Checkpoint cache by combination key of checkpoint and vae

modules/sd_models.py

@@ -160,11 +160,15 @@ def get_state_dict_from_checkpoint(pl_sd):
 
 vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
 
-def load_model_weights(model, checkpoint_info, force=False):
+def load_model_weights(model, checkpoint_info, vae_file="auto"):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
-    if force or checkpoint_info not in checkpoints_loaded:
+    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
+
+    checkpoint_key = (checkpoint_info, vae_file)
+
+    if checkpoint_key not in checkpoints_loaded:
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
         pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
@@ -185,24 +189,25 @@ def load_model_weights(model, checkpoint_info, force=False):
         devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
         devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
 
-        sd_vae.load_vae(model, checkpoint_file)
+        sd_vae.load_vae(model, vae_file)
         model.first_stage_model.to(devices.dtype_vae)
 
         if shared.opts.sd_checkpoint_cache > 0:
-            checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
+            checkpoints_loaded[checkpoint_key] = model.state_dict().copy()
             while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
                 checkpoints_loaded.popitem(last=False)  # LRU
     else:
-        print(f"Loading weights [{sd_model_hash}] from cache")
-        checkpoints_loaded.move_to_end(checkpoint_info)
-        model.load_state_dict(checkpoints_loaded[checkpoint_info])
+        vae_name = sd_vae.get_filename(vae_file)
+        print(f"Loading weights [{sd_model_hash}] with {vae_name} VAE from cache")
+        checkpoints_loaded.move_to_end(checkpoint_key)
+        model.load_state_dict(checkpoints_loaded[checkpoint_key])
 
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
 
-def load_model(checkpoint_info=None, force=False):
+def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
 
@@ -223,7 +228,7 @@ def load_model(checkpoint_info=None, force=False):
 
     do_inpainting_hijack()
     sd_model = instantiate_from_config(sd_config.model)
-    load_model_weights(sd_model, checkpoint_info, force=force)
+    load_model_weights(sd_model, checkpoint_info)
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
@@ -250,7 +255,7 @@ def reload_model_weights(sd_model, info=None, force=False):
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
         checkpoints_loaded.clear()
-        load_model(checkpoint_info, force=force)
+        load_model(checkpoint_info)
         return shared.sd_model
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
@@ -260,7 +265,7 @@ def reload_model_weights(sd_model, info=None, force=False):
 
     sd_hijack.model_hijack.undo_hijack(sd_model)
 
-    load_model_weights(sd_model, checkpoint_info, force=force)
+    load_model_weights(sd_model, checkpoint_info)
 
     sd_hijack.model_hijack.hijack(sd_model)
     script_callbacks.model_loaded_callback(sd_model)

modules/sd_vae.py

@@ -43,7 +43,7 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     vae_dict.update(res)
     return vae_list
 
-def load_vae(model, checkpoint_file, vae_file="auto"):
+def resolve_vae(checkpoint_file, vae_file="auto"):
     global first_load, vae_dict, vae_list
     # save_settings = False
 
@@ -94,6 +94,12 @@ def load_vae(model, checkpoint_file, vae_file="auto"):
     if vae_file and not os.path.exists(vae_file):
         vae_file = None
 
+    return vae_file
+
+def load_vae(model, vae_file):
+    global first_load, vae_dict, vae_list
+    # 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)