fix bug with "Ignore selected VAE for..." option completely disabling VAE election
rework VAE resolving code to be more simple
This commit is contained in:
AUTOMATIC
parent
69781031e7
commit
a5bbcd2153
@ -224,7 +224,7 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None
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return sd
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def load_model_weights(model, checkpoint_info: CheckpointInfo, vae_file="auto"):
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def load_model_weights(model, checkpoint_info: CheckpointInfo):
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sd_model_hash = checkpoint_info.calculate_shorthash()
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cache_enabled = shared.opts.sd_checkpoint_cache > 0
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@ -277,8 +277,8 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, vae_file="auto"):
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sd_vae.delete_base_vae()
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sd_vae.clear_loaded_vae()
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vae_file = sd_vae.resolve_vae(checkpoint_info.filename, vae_file=vae_file)
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sd_vae.load_vae(model, vae_file)
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vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename)
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sd_vae.load_vae(model, vae_file, vae_source)
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def enable_midas_autodownload():
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@ -9,23 +9,9 @@ import glob
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from copy import deepcopy
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model_dir = "Stable-diffusion"
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model_path = os.path.abspath(os.path.join(models_path, model_dir))
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vae_dir = "VAE"
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vae_path = os.path.abspath(os.path.join(models_path, vae_dir))
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vae_path = os.path.abspath(os.path.join(models_path, "VAE"))
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vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
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default_vae_dict = {"auto": "auto", "None": None, None: None}
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default_vae_list = ["auto", "None"]
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default_vae_values = [default_vae_dict[x] for x in default_vae_list]
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vae_dict = dict(default_vae_dict)
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vae_list = list(default_vae_list)
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first_load = True
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vae_dict = {}
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base_vae = None
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@ -64,100 +50,69 @@ def restore_base_vae(model):
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def get_filename(filepath):
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return os.path.splitext(os.path.basename(filepath))[0]
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return os.path.basename(filepath)
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def refresh_vae_list(vae_path=vae_path, model_path=model_path):
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global vae_dict, vae_list
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res = {}
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candidates = [
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*glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True),
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*glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True),
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*glob.iglob(os.path.join(model_path, '**/*.vae.safetensors'), recursive=True),
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*glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True),
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*glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True),
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*glob.iglob(os.path.join(vae_path, '**/*.safetensors'), recursive=True),
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def refresh_vae_list():
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vae_dict.clear()
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paths = [
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os.path.join(sd_models.model_path, '**/*.vae.ckpt'),
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os.path.join(sd_models.model_path, '**/*.vae.pt'),
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os.path.join(sd_models.model_path, '**/*.vae.safetensors'),
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os.path.join(vae_path, '**/*.ckpt'),
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os.path.join(vae_path, '**/*.pt'),
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os.path.join(vae_path, '**/*.safetensors'),
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]
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if shared.cmd_opts.vae_path is not None and os.path.isfile(shared.cmd_opts.vae_path):
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candidates.append(shared.cmd_opts.vae_path)
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if shared.cmd_opts.ckpt_dir is not None and os.path.isdir(shared.cmd_opts.ckpt_dir):
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paths += [
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os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.ckpt'),
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os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.pt'),
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os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.safetensors'),
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]
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candidates = []
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for path in paths:
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candidates += glob.iglob(path, recursive=True)
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for filepath in candidates:
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name = get_filename(filepath)
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res[name] = filepath
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vae_list.clear()
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vae_list.extend(default_vae_list)
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vae_list.extend(list(res.keys()))
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vae_dict.clear()
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vae_dict.update(res)
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vae_dict.update(default_vae_dict)
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return vae_list
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vae_dict[name] = filepath
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def get_vae_from_settings(vae_file="auto"):
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# else, we load from settings, if not set to be default
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if vae_file == "auto" and shared.opts.sd_vae is not None:
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# if saved VAE settings isn't recognized, fallback to auto
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vae_file = vae_dict.get(shared.opts.sd_vae, "auto")
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# if VAE selected but not found, fallback to auto
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if vae_file not in default_vae_values and not os.path.isfile(vae_file):
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vae_file = "auto"
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print(f"Selected VAE doesn't exist: {vae_file}")
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return vae_file
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def find_vae_near_checkpoint(checkpoint_file):
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checkpoint_path = os.path.splitext(checkpoint_file)[0]
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for vae_location in [checkpoint_path + ".vae.pt", checkpoint_path + ".vae.ckpt", checkpoint_path + ".vae.safetensors"]:
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if os.path.isfile(vae_location):
