From b50ff4f4e4d4d6bf31e222832d3fe4cfde4703c9 Mon Sep 17 00:00:00 2001 From: Josh Watzman Date: Thu, 27 Oct 2022 21:59:16 +0100 Subject: [PATCH] Reduce peak memory usage when changing models A few tweaks to reduce peak memory usage, the biggest being that if we aren't using the checkpoint cache, we shouldn't duplicate the model state dict just to immediately throw it away. On my machine with 16GB of RAM, this change means I can typically change models, whereas before it would typically OOM. --- modules/sd_models.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/modules/sd_models.py b/modules/sd_models.py index e697bb72..203e99a8 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -170,7 +170,9 @@ def load_model_weights(model, checkpoint_info): print(f"Global Step: {pl_sd['global_step']}") sd = get_state_dict_from_checkpoint(pl_sd) - missing, extra = model.load_state_dict(sd, strict=False) + del pl_sd + model.load_state_dict(sd, strict=False) + del sd if shared.cmd_opts.opt_channelslast: model.to(memory_format=torch.channels_last) @@ -194,9 +196,10 @@ def load_model_weights(model, checkpoint_info): model.first_stage_model.to(devices.dtype_vae) - checkpoints_loaded[checkpoint_info] = model.state_dict().copy() - while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache: - checkpoints_loaded.popitem(last=False) # LRU + if shared.opts.sd_checkpoint_cache > 0: + checkpoints_loaded[checkpoint_info] = model.state_dict().copy() + while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache: + checkpoints_loaded.popitem(last=False) # LRU else: print(f"Loading weights [{sd_model_hash}] from cache") checkpoints_loaded.move_to_end(checkpoint_info)