import torch import os from collections import namedtuple from modules import shared, devices, script_callbacks from modules.paths import models_path import glob 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 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 = model.first_stage_model.state_dict().copy() 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(vae_path, '**/*.ckpt'), recursive=True), *glob.iglob(os.path.join(vae_path, '**/*.pt'), 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 resolve_vae(checkpoint_file, 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): vae_file = "auto" print("VAE provided as function argument doesn't exist") # 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("VAE provided as command line argument doesn't exist") # else, we load from settings 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("Selected VAE doesn't exist") # 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("Using VAE provided as command line argument") # 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("Using VAE found beside selected model") # 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("Using VAE found beside selected model") # 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 if vae_file: print(f"Loading VAE weights from: {vae_file}") store_base_vae(model) vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location) vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys} load_vae_dict(model, vae_dict_1) # 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) # shared.opts.data['sd_vae'] = vae_opt else: 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(f"VAE Weights loaded.") return sd_model