feat(mm): port TIs to new API

invokeai/backend/model_manager/config.py

@@ -30,6 +30,7 @@ from inspect import isabstract
 from pathlib import Path
 from typing import ClassVar, Literal, Optional, Type, TypeAlias, Union
 
+import torch
 from pydantic import BaseModel, ConfigDict, Discriminator, Field, Tag, TypeAdapter
 from typing_extensions import Annotated, Any, Dict
 
@@ -601,19 +602,137 @@ class ControlNetCheckpointConfig(CheckpointConfigBase, ControlAdapterConfigBase,
     type: Literal[ModelType.ControlNet] = ModelType.ControlNet
 
 
-class TextualInversionFileConfig(LegacyProbeMixin, ModelConfigBase):
+class TextualInversionConfigBase(ABC, BaseModel):
+    type: Literal[ModelType.TextualInversion] = ModelType.TextualInversion
+
+    @classmethod
+    def file_looks_like_embedding(cls, mod: ModelOnDisk, path: Path | None = None) -> bool:
+        p = path or mod.path
+
+        if not p.exists():
+            return False
+
+        if p.is_dir():
+            return False
+
+        if p.name in {"learned_embeds.bin", "learned_embeds.safetensors"}:
+            return True
+
+        state_dict = mod.load_state_dict(p)
+
+        # Heuristic: textual inversion embeddings have these keys
+        if any(key in {"string_to_param", "emb_params"} for key in state_dict.keys()):
+            return True
+
+        # Heuristic: small state dict with all tensor values
+        if (len(state_dict)) < 10 and all(isinstance(v, torch.Tensor) for v in state_dict.values()):
+            return True
+
+        return False
+
+    @classmethod
+    def get_base(cls, mod: ModelOnDisk, path: Path | None = None) -> BaseModelType:
+        p = path or mod.path
+
+        try:
+            state_dict = mod.load_state_dict(p)
+            if "string_to_token" in state_dict:
+                token_dim = list(state_dict["string_to_param"].values())[0].shape[-1]
+            elif "emb_params" in state_dict:
+                token_dim = state_dict["emb_params"].shape[-1]
+            elif "clip_g" in state_dict:
+                token_dim = state_dict["clip_g"].shape[-1]
+            else:
+                token_dim = list(state_dict.values())[0].shape[0]
+
+            match token_dim:
+                case 768:
+                    return BaseModelType.StableDiffusion1
+                case 1024:
+                    return BaseModelType.StableDiffusion2
+                case 1280:
+                    return BaseModelType.StableDiffusionXL
+                case _:
+                    pass
+        except Exception:
+            pass
+
+        raise InvalidModelConfigException(f"{p}: Could not determine base type")
+
+
+class TextualInversionFileConfig(TextualInversionConfigBase, ModelConfigBase):
     """Model config for textual inversion embeddings."""
 
-    type: Literal[ModelType.TextualInversion] = ModelType.TextualInversion
     format: Literal[ModelFormat.EmbeddingFile] = ModelFormat.EmbeddingFile
 
+    @classmethod
+    def get_tag(cls) -> Tag:
+        return Tag(f"{ModelType.TextualInversion.value}.{ModelFormat.EmbeddingFile.value}")
 
-class TextualInversionFolderConfig(LegacyProbeMixin, ModelConfigBase):
+    @classmethod
+    def matches(cls, mod: ModelOnDisk, **overrides) -> MatchCertainty:
+        is_embedding_override = overrides.get("type") is ModelType.TextualInversion
+        is_file_override = overrides.get("format") is ModelFormat.EmbeddingFile
+
+        if is_embedding_override and is_file_override:
+            return MatchCertainty.OVERRIDE
+
+        if mod.path.is_dir():
+            return MatchCertainty.NEVER
+
+        if cls.file_looks_like_embedding(mod):
+            return MatchCertainty.MAYBE
+
+        return MatchCertainty.NEVER
+
+    @classmethod
+    def parse(cls, mod: ModelOnDisk) -> dict[str, Any]:
+        try:
+            base = cls.get_base(mod)
+            return {"base": base}
+        except Exception:
+            pass
+
+        raise InvalidModelConfigException(f"{mod.path}: Could not determine base type")
+
+
+class TextualInversionFolderConfig(TextualInversionConfigBase, ModelConfigBase):
     """Model config for textual inversion embeddings."""
 
-    type: Literal[ModelType.TextualInversion] = ModelType.TextualInversion
     format: Literal[ModelFormat.EmbeddingFolder] = ModelFormat.EmbeddingFolder
 
+    @classmethod
+    def get_tag(cls) -> Tag:
+        return Tag(f"{ModelType.TextualInversion.value}.{ModelFormat.EmbeddingFolder.value}")
+
+    @classmethod
+    def matches(cls, mod: ModelOnDisk, **overrides) -> MatchCertainty:
+        is_embedding_override = overrides.get("type") is ModelType.TextualInversion
+        is_folder_override = overrides.get("format") is ModelFormat.EmbeddingFolder
+
+        if is_embedding_override and is_folder_override:
+            return MatchCertainty.OVERRIDE
+
+        if mod.path.is_file():
+            return MatchCertainty.NEVER
+
+        for filename in {"learned_embeds.bin", "learned_embeds.safetensors"}:
+            if cls.file_looks_like_embedding(mod, mod.path / filename):
+                return MatchCertainty.MAYBE
+
+        return MatchCertainty.NEVER
+
+    @classmethod
+    def parse(cls, mod: ModelOnDisk) -> dict[str, Any]:
+        try:
+            for filename in {"learned_embeds.bin", "learned_embeds.safetensors"}:
+                base = cls.get_base(mod, mod.path / filename)
+                return {"base": base}
+        except Exception:
+            pass
+
+        raise InvalidModelConfigException(f"{mod.path}: Could not determine base type")
+
 
 class MainConfigBase(ABC, BaseModel):
     type: Literal[ModelType.Main] = ModelType.Main