import torch import safetensors.torch import os import collections from collections import namedtuple from modules import shared, devices, script_callbacks from modules.paths import models_path import glob from copy import deepcopy model_dir = "Stable-diffusion" model_path = os.path.abspath(os.path.join(models_path, model_dir)) vae_dir = "VAE" vae_path = os.path.abspath(os.path.join(models_path, vae_dir)) vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"} default_vae_dict = {"auto": "auto", "None": None, None: None} default_vae_list = ["auto", "None"] default_vae_values = [default_vae_dict[x] for x in default_vae_list] vae_dict = dict(default_vae_dict) vae_list = list(default_vae_list) first_load = True 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.splitext(os.path.basename(filepath))[0] def refresh_vae_list(vae_path=vae_path, model_path=model_path): global vae_dict, vae_list res = {} candidates = [ *glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True), *glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True), *glob.iglob(os.path.join(model_path, '**/*.vae.safetensors'), recursive=True), *glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True), *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True), *glob.iglob(os.path.join(vae_path, '**/*.safetensors'), recursive=True), ] if shared.cmd_opts.vae_path is not None and os.path.isfile(shared.cmd_opts.vae_path): candidates.append(shared.cmd_opts.vae_path) for filepath in candidates: name = get_filename(filepath) res[name] = filepath vae_list.clear() vae_list.extend(default_vae_list) vae_list.extend(list(res.keys())) vae_dict.clear() vae_dict.update(res) vae_dict.update(default_vae_dict) return vae_list def get_vae_from_settings(vae_file="auto"): # else, we load from settings, if not set to be default if vae_file == "auto" and shared.opts.sd_vae is not None: # if saved VAE settings isn't recognized, fallback to auto vae_file = vae_dict.get(shared.opts.sd_vae, "auto") # if VAE selected but not found, fallback to auto if vae_file not in default_vae_values and not os.path.isfile(vae_file): vae_file = "auto" print(f"Selected VAE doesn't exist: {vae_file}") return vae_file def resolve_vae(checkpoint_file=None, vae_file="auto"): global first_load, vae_dict, vae_list # if vae_file argument is provided, it takes priority, but not saved if vae_file and vae_file not in default_vae_list: if not os.path.isfile(vae_file): print(f"VAE provided as function argument doesn't exist: {vae_file}") vae_file = "auto" # for the first load, if vae-path is provided, it takes priority, saved, and failure is reported if first_load and shared.cmd_opts.vae_path is not None: if os.path.isfile(shared.cmd_opts.vae_path): vae_file = shared.cmd_opts.vae_path shared.opts.data['sd_vae'] = get_filename(vae_file) else: print(f"VAE provided as command line argument doesn't exist: {vae_file}") # fallback to selector in settings, if vae selector not set to act as default fallback if not shared.opts.sd_vae_as_default: vae_file = get_vae_from_settings(vae_file) # vae-path cmd arg takes priority for auto if vae_file == "auto" and shared.cmd_opts.vae_path is not None: if os.path.isfile(shared.cmd_opts.vae_path): vae_file = shared.cmd_opts.vae_path print(f"Using VAE provided as command line argument: {vae_file}") # if still not found, try look for ".vae.pt" beside model model_path = os.path.splitext(checkpoint_file)[0] if vae_file == "auto": vae_file_try = model_path + ".vae.pt" if os.path.isfile(vae_file_try): vae_file = vae_file_try print(f"Using VAE found similar to selected model: {vae_file}") # if still not found, try look for ".vae.ckpt" beside model if vae_file == "auto": vae_file_try = model_path + ".vae.ckpt" if os.path.isfile(vae_file_try): vae_file = vae_file_try print(f"Using VAE found similar to selected model: {vae_file}") # if still not found, try look for ".vae.safetensors" beside model if vae_file == "auto": vae_file_try = model_path + ".vae.safetensors" if os.path.isfile(vae_file_try): vae_file = vae_file_try print(f"Using VAE found similar to selected model: {vae_file}") # No more fallbacks for auto if vae_file == "auto": vae_file = None # Last check, just because if vae_file and not os.path.exists(vae_file): vae_file = None return vae_file def load_vae(model, vae_file=None): global first_load, vae_dict, vae_list, 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 [{get_filename(vae_file)}] from cache") store_base_vae(model) _load_vae_dict(model, checkpoints_loaded[vae_file]) else: assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}" print(f"Loading VAE weights from: {vae_file}") store_base_vae(model) _, extension = os.path.splitext(vae_file) if extension.lower() == ".safetensors": vae_ckpt = safetensors.torch.load_file(vae_file, device=shared.weight_load_location) else: vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location) if "state_dict" in vae_ckpt: vae_ckpt = vae_ckpt["state_dict"] vae_dict_1 = {k: v for k, v in vae_ckpt.items() if k[0:4] != "loss" and k not in vae_ignore_keys} _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 vae_list.append(vae_opt) elif loaded_vae_file: restore_base_vae(model) loaded_vae_file = vae_file first_load = False # 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 def reload_vae_weights(sd_model=None, vae_file="auto"): 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 vae_file = resolve_vae(checkpoint_file, vae_file=vae_file) 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) 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