Merge pull request #4841 from R-N/vae-fix-none

Fix None option of VAE selector

modules/sd_models.py

@@ -227,6 +227,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
+    sd_vae.delete_base_vae()
+    sd_vae.clear_loaded_vae()
     vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
     sd_vae.load_vae(model, vae_file)
 

modules/sd_vae.py

@@ -4,6 +4,7 @@ from collections import namedtuple
 from modules import shared, devices, script_callbacks
 from modules.paths import models_path
 import glob
+from copy import deepcopy
 
 
 model_dir = "Stable-diffusion"
@@ -15,7 +16,7 @@ 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_dict = {"auto": "auto", "None": None, None: None}
 default_vae_list = ["auto", "None"]
 
 
@@ -39,7 +40,8 @@ def get_base_vae(model):
 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()
+        assert not loaded_vae_file, "Trying to store non-base VAE!"
+        base_vae = deepcopy(model.first_stage_model.state_dict())
         checkpoint_info = model.sd_checkpoint_info
 
 
@@ -50,9 +52,11 @@ def delete_base_vae():
 
 
 def restore_base_vae(model):
-    global base_vae, checkpoint_info
+    global loaded_vae_file
     if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
-        load_vae_dict(model, base_vae)
+        print("Restoring base VAE")
+        _load_vae_dict(model, base_vae)
+        loaded_vae_file = None
     delete_base_vae()
 
 
@@ -148,9 +152,10 @@ def load_vae(model, vae_file=None):
     if vae_file:
         assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"
         print(f"Loading VAE weights from: {vae_file}")
+        store_base_vae(model)
         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}
-        load_vae_dict(model, 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
@@ -158,30 +163,22 @@ def load_vae(model, vae_file=None):
         if vae_opt not in vae_dict:
             vae_dict[vae_opt] = vae_file
             vae_list.append(vae_opt)
+    elif loaded_vae_file:
+        restore_base_vae(model)
 
     loaded_vae_file = vae_file
 
-    """
-    # Save current VAE to VAE settings, maybe? will it work?
-    if save_settings:
-        if vae_file is None:
-            vae_opt = "None"
-
-        # shared.opts.sd_vae = vae_opt
-    """
-
     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)
+def _load_vae_dict(model, vae_dict_1):
     model.first_stage_model.load_state_dict(vae_dict_1)
-    else:
-        restore_base_vae()
     model.first_stage_model.to(devices.dtype_vae)
 
+def clear_loaded_vae():
+    global loaded_vae_file
+    loaded_vae_file = None
 
 def reload_vae_weights(sd_model=None, vae_file="auto"):
     from modules import lowvram, devices, sd_hijack