stable-diffusion-webui/scripts/prune-ckpt.py

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import os
import torch
import argparse
parser = argparse.ArgumentParser(description="Pruning")
parser.add_argument("--ckpt", type=str, default=None, help="path to model ckpt")
args = parser.parse_args()
ckpt = args.ckpt
def prune_it(p, keep_only_ema=False):
print(f"prunin' in path: {p}")
size_initial = os.path.getsize(p)
nsd = dict()
sd = torch.load(p, map_location="cpu")
print(sd.keys())
for k in sd.keys():
if k != "optimizer_states":
nsd[k] = sd[k]
else:
print(f"removing optimizer states for path {p}")
if "global_step" in sd:
print(f"This is global step {sd['global_step']}.")
if keep_only_ema:
sd = nsd["state_dict"].copy()
# infer ema keys
ema_keys = {
k: "model_ema." + k[6:].replace(".", ".")
for k in sd.keys()
if k.startswith("model.")
}
new_sd = dict()
for k in sd:
if k in ema_keys:
new_sd[k] = sd[ema_keys[k]].half()
elif not k.startswith("model_ema.") or k in [
"model_ema.num_updates",
"model_ema.decay",
]:
new_sd[k] = sd[k].half()
assert len(new_sd) == len(sd) - len(ema_keys)
nsd["state_dict"] = new_sd
else:
sd = nsd["state_dict"].copy()
new_sd = dict()
for k in sd:
new_sd[k] = sd[k].half()
nsd["state_dict"] = new_sd
fn = (
f"{os.path.splitext(p)[0]}-pruned.ckpt"
if not keep_only_ema
else f"{os.path.splitext(p)[0]}-ema-pruned.ckpt"
)
print(f"saving pruned checkpoint at: {fn}")
torch.save(nsd, fn)
newsize = os.path.getsize(fn)
MSG = (
f"New ckpt size: {newsize*1e-9:.2f} GB. "
+ f"Saved {(size_initial - newsize)*1e-9:.2f} GB by removing optimizer states"
)
if keep_only_ema:
MSG += " and non-EMA weights"
print(MSG)
if __name__ == "__main__":
prune_it(ckpt)