Fix model wasn't restored even when choosing "None"
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@ -50,8 +50,8 @@ def delete_base_vae():
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def restore_base_vae(model):
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global base_vae, 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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load_vae_dict(model, base_vae)
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delete_base_vae()
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@ -143,6 +143,7 @@ def load_vae(model, vae_file=None):
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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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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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# It will be removed on refresh though
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@ -150,6 +151,9 @@ def load_vae(model, vae_file=None):
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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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# shared.opts.data['sd_vae'] = vae_opt
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else:
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restore_base_vae(model)
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loaded_vae_file = vae_file
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@ -166,12 +170,8 @@ def load_vae(model, vae_file=None):
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# don't call this from outside
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def load_vae_dict(model, vae_dict_1=None):
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if vae_dict_1:
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store_base_vae(model)
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model.first_stage_model.load_state_dict(vae_dict_1)
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else:
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restore_base_vae()
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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.to(devices.dtype_vae)
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