Load Model only when click Generate

#964

modules/sd_vae.py

@@ -187,87 +187,24 @@ def resolve_vae(checkpoint_file) -> VaeResolution:
 
 
 def load_vae_dict(filename, map_location):
-    return load_torch_file(filename)
+    pass
 
 
 def load_vae(model, vae_file=None, vae_source="from unknown source"):
-    global vae_dict, base_vae, loaded_vae_file
-    # save_settings = False
-
-    cache_enabled = shared.opts.sd_vae_checkpoint_cache > 0
-
-    if vae_file:
-        if cache_enabled and vae_file in checkpoints_loaded:
-            # use vae checkpoint cache
-            print(f"Loading VAE weights {vae_source}: cached {get_filename(vae_file)}")
-            store_base_vae(model)
-            _load_vae_dict(model, checkpoints_loaded[vae_file])
-        else:
-            assert os.path.isfile(vae_file), f"VAE {vae_source} doesn't exist: {vae_file}"
-            print(f"Loading VAE weights {vae_source}: {vae_file}")
-            store_base_vae(model)
-
-            vae_dict_1 = load_vae_dict(vae_file, map_location=shared.weight_load_location)
-            _load_vae_dict(model, vae_dict_1)
-
-            if cache_enabled:
-                # cache newly loaded vae
-                checkpoints_loaded[vae_file] = vae_dict_1.copy()
-
-        # clean up cache if limit is reached
-        if cache_enabled:
-            while len(checkpoints_loaded) > shared.opts.sd_vae_checkpoint_cache + 1: # we need to count the current model
-                checkpoints_loaded.popitem(last=False)  # LRU
-
-        # 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
-
-    elif loaded_vae_file:
-        restore_base_vae(model)
-
-    loaded_vae_file = vae_file
-    model.base_vae = base_vae
-    model.loaded_vae_file = loaded_vae_file
+    raise NotImplementedError('Forge does not use this!')
 
 
 # don't call this from outside
 def _load_vae_dict(model, vae_dict_1):
-    model.first_stage_model.load_state_dict(vae_dict_1, strict=False)
+    pass
 
 
 def clear_loaded_vae():
-    global loaded_vae_file
-    loaded_vae_file = None
+    pass
 
 
 unspecified = object()
 
 
 def reload_vae_weights(sd_model=None, vae_file=unspecified):
-    if not sd_model:
-        sd_model = shared.sd_model
-
-    checkpoint_info = sd_model.sd_checkpoint_info
-    checkpoint_file = checkpoint_info.filename
-
-    if vae_file == unspecified:
-        vae_file, vae_source = resolve_vae(checkpoint_file).tuple()
-    else:
-        vae_source = "from function argument"
-
-    if loaded_vae_file == vae_file:
-        return
-
-    # sd_hijack.model_hijack.undo_hijack(sd_model)
-
-    load_vae(sd_model, vae_file, vae_source)
-
-    # sd_hijack.model_hijack.hijack(sd_model)
-
-    script_callbacks.model_loaded_callback(sd_model)
-
-    print("VAE weights loaded.")
-    return sd_model
+    raise NotImplementedError('Forge does not use this!')