoint_tiles(use_short=False):
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return [x.short_title if use_short else x.title for x in checkpoints_list.values()]
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def list_models():
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checkpoints_list.clear()
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checkpoint_aliases.clear()
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cmd_ckpt = shared.cmd_opts.ckpt
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if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt):
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model_url = None
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else:
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model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
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model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
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if os.path.exists(cmd_ckpt):
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checkpoint_info = CheckpointInfo(cmd_ckpt)
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checkpoint_info.register()
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shared.opts.data['sd_model_checkpoint'] = checkpoint_info.title
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elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
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print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
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for filename in model_list:
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checkpoint_info = CheckpointInfo(filename)
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checkpoint_info.register()
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re_strip_checksum = re.compile(r"\s*\[[^]]+]\s*$")
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def get_closet_checkpoint_match(search_string):
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if not search_string:
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return None
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checkpoint_info = checkpoint_aliases.get(search_string, None)
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if checkpoint_info is not None:
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return checkpoint_info
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found = sorted([info for info in checkpoints_list.values() if search_string in info.title], key=lambda x: len(x.title))
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if found:
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return found[0]
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search_string_without_checksum = re.sub(re_strip_checksum, '', search_string)
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found = sorted([info for info in checkpoints_list.values() if search_string_without_checksum in info.title], key=lambda x: len(x.title))
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if found:
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return found[0]
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return None
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def model_hash(filename):
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"""old hash that only looks at a small part of the file and is prone to collisions"""
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try:
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with open(filename, "rb") as file:
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import hashlib
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m = hashlib.sha256()
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file.seek(0x100000)
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m.update(file.read(0x10000))
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return m.hexdigest()[0:8]
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except FileNotFoundError:
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return 'NOFILE'
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def select_checkpoint():
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"""Raises `FileNotFoundError` if no checkpoints are found."""
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model_checkpoint = shared.opts.sd_model_checkpoint
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checkpoint_info = checkpoint_aliases.get(model_checkpoint, None)
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if checkpoint_info is not None:
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return checkpoint_info
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if len(checkpoints_list) == 0:
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error_message = "No checkpoints found. When searching for checkpoints, looked at:"
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if shared.cmd_opts.ckpt is not None:
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error_message += f"\n - file {os.path.abspath(shared.cmd_opts.ckpt)}"
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error_message += f"\n - directory {model_path}"
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if shared.cmd_opts.ckpt_dir is not None:
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error_message += f"\n - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}"
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error_message += "Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations."
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raise FileNotFoundError(error_message)
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checkpoint_info = next(iter(checkpoints_list.values()))
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if model_checkpoint is not None:
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print(f"Checkpoint {model_checkpoint} not found; loading fallback {checkpoint_info.title}", file=sys.stderr)
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return checkpoint_info
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checkpoint_dict_replacements = {
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'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
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'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
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'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
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}
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def transform_checkpoint_dict_key(k):
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for text, replacement in checkpoint_dict_replacements.items():
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if k.startswith(text):
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k = replacement + k[len(text):]
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return k
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def get_state_dict_from_checkpoint(pl_sd):
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pl_sd = pl_sd.pop("state_dict", pl_sd)
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pl_sd.pop("state_dict", None)
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sd = {}
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for k, v in pl_sd.items():
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new_key = transform_checkpoint_dict_key(k)
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if new_key is not None:
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sd[new_key] = v
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pl_sd.clear()
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pl_sd.update(sd)
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return pl_sd
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def read_metadata_from_safetensors(filename):
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import json
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with open(filename, mode="rb") as file:
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metadata_len = file.read(8)
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metadata_len = int.from_bytes(metadata_len, "little")
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json_start = file.read(2)
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assert metadata_len > 2 and json_start in (b'{"', b"{'"), f"{filename} is not a safetensors file"
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json_data = json_start + file.read(metadata_len-2)
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json_obj = json.loads(json_data)
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res = {}
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for k, v in json_obj.get("__metadata__", {}).items():
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res[k] = v
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if isinstance(v, str) and v[0:1] == '{':
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try:
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res[k] = json.loads(v)
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except Exception:
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pass
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return res
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def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
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_, extension = os.path.splitext(checkpoint_file)
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if extension.lower() == ".safetensors":
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device = map_location or shared.weight_load_location or devices.get_optimal_device_name()
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if not shared.opts.disable_mmap_load_safetensors:
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pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
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else:
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pl_sd = safetensors.torch.load(open(checkpoint_file, 'rb').read())
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pl_sd = {k: v.to(device) for k, v in pl_sd.items()}
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else:
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pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
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if print_global_state and "global_step" in pl_sd:
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print(f"Global Step: {pl_sd['global_step']}")
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sd = get_state_dict_from_checkpoint(pl_sd)
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return sd
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def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
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sd_model_hash = checkpoint_info.calculate_shorthash()
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timer.record("calculate hash")
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if checkpoint_info in checkpoints_loaded:
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# use checkpoint cache
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print(f"Loading weights [{sd_model_hash}] from cache")
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# move to end as latest
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checkpoints_loaded.move_to_end(checkpoint_info)
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return checkpoints_loaded[checkpoint_info]
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print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
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res = read_state_dict(checkpoint_info.filename)
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timer.record("load weights from disk")
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return res
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class SkipWritingToConfig:
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"""This context manager prevents load_model_weights from writing checkpoint name to the config when it loads weight."""
