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Merge pull request #3818 from jwatzman/master 

Reduce peak memory usage when changing models
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AUTOMATIC1111 2022-10-29 09:16:00 +03:00 committed by GitHub
parent 28e6d4a54e b50ff4f4e4
commit 9553a7e071
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GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 7 additions and 4 deletions
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11
modules/sd_models.py
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@ -173,7 +173,9 @@ 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)
@ -197,9 +199,10 @@ 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)