Remove no longer necessary parts and add vae_file safeguard
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@ -165,16 +165,9 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
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cache_enabled = shared.opts.sd_checkpoint_cache > 0
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if cache_enabled:
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sd_vae.restore_base_vae(model)
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vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
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if cache_enabled and checkpoint_info in checkpoints_loaded:
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# use checkpoint cache
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vae_name = sd_vae.get_filename(vae_file) if vae_file else None
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vae_message = f" with {vae_name} VAE" if vae_name else ""
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print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
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print(f"Loading weights [{sd_model_hash}] from cache")
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model.load_state_dict(checkpoints_loaded[checkpoint_info])
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else:
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# load from file
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@ -220,6 +213,7 @@ 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_checkpoint_info = checkpoint_info
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vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
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sd_vae.load_vae(model, vae_file)
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@ -139,6 +139,7 @@ def load_vae(model, vae_file=None):
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# save_settings = False
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if vae_file:
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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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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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