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skip = False
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previous = None
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def __enter__(self):
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self.previous = SkipWritingToConfig.skip
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SkipWritingToConfig.skip = True
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return self
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def __exit__(self, exc_type, exc_value, exc_traceback):
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SkipWritingToConfig.skip = self.previous
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def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer):
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sd_model_hash = checkpoint_info.calculate_shorthash()
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timer.record("calculate hash")
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if not SkipWritingToConfig.skip:
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shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
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if state_dict is None:
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state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
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model.is_sdxl = hasattr(model, 'conditioner')
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model.is_sd2 = not model.is_sdxl and hasattr(model.cond_stage_model, 'model')
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model.is_sd1 = not model.is_sdxl and not model.is_sd2
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model.is_ssd = model.is_sdxl and 'model.diffusion_model.middle_block.1.transformer_blocks.0.attn1.to_q.weight' not in state_dict.keys()
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if model.is_sdxl:
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sd_models_xl.extend_sdxl(model)
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if model.is_ssd:
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sd_hijack.model_hijack.convert_sdxl_to_ssd(model)
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if shared.opts.sd_checkpoint_cache > 0:
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# cache newly loaded model
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checkpoints_loaded[checkpoint_info] = state_dict.copy()
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model.load_state_dict(state_dict, strict=False)
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timer.record("apply weights to model")
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del state_dict
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if shared.cmd_opts.opt_channelslast:
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model.to(memory_format=torch.channels_last)
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timer.record("apply channels_last")
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if shared.cmd_opts.no_half:
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model.float()
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devices.dtype_unet = torch.float32
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timer.record("apply float()")
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else:
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vae = model.first_stage_model
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depth_model = getattr(model, 'depth_model', None)
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# with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
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if shared.cmd_opts.no_half_vae:
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model.first_stage_model = None
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# with --upcast-sampling, don't convert the depth model weights to float16
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if shared.cmd_opts.upcast_sampling and depth_model:
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model.depth_model = None
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model.half()
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model.first_stage_model = vae
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if depth_model:
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model.depth_model = depth_model
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devices.dtype_unet = torch.float16
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timer.record("apply half()")
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if devices.get_optimal_device_name() == "mps":
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enable_fp8 = False
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elif shared.cmd_opts.opt_unet_fp8_storage:
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enable_fp8 = True
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elif model.is_sdxl and shared.cmd_opts.opt_unet_fp8_storage_xl:
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enable_fp8 = True
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else:
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enable_fp8 = False
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if enable_fp8:
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devices.fp8 = True
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if model.is_sdxl:
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cond_stage = model.conditioner
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else:
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cond_stage = model.cond_stage_model
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for module in cond_stage.modules():
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if isinstance(module, torch.nn.Linear):
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module.to(torch.float8_e4m3fn)
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if devices.device == devices.cpu:
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for module in model.model.diffusion_model.modules():
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if isinstance(module, torch.nn.Conv2d):
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module.to(torch.float8_e4m3fn)
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elif isinstance(module, torch.nn.Linear):
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module.to(torch.float8_e4m3fn)
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else:
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model.model.diffusion_model = model.model.diffusion_model.to(torch.float8_e4m3fn)
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timer.record("apply fp8")
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else:
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devices.fp8 = False
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devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
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model.first_stage_model.to(devices.dtype_vae)
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timer.record("apply dtype to VAE")
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# clean up cache if limit is reached
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while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
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checkpoints_loaded.popitem(last=False)
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model.sd_model_hash = sd_model_hash
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model.sd_model_checkpoint = checkpoint_info.filename
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model.sd_checkpoint_info = checkpoint_info
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shared.opts.data["sd_checkpoint_hash"] = checkpoint_info.sha256
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if hasattr(model, 'logvar'):
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model.logvar = model.logvar.to(devices.device) # fix for training
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sd_vae.delete_base_vae()
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sd_vae.clear_loaded_vae()
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vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename).tuple()
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sd_vae.load_vae(model, vae_file, vae_source)
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timer.record("load VAE")
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def enable_midas_autodownload():
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"""
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Gives the ldm.modules.midas.api.load_model function automatic downloading.
