From 23cbc19adb57a4ab1d5649bae2a3e72fcc675898 Mon Sep 17 00:00:00 2001
From: Cyberes <64224601+Cyberes@users.noreply.github.com>
Date: Thu, 20 Oct 2022 21:01:19 -0600
Subject: [PATCH] Add files via upload

---
 other/prune_stable_diffusion_ckpt.py | 61 ++++++++++++++++++++++++++++
 1 file changed, 61 insertions(+)
 create mode 100644 other/prune_stable_diffusion_ckpt.py

diff --git a/other/prune_stable_diffusion_ckpt.py b/other/prune_stable_diffusion_ckpt.py
new file mode 100644
index 0000000..4ab776c
--- /dev/null
+++ b/other/prune_stable_diffusion_ckpt.py
@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+
+import argparse
+import glob
+import os
+
+import torch
+
+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)