Merge pull request #4841 from R-N/vae-fix-none (ec5e0721) · Commits · github_fork / Stable Diffusion Webui

modules/sd_models.py

+2 −0

Original line number	Diff line number	Diff line
		@@ -227,6 +227,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
		model.sd_model_checkpoint = checkpoint_file
		model.sd_checkpoint_info = checkpoint_info

		sd_vae.delete_base_vae()
		sd_vae.clear_loaded_vae()
		vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
		sd_vae.load_vae(model, vae_file)

modules/sd_vae.py

+17 −20

Original line number	Diff line number	Diff line
		@@ -4,6 +4,7 @@ from collections import namedtuple
		from modules import shared, devices, script_callbacks
		from modules.paths import models_path
		import glob
		from copy import deepcopy


		model_dir = "Stable-diffusion"
		@@ -15,7 +16,7 @@ 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_dict = {"auto": "auto", "None": None, None: None}
		default_vae_list = ["auto", "None"]


		@@ -39,7 +40,8 @@ def get_base_vae(model):
		def store_base_vae(model):
		global base_vae, checkpoint_info
		if checkpoint_info != model.sd_checkpoint_info:
		base_vae = model.first_stage_model.state_dict().copy()
		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


		@@ -50,9 +52,11 @@ def delete_base_vae():


		def restore_base_vae(model):
		global base_vae, checkpoint_info
		global loaded_vae_file
		if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
		load_vae_dict(model, base_vae)
		print("Restoring base VAE")
		_load_vae_dict(model, base_vae)
		loaded_vae_file = None
		delete_base_vae()


		@@ -148,9 +152,10 @@ def load_vae(model, vae_file=None):
		if vae_file:
		assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"
		print(f"Loading VAE weights from: {vae_file}")
		store_base_vae(model)
		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}
		load_vae_dict(model, vae_dict_1)
		_load_vae_dict(model, vae_dict_1)

		# If vae used is not in dict, update it
		# It will be removed on refresh though
		@@ -158,30 +163,22 @@ def load_vae(model, vae_file=None):
		if vae_opt not in vae_dict:
		vae_dict[vae_opt] = vae_file
		vae_list.append(vae_opt)
		elif loaded_vae_file:
		restore_base_vae(model)

		loaded_vae_file = vae_file

		"""
		# 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


		# don't call this from outside
		def load_vae_dict(model, vae_dict_1=None):
		if vae_dict_1:
		store_base_vae(model)
		def _load_vae_dict(model, vae_dict_1):
		model.first_stage_model.load_state_dict(vae_dict_1)
		else:
		restore_base_vae()
		model.first_stage_model.to(devices.dtype_vae)

		def clear_loaded_vae():
		global loaded_vae_file
		loaded_vae_file = None

		def reload_vae_weights(sd_model=None, vae_file="auto"):
		from modules import lowvram, devices, sd_hijack

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