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return vae_location
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return None
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def resolve_vae(checkpoint_file=None, vae_file="auto"):
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global first_load, vae_dict, vae_list
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def resolve_vae(checkpoint_file):
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if shared.cmd_opts.vae_path is not None:
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return shared.cmd_opts.vae_path, 'from commandline argument'
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# if vae_file argument is provided, it takes priority, but not saved
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if vae_file and vae_file not in default_vae_list:
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if not os.path.isfile(vae_file):
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print(f"VAE provided as function argument doesn't exist: {vae_file}")
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vae_file = "auto"
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# for the first load, if vae-path is provided, it takes priority, saved, and failure is reported
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if first_load and shared.cmd_opts.vae_path is not None:
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if os.path.isfile(shared.cmd_opts.vae_path):
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vae_file = shared.cmd_opts.vae_path
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shared.opts.data['sd_vae'] = get_filename(vae_file)
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else:
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print(f"VAE provided as command line argument doesn't exist: {vae_file}")
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# fallback to selector in settings, if vae selector not set to act as default fallback
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if not shared.opts.sd_vae_as_default:
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vae_file = get_vae_from_settings(vae_file)
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# vae-path cmd arg takes priority for auto
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if vae_file == "auto" and shared.cmd_opts.vae_path is not None:
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if os.path.isfile(shared.cmd_opts.vae_path):
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vae_file = shared.cmd_opts.vae_path
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print(f"Using VAE provided as command line argument: {vae_file}")
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# if still not found, try look for ".vae.pt" beside model
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model_path = os.path.splitext(checkpoint_file)[0]
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if vae_file == "auto":
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vae_file_try = model_path + ".vae.pt"
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if os.path.isfile(vae_file_try):
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vae_file = vae_file_try
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print(f"Using VAE found similar to selected model: {vae_file}")
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# if still not found, try look for ".vae.ckpt" beside model
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if vae_file == "auto":
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vae_file_try = model_path + ".vae.ckpt"
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if os.path.isfile(vae_file_try):
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vae_file = vae_file_try
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print(f"Using VAE found similar to selected model: {vae_file}")
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# if still not found, try look for ".vae.safetensors" beside model
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if vae_file == "auto":
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vae_file_try = model_path + ".vae.safetensors"
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if os.path.isfile(vae_file_try):
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vae_file = vae_file_try
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print(f"Using VAE found similar to selected model: {vae_file}")
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# No more fallbacks for auto
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if vae_file == "auto":
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vae_file = None
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# Last check, just because
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if vae_file and not os.path.exists(vae_file):
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vae_file = None
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vae_near_checkpoint = find_vae_near_checkpoint(checkpoint_file)
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if vae_near_checkpoint is not None and (shared.opts.sd_vae_as_default or shared.opts.sd_vae == "auto"):
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return vae_near_checkpoint, 'found near the checkpoint'
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return vae_file
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if shared.opts.sd_vae == "None":
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return None, None
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vae_from_options = vae_dict.get(shared.opts.sd_vae, None)
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if vae_from_options is not None:
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return vae_from_options, 'specified in settings'
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if shared.opts.sd_vae != "Automatic":
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print(f"Couldn't find VAE named {shared.opts.sd_vae}; using None instead")
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return None, None
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def load_vae(model, vae_file=None):
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global first_load, vae_dict, vae_list, loaded_vae_file
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def load_vae(model, vae_file=None, vae_source="from unknown source"):
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global vae_dict, loaded_vae_file
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# save_settings = False
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cache_enabled = shared.opts.sd_vae_checkpoint_cache > 0
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@ -165,12 +120,12 @@ def load_vae(model, vae_file=None):
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if vae_file:
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if cache_enabled and vae_file in checkpoints_loaded:
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# use vae checkpoint cache
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print(f"Loading VAE weights [{get_filename(vae_file)}] from cache")
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print(f"Loading VAE weights {vae_source}: cached {get_filename(vae_file)}")
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store_base_vae(model)
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_load_vae_dict(model, checkpoints_loaded[vae_file])
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else:
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assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"
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print(f"Loading VAE weights from: {vae_file}")
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assert os.path.isfile(vae_file), f"VAE {vae_source} doesn't exist: {vae_file}"
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print(f"Loading VAE weights {vae_source}: {vae_file}")
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store_base_vae(model)
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vae_ckpt = sd_models.read_state_dict(vae_file, map_location=shared.weight_load_location)
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@ -191,14 +146,12 @@ def load_vae(model, vae_file=None):
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vae_opt = get_filename(vae_file)
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if vae_opt not in vae_dict:
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vae_dict[vae_opt] = vae_file
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vae_list.append(vae_opt)
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elif loaded_vae_file:
