import os import collections from dataclasses import dataclass from modules import paths, shared, devices, script_callbacks, sd_models, extra_networks, lowvram, sd_hijack, hashes 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_loaded_vae_name(): if loaded_vae_file is None: return None return os.path.basename(loaded_vae_file) def get_loaded_vae_hash(): if loaded_vae_file is None: return None sha256 = hashes.sha256(loaded_vae_file, 'vae') return sha256[0:10] if sha256 else None 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(): global vae_dict 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 vae_dict = dict(sorted(vae_dict.items(), key=lambda item: shared.natural_sort_key(item[0]))) def find_vae_near_checkpoint(checkpoint_file): checkpoint_path = os.path.basename(checkpoint_file).rsplit('.', 1)[0] for vae_file in vae_dict.values(): if os.path.basename(vae_file).startswith(checkpoint_path): return vae_file return None @dataclass class VaeResolution: vae: str = None source: str = None resolved: bool = True def tuple(self): return self.vae, self.source def is_automatic(): return shared.opts.sd_vae in {"Automatic", "auto"} # "auto" for people with old config def resolve_vae_from_setting() -> VaeResolution: if shared.opts.sd_vae == "None": return VaeResolution() vae_from_options = vae_dict.get(shared.opts.sd_vae, None) if vae_from_options is not None: return VaeResolution(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 VaeResolution(resolved=False) def resolve_vae_from_user_metadata(checkpoint_file) -> VaeResolution: metadata = extra_networks.get_user_metadata(checkpoint_file) vae_metadata = metadata.get("vae", None) if vae_metadata is not None and vae_metadata != "Automatic": if vae_metadata == "None": return VaeResolution() vae_from_metadata = vae_dict.get(vae_metadata, None) if vae_from_metadata is not None: return VaeResolution(vae_from_metadata, "from user metadata") return VaeResolution(resolved=False) def resolve_vae_near_checkpoint(checkpoint_file) -> VaeResolution: 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 VaeResolution(vae_near_checkpoint, 'found near the checkpoint') return VaeResolution(resolved=False) def resolve_vae(checkpoint_file) -> VaeResolution: if shared.cmd_opts.vae_path is not None: return VaeResolution(shared.cmd_opts.vae_path, 'from commandline argument') if shared.opts.sd_vae_overrides_per_model_preferences and not is_automatic(): return resolve_vae_from_setting() res = resolve_vae_from_user_metadata(checkpoint_file) if res.resolved: return res res = resolve_vae_near_checkpoint(checkpoint_file) if res.resolved: return res res = resolve_vae_from_setting() return res 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): 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).tuple() 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