load models to VRAM when using --lowram
param
load models to VRM instead of RAM (for machines which have bigger VRM than RAM such as free Google Colab server)
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1 changed files with 13 additions and 2 deletions
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@ -134,7 +134,12 @@ def load_model_weights(model, checkpoint_info):
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print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
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pl_sd = torch.load(checkpoint_file, map_location="cpu")
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if shared.cmd_opts.lowram:
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print("Load to VRAM if GPU is available (low RAM)")
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pl_sd = torch.load(checkpoint_file)
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else:
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pl_sd = torch.load(checkpoint_file, map_location="cpu")
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if "global_step" in pl_sd:
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print(f"Global Step: {pl_sd['global_step']}")
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@ -158,7 +163,13 @@ def load_model_weights(model, checkpoint_info):
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if os.path.exists(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="cpu")
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if shared.cmd_opts.lowram:
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print("Load to VRAM if GPU is available (low RAM)")
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vae_ckpt = torch.load(vae_file)
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else:
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vae_ckpt = torch.load(vae_file, map_location="cpu")
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vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
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model.first_stage_model.load_state_dict(vae_dict)
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