From 247f58a5e740a7bd3980815961425b778d77ec28 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 17 Sep 2022 12:05:04 +0300
Subject: add support for switching model checkpoints at runtime

---
 modules/sd_models.py | 148 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 148 insertions(+)
 create mode 100644 modules/sd_models.py

(limited to 'modules/sd_models.py')

diff --git a/modules/sd_models.py b/modules/sd_models.py
new file mode 100644
index 00000000..036af0e4
--- /dev/null
+++ b/modules/sd_models.py
@@ -0,0 +1,148 @@
+import glob
+import os.path
+import sys
+from collections import namedtuple
+import torch
+from omegaconf import OmegaConf
+
+
+from ldm.util import instantiate_from_config
+
+from modules import shared
+
+CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash'])
+checkpoints_list = {}
+
+try:
+    # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
+
+    from transformers import logging
+
+    logging.set_verbosity_error()
+except Exception:
+    pass
+
+
+def list_models():
+    checkpoints_list.clear()
+
+    model_dir = os.path.abspath(shared.cmd_opts.ckpt_dir)
+
+    def modeltitle(path, h):
+        abspath = os.path.abspath(path)
+
+        if abspath.startswith(model_dir):
+            name = abspath.replace(model_dir, '')
+        else:
+            name = os.path.basename(path)
+
+        if name.startswith("\\") or name.startswith("/"):
+            name = name[1:]
+
+        return f'{name} [{h}]'
+
+    cmd_ckpt = shared.cmd_opts.ckpt
+    if os.path.exists(cmd_ckpt):
+        h = model_hash(cmd_ckpt)
+        title = modeltitle(cmd_ckpt, h)
+        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h)
+    elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
+        print(f"Checkpoint in --ckpt argument not found: {cmd_ckpt}", file=sys.stderr)
+
+    if os.path.exists(model_dir):
+        for filename in glob.glob(model_dir + '/**/*.ckpt', recursive=True):
+            h = model_hash(filename)
+            title = modeltitle(filename, h)
+            checkpoints_list[title] = CheckpointInfo(filename, title, h)
+
+
+def model_hash(filename):
+    try:
+        with open(filename, "rb") as file:
+            import hashlib
+            m = hashlib.sha256()
+
+            file.seek(0x100000)
+            m.update(file.read(0x10000))
+            return m.hexdigest()[0:8]
+    except FileNotFoundError:
+        return 'NOFILE'
+
+
+def select_checkpoint():
+    model_checkpoint = shared.opts.sd_model_checkpoint
+    checkpoint_info = checkpoints_list.get(model_checkpoint, None)
+    if checkpoint_info is not None:
+        return checkpoint_info
+
+    if len(checkpoints_list) == 0:
+        print(f"Checkpoint {model_checkpoint} not found and no other checkpoints found", file=sys.stderr)
+        return None
+
+    checkpoint_info = next(iter(checkpoints_list.values()))
+    if model_checkpoint is not None:
+        print(f"Checkpoint {model_checkpoint} not found; loading fallback {checkpoint_info.title}", file=sys.stderr)
+
+    return checkpoint_info
+
+
+def load_model_weights(model, checkpoint_file, sd_model_hash):
+    print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
+
+    pl_sd = torch.load(checkpoint_file, map_location="cpu")
+    if "global_step" in pl_sd:
+        print(f"Global Step: {pl_sd['global_step']}")
+    sd = pl_sd["state_dict"]
+
+    model.load_state_dict(sd, strict=False)
+
+    if shared.cmd_opts.opt_channelslast:
+        model.to(memory_format=torch.channels_last)
+
+    if not shared.cmd_opts.no_half:
+        model.half()
+
+    model.sd_model_hash = sd_model_hash
+    model.sd_model_checkpint = checkpoint_file
+
+
+def load_model():
+    from modules import lowvram, sd_hijack
+    checkpoint_info = select_checkpoint()
+
+    sd_config = OmegaConf.load(shared.cmd_opts.config)
+    sd_model = instantiate_from_config(sd_config.model)
+    load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
+
+    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+        lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
+    else:
+        sd_model.to(shared.device)
+
+    sd_hijack.model_hijack.hijack(sd_model)
+
+    sd_model.eval()
+
+    print(f"Model loaded.")
+    return sd_model
+
+
+def reload_model_weights(sd_model):
+    from modules import lowvram, devices
+    checkpoint_info = select_checkpoint()
+
+    if sd_model.sd_model_checkpint == checkpoint_info.filename:
+        return
+
+    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+        lowvram.send_everything_to_cpu()
+    else:
+        sd_model.to(devices.cpu)
+
+    load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
+
+    if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
+        sd_model.to(devices.device)
+
+    print(f"Weights loaded.")
+    return sd_model
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