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When the 512-depth-ema model, and other future models like it, is loaded,
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it calls midas.api.load_model to load the associated midas depth model.
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This function applies a wrapper to download the model to the correct
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location automatically.
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"""
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midas_path = os.path.join(paths.models_path, 'midas')
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# stable-diffusion-stability-ai hard-codes the midas model path to
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# a location that differs from where other scripts using this model look.
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# HACK: Overriding the path here.
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for k, v in midas.api.ISL_PATHS.items():
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file_name = os.path.basename(v)
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midas.api.ISL_PATHS[k] = os.path.join(midas_path, file_name)
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midas_urls = {
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"dpt_large": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
|
|
"dpt_hybrid": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt",
|
|
"midas_v21": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21-f6b98070.pt",
|
|
"midas_v21_small": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21_small-70d6b9c8.pt",
|
|
}
|
|
|
|
midas.api.load_model_inner = midas.api.load_model
|
|
|
|
def load_model_wrapper(model_type):
|
|
path = midas.api.ISL_PATHS[model_type]
|
|
if not os.path.exists(path):
|
|
if not os.path.exists(midas_path):
|
|
mkdir(midas_path)
|
|
|
|
print(f"Downloading midas model weights for {model_type} to {path}")
|
|
request.urlretrieve(midas_urls[model_type], path)
|
|
print(f"{model_type} downloaded")
|
|
|
|
return midas.api.load_model_inner(model_type)
|
|
|
|
midas.api.load_model = load_model_wrapper
|
|
|
|
|
|
def patch_given_betas():
|
|
import ldm.models.diffusion.ddpm
|
|
|
|
def patched_register_schedule(*args, **kwargs):
|
|
"""a modified version of register_schedule function that converts plain list from Omegaconf into numpy"""
|
|
|
|
if isinstance(args[1], ListConfig):
|
|
args = (args[0], np.array(args[1]), *args[2:])
|
|
|
|
original_register_schedule(*args, **kwargs)
|
|
|
|
original_register_schedule = patches.patch(__name__, ldm.models.diffusion.ddpm.DDPM, 'register_schedule', patched_register_schedule)
|
|
|
|
|
|
def repair_config(sd_config):
|
|
|
|
if not hasattr(sd_config.model.params, "use_ema"):
|
|
sd_config.model.params.use_ema = False
|
|
|
|
if hasattr(sd_config.model.params, 'unet_config'):
|
|
if shared.cmd_opts.no_half:
|
|
sd_config.model.params.unet_config.params.use_fp16 = False
|
|
elif shared.cmd_opts.upcast_sampling:
|
|
sd_config.model.params.unet_config.params.use_fp16 = True
|
|
|
|
if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
|
|
sd_config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla"
|
|
|
|
# For UnCLIP-L, override the hardcoded karlo directory
|
|
if hasattr(sd_config.model.params, "noise_aug_config") and hasattr(sd_config.model.params.noise_aug_config.params, "clip_stats_path"):
|
|
karlo_path = os.path.join(paths.models_path, 'karlo')
