From ac7ecd2d847bf4e3a9503db0f2a291e32b82302c Mon Sep 17 00:00:00 2001
From: Tim Patton <38817597+pattontim@users.noreply.github.com>
Date: Sat, 19 Nov 2022 14:49:22 -0500
Subject: Label and load SD .safetensors model files

---
 modules/sd_models.py | 24 ++++++++++++++++--------
 1 file changed, 16 insertions(+), 8 deletions(-)

(limited to 'modules/sd_models.py')

diff --git a/modules/sd_models.py b/modules/sd_models.py
index c59151e0..4ccdf30b 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -4,6 +4,7 @@ import sys
 import gc
 from collections import namedtuple
 import torch
+from safetensors.torch import load_file
 import re
 from omegaconf import OmegaConf
 
@@ -16,9 +17,10 @@ from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inp
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(models_path, model_dir))
 
-CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
+CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config', 'exttype'])
 checkpoints_list = {}
 checkpoints_loaded = collections.OrderedDict()
+checkpoint_types = {'.ckpt':'pickle','.safetensors':'safetensors'}
 
 try:
     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
@@ -45,7 +47,7 @@ def checkpoint_tiles():
 
 def list_models():
     checkpoints_list.clear()
-    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt"])
+    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt",".safetensors"])
 
     def modeltitle(path, shorthash):
         abspath = os.path.abspath(path)
@@ -60,15 +62,15 @@ def list_models():
         if name.startswith("\\") or name.startswith("/"):
             name = name[1:]
 
-        shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
+        shortname, ext = os.path.splitext(name.replace("/", "_").replace("\\", "_"))
 
-        return f'{name} [{shorthash}]', shortname
+        return f'{name} [{checkpoint_types[ext]}] [{shorthash}]', shortname
 
     cmd_ckpt = shared.cmd_opts.ckpt
     if os.path.exists(cmd_ckpt):
         h = model_hash(cmd_ckpt)
         title, short_model_name = modeltitle(cmd_ckpt, h)
-        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config)
+        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config, '')
         shared.opts.data['sd_model_checkpoint'] = title
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
         print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
@@ -76,12 +78,12 @@ def list_models():
         h = model_hash(filename)
         title, short_model_name = modeltitle(filename, h)
 
-        basename, _ = os.path.splitext(filename)
+        basename, ext = os.path.splitext(filename)
         config = basename + ".yaml"
         if not os.path.exists(config):
             config = shared.cmd_opts.config
 
-        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config)
+        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config, ext)
 
 
 def get_closet_checkpoint_match(searchString):
@@ -173,7 +175,13 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         # load from file
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-        pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
+        if(checkpoint_types[checkpoint_info.exttype] == 'safetensors'):
+            # safely load weights
+            # TODO: safetensors supports zero copy fast load to gpu, see issue #684
+            pl_sd = load_file(checkpoint_file, device=shared.weight_load_location)
+        else:
+            pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
+
         if "global_step" in pl_sd:
             print(f"Global Step: {pl_sd['global_step']}")
 
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