diff options
Diffstat (limited to 'modules/sd_vae.py')
| -rw-r--r-- | modules/sd_vae.py | 100 |
1 files changed, 62 insertions, 38 deletions
diff --git a/modules/sd_vae.py b/modules/sd_vae.py index 71e7a6e6..ac71d62d 100644 --- a/modules/sd_vae.py +++ b/modules/sd_vae.py @@ -1,9 +1,11 @@ import 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" @@ -15,7 +17,7 @@ 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"} +default_vae_dict = {"auto": "auto", "None": None, None: None} default_vae_list = ["auto", "None"] @@ -29,6 +31,7 @@ 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: @@ -39,7 +42,8 @@ def get_base_vae(model): def store_base_vae(model): global base_vae, checkpoint_info if checkpoint_info != model.sd_checkpoint_info: - base_vae = model.first_stage_model.state_dict().copy() + 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 @@ -50,9 +54,11 @@ def delete_base_vae(): def restore_base_vae(model): - global base_vae, checkpoint_info + global loaded_vae_file if base_vae is not None and checkpoint_info == model.sd_checkpoint_info: - load_vae_dict(model, base_vae) + print("Restoring base VAE") + _load_vae_dict(model, base_vae) + loaded_vae_file = None delete_base_vae() @@ -83,47 +89,54 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path): return vae_list -def resolve_vae(checkpoint_file, vae_file="auto"): +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" - 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") + 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("Using VAE provided as command line argument") + 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("Using VAE found beside selected model") + 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("Using VAE found beside selected model") + print(f"Using VAE found similar to selected model: {vae_file}") # No more fallbacks for auto if vae_file == "auto": vae_file = None @@ -138,11 +151,30 @@ 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: - print(f"Loading VAE weights from: {vae_file}") - 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 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) + 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 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 @@ -150,30 +182,22 @@ def load_vae(model, vae_file=None): 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 - """ - # Save current VAE to VAE settings, maybe? will it work? - if save_settings: - if vae_file is None: - vae_opt = "None" - - # shared.opts.sd_vae = vae_opt - """ - first_load = False # don't call this from outside -def load_vae_dict(model, vae_dict_1=None): - if vae_dict_1: - store_base_vae(model) - model.first_stage_model.load_state_dict(vae_dict_1) - else: - restore_base_vae() +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 @@ -203,5 +227,5 @@ def reload_vae_weights(sd_model=None, vae_file="auto"): if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram: sd_model.to(devices.device) - print(f"VAE Weights loaded.") + print("VAE Weights loaded.") return sd_model |