|
|
sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
|
|
|
|
|
|
sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
|
|
sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
|
|
sdxl_clip_weight = 'conditioner.embedders.1.model.ln_final.weight'
|
|
sdxl_refiner_clip_weight = 'conditioner.embedders.0.model.ln_final.weight'
|
|
|
|
|
|
class SdModelData:
|
|
def __init__(self):
|
|
self.sd_model = None
|
|
self.loaded_sd_models = []
|
|
self.was_loaded_at_least_once = False
|
|
self.lock = threading.Lock()
|
|
|
|
def get_sd_model(self):
|
|
if self.was_loaded_at_least_once:
|
|
return self.sd_model
|
|
|
|
if self.sd_model is None:
|
|
with self.lock:
|
|
if self.sd_model is not None or self.was_loaded_at_least_once:
|
|
return self.sd_model
|
|
|
|
try:
|
|
load_model()
|
|
|
|
except Exception as e:
|
|
errors.display(e, "loading stable diffusion model", full_traceback=True)
|
|
print("", file=sys.stderr)
|
|
print("Stable diffusion model failed to load", file=sys.stderr)
|
|
self.sd_model = None
|
|
|
|
return self.sd_model
|
|
|
|
def set_sd_model(self, v, already_loaded=False):
|
|
self.sd_model = v
|
|
if already_loaded:
|
|
sd_vae.base_vae = getattr(v, "base_vae", None)
|
|
sd_vae.loaded_vae_file = getattr(v, "loaded_vae_file", None)
|
|
sd_vae.checkpoint_info = v.sd_checkpoint_info
|
|
|
|
try:
|
|
self.loaded_sd_models.remove(v)
|
|
except ValueError:
|
|
pass
|
|
|
|
if v is not None:
|
|
self.loaded_sd_models.insert(0, v)
|
|
|
|
|
|
model_data = SdModelData()
|
|
|
|
|
|
def get_empty_cond(sd_model):
|
|
|
|
p = processing.StableDiffusionProcessingTxt2Img()
|
|
extra_networks.activate(p, {})
|
|
|
|
if hasattr(sd_model, 'conditioner'):
|
|
d = sd_model.get_learned_conditioning([""])
|
|
return d['crossattn']
|
|
else:
|
|
return sd_model.cond_stage_model([""])
|
|
|
|
|
|
def send_model_to_cpu(m):
|
|
if m.lowvram:
|
|
lowvram.send_everything_to_cpu()
|
|
else:
|
|
m.to(devices.cpu)
|
|
|
|
devices.torch_gc()
|
|
|
|
|
|
def model_target_device(m):
|
|
if lowvram.is_needed(m):
|
|
return devices.cpu
|
|
else:
|
|
return devices.device
|
|
|
|
|
|
def send_model_to_device(m):
|
|
lowvram.apply(m)
|
|
|
|
if not m.lowvram:
|
|
m.to(shared.device)
|
|
|
|
|
|
def send_model_to_trash(m):
|
|
m.to(device="meta")
|
|
devices.torch_gc()
|
|
|
|
|
|
def load_model(checkpoint_info=None, already_loaded_state_dict=None):
|
|
from modules import sd_hijack
|
|
checkpoint_info = checkpoint_info or select_checkpoint()
|
|
|
|
timer = Timer()
|
|
|
|
if model_data.sd_model:
|
|
send_model_to_trash(model_data.sd_model)
|
|
model_data.sd_model = None
|
|
devices.torch_gc()
|
|
|
|
timer.record("unload existing model")
|
|
|
|
if already_loaded_state_dict is not None:
|
|
state_dict = already_loaded_state_dict
|
|
else:
|
|
state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
|
|
|
|
checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
|
|
clip_is_included_into_sd = any(x for x in [sd1_clip_weight, sd2_clip_weight, sdxl_clip_weight, sdxl_refiner_clip_weight] if x in state_dict)
|
|
|
|
timer.record("find config")
|
|
|
|
sd_config = OmegaConf.load(checkpoint_config)
|
|
repair_config(sd_config)
|
|
|
|
timer.record("load config")
|
|
|
|
print(f"Creating model from config: {checkpoint_config}")
|
|
|
|
sd_model = None
|
|
try:
|
|
with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd or shared.cmd_opts.do_not_download_clip):
|
|
with sd_disable_initialization.InitializeOnMeta():
|
|
sd_model = instantiate_from_config(sd_config.model)
|
|
