Reduce peak memory usage when changing models

A few tweaks to reduce peak memory usage, the biggest being that if we
aren't using the checkpoint cache, we shouldn't duplicate the model
state dict just to immediately throw it away.

On my machine with 16GB of RAM, this change means I can typically change
models, whereas before it would typically OOM.

modules/sd_models.py

@@ -170,7 +170,9 @@ def load_model_weights(model, checkpoint_info):
             print(f"Global Step: {pl_sd['global_step']}")
 
         sd = get_state_dict_from_checkpoint(pl_sd)
-        missing, extra = model.load_state_dict(sd, strict=False)
+        del pl_sd
+        model.load_state_dict(sd, strict=False)
+        del sd
 
         if shared.cmd_opts.opt_channelslast:
             model.to(memory_format=torch.channels_last)
@@ -194,9 +196,10 @@ def load_model_weights(model, checkpoint_info):
 
         model.first_stage_model.to(devices.dtype_vae)
 
-        checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
-        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
-            checkpoints_loaded.popitem(last=False)  # LRU
+        if shared.opts.sd_checkpoint_cache > 0:
+            checkpoints_loaded[checkpoint_info] = 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)