Reload VAE without reloading sd checkpoint

modules/sd_vae.py

@@ -1,26 +1,65 @@
 import torch
 import os
 from collections import namedtuple
-from modules import shared, devices
+from modules import shared, devices, script_callbacks
 from modules.paths import models_path
 import glob
 
+
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(models_path, model_dir))
 vae_dir = "VAE"
 vae_path = os.path.abspath(os.path.join(models_path, vae_dir))
 
+
 vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
+
+
 default_vae_dict = {"auto": "auto", "None": "None"}
 default_vae_list = ["auto", "None"]
+
+
 default_vae_values = [default_vae_dict[x] for x in default_vae_list]
 vae_dict = dict(default_vae_dict)
 vae_list = list(default_vae_list)
 first_load = True
 
+
+base_vae = None
+loaded_vae_file = None
+checkpoint_info = None
+
+
+def get_base_vae(model):
+    if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model:
+        return base_vae
+    return None
+
+
+def store_base_vae(model):
+    global base_vae, checkpoint_info
+    if checkpoint_info != model.sd_checkpoint_info:
+        base_vae = model.first_stage_model.state_dict().copy()
+        checkpoint_info = model.sd_checkpoint_info
+
+
+def delete_base_vae():
+    global base_vae, checkpoint_info
+    base_vae = None
+    checkpoint_info = None
+
+
+def restore_base_vae(model):
+    global base_vae, checkpoint_info
+    if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
+        load_vae_dict(model, base_vae)
+    delete_base_vae()
+
+
 def get_filename(filepath):
     return os.path.splitext(os.path.basename(filepath))[0]
 
+
 def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     global vae_dict, vae_list
     res = {}
@@ -43,6 +82,7 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     vae_dict.update(res)
     return vae_list
 
+
 def resolve_vae(checkpoint_file, vae_file="auto"):
     global first_load, vae_dict, vae_list
     # save_settings = False
@@ -96,24 +136,26 @@ def resolve_vae(checkpoint_file, vae_file="auto"):
 
     return vae_file
 
-def load_vae(model, vae_file):
-    global first_load, vae_dict, vae_list
+
+def load_vae(model, vae_file=None):
+    global first_load, vae_dict, vae_list, loaded_vae_file
     # save_settings = False
 
     if vae_file:
         print(f"Loading VAE weights from: {vae_file}")
         vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
         vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
-        model.first_stage_model.load_state_dict(vae_dict_1)
+        load_vae_dict(model, vae_dict_1)
 
-    # If vae used is not in dict, update it
-    # It will be removed on refresh though
-    if vae_file is not None:
+        # If vae used is not in dict, update it
+        # It will be removed on refresh though
         vae_opt = get_filename(vae_file)
         if vae_opt not in vae_dict:
             vae_dict[vae_opt] = vae_file
             vae_list.append(vae_opt)
 
+    loaded_vae_file = vae_file
+
     """
     # Save current VAE to VAE settings, maybe? will it work?
     if save_settings:
@@ -124,4 +166,45 @@ def load_vae(model, vae_file):
     """
 
     first_load = False
+
+
+# don't call this from outside
+def load_vae_dict(model, vae_dict_1=None):
+    if vae_dict_1:
+        store_base_vae(model)
+        model.first_stage_model.load_state_dict(vae_dict_1)
+    else:
+        restore_base_vae()
     model.first_stage_model.to(devices.dtype_vae)
+
+
+def reload_vae_weights(sd_model=None, vae_file="auto"):
+    from modules import lowvram, devices, sd_hijack
+
+    if not sd_model:
+        sd_model = shared.sd_model
+
+    checkpoint_info = sd_model.sd_checkpoint_info
+    checkpoint_file = checkpoint_info.filename
+    vae_file = resolve_vae(checkpoint_file, vae_file=vae_file)
+
+    if loaded_vae_file == vae_file:
+        return
+
+    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+        lowvram.send_everything_to_cpu()
+    else:
+        sd_model.to(devices.cpu)
+
+    sd_hijack.model_hijack.undo_hijack(sd_model)
+
+    load_vae(sd_model, vae_file)
+
+    sd_hijack.model_hijack.hijack(sd_model)
+    script_callbacks.model_loaded_callback(sd_model)
+
+    if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
+        sd_model.to(devices.device)
+
+    print(f"VAE Weights loaded.")
+    return sd_model