|
|
except Exception as e:
|
|
errors.display(e, "creating model quickly", full_traceback=True)
|
|
|
|
if sd_model is None:
|
|
print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
|
|
|
|
with sd_disable_initialization.InitializeOnMeta():
|
|
sd_model = instantiate_from_config(sd_config.model)
|
|
|
|
sd_model.used_config = checkpoint_config
|
|
|
|
timer.record("create model")
|
|
|
|
if shared.cmd_opts.no_half:
|
|
weight_dtype_conversion = None
|
|
else:
|
|
weight_dtype_conversion = {
|
|
'first_stage_model': None,
|
|
'': torch.float16,
|
|
}
|
|
|
|
with sd_disable_initialization.LoadStateDictOnMeta(state_dict, device=model_target_device(sd_model), weight_dtype_conversion=weight_dtype_conversion):
|
|
load_model_weights(sd_model, checkpoint_info, state_dict, timer)
|
|
timer.record("load weights from state dict")
|
|
|
|
send_model_to_device(sd_model)
|
|
timer.record("move model to device")
|
|
|
|
sd_hijack.model_hijack.hijack(sd_model)
|
|
|
|
timer.record("hijack")
|
|
|
|
sd_model.eval()
|
|
model_data.set_sd_model(sd_model)
|
|
model_data.was_loaded_at_least_once = True
|
|
|
|
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True) # Reload embeddings after model load as they may or may not fit the model
|
|
|
|
timer.record("load textual inversion embeddings")
|
|
|
|
script_callbacks.model_loaded_callback(sd_model)
|
|
|
|
timer.record("scripts callbacks")
|
|
|
|
with devices.autocast(), torch.no_grad():
|
|
sd_model.cond_stage_model_empty_prompt = get_empty_cond(sd_model)
|
|
|
|
timer.record("calculate empty prompt")
|
|
|
|
print(f"Model loaded in {timer.summary()}.")
|
|
|
|
return sd_model
|
|
|
|
|
|
def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
|
|
"""
|
|
Checks if the desired checkpoint from checkpoint_info is not already loaded in model_data.loaded_sd_models.
|
|
If it is loaded, returns that (moving it to GPU if necessary, and moving the currently loadded model to CPU if necessary).
|
|
If not, returns the model that can be used to load weights from checkpoint_info's file.
|
|
If no such model exists, returns None.
|
|
Additionaly deletes loaded models that are over the limit set in settings (sd_checkpoints_limit).
|
|
"""
|
|
|
|
already_loaded = None
|
|
for i in reversed(range(len(model_data.loaded_sd_models))):
|
|
loaded_model = model_data.loaded_sd_models[i]
|
|
if loaded_model.sd_checkpoint_info.filename == checkpoint_info.filename:
|
|
already_loaded = loaded_model
|
|
continue
|
|
|
|
if len(model_data.loaded_sd_models) > shared.opts.sd_checkpoints_limit > 0:
|
|
print(f"Unloading model {len(model_data.loaded_sd_models)} over the limit of {shared.opts.sd_checkpoints_limit}: {loaded_model.sd_checkpoint_info.title}")
|
|
model_data.loaded_sd_models.pop()
|
|
send_model_to_trash(loaded_model)
|
|
timer.record("send model to trash")
|
|
|
|
if shared.opts.sd_checkpoints_keep_in_cpu:
|
|
send_model_to_cpu(sd_model)
|
|
timer.record("send model to cpu")
|
|
|
|
if already_loaded is not None:
|
|
send_model_to_device(already_loaded)
|
|
timer.record("send model to device")
|
|
|
|
model_data.set_sd_model(already_loaded, already_loaded=True)
|
|
|
|
if not SkipWritingToConfig.skip:
|
|
shared.opts.data["sd_model_checkpoint"] = already_loaded.sd_checkpoint_info.title
|
|
shared.opts.data["sd_checkpoint_hash"] = already_loaded.sd_checkpoint_info.sha256
|
|
|
|
print(f"Using already loaded model {already_loaded.sd_checkpoint_info.title}: done in {timer.summary()}")
|
|
sd_vae.reload_vae_weights(already_loaded)
|
|
