import collections import os.path import sys import gc import torch import re import safetensors.torch from omegaconf import OmegaConf from os import mkdir from urllib import request import ldm.modules.midas as midas from ldm.util import instantiate_from_config from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config from modules.paths import models_path from modules.sd_hijack_inpainting import do_inpainting_hijack from modules.timer import Timer model_dir = "Stable-diffusion" model_path = os.path.abspath(os.path.join(paths.models_path, model_dir)) checkpoints_list = {} checkpoint_alisases = {} checkpoints_loaded = collections.OrderedDict() class CheckpointInfo: def __init__(self, filename): self.filename = filename abspath = os.path.abspath(filename) if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir): name = abspath.replace(shared.cmd_opts.ckpt_dir, '') elif abspath.startswith(model_path): name = abspath.replace(model_path, '') else: name = os.path.basename(filename) if name.startswith("\\") or name.startswith("/"): name = name[1:] self.name = name self.name_for_extra = os.path.splitext(os.path.basename(filename))[0] self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0] self.hash = model_hash(filename) self.sha256 = hashes.sha256_from_cache(self.filename, "checkpoint/" + name) self.shorthash = self.sha256[0:10] if self.sha256 else None self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]' self.ids = [self.hash, self.model_name, self.title, name, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else []) def register(self): checkpoints_list[self.title] = self for id in self.ids: checkpoint_alisases[id] = self def calculate_shorthash(self): self.sha256 = hashes.sha256(self.filename, "checkpoint/" + self.name) if self.sha256 is None: return self.shorthash = self.sha256[0:10] if self.shorthash not in self.ids: self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] checkpoints_list.pop(self.title) self.title = f'{self.name} [{self.shorthash}]' self.register() return self.shorthash try: # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start. from transformers import logging, CLIPModel logging.set_verbosity_error() except Exception: pass def setup_model(): if not os.path.exists(model_path): os.makedirs(model_path) list_models() enable_midas_autodownload() def checkpoint_tiles(): def convert(name): return int(name) if name.isdigit() else name.lower() def alphanumeric_key(key): return [convert(c) for c in re.split('([0-9]+)', key)] return sorted([x.title for x in checkpoints_list.values()], key=alphanumeric_key) def list_models(): checkpoints_list.clear() checkpoint_alisases.clear() cmd_ckpt = shared.cmd_opts.ckpt if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt): model_url = None else: model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors" 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"]) if os.path.exists(cmd_ckpt): checkpoint_info = CheckpointInfo(cmd_ckpt) checkpoint_info.register() shared.opts.data['sd_model_checkpoint'] = checkpoint_info.title elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file: print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr) for filename in model_list: checkpoint_info = CheckpointInfo(filename) checkpoint_info.register() def get_closet_checkpoint_match(search_string): checkpoint_info = checkpoint_alisases.get(search_string, None) if checkpoint_info is not None: return checkpoint_info found = sorted([info for info in checkpoints_list.values() if search_string in info.title], key=lambda x: len(x.title)) if found: return found[0] return None def model_hash(filename): """old hash that only looks at a small part of the file and is prone to collisions""" try: with open(filename, "rb") as file: import hashlib m = hashlib.sha256() file.seek(0x100000) m.update(file.read(0x10000)) return m.hexdigest()[0:8] except FileNotFoundError: return 'NOFILE' def select_checkpoint(): model_checkpoint = shared.opts.sd_model_checkpoint checkpoint_info = checkpoint_alisases.get(model_checkpoint, None) if checkpoint_info is not None: return checkpoint_info if len(checkpoints_list) == 0: print("No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr) if shared.cmd_opts.ckpt is not None: print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr) print(f" - directory {model_path}", file=sys.stderr) if shared.cmd_opts.ckpt_dir is not None: print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr) print("Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations. The program will exit.", file=sys.stderr) exit(1) checkpoint_info = next(iter(checkpoints_list.values())) if model_checkpoint is not None: print(f"Checkpoint {model_checkpoint} not found; loading fallback {checkpoint_info.title}", file=sys.stderr) return checkpoint_info checkpoint_dict_replacements = { 'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.', 'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.', 'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.', } def transform_checkpoint_dict_key(k): for text, replacement in checkpoint_dict_replacements.items(): if k.startswith(text): k = replacement + k[len(text):] return k def get_state_dict_from_checkpoint(pl_sd): pl_sd = pl_sd.pop("state_dict", pl_sd) pl_sd.pop("state_dict", None) sd = {} for k, v in pl_sd.items(): new_key = transform_checkpoint_dict_key(k) if new_key is not None: sd[new_key] = v pl_sd.clear() pl_sd.update(sd) return pl_sd def read_metadata_from_safetensors(filename): import json with open(filename, mode="rb") as file: metadata_len = file.read(8) metadata_len = int.from_bytes(metadata_len, "little") json_start = file.read(2) assert metadata_len > 2 and json_start in (b'{"', b"{'"), f"{filename} is not a safetensors file" json_data = json_start + file.read(metadata_len-2) json_obj = json.loads(json_data) res = {} for k, v in json_obj.get("__metadata__", {}).items(): res[k] = v if isinstance(v, str) and v[0:1] == '{': try: res[k] = json.loads(v) except Exception as e: pass return res def read_state_dict(checkpoint_file, print_global_state=False, map_location=None): _, extension = os.path.splitext(checkpoint_file) if extension.lower() == ".safetensors": device = map_location or shared.weight_load_location or devices.get_optimal_device_name() pl_sd = safetensors.torch.load_file(checkpoint_file, device=device) else: pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location) if print_global_state and "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") sd = get_state_dict_from_checkpoint(pl_sd) return sd def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer): sd_model_hash = checkpoint_info.calculate_shorthash() timer.record("calculate hash") if checkpoint_info in checkpoints_loaded: # use checkpoint cache print(f"Loading weights [{sd_model_hash}] from cache") return checkpoints_loaded[checkpoint_info] print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}") res = read_state_dict(checkpoint_info.filename) timer.record("load weights from disk") return res def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer): sd_model_hash = checkpoint_info.calculate_shorthash() timer.record("calculate hash") shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title if state_dict is None: state_dict = get_checkpoint_state_dict(checkpoint_info, timer) model.load_state_dict(state_dict, strict=False) del state_dict timer.record("apply weights to model") if shared.opts.sd_checkpoint_cache > 0: # cache newly loaded model checkpoints_loaded[checkpoint_info] = model.state_dict().copy() if shared.cmd_opts.opt_channelslast: model.to(memory_format=torch.channels_last) timer.record("apply channels_last") if not shared.cmd_opts.no_half: 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 model.half() model.first_stage_model = vae if depth_model: model.depth_model = depth_model timer.record("apply half()") devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16 devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16 devices.dtype_unet = model.model.diffusion_model.dtype 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 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) 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. """ 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): 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 repair_config(sd_config): if not hasattr(sd_config.model.params, "use_ema"): sd_config.model.params.use_ema = False 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 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' def load_model(checkpoint_info=None, already_loaded_state_dict=None, time_taken_to_load_state_dict=None): from modules import lowvram, sd_hijack checkpoint_info = checkpoint_info or select_checkpoint() if shared.sd_model: sd_hijack.model_hijack.undo_hijack(shared.sd_model) shared.sd_model = None gc.collect() devices.torch_gc() do_inpainting_hijack() timer = Timer() 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 = sd1_clip_weight in state_dict or sd2_clip_weight 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): sd_model = instantiate_from_config(sd_config.model) except Exception as e: pass if sd_model is None: print('Failed to create model quickly; will retry using slow method.', file=sys.stderr) sd_model = instantiate_from_config(sd_config.model) sd_model.used_config = checkpoint_config timer.record("create model") load_model_weights(sd_model, checkpoint_info, state_dict, timer) if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram) else: sd_model.to(shared.device) timer.record("move model to device") sd_hijack.model_hijack.hijack(sd_model) timer.record("hijack") sd_model.eval() shared.sd_model = sd_model 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") print(f"Model loaded in {timer.summary()}.") return sd_model def reload_model_weights(sd_model=None, info=None): from modules import lowvram, devices, sd_hijack checkpoint_info = info or select_checkpoint() if not sd_model: sd_model = shared.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 if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: lowvram.send_everything_to_cpu() else: sd_model.to(devices.cpu) sd_hijack.model_hijack.undo_hijack(sd_model) timer = Timer() 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: del sd_model checkpoints_loaded.clear() load_model(checkpoint_info, already_loaded_state_dict=state_dict) return shared.sd_model try: load_model_weights(sd_model, checkpoint_info, state_dict, timer) except Exception as e: 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 shared.cmd_opts.lowvram and not shared.cmd_opts.medvram: sd_model.to(devices.device) timer.record("move model to device") print(f"Weights loaded in {timer.summary()}.") return sd_model def unload_model_weights(sd_model=None, info=None): from modules import lowvram, devices, sd_hijack timer = Timer() if shared.sd_model: # shared.sd_model.cond_stage_model.to(devices.cpu) # shared.sd_model.first_stage_model.to(devices.cpu) shared.sd_model.to(devices.cpu) sd_hijack.model_hijack.undo_hijack(shared.sd_model) shared.sd_model = None sd_model = None gc.collect() devices.torch_gc() torch.cuda.empty_cache() print(f"Unloaded weights {timer.summary()}.") return sd_model