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import torch
import os
from collections import namedtuple
from modules import shared, devices
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"}
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
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.pt'), recursive=True),
*glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True),
*glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True),
*glob.iglob(os.path.join(vae_path, '**/*.ckpt'), 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(default_vae_dict)
vae_dict.update(res)
return vae_list
def load_vae(model, checkpoint_file, vae_file="auto"):
global first_load, vae_dict, vae_list
# save_settings = False
# if vae_file argument is provided, it takes priority
if vae_file and vae_file not in default_vae_list:
if not os.path.isfile(vae_file):
vae_file = "auto"
# save_settings = True
print("VAE provided as function argument doesn't exist")
# for the first load, if vae-path is provided, it takes priority 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
# save_settings = True
# print("Using VAE provided as command line argument")
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
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}
model.first_stage_model.load_state_dict(vae_dict_1)
# If vae used is not in dict, update it
# It will be removed on refresh though
if vae_file is not None:
vae_opt = get_filename(vae_file)
if vae_opt not in vae_dict:
vae_dict[vae_opt] = vae_file
vae_list.append(vae_opt)
"""
# 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
model.first_stage_model.to(devices.dtype_vae)
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