- do not use ckpt cache, if disabled
- cache model after is has been loaded from file
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@ -163,13 +163,21 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
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checkpoint_file = checkpoint_info.filename
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sd_model_hash = checkpoint_info.hash
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if shared.opts.sd_checkpoint_cache > 0 and hasattr(model, "sd_checkpoint_info"):
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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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checkpoints_loaded[model.sd_checkpoint_info] = model.state_dict().copy()
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vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
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if checkpoint_info not in checkpoints_loaded:
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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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model.load_state_dict(checkpoints_loaded[checkpoint_info])
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else:
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# load from file
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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=shared.weight_load_location)
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@ -180,6 +188,10 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
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del pl_sd
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model.load_state_dict(sd, strict=False)
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del sd
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if cache_enabled:
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# cache newly loaded model
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checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
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if shared.cmd_opts.opt_channelslast:
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model.to(memory_format=torch.channels_last)
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@ -199,13 +211,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
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model.first_stage_model.to(devices.dtype_vae)
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else:
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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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model.load_state_dict(checkpoints_loaded[checkpoint_info])
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if shared.opts.sd_checkpoint_cache > 0:
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# clean up cache if limit is reached
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if cache_enabled:
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while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
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checkpoints_loaded.popitem(last=False) # LRU
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