mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2026-03-23 06:40:23 -07:00
322feb4063
- Set PYTHON path to the specific Python executable. - Add COMMANDLINE_ARGS to skip CUDA test during setup. - Define STABLE_DIFFUSION_REPO and STABLE_DIFFUSION_COMMIT_HASH for repository management. - Create preflight log files to capture errors during the fetching of the Stable Diffusion repository, including issues with malformed URLs and repository not found errors.
1041 lines
36 KiB
Python
1041 lines
36 KiB
Python
import collections
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import importlib
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import os
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import sys
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import threading
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import enum
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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 urllib import request
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try:
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import ldm.modules.midas as midas
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except Exception:
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midas = None
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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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from modules.shared import opts
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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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class ModelType(enum.Enum):
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SD1 = 1
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SD2 = 2
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SDXL = 3
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SSD = 4
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SD3 = 5
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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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if midas is not None:
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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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expected_sha256 = None
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else:
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model_url = f"{shared.hf_endpoint}/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
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expected_sha256 = '6ce0161689b3853acaa03779ec93eafe75a02f4ced659bee03f50797806fa2fa'
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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"], hash_prefix=expected_sha256)
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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_sd1 = {
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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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checkpoint_dict_replacements_sd2_turbo = { # Converts SD 2.1 Turbo from SGM to LDM format.
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'conditioner.embedders.0.': 'cond_stage_model.',
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}
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def transform_checkpoint_dict_key(k, replacements):
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for text, replacement in 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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is_sd2_turbo = 'conditioner.embedders.0.model.ln_final.weight' in pl_sd and pl_sd['conditioner.embedders.0.model.ln_final.weight'].size()[0] == 1024
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sd = {}
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for k, v in pl_sd.items():
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if is_sd2_turbo:
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new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd2_turbo)
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else:
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new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd1)
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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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res = {}
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try:
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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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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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except Exception:
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errors.report(f"Error reading metadata from file: {filename}", exc_info=True)
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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 check_fp8(model):
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if model is None:
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return None
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if devices.get_optimal_device_name() == "mps":
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enable_fp8 = False
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elif shared.opts.fp8_storage == "Enable":
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enable_fp8 = True
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elif getattr(model, "is_sdxl", False) and shared.opts.fp8_storage == "Enable for SDXL":
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enable_fp8 = True
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else:
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enable_fp8 = False
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return enable_fp8
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def set_model_type(model, state_dict):
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model.is_sd1 = False
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model.is_sd2 = False
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model.is_sdxl = False
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model.is_ssd = False
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model.is_sd3 = False
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if "model.diffusion_model.x_embedder.proj.weight" in state_dict:
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model.is_sd3 = True
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model.model_type = ModelType.SD3
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elif hasattr(model, 'conditioner'):
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model.is_sdxl = True
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if 'model.diffusion_model.middle_block.1.transformer_blocks.0.attn1.to_q.weight' not in state_dict.keys():
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model.is_ssd = True
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model.model_type = ModelType.SSD
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else:
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model.model_type = ModelType.SDXL
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elif hasattr(model.cond_stage_model, 'model'):
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model.is_sd2 = True
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model.model_type = ModelType.SD2
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else:
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model.is_sd1 = True
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model.model_type = ModelType.SD1
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def set_model_fields(model):
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if not hasattr(model, 'latent_channels'):
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model.latent_channels = 4
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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 devices.fp8:
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# prevent model to load state dict in fp8
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model.half()
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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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set_model_type(model, state_dict)
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set_model_fields(model)
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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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if hasattr(model, "before_load_weights"):
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model.before_load_weights(state_dict)
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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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if hasattr(model, "after_load_weights"):
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model.after_load_weights(state_dict)
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del state_dict
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# Set is_sdxl_inpaint flag.
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# Checks Unet structure to detect inpaint model. The inpaint model's
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# checkpoint state_dict does not contain the key
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# 'diffusion_model.input_blocks.0.0.weight'.
