From cb31abcf58ea1f64266e6d821937eed058c35f4d Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Sun, 30 Oct 2022 21:54:31 +0700 Subject: Settings to select VAE --- modules/sd_vae.py | 121 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 121 insertions(+) create mode 100644 modules/sd_vae.py (limited to 'modules/sd_vae.py') diff --git a/modules/sd_vae.py b/modules/sd_vae.py new file mode 100644 index 00000000..82764e55 --- /dev/null +++ b/modules/sd_vae.py @@ -0,0 +1,121 @@ +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) -- cgit v1.2.3