- do not use ckpt cache, if disabled

- cache model after is has been loaded from file

modules/sd_models.py

@@ -163,13 +163,21 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
-    if shared.opts.sd_checkpoint_cache > 0 and hasattr(model, "sd_checkpoint_info"):
+    cache_enabled = shared.opts.sd_checkpoint_cache > 0
+
+    if cache_enabled:
         sd_vae.restore_base_vae(model)
-        checkpoints_loaded[model.sd_checkpoint_info] = model.state_dict().copy()
 
     vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
 
-    if checkpoint_info not in checkpoints_loaded:
+    if cache_enabled and checkpoint_info in checkpoints_loaded:
+        # use checkpoint cache
+        vae_name = sd_vae.get_filename(vae_file) if vae_file else None
+        vae_message = f" with {vae_name} VAE" if vae_name else ""
+        print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
+        model.load_state_dict(checkpoints_loaded[checkpoint_info])
+    else:
+        # 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)
@@ -180,6 +188,10 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         del pl_sd
         model.load_state_dict(sd, strict=False)
         del sd
+        
+        if cache_enabled:
+            # cache newly loaded model
+            checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
 
         if shared.cmd_opts.opt_channelslast:
             model.to(memory_format=torch.channels_last)
@@ -199,13 +211,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
 
         model.first_stage_model.to(devices.dtype_vae)
 
-    else:
-        vae_name = sd_vae.get_filename(vae_file) if vae_file else None
-        vae_message = f" with {vae_name} VAE" if vae_name else ""
-        print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
-        model.load_state_dict(checkpoints_loaded[checkpoint_info])
-
-    if shared.opts.sd_checkpoint_cache > 0:
+    # clean up cache if limit is reached
+    if cache_enabled:
         while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
             checkpoints_loaded.popitem(last=False)  # LRU