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_models.py | 31 +++++-------- modules/sd_vae.py | 121 +++++++++++++++++++++++++++++++++++++++++++++++++++ modules/shared.py | 8 ++-- 3 files changed, 136 insertions(+), 24 deletions(-) create mode 100644 modules/sd_vae.py (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index f86dc3ed..91ad4b5e 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -8,7 +8,7 @@ from omegaconf import OmegaConf from ldm.util import instantiate_from_config -from modules import shared, modelloader, devices, script_callbacks +from modules import shared, modelloader, devices, script_callbacks, sd_vae from modules.paths import models_path from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting @@ -160,12 +160,11 @@ def get_state_dict_from_checkpoint(pl_sd): vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"} - -def load_model_weights(model, checkpoint_info): +def load_model_weights(model, checkpoint_info, force=False): checkpoint_file = checkpoint_info.filename sd_model_hash = checkpoint_info.hash - if checkpoint_info not in checkpoints_loaded: + if force or checkpoint_info not in checkpoints_loaded: print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}") pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location) @@ -186,17 +185,7 @@ def load_model_weights(model, checkpoint_info): devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16 devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16 - vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt" - - if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None: - vae_file = shared.cmd_opts.vae_path - - if os.path.exists(vae_file): - print(f"Loading VAE weights from: {vae_file}") - vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location) - vae_dict = {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) - + sd_vae.load_vae(model, checkpoint_file) model.first_stage_model.to(devices.dtype_vae) if shared.opts.sd_checkpoint_cache > 0: @@ -213,7 +202,7 @@ def load_model_weights(model, checkpoint_info): model.sd_checkpoint_info = checkpoint_info -def load_model(checkpoint_info=None): +def load_model(checkpoint_info=None, force=False): from modules import lowvram, sd_hijack checkpoint_info = checkpoint_info or select_checkpoint() @@ -234,7 +223,7 @@ def load_model(checkpoint_info=None): do_inpainting_hijack() sd_model = instantiate_from_config(sd_config.model) - load_model_weights(sd_model, checkpoint_info) + load_model_weights(sd_model, checkpoint_info, force=force) if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram) @@ -252,16 +241,16 @@ def load_model(checkpoint_info=None): return sd_model -def reload_model_weights(sd_model, info=None): +def reload_model_weights(sd_model, info=None, force=False): from modules import lowvram, devices, sd_hijack checkpoint_info = info or select_checkpoint() - if sd_model.sd_model_checkpoint == checkpoint_info.filename: + if sd_model.sd_model_checkpoint == checkpoint_info.filename and not force: return if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info): checkpoints_loaded.clear() - load_model(checkpoint_info) + load_model(checkpoint_info, force=force) return shared.sd_model if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: @@ -271,7 +260,7 @@ def reload_model_weights(sd_model, info=None): sd_hijack.model_hijack.undo_hijack(sd_model) - load_model_weights(sd_model, checkpoint_info) + load_model_weights(sd_model, checkpoint_info, force=force) sd_hijack.model_hijack.hijack(sd_model) script_callbacks.model_loaded_callback(sd_model) 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) diff --git a/modules/shared.py b/modules/shared.py index e4f163c1..06440ac4 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -14,7 +14,7 @@ import modules.memmon import modules.sd_models import modules.styles import modules.devices as devices -from modules import sd_samplers, sd_models, localization +from modules import sd_samplers, sd_models, localization, sd_vae from modules.hypernetworks import hypernetwork from modules.paths import models_path, script_path, sd_path @@ -295,6 +295,7 @@ options_templates.update(options_section(('training', "Training"), { options_templates.update(options_section(('sd', "Stable Diffusion"), { "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models), "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}), + "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": list(sd_vae.vae_list)}, refresh=sd_vae.refresh_vae_list), "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks), "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}), "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), @@ -407,11 +408,12 @@ class Options: if bad_settings > 0: print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr) - def onchange(self, key, func): + def onchange(self, key, func, call=True): item = self.data_labels.get(key) item.onchange = func - func() + if call: + func() def dumpjson(self): d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()} -- cgit v1.2.1