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diffusion_model_input = model.model.state_dict().get(
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'diffusion_model.input_blocks.0.0.weight'
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)
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model.is_sdxl_inpaint = (
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model.is_sdxl and
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diffusion_model_input is not None and
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diffusion_model_input.shape[1] == 9
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)
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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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|
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if shared.cmd_opts.no_half:
|
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model.float()
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model.alphas_cumprod_original = model.alphas_cumprod
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devices.dtype_unet = torch.float32
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assert shared.cmd_opts.precision != "half", "Cannot use --precision half with --no-half"
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timer.record("apply float()")
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else:
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vae = model.first_stage_model
|
|
depth_model = getattr(model, 'depth_model', None)
|
|
|
|
# with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
|
|
if shared.cmd_opts.no_half_vae:
|
|
model.first_stage_model = None
|
|
# with --upcast-sampling, don't convert the depth model weights to float16
|
|
if shared.cmd_opts.upcast_sampling and depth_model:
|
|
model.depth_model = None
|
|
|
|
alphas_cumprod = model.alphas_cumprod
|
|
model.alphas_cumprod = None
|
|
model.half()
|
|
model.alphas_cumprod = alphas_cumprod
|
|
model.alphas_cumprod_original = alphas_cumprod
|
|
model.first_stage_model = vae
|
|
if depth_model:
|
|
model.depth_model = depth_model
|
|
|
|
devices.dtype_unet = torch.float16
|
|
timer.record("apply half()")
|
|
|
|
apply_alpha_schedule_override(model)
|
|
|
|
for module in model.modules():
|
|
if hasattr(module, 'fp16_weight'):
|
|
del module.fp16_weight
|
|
if hasattr(module, 'fp16_bias'):
|
|
del module.fp16_bias
|
|
|
|
if check_fp8(model):
|
|
devices.fp8 = True
|
|
first_stage = model.first_stage_model
|
|
model.first_stage_model = None
|
|
for module in model.modules():
|
|
if isinstance(module, (torch.nn.Conv2d, torch.nn.Linear)):
|
|
if shared.opts.cache_fp16_weight:
|
|
module.fp16_weight = module.weight.data.clone().cpu().half()
|
|
if module.bias is not None:
|
|
module.fp16_bias = module.bias.data.clone().cpu().half()
|
|
module.to(torch.float8_e4m3fn)
|
|
model.first_stage_model = first_stage
|
|
timer.record("apply fp8")
|
|
else:
|
|
devices.fp8 = False
|
|
|
|
devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
|
|
|
|
model.first_stage_model.to(devices.dtype_vae)
|
|
timer.record("apply dtype to VAE")
|
|
|
|
# clean up cache if limit is reached
|
|
while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
|
|
checkpoints_loaded.popitem(last=False)
|
|
|
|
model.sd_model_hash = sd_model_hash
|
|
model.sd_model_checkpoint = checkpoint_info.filename
|
|
model.sd_checkpoint_info = checkpoint_info
|
|
shared.opts.data["sd_checkpoint_hash"] = checkpoint_info.sha256
|
|
|
|
if hasattr(model, 'logvar'):
|
|
model.logvar = model.logvar.to(devices.device) # fix for training
|
|
|
|
sd_vae.delete_base_vae()
|
|
sd_vae.clear_loaded_vae()
|
|
vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename).tuple()
|
|
sd_vae.load_vae(model, vae_file, vae_source)
|
|
timer.record("load VAE")
|
|
|
|
|
|
def enable_midas_autodownload():
|
|
"""
|
|
Gives the ldm.modules.midas.api.load_model function automatic downloading.
|
|
|
|
When the 512-depth-ema model, and other future models like it, is loaded,
|
|
it calls midas.api.load_model to load the associated midas depth model.
|
|
This function applies a wrapper to download the model to the correct
|
|
location automatically.
|
|
"""
|
|
|
|
if midas is None:
|
|
return
|
|
|
|
midas_path = os.path.join(paths.models_path, 'midas')
|
|
|
|
# stable-diffusion-stability-ai hard-codes the midas model path to
|
|
# a location that differs from where other scripts using this model look.
|
|
# HACK: Overriding the path here.
|
|
for k, v in midas.api.ISL_PATHS.items():
|
|
file_name = os.path.basename(v)
|
|
midas.api.ISL_PATHS[k] = os.path.join(midas_path, file_name)
|
|
|
|
midas_urls = {
|
|
"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):
|
|
os.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, state_dict=None):
|
|
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 or shared.cmd_opts.precision == "half":
|
|
sd_config.model.params.unet_config.params.use_fp16 = True
|
|
|
|
if hasattr(sd_config.model.params, 'first_stage_config'):
|
|
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)
|
|
|
|
# Do not use checkpoint for inference.
|
|
# This helps prevent extra performance overhead on checking parameters.
|
|
# The perf overhead is about 100ms/it on 4090 for SDXL.
|
|
if hasattr(sd_config.model.params, "network_config"):
|
|
sd_config.model.params.network_config.params.use_checkpoint = False
|
|
if hasattr(sd_config.model.params, "unet_config"):
|
|
sd_config.model.params.unet_config.params.use_checkpoint = False
|
|
|
|
|
|
|
|
def rescale_zero_terminal_snr_abar(alphas_cumprod):
|
|
alphas_bar_sqrt = alphas_cumprod.sqrt()
