import torch import safetensors.torch import os import collections from collections import namedtuple from modules import paths, shared, devices, script_callbacks, sd_models import glob from copy import deepcopy vae_path = os.path.abspath(os.path.join(paths.models_path, "VAE")) vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"} vae_dict = {} base_vae = None loaded_vae_file = None checkpoint_info = None checkpoints_loaded = collections.OrderedDict() def get_base_vae(model): if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model: return base_vae return None def store_base_vae(model): global base_vae, checkpoint_info if checkpoint_info != model.sd_checkpoint_info: assert not loaded_vae_file, "Trying to store non-base VAE!" base_vae = deepcopy(model.first_stage_model.state_dict()) checkpoint_info = model.sd_checkpoint_info def delete_base_vae(): global base_vae, checkpoint_info base_vae = None checkpoint_info = None def restore_base_vae(model): global loaded_vae_file if base_vae is not None and checkpoint_info == model.sd_checkpoint_info: print("Restoring base VAE") _load_vae_dict(model, base_vae) loaded_vae_file = None delete_base_vae() def get_filename(filepath): return os.path.basename(filepath) def refresh_vae_list(): vae_dict.clear() paths = [ os.path.join(sd_models.model_path, '**/*.vae.ckpt'), os.path.join(sd_models.model_path, '**/*.vae.pt'), os.path.join(sd_models.model_path, '**/*.vae.safetensors'), os.path.join(vae_path, '**/*.ckpt'), os.path.join(vae_path, '**/*.pt'), os.path.join(vae_path, '**/*.safetensors'), ] if shared.cmd_opts.ckpt_dir is not None and os.path.isdir(shared.cmd_opts.ckpt_dir): paths += [ os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.ckpt'), os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.pt'), os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.safetensors'), ] if shared.cmd_opts.vae_dir is not None and os.path.isdir(shared.cmd_opts.vae_dir): paths += [ os.path.join(shared.cmd_opts.vae_dir, '**/*.ckpt'), os.path.join(shared.cmd_opts.vae_dir, '**/*.pt'), os.path.join(shared.cmd_opts.vae_dir, '**/*.safetensors'), ] candidates = [] for path in paths: candidates += glob.iglob(path, recursive=True) for filepath in candidates: name = get_filename(filepath) vae_dict[name] = filepath def find_vae_near_checkpoint(checkpoint_file): checkpoint_path = os.path.splitext(checkpoint_file)[0] for vae_location in [f"{checkpoint_path}.vae.pt", f"{checkpoint_path}.vae.ckpt", f"{checkpoint_path}.vae.safetensors"]: if os.path.isfile(vae_location): return vae_location return None def resolve_vae(checkpoint_file): if shared.cmd_opts.vae_path is not None: return shared.cmd_opts.vae_path, 'from commandline argument' is_automatic = shared.opts.sd_vae in {"Automatic", "auto"} # "auto" for people with old config vae_near_checkpoint = find_vae_near_checkpoint(checkpoint_file) if vae_near_checkpoint is not None and (shared.opts.sd_vae_as_default or is_automatic): return vae_near_checkpoint, 'found near the checkpoint' if shared.opts.sd_vae == "None": return None, None vae_from_options = vae_dict.get(shared.opts.sd_vae, None) if vae_from_options is not None: return vae_from_options, 'specified in settings' if not is_automatic: print(f"Couldn't find VAE named {shared.opts.sd_vae}; using None instead") return None, None def load_vae_dict(filename, map_location): vae_ckpt = sd_models.read_state_dict(filename, map_location=map_location) vae_dict_1 = {k: v for k, v in vae_ckpt.items() if k[0:4] != "loss" and k not in vae_ignore_keys} return vae_dict_1 def load_vae(model, vae_file=None, vae_source="from unknown source"): global vae_dict, loaded_vae_file # save_settings = False cache_enabled = shared.opts.sd_vae_checkpoint_cache > 0 if vae_file: if cache_enabled and vae_file in checkpoints_loaded: # use vae checkpoint cache print(f"Loading VAE weights {vae_source}: cached {get_filename(vae_file)}") store_base_vae(model) _load_vae_dict(model, checkpoints_loaded[vae_file]) else: assert os.path.isfile(vae_file), f"VAE {vae_source} doesn't exist: {vae_file}" print(f"Loading VAE weights {vae_source}: {vae_file}") store_base_vae(model) vae_dict_1 = load_vae_dict(vae_file, map_location=shared.weight_load_location) _load_vae_dict(model, vae_dict_1) if cache_enabled: # cache newly loaded vae checkpoints_loaded[vae_file] = vae_dict_1.copy() # clean up cache if limit is reached if cache_enabled: while len(checkpoints_loaded) > shared.opts.sd_vae_checkpoint_cache + 1: # we need to count the current model checkpoints_loaded.popitem(last=False) # LRU # If vae used is not in dict, update it # It will be removed on refresh though vae_opt = get_filename(vae_file) if vae_opt not in vae_dict: vae_dict[vae_opt] = vae_file elif loaded_vae_file: restore_base_vae(model) loaded_vae_file = vae_file # don't call this from outside def _load_vae_dict(model, vae_dict_1): model.first_stage_model.load_state_dict(vae_dict_1) model.first_stage_model.to(devices.dtype_vae) def clear_loaded_vae(): global loaded_vae_file loaded_vae_file = None unspecified = object() def reload_vae_weights(sd_model=None, vae_file=unspecified): from modules import lowvram, devices, sd_hijack if not sd_model: sd_model = shared.sd_model checkpoint_info = sd_model.sd_checkpoint_info checkpoint_file = checkpoint_info.filename if vae_file == unspecified: vae_file, vae_source = resolve_vae(checkpoint_file) else: vae_source = "from function argument" if loaded_vae_file == vae_file: 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) load_vae(sd_model, vae_file, vae_source) sd_hijack.model_hijack.hijack(sd_model) script_callbacks.model_loaded_callback(sd_model) if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram: sd_model.to(devices.device) print("VAE weights loaded.") return sd_model