stable-diffusion-webui/modules/sd_vae.py
2023-01-14 20:00:12 +03:00

203 lines
6.2 KiB
Python

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