203 lines
6.2 KiB
Python
203 lines
6.2 KiB
Python
import torch
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import safetensors.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, sd_models
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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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vae_path = os.path.abspath(os.path.join(models_path, "VAE"))
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vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
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vae_dict = {}
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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.basename(filepath)
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def refresh_vae_list():
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vae_dict.clear()
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paths = [
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os.path.join(sd_models.model_path, '**/*.vae.ckpt'),
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os.path.join(sd_models.model_path, '**/*.vae.pt'),
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os.path.join(sd_models.model_path, '**/*.vae.safetensors'),
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os.path.join(vae_path, '**/*.ckpt'),
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os.path.join(vae_path, '**/*.pt'),
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os.path.join(vae_path, '**/*.safetensors'),
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]
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if shared.cmd_opts.ckpt_dir is not None and os.path.isdir(shared.cmd_opts.ckpt_dir):
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paths += [
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os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.ckpt'),
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os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.pt'),
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os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.safetensors'),
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]
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candidates = []
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for path in paths:
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candidates += glob.iglob(path, recursive=True)
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for filepath in candidates:
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name = get_filename(filepath)
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vae_dict[name] = filepath
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def find_vae_near_checkpoint(checkpoint_file):
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checkpoint_path = os.path.splitext(checkpoint_file)[0]
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for vae_location in [checkpoint_path + ".vae.pt", checkpoint_path + ".vae.ckpt", checkpoint_path + ".vae.safetensors"]:
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if os.path.isfile(vae_location):
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return vae_location
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return None
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def resolve_vae(checkpoint_file):
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if shared.cmd_opts.vae_path is not None:
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return shared.cmd_opts.vae_path, 'from commandline argument'
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vae_near_checkpoint = find_vae_near_checkpoint(checkpoint_file)
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if vae_near_checkpoint is not None and (shared.opts.sd_vae_as_default or shared.opts.sd_vae == "Automatic"):
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return vae_near_checkpoint, 'found near the checkpoint'
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if shared.opts.sd_vae == "None":
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return None, None
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vae_from_options = vae_dict.get(shared.opts.sd_vae, None)
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if vae_from_options is not None:
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return vae_from_options, 'specified in settings'
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if shared.opts.sd_vae != "Automatic":
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print(f"Couldn't find VAE named {shared.opts.sd_vae}; using None instead")
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return None, None
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def load_vae(model, vae_file=None, vae_source="from unknown source"):
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global vae_dict, 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 {vae_source}: cached {get_filename(vae_file)}")
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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 {vae_source} doesn't exist: {vae_file}"
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print(f"Loading VAE weights {vae_source}: {vae_file}")
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store_base_vae(model)
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vae_ckpt = sd_models.read_state_dict(vae_file, map_location=shared.weight_load_location)
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vae_dict_1 = {k: v for k, v in vae_ckpt.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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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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# 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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unspecified = object()
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def reload_vae_weights(sd_model=None, vae_file=unspecified):
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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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if vae_file == unspecified:
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vae_file, vae_source = resolve_vae(checkpoint_file)
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else:
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vae_source = "from function argument"
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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, vae_source)
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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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