refactor(model_cache): factor out load_ckpt

ldm/invoke/model_cache.py

@@ -94,7 +94,7 @@ def get_model(self, model_name:str):
             'hash': hash
         }
 
-    def default_model(self) -> str:
+    def default_model(self) -> str | None:
         '''
         Returns the name of the default model, or None
         if none is defined.
@@ -191,13 +191,6 @@ def _load_model(self, model_name:str):
             return None
 
         mconfig = self.config[model_name]
-        config = mconfig.config
-        weights = mconfig.weights
-        vae = mconfig.get('vae',None)
-        width = mconfig.width
-        height = mconfig.height
-
-        print(f'>> Loading {model_name} from {weights}')
 
         # for usage statistics
         if self._has_cuda():
@@ -207,15 +200,44 @@ def _load_model(self, model_name:str):
         tic = time.time()
 
         # this does the work
-        c     = OmegaConf.load(config)
-        with open(weights,'rb') as f:
+        model_format = mconfig.get('format', 'ckpt')
+        if model_format == 'ckpt':
+            weights = mconfig.weights
+            print(f'>> Loading {model_name} from {weights}')
+            model, width, height, model_hash = self._load_ckpt_model(mconfig)
+        elif model_format == 'diffusers':
+            model, width, height, model_hash = self._load_diffusers_model(mconfig)
+        else:
+            raise NotImplementedError(f"Unknown model format {model_name}: {model_format}")
+
+        # usage statistics
+        toc = time.time()
+        print(f'>> Model loaded in', '%4.2fs' % (toc - tic))
+        if self._has_cuda():
+            print(
+                '>> Max VRAM used to load the model:',
+                '%4.2fG' % (torch.cuda.max_memory_allocated() / 1e9),
+                '\n>> Current VRAM usage:'
+                '%4.2fG' % (torch.cuda.memory_allocated() / 1e9),
+            )
+        return model, width, height, model_hash
+
+    def _load_ckpt_model(self, mconfig):
+        config = mconfig.config
+        weights = mconfig.weights
+        vae = mconfig.get('vae', None)
+        width = mconfig.width
+        height = mconfig.height
+
+        c = OmegaConf.load(config)
+        with open(weights, 'rb') as f:
             weight_bytes = f.read()
-        model_hash  = self._cached_sha256(weights,weight_bytes)
+        model_hash = self._cached_sha256(weights, weight_bytes)
         pl_sd = torch.load(io.BytesIO(weight_bytes), map_location='cpu')
         del weight_bytes
-        sd    = pl_sd['state_dict']
+        sd = pl_sd['state_dict']
         model = instantiate_from_config(c.model)
-        m, u  = model.load_state_dict(sd, strict=False)
+        m, u = model.load_state_dict(sd, strict=False)
 
         if self.precision == 'float16':
             print('   | Using faster float16 precision')
@@ -243,18 +265,11 @@ def _load_model(self, model_name:str):
             if isinstance(m, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)):
                 m._orig_padding_mode = m.padding_mode
 
-        # usage statistics
-        toc = time.time()
-        print(f'>> Model loaded in', '%4.2fs' % (toc - tic))
-        if self._has_cuda():
-            print(
-                '>> Max VRAM used to load the model:',
-                '%4.2fG' % (torch.cuda.max_memory_allocated() / 1e9),
-                '\n>> Current VRAM usage:'
-                '%4.2fG' % (torch.cuda.memory_allocated() / 1e9),
-            )
         return model, width, height, model_hash
-
+
+    def _load_diffusers_model(self, mconfig):
+        raise NotImplementedError()  # return pipeline, width, height, model_hash
+
     def offload_model(self, model_name:str):
         '''
         Offload the indicated model to CPU. Will call