Fix base VAE caching was done after loading VAE, also add safeguard
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@ -220,6 +220,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
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model.sd_model_checkpoint = checkpoint_file
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model.sd_model_checkpoint = checkpoint_file
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model.sd_checkpoint_info = checkpoint_info
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model.sd_checkpoint_info = checkpoint_info
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sd_vae.clear_loaded_vae()
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sd_vae.load_vae(model, vae_file)
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sd_vae.load_vae(model, vae_file)
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@ -15,7 +15,7 @@ vae_path = os.path.abspath(os.path.join(models_path, vae_dir))
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vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
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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"}
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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_list = ["auto", "None"]
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@ -39,6 +39,7 @@ def get_base_vae(model):
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def store_base_vae(model):
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def store_base_vae(model):
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global base_vae, checkpoint_info
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global base_vae, checkpoint_info
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if checkpoint_info != model.sd_checkpoint_info:
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if checkpoint_info != model.sd_checkpoint_info:
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assert not loaded_vae_file, "Trying to store non-base VAE!"
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base_vae = model.first_stage_model.state_dict().copy()
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base_vae = model.first_stage_model.state_dict().copy()
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checkpoint_info = model.sd_checkpoint_info
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checkpoint_info = model.sd_checkpoint_info
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@ -50,9 +51,11 @@ def delete_base_vae():
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def restore_base_vae(model):
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def restore_base_vae(model):
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global loaded_vae_file
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if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
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if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
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print("Restoring base VAE")
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print("Restoring base VAE")
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load_vae_dict(model, base_vae)
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load_vae_dict(model, base_vae)
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loaded_vae_file = None
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delete_base_vae()
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delete_base_vae()
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@ -140,10 +143,10 @@ def load_vae(model, vae_file=None):
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if vae_file:
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if vae_file:
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print(f"Loading VAE weights from: {vae_file}")
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print(f"Loading VAE weights from: {vae_file}")
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store_base_vae(model)
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vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
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vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
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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}
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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}
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load_vae_dict(model, vae_dict_1)
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load_vae_dict(model, vae_dict_1)
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store_base_vae(model)
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# If vae used is not in dict, update it
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# If vae used is not in dict, update it
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# It will be removed on refresh though
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# It will be removed on refresh though
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@ -157,15 +160,6 @@ def load_vae(model, vae_file=None):
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loaded_vae_file = vae_file
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loaded_vae_file = vae_file
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"""
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# Save current VAE to VAE settings, maybe? will it work?
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if save_settings:
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if vae_file is None:
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vae_opt = "None"
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# shared.opts.sd_vae = vae_opt
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"""
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first_load = False
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first_load = False
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@ -174,6 +168,9 @@ def load_vae_dict(model, vae_dict_1):
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model.first_stage_model.load_state_dict(vae_dict_1)
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model.first_stage_model.load_state_dict(vae_dict_1)
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model.first_stage_model.to(devices.dtype_vae)
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model.first_stage_model.to(devices.dtype_vae)
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def clear_loaded_vae():
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global loaded_vae_file
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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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def reload_vae_weights(sd_model=None, vae_file="auto"):
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from modules import lowvram, devices, sd_hijack
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from modules import lowvram, devices, sd_hijack
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