|
|
|
|
# Store old values.
|
|
alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
|
|
alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
|
|
|
|
# Shift so the last timestep is zero.
|
|
alphas_bar_sqrt -= (alphas_bar_sqrt_T)
|
|
|
|
# Scale so the first timestep is back to the old value.
|
|
alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
|
|
|
|
# Convert alphas_bar_sqrt to betas
|
|
alphas_bar = alphas_bar_sqrt ** 2 # Revert sqrt
|
|
alphas_bar[-1] = 4.8973451890853435e-08
|
|
return alphas_bar
|
|
|
|
|
|
def apply_alpha_schedule_override(sd_model, p=None):
|
|
"""
|
|
Applies an override to the alpha schedule of the model according to settings.
|
|
- downcasts the alpha schedule to half precision
|
|
- rescales the alpha schedule to have zero terminal SNR
|
|
"""
|
|
|
|
if not hasattr(sd_model, 'alphas_cumprod') or not hasattr(sd_model, 'alphas_cumprod_original'):
|
|
return
|
|
|
|
sd_model.alphas_cumprod = sd_model.alphas_cumprod_original.to(shared.device)
|
|
|
|
if opts.use_downcasted_alpha_bar:
|
|
if p is not None:
|
|
p.extra_generation_params['Downcast alphas_cumprod'] = opts.use_downcasted_alpha_bar
|
|
sd_model.alphas_cumprod = sd_model.alphas_cumprod.half().to(shared.device)
|
|
|
|
if opts.sd_noise_schedule == "Zero Terminal SNR":
|
|
if p is not None:
|
|
p.extra_generation_params['Noise Schedule'] = opts.sd_noise_schedule
|
|
sd_model.alphas_cumprod = rescale_zero_terminal_snr_abar(sd_model.alphas_cumprod).to(shared.device)
|
|
|
|
|
|
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, 'get_learned_conditioning'):
|
|
d = sd_model.get_learned_conditioning([""])
|
|
else:
|
|
d = sd_model.cond_stage_model([""])
|
|
|
|
if isinstance(d, dict):
|
|
d = d['crossattn']
|
|
|
|
return d
|
|
|
|
|
|
def send_model_to_cpu(m):
|
|
if m is not None:
|
|
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 instantiate_from_config(config, state_dict=None):
|
|
constructor = get_obj_from_str(config["target"])
|
|
|
|
params = {**config.get("params", {})}
|
|
|
|
if state_dict and "state_dict" in params and params["state_dict"] is None:
|
|
params["state_dict"] = state_dict
|
|
|
|
return constructor(**params)
|
|
|
|
|
|
def get_obj_from_str(string, reload=False):
|
|
module, cls = string.rsplit(".", 1)
|
|
if reload:
|
|
module_imp = importlib.import_module(module)
|
|
importlib.reload(module_imp)
|
|
return getattr(importlib.import_module(module, package=None), cls)
|
|
|
|
|
|
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, state_dict)
|
|
|
|
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, state_dict)
|
|
|
|
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, state_dict)
|
|
|
|
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,
|
|
'alphas_cumprod': 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.
|
|
Additionally deletes loaded models that are over the limit set in settings (sd_checkpoints_limit).
|
|
"""
|
|
|
|
if sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename:
|
|
return sd_model
|
|
|
|
if shared.opts.sd_checkpoints_keep_in_cpu:
|
|
send_model_to_cpu(sd_model)
|
|
timer.record("send model to cpu")
|
|
|
|
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}")
|
|
del model_data.loaded_sd_models[i]
|
|
send_model_to_trash(loaded_model)
|
|
timer.record("send model to trash")
|
|
|
|
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, forced_reload=False):
|
|
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 check_fp8(sd_model) != devices.fp8:
|
|
# load from state dict again to prevent extra numerical errors
|
|
forced_reload = True
|
|
elif sd_model.sd_model_checkpoint == checkpoint_info.filename and not forced_reload:
|
|
return sd_model
|
|
|
|
sd_model = reuse_model_from_already_loaded(sd_model, checkpoint_info, timer)
|
|
if not forced_reload and 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")
|
|
|
|
if not sd_model.lowvram:
|
|
sd_model.to(devices.device)
|
|
timer.record("move model to device")
|
|
|
|
script_callbacks.model_loaded_callback(sd_model)
|
|
timer.record("script callbacks")
|
|
|
|
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
|