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restore_base_vae(model)
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loaded_vae_file = vae_file
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first_load = False
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# don't call this from outside
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def _load_vae_dict(model, vae_dict_1):
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@ -211,7 +164,10 @@ def clear_loaded_vae():
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loaded_vae_file = None
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def reload_vae_weights(sd_model=None, vae_file="auto"):
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unspecified = object()
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def reload_vae_weights(sd_model=None, vae_file=unspecified):
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from modules import lowvram, devices, sd_hijack
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if not sd_model:
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@ -219,7 +175,11 @@ def reload_vae_weights(sd_model=None, vae_file="auto"):
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checkpoint_info = sd_model.sd_checkpoint_info
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checkpoint_file = checkpoint_info.filename
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vae_file = resolve_vae(checkpoint_file, vae_file=vae_file)
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if vae_file == unspecified:
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vae_file, vae_source = resolve_vae(checkpoint_file)
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else:
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vae_source = "from function argument"
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if loaded_vae_file == vae_file:
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return
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@ -231,7 +191,7 @@ def reload_vae_weights(sd_model=None, vae_file="auto"):
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sd_hijack.model_hijack.undo_hijack(sd_model)
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load_vae(sd_model, vae_file)
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load_vae(sd_model, vae_file, vae_source)
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sd_hijack.model_hijack.hijack(sd_model)
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script_callbacks.model_loaded_callback(sd_model)
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@ -239,5 +199,5 @@ def reload_vae_weights(sd_model=None, vae_file="auto"):
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if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
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sd_model.to(devices.device)
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print("VAE Weights loaded.")
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print("VAE weights loaded.")
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return sd_model
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@ -83,7 +83,7 @@ parser.add_argument("--theme", type=str, help="launches the UI with light or dar
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parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
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parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
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parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
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parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
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parser.add_argument('--vae-path', type=str, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
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parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
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parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
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parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
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@ -383,7 +383,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
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"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
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"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
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"sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
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"sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list),
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"sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": ["Automatic", "None"] + list(sd_vae.vae_dict)}, refresh=sd_vae.refresh_vae_list),
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"sd_vae_as_default": OptionInfo(False, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
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"sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
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"sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
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@ -125,24 +125,21 @@ def apply_upscale_latent_space(p, x, xs):
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def find_vae(name: str):
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if name.lower() in ['auto', 'none']:
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return name
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if name.lower() in ['auto', 'automatic']:
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return modules.sd_vae.unspecified
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if name.lower() == 'none':
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return None
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else:
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vae_path = os.path.abspath(os.path.join(paths.models_path, 'VAE'))
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found = glob.glob(os.path.join(vae_path, f'**/{name}.*pt'), recursive=True)
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if found:
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return found[0]
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choices = [x for x in sorted(modules.sd_vae.vae_dict, key=lambda x: len(x)) if name.lower().strip() in x.lower()]
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if len(choices) == 0:
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print(f"No VAE found for {name}; using automatic")
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return modules.sd_vae.unspecified
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else:
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return 'auto'
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return modules.sd_vae.vae_dict[choices[0]]
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def apply_vae(p, x, xs):
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if x.lower().strip() == 'none':
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modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file='None')
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else:
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found = find_vae(x)
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if found:
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v = modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=found)
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modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=find_vae(x))
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def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
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@ -271,7 +268,9 @@ class SharedSettingsStackHelper(object):
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def __exit__(self, exc_type, exc_value, tb):
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modules.sd_models.reload_model_weights(self.model)
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modules.sd_vae.reload_vae_weights(self.model, vae_file=find_vae(self.vae))
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opts.data["sd_vae"] = self.vae
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modules.sd_vae.reload_vae_weights(self.model)
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hypernetwork.load_hypernetwork(self.hypernetwork)
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hypernetwork.apply_strength()
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