232 lines
7.9 KiB
Python
232 lines
7.9 KiB
Python
import torch
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import os
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import collections
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from collections import namedtuple
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from modules import shared, devices, script_callbacks
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from modules.paths import models_path
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import glob
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from copy import deepcopy
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model_dir = "Stable-diffusion"
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model_path = os.path.abspath(os.path.join(models_path, model_dir))
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vae_dir = "VAE"
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vae_path = os.path.abspath(os.path.join(models_path, vae_dir))
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vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
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default_vae_dict = {"auto": "auto", "None": None, None: None}
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default_vae_list = ["auto", "None"]
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default_vae_values = [default_vae_dict[x] for x in default_vae_list]
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vae_dict = dict(default_vae_dict)
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vae_list = list(default_vae_list)
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first_load = True
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base_vae = None
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loaded_vae_file = None
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checkpoint_info = None
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checkpoints_loaded = collections.OrderedDict()
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def get_base_vae(model):
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if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model:
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return base_vae
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return None
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def store_base_vae(model):
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global base_vae, checkpoint_info
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if checkpoint_info != model.sd_checkpoint_info:
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assert not loaded_vae_file, "Trying to store non-base VAE!"
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base_vae = deepcopy(model.first_stage_model.state_dict())
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checkpoint_info = model.sd_checkpoint_info
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def delete_base_vae():
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global base_vae, checkpoint_info
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base_vae = None
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checkpoint_info = None
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def restore_base_vae(model):
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global loaded_vae_file
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if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
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print("Restoring base VAE")
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_load_vae_dict(model, base_vae)
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loaded_vae_file = None
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delete_base_vae()
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def get_filename(filepath):
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return os.path.splitext(os.path.basename(filepath))[0]
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def refresh_vae_list(vae_path=vae_path, model_path=model_path):
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global vae_dict, vae_list
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res = {}
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candidates = [
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*glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True),
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*glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True),
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*glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True),
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*glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True)
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]
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if shared.cmd_opts.vae_path is not None and os.path.isfile(shared.cmd_opts.vae_path):
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candidates.append(shared.cmd_opts.vae_path)
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for filepath in candidates:
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name = get_filename(filepath)
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res[name] = filepath
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vae_list.clear()
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vae_list.extend(default_vae_list)
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vae_list.extend(list(res.keys()))
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vae_dict.clear()
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vae_dict.update(res)
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vae_dict.update(default_vae_dict)
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return vae_list
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def get_vae_from_settings(vae_file="auto"):
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# else, we load from settings, if not set to be default
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if vae_file == "auto" and shared.opts.sd_vae is not None:
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# if saved VAE settings isn't recognized, fallback to auto
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vae_file = vae_dict.get(shared.opts.sd_vae, "auto")
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# if VAE selected but not found, fallback to auto
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if vae_file not in default_vae_values and not os.path.isfile(vae_file):
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vae_file = "auto"
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print(f"Selected VAE doesn't exist: {vae_file}")
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return vae_file
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def resolve_vae(checkpoint_file=None, vae_file="auto"):
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global first_load, vae_dict, vae_list
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# if vae_file argument is provided, it takes priority, but not saved
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if vae_file and vae_file not in default_vae_list:
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if not os.path.isfile(vae_file):
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print(f"VAE provided as function argument doesn't exist: {vae_file}")
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vae_file = "auto"
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# for the first load, if vae-path is provided, it takes priority, saved, and failure is reported
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if first_load and shared.cmd_opts.vae_path is not None:
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if os.path.isfile(shared.cmd_opts.vae_path):
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vae_file = shared.cmd_opts.vae_path
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shared.opts.data['sd_vae'] = get_filename(vae_file)
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else:
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print(f"VAE provided as command line argument doesn't exist: {vae_file}")
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# fallback to selector in settings, if vae selector not set to act as default fallback
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if not shared.opts.sd_vae_as_default:
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vae_file = get_vae_from_settings(vae_file)
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# vae-path cmd arg takes priority for auto
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if vae_file == "auto" and shared.cmd_opts.vae_path is not None:
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if os.path.isfile(shared.cmd_opts.vae_path):
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vae_file = shared.cmd_opts.vae_path
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print(f"Using VAE provided as command line argument: {vae_file}")
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# if still not found, try look for ".vae.pt" beside model
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model_path = os.path.splitext(checkpoint_file)[0]
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if vae_file == "auto":
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vae_file_try = model_path + ".vae.pt"
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if os.path.isfile(vae_file_try):
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vae_file = vae_file_try
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print(f"Using VAE found similar to selected model: {vae_file}")
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# if still not found, try look for ".vae.ckpt" beside model
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if vae_file == "auto":
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vae_file_try = model_path + ".vae.ckpt"
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if os.path.isfile(vae_file_try):
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vae_file = vae_file_try
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print(f"Using VAE found similar to selected model: {vae_file}")
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# No more fallbacks for auto
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if vae_file == "auto":
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vae_file = None
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# Last check, just because
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if vae_file and not os.path.exists(vae_file):
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vae_file = None
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return vae_file
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def load_vae(model, vae_file=None):
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global first_load, vae_dict, vae_list, loaded_vae_file
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# save_settings = False
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cache_enabled = shared.opts.sd_vae_checkpoint_cache > 0
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if vae_file:
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if cache_enabled and vae_file in checkpoints_loaded:
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# use vae checkpoint cache
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print(f"Loading VAE weights [{get_filename(vae_file)}] from cache")
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store_base_vae(model)
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_load_vae_dict(model, checkpoints_loaded[vae_file])
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else:
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assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"
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print(f"Loading VAE weights from: {vae_file}")
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store_base_vae(model)
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vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
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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}
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_load_vae_dict(model, vae_dict_1)
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if cache_enabled:
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# cache newly loaded vae
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checkpoints_loaded[vae_file] = vae_dict_1.copy()
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# clean up cache if limit is reached
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if cache_enabled:
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while len(checkpoints_loaded) > shared.opts.sd_vae_checkpoint_cache + 1: # we need to count the current model
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checkpoints_loaded.popitem(last=False) # LRU
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# If vae used is not in dict, update it
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# It will be removed on refresh though
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vae_opt = get_filename(vae_file)
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if vae_opt not in vae_dict:
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vae_dict[vae_opt] = vae_file
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vae_list.append(vae_opt)
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elif loaded_vae_file:
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restore_base_vae(model)
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loaded_vae_file = vae_file
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first_load = False
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# don't call this from outside
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def _load_vae_dict(model, vae_dict_1):
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model.first_stage_model.load_state_dict(vae_dict_1)
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model.first_stage_model.to(devices.dtype_vae)
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def clear_loaded_vae():
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global loaded_vae_file
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loaded_vae_file = None
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def reload_vae_weights(sd_model=None, vae_file="auto"):
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from modules import lowvram, devices, sd_hijack
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if not sd_model:
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sd_model = shared.sd_model
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checkpoint_info = sd_model.sd_checkpoint_info
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checkpoint_file = checkpoint_info.filename
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vae_file = resolve_vae(checkpoint_file, vae_file=vae_file)
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if loaded_vae_file == vae_file:
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return
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if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
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lowvram.send_everything_to_cpu()
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else:
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sd_model.to(devices.cpu)
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sd_hijack.model_hijack.undo_hijack(sd_model)
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load_vae(sd_model, vae_file)
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sd_hijack.model_hijack.hijack(sd_model)
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script_callbacks.model_loaded_callback(sd_model)
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if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
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sd_model.to(devices.device)
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print("VAE Weights loaded.")
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return sd_model
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