import glob import os.path import sys from collections import namedtuple import torch from omegaconf import OmegaConf from ldm.util import instantiate_from_config from modules import shared, modelloader, devices from modules.paths import models_path model_dir = "Stable-diffusion" model_path = os.path.abspath(os.path.join(models_path, model_dir)) CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config']) checkpoints_list = {} try: # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start. from transformers import logging logging.set_verbosity_error() except Exception: pass def setup_model(): if not os.path.exists(model_path): os.makedirs(model_path) list_models() def checkpoint_tiles(): return sorted([x.title for x in checkpoints_list.values()]) def list_models(): checkpoints_list.clear() model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt"]) def modeltitle(path, shorthash): abspath = os.path.abspath(path) 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(path) if name.startswith("\\") or name.startswith("/"): name = name[1:] shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0] return f'{name} [{shorthash}]', shortname cmd_ckpt = shared.cmd_opts.ckpt if os.path.exists(cmd_ckpt): h = model_hash(cmd_ckpt) title, short_model_name = modeltitle(cmd_ckpt, h) checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config) shared.opts.data['sd_model_checkpoint'] = 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: h = model_hash(filename) title, short_model_name = modeltitle(filename, h) basename, _ = os.path.splitext(filename) config = basename + ".yaml" if not os.path.exists(config): config = shared.cmd_opts.config checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config) def get_closet_checkpoint_match(searchString): applicable = sorted([info for info in checkpoints_list.values() if searchString in info.title], key = lambda x:len(x.title)) if len(applicable) > 0: return applicable[0] return None def model_hash(filename): 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 = checkpoints_list.get(model_checkpoint, None) if checkpoint_info is not None: return checkpoint_info if len(checkpoints_list) == 0: print(f"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(f"Can't run without a checkpoint. Find and place a .ckpt 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 def get_state_dict_from_checkpoint(pl_sd): if "state_dict" in pl_sd: return pl_sd["state_dict"] return pl_sd def load_model_weights(model, checkpoint_info): checkpoint_file = checkpoint_info.filename sd_model_hash = checkpoint_info.hash print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}") pl_sd = torch.load(checkpoint_file, map_location="cpu") if "global_step" in pl_sd: print(f"Global Step: {pl_sd['global_step']}") sd = get_state_dict_from_checkpoint(pl_sd) model.load_state_dict(sd, strict=False) if shared.cmd_opts.opt_channelslast: model.to(memory_format=torch.channels_last) if not shared.cmd_opts.no_half: model.half() devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16 vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt" if os.path.exists(vae_file): print(f"Loading VAE weights from: {vae_file}") vae_ckpt = torch.load(vae_file, map_location="cpu") vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"} model.first_stage_model.load_state_dict(vae_dict) model.sd_model_hash = sd_model_hash model.sd_model_checkpoint = checkpoint_file model.sd_checkpoint_info = checkpoint_info def load_model(): from modules import lowvram, sd_hijack checkpoint_info = select_checkpoint() if checkpoint_info.config != shared.cmd_opts.config: print(f"Loading config from: {checkpoint_info.config}") sd_config = OmegaConf.load(checkpoint_info.config) sd_model = instantiate_from_config(sd_config.model) load_model_weights(sd_model, checkpoint_info) 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) sd_hijack.model_hijack.hijack(sd_model) sd_model.eval() print(f"Model loaded.") return sd_model def reload_model_weights(sd_model, info=None): from modules import lowvram, devices, sd_hijack checkpoint_info = info or select_checkpoint() if sd_model.sd_model_checkpoint == checkpoint_info.filename: return if sd_model.sd_checkpoint_info.config != checkpoint_info.config: return load_model() 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) load_model_weights(sd_model, checkpoint_info) sd_hijack.model_hijack.hijack(sd_model) if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram: sd_model.to(devices.device) print(f"Weights loaded.") return sd_model