Remove need for the image_encoder param in IPAdapter.initialize().

invokeai/backend/ip_adapter/resampler.py

@@ -109,6 +109,42 @@ class Resampler(nn.Module):
                 )
             )
 
+    @classmethod
+    def from_state_dict(cls, state_dict: dict[torch.Tensor], depth=8, dim_head=64, heads=16, num_queries=8, ff_mult=4):
+        """A convenience function that initializes a Resampler from a state_dict.
+
+        Some of the shape parameters are inferred from the state_dict (e.g. dim, embedding_dim, etc.). At the time of
+        writing, we did not have a need for inferring ALL of the shape parameters from the state_dict, but this would be
+        possible if needed in the future.
+
+        Args:
+            state_dict (dict[torch.Tensor]): The state_dict to load.
+            depth (int, optional):
+            dim_head (int, optional):
+            heads (int, optional):
+            ff_mult (int, optional):
+
+        Returns:
+            Resampler
+        """
+        dim = state_dict["latents"].shape[2]
+        num_queries = state_dict["latents"].shape[1]
+        embedding_dim = state_dict["proj_in.weight"].shape[-1]
+        output_dim = state_dict["norm_out.weight"].shape[0]
+
+        model = cls(
+            dim=dim,
+            depth=depth,
+            dim_head=dim_head,
+            heads=heads,
+            num_queries=num_queries,
+            embedding_dim=embedding_dim,
+            output_dim=output_dim,
+            ff_mult=ff_mult,
+        )
+        model.load_state_dict(state_dict)
+        return model
+
     def forward(self, x):
         latents = self.latents.repeat(x.size(0), 1, 1)