model probe detects sdxl lora models

invokeai/app/invocations/sdxl.py

@@ -306,9 +306,7 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
         unet_info = context.services.model_manager.get_model(**self.unet.unet.dict(), context=context)
         do_classifier_free_guidance = True
         cross_attention_kwargs = None
-        with ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
-            unet_info as unet:
-
+        with ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()), unet_info as unet:
             scheduler.set_timesteps(num_inference_steps, device=unet.device)
             timesteps = scheduler.timesteps
 

invokeai/backend/model_management/lora.py

@@ -101,7 +101,6 @@ class ModelPatcher:
         with cls.apply_lora(text_encoder, loras, "lora_te_"):
             yield
 
-
     @classmethod
     @contextmanager
     def apply_sdxl_lora_text_encoder(

invokeai/backend/model_management/model_probe.py

@@ -315,21 +315,38 @@ class LoRACheckpointProbe(CheckpointProbeBase):
 
     def get_base_type(self) -> BaseModelType:
         checkpoint = self.checkpoint
+
+        # SD-2 models are very hard to probe. These probes are brittle and likely to fail in the future
+        # There are also some "SD-2 LoRAs" that have identical keys and shapes to SD-1 and will be
+        # misclassified as SD-1
+        key = "lora_te_text_model_encoder_layers_0_mlp_fc1.lora_down.weight"
+        if key in checkpoint and checkpoint[key].shape[0] == 320:
+            return BaseModelType.StableDiffusion2
+
+        key = "lora_unet_output_blocks_5_1_transformer_blocks_1_ff_net_2.lora_up.weight"
+        if key in checkpoint:
+            return BaseModelType.StableDiffusionXL
+
         key1 = "lora_te_text_model_encoder_layers_0_mlp_fc1.lora_down.weight"
-        key2 = "lora_te_text_model_encoder_layers_0_self_attn_k_proj.hada_w1_a"
+        key2 = "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
+        key3 = "lora_te_text_model_encoder_layers_0_self_attn_k_proj.hada_w1_a"
+
         lora_token_vector_length = (
             checkpoint[key1].shape[1]
             if key1 in checkpoint
-            else checkpoint[key2].shape[0]
+            else checkpoint[key2].shape[1]
             if key2 in checkpoint
-            else 768
+            else checkpoint[key3].shape[0]
+            if key3 in checkpoint
+            else None
         )
+
         if lora_token_vector_length == 768:
             return BaseModelType.StableDiffusion1
         elif lora_token_vector_length == 1024:
             return BaseModelType.StableDiffusion2
         else:
-            return None
+            raise InvalidModelException(f"Unknown LoRA type")
 
 
 class TextualInversionCheckpointProbe(CheckpointProbeBase):

invokeai/backend/model_management/models/lora.py

@@ -88,6 +88,7 @@ class LoRAModel(ModelBase):
         else:
             return model_path
 
+
 class LoRALayerBase:
     # rank: Optional[int]
     # alpha: Optional[float]
@@ -173,6 +174,7 @@ class LoRALayerBase:
         if self.bias is not None:
             self.bias = self.bias.to(device=device, dtype=dtype)
 
+
 # TODO: find and debug lora/locon with bias
 class LoRALayer(LoRALayerBase):
     # up: torch.Tensor
@@ -225,6 +227,7 @@ class LoRALayer(LoRALayerBase):
         if self.mid is not None:
             self.mid = self.mid.to(device=device, dtype=dtype)
 
+
 class LoHALayer(LoRALayerBase):
     # w1_a: torch.Tensor
     # w1_b: torch.Tensor
@@ -292,6 +295,7 @@ class LoHALayer(LoRALayerBase):
         if self.t2 is not None:
             self.t2 = self.t2.to(device=device, dtype=dtype)
 
+
 class LoKRLayer(LoRALayerBase):
     # w1: Optional[torch.Tensor] = None
     # w1_a: Optional[torch.Tensor] = None
@@ -386,6 +390,7 @@ class LoKRLayer(LoRALayerBase):
         if self.t2 is not None:
             self.t2 = self.t2.to(device=device, dtype=dtype)
 
+
 # TODO: rename all methods used in model logic with Info postfix and remove here Raw postfix
 class LoRAModelRaw:  # (torch.nn.Module):
     _name: str
@@ -439,7 +444,7 @@ class LoRAModelRaw:  # (torch.nn.Module):
         new_state_dict = dict()
         for full_key, value in state_dict.items():
             if full_key.startswith("lora_te1_") or full_key.startswith("lora_te2_"):
-                continue # clip same
+                continue  # clip same
 
             if not full_key.startswith("lora_unet_"):
                 raise NotImplementedError(f"Unknown prefix for sdxl lora key - {full_key}")
@@ -450,7 +455,7 @@ class LoRAModelRaw:  # (torch.nn.Module):
                     if src_key in SDXL_UNET_COMPVIS_MAP:
                         dst_key = SDXL_UNET_COMPVIS_MAP[src_key]
                         break
-                    src_key = "_".join(src_key.split('_')[:-1])
+                    src_key = "_".join(src_key.split("_")[:-1])
 
                 if dst_key is None:
                     raise Exception(f"Unknown sdxl lora key - {full_key}")
@@ -614,5 +619,9 @@ def make_sdxl_unet_conversion_map():
 
     return unet_conversion_map
 
-#_sdxl_conversion_map = {f"lora_unet_{sd}".rstrip(".").replace(".", "_"): f"lora_unet_{hf}".rstrip(".").replace(".", "_") for sd, hf in make_sdxl_unet_conversion_map()}
-SDXL_UNET_COMPVIS_MAP = {f"{sd}".rstrip(".").replace(".", "_"): f"{hf}".rstrip(".").replace(".", "_") for sd, hf in make_sdxl_unet_conversion_map()}
+
+# _sdxl_conversion_map = {f"lora_unet_{sd}".rstrip(".").replace(".", "_"): f"lora_unet_{hf}".rstrip(".").replace(".", "_") for sd, hf in make_sdxl_unet_conversion_map()}
+SDXL_UNET_COMPVIS_MAP = {
+    f"{sd}".rstrip(".").replace(".", "_"): f"{hf}".rstrip(".").replace(".", "_")
+    for sd, hf in make_sdxl_unet_conversion_map()
+}

scripts/probe-model.py

@@ -9,8 +9,10 @@ parser = argparse.ArgumentParser(description="Probe model type")
 parser.add_argument(
     "model_path",
     type=Path,
+    nargs="+",
 )
 args = parser.parse_args()
 
-info = ModelProbe().probe(args.model_path)
-print(info)
+for path in args.model_path:
+    info = ModelProbe().probe(path)
+    print(f"{path}: {info}")