Add support for IPAdapterFull models. The changes are based on this upstream PR: https://github.com/tencent-ailab/IP-Adapter/pull/139 .

invokeai/backend/ip_adapter/ip_adapter.py

@@ -54,6 +54,44 @@ class ImageProjModel(torch.nn.Module):
         return clip_extra_context_tokens
 
 
+class MLPProjModel(torch.nn.Module):
+    """SD model with image prompt"""
+
+    def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024):
+        super().__init__()
+
+        self.proj = torch.nn.Sequential(
+            torch.nn.Linear(clip_embeddings_dim, clip_embeddings_dim),
+            torch.nn.GELU(),
+            torch.nn.Linear(clip_embeddings_dim, cross_attention_dim),
+            torch.nn.LayerNorm(cross_attention_dim),
+        )
+
+    @classmethod
+    def from_state_dict(cls, state_dict: dict[torch.Tensor]):
+        """Initialize an MLPProjModel from a state_dict.
+
+        The cross_attention_dim and clip_embeddings_dim are inferred from the shape of the tensors in the state_dict.
+
+        Args:
+            state_dict (dict[torch.Tensor]): The state_dict of model weights.
+
+        Returns:
+            MLPProjModel
+        """
+        cross_attention_dim = state_dict["proj.3.weight"].shape[0]
+        clip_embeddings_dim = state_dict["proj.0.weight"].shape[0]
+
+        model = cls(cross_attention_dim, clip_embeddings_dim)
+
+        model.load_state_dict(state_dict)
+        return model
+
+    def forward(self, image_embeds):
+        clip_extra_context_tokens = self.proj(image_embeds)
+        return clip_extra_context_tokens
+
+
 class IPAdapter:
     """IP-Adapter: https://arxiv.org/pdf/2308.06721.pdf"""
 
@@ -130,6 +168,13 @@ class IPAdapterPlus(IPAdapter):
         return image_prompt_embeds, uncond_image_prompt_embeds
 
 
+class IPAdapterFull(IPAdapterPlus):
+    """IP-Adapter Plus with full features."""
+
+    def _init_image_proj_model(self, state_dict: dict[torch.Tensor]):
+        return MLPProjModel.from_state_dict(state_dict).to(self.device, dtype=self.dtype)
+
+
 class IPAdapterPlusXL(IPAdapterPlus):
     """IP-Adapter Plus for SDXL."""
 
@@ -149,11 +194,9 @@ def build_ip_adapter(
 ) -> Union[IPAdapter, IPAdapterPlus]:
     state_dict = torch.load(ip_adapter_ckpt_path, map_location="cpu")
 
-    # Determine if the state_dict is from an IPAdapter or IPAdapterPlus based on the image_proj weights that it
-    # contains.
-    is_plus = "proj.weight" not in state_dict["image_proj"]
-
-    if is_plus:
+    if "proj.weight" in state_dict["image_proj"]:  # IPAdapter (with ImageProjModel).
+        return IPAdapter(state_dict, device=device, dtype=dtype)
+    elif "proj_in.weight" in state_dict["image_proj"]:  # IPAdaterPlus or IPAdapterPlusXL (with Resampler).
         cross_attention_dim = state_dict["ip_adapter"]["1.to_k_ip.weight"].shape[-1]
         if cross_attention_dim == 768:
             # SD1 IP-Adapter Plus
@@ -163,5 +206,7 @@ def build_ip_adapter(
             return IPAdapterPlusXL(state_dict, device=device, dtype=dtype)
         else:
             raise Exception(f"Unsupported IP-Adapter Plus cross-attention dimension: {cross_attention_dim}.")
+    elif "proj.0.weight" in state_dict["image_proj"]:  # IPAdapterFull (with MLPProjModel).
+        return IPAdapterFull(state_dict, device=device, dtype=dtype)
     else:
-        return IPAdapter(state_dict, device=device, dtype=dtype)
+        raise ValueError(f"'{ip_adapter_ckpt_path}' has an unrecognized IP-Adapter model architecture.")

tests/backend/ip_adapter/test_ip_adapter.py

@@ -37,6 +37,14 @@ def build_dummy_sd15_unet_input(torch_device):
             "unet_model_id": "runwayml/stable-diffusion-v1-5",
             "unet_model_name": "stable-diffusion-v1-5",
         },
+        # SD1.5, IPAdapterFull
+        {
+            "ip_adapter_model_id": "InvokeAI/ip-adapter-full-face_sd15",
+            "ip_adapter_model_name": "ip-adapter-full-face_sd15",
+            "base_model": BaseModelType.StableDiffusion1,
+            "unet_model_id": "runwayml/stable-diffusion-v1-5",
+            "unet_model_name": "stable-diffusion-v1-5",
+        },
     ],
 )
 @pytest.mark.slow