return model_data.sd_model
|
|
elif shared.opts.sd_checkpoints_limit > 1 and len(model_data.loaded_sd_models) < shared.opts.sd_checkpoints_limit:
|
|
print(f"Loading model {checkpoint_info.title} ({len(model_data.loaded_sd_models) + 1} out of {shared.opts.sd_checkpoints_limit})")
|
|
|
|
model_data.sd_model = None
|
|
load_model(checkpoint_info)
|
|
return model_data.sd_model
|
|
elif len(model_data.loaded_sd_models) > 0:
|
|
sd_model = model_data.loaded_sd_models.pop()
|
|
model_data.sd_model = sd_model
|
|
|
|
sd_vae.base_vae = getattr(sd_model, "base_vae", None)
|
|
sd_vae.loaded_vae_file = getattr(sd_model, "loaded_vae_file", None)
|
|
sd_vae.checkpoint_info = sd_model.sd_checkpoint_info
|
|
|
|
print(f"Reusing loaded model {sd_model.sd_checkpoint_info.title} to load {checkpoint_info.title}")
|
|
return sd_model
|
|
else:
|
|
return None
|
|
|
|
|
|
def reload_model_weights(sd_model=None, info=None):
|
|
checkpoint_info = info or select_checkpoint()
|
|
|
|
timer = Timer()
|
|
|
|
if not sd_model:
|
|
sd_model = model_data.sd_model
|
|
|
|
if sd_model is None: # previous model load failed
|
|
current_checkpoint_info = None
|
|
else:
|
|
current_checkpoint_info = sd_model.sd_checkpoint_info
|
|
if sd_model.sd_model_checkpoint == checkpoint_info.filename:
|
|
return sd_model
|
|
|
|
sd_model = reuse_model_from_already_loaded(sd_model, checkpoint_info, timer)
|
|
if sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename:
|
|
return sd_model
|
|
|
|
if sd_model is not None:
|
|
sd_unet.apply_unet("None")
|
|
send_model_to_cpu(sd_model)
|
|
sd_hijack.model_hijack.undo_hijack(sd_model)
|
|
|
|
state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
|
|
|
|
checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
|
|
|
|
timer.record("find config")
|
|
|
|
if sd_model is None or checkpoint_config != sd_model.used_config:
|
|
if sd_model is not None:
|
|
send_model_to_trash(sd_model)
|
|
|
|
load_model(checkpoint_info, already_loaded_state_dict=state_dict)
|
|
return model_data.sd_model
|
|
|
|
try:
|
|
load_model_weights(sd_model, checkpoint_info, state_dict, timer)
|
|
except Exception:
|
|
print("Failed to load checkpoint, restoring previous")
|
|
load_model_weights(sd_model, current_checkpoint_info, None, timer)
|
|
raise
|
|
finally:
|
|
sd_hijack.model_hijack.hijack(sd_model)
|
|
timer.record("hijack")
|
|
|
|
script_callbacks.model_loaded_callback(sd_model)
|
|
timer.record("script callbacks")
|
|
|
|
if not sd_model.lowvram:
|
|
sd_model.to(devices.device)
|
|
timer.record("move model to device")
|
|
|
|
print(f"Weights loaded in {timer.summary()}.")
|
|
|
|
model_data.set_sd_model(sd_model)
|
|
sd_unet.apply_unet()
|
|
|
|
return sd_model
|
|
|
|
|
|
def unload_model_weights(sd_model=None, info=None):
|
|
send_model_to_cpu(sd_model or shared.sd_model)
|
|
|
|
return sd_model
|
|
|
|
|
|
def apply_token_merging(sd_model, token_merging_ratio):
|
|
"""
|
|
Applies speed and memory optimizations from tomesd.
|
|
"""
|
|
|
|
current_token_merging_ratio = getattr(sd_model, 'applied_token_merged_ratio', 0)
|
|
|
|
if current_token_merging_ratio == token_merging_ratio:
|
|
return
|
|
|
|
if current_token_merging_ratio > 0:
|
|
tomesd.remove_patch(sd_model)
|
|
|
|
if token_merging_ratio > 0:
|
|
tomesd.apply_patch(
|
|
sd_model,
|
|
ratio=token_merging_ratio,
|
|
use_rand=False, # can cause issues with some samplers
|
|
merge_attn=True,
|
|
merge_crossattn=False,
|
|
merge_mlp=False
|
|
)
|
|
|
|
sd_model.applied_token_merged_ratio = token_merging_ratio
|