diffusion in fp8 landed

modules/sd_models.py

@@ -426,16 +426,45 @@ def get_obj_from_str(string, reload=False):
     pass
 
 
-@torch.no_grad()
 def load_model(checkpoint_info=None, already_loaded_state_dict=None):
-    checkpoint_info = checkpoint_info or select_checkpoint()
+    pass
+
+
+def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
+    pass
+
+
+def reload_model_weights(sd_model=None, info=None, forced_reload=False):
+    pass
+
+
+def unload_model_weights(sd_model=None, info=None):
+    pass
+
+
+def apply_token_merging(sd_model, token_merging_ratio):
+    if token_merging_ratio <= 0:
+        return
+
+    print(f'token_merging_ratio = {token_merging_ratio}')
+
+    from backend.misc.tomesd import TomePatcher
+
+    sd_model.forge_objects.unet = TomePatcher().patch(
+        model=sd_model.forge_objects.unet,
+        ratio=token_merging_ratio
+    )
+
+    return
+
+
+@torch.no_grad()
+def forge_model_reload():
+    checkpoint_info = select_checkpoint()
 
     timer = Timer()
 
     if model_data.sd_model:
-        if model_data.sd_model.filename == checkpoint_info.filename:
-            return model_data.sd_model
-
         model_data.sd_model = None
         model_data.loaded_sd_models = []
         memory_management.unload_all_models()
@@ -444,10 +473,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
 
     timer.record("unload existing model")
 
-    if already_loaded_state_dict is not None:
-        state_dict = already_loaded_state_dict
-    else:
-        state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
+    state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
 
     if shared.opts.sd_checkpoint_cache > 0:
         # cache newly loaded model
@@ -489,31 +515,3 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
     print(f"Model loaded in {timer.summary()}.")
 
     return sd_model
-
-
-def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
-    pass
-
-
-def reload_model_weights(sd_model=None, info=None, forced_reload=False):
-    pass
-
-
-def unload_model_weights(sd_model=None, info=None):
-    pass
-
-
-def apply_token_merging(sd_model, token_merging_ratio):
-    if token_merging_ratio <= 0:
-        return
-
-    print(f'token_merging_ratio = {token_merging_ratio}')
-
-    from backend.misc.tomesd import TomePatcher
-
-    sd_model.forge_objects.unet = TomePatcher().patch(
-        model=sd_model.forge_objects.unet,
-        ratio=token_merging_ratio
-    )
-
-    return