Add nf4 bnb quantized format

invokeai/app/invocations/model.py

@@ -136,12 +136,12 @@ class ModelIdentifierInvocation(BaseInvocation):
 T5_ENCODER_OPTIONS = Literal["base", "8b_quantized"]
 T5_ENCODER_MAP: Dict[str, Dict[str, str]] = {
     "base": {
-        "repo": "invokeai/flux_dev::t5_xxl_encoder/base",
+        "repo": "InvokeAI/flux_schnell::t5_xxl_encoder/base",
         "name": "t5_base_encoder",
         "format": ModelFormat.T5Encoder,
     },
     "8b_quantized": {
-        "repo": "invokeai/flux_dev::t5_xxl_encoder/8b_quantized",
+        "repo": "invokeai/flux_dev::t5_xxl_encoder/optimum_quanto_qfloat8",
         "name": "t5_8b_quantized_encoder",
         "format": ModelFormat.T5Encoder,
     },

invokeai/backend/model_manager/config.py

@@ -111,6 +111,7 @@ class ModelFormat(str, Enum):
     T5Encoder = "t5_encoder"
     T5Encoder8b = "t5_encoder_8b"
     T5Encoder4b = "t5_encoder_4b"
+    BnbQuantizednf4b = "bnb_quantized_nf4b"
 
 
 class SchedulerPredictionType(str, Enum):
@@ -193,7 +194,7 @@ class ModelConfigBase(BaseModel):
 class CheckpointConfigBase(ModelConfigBase):
     """Model config for checkpoint-style models."""
 
-    format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
+    format: Literal[ModelFormat.Checkpoint, ModelFormat.BnbQuantizednf4b] = Field(description="Format of the provided checkpoint model", default=ModelFormat.Checkpoint)
     config_path: str = Field(description="path to the checkpoint model config file")
     converted_at: Optional[float] = Field(
         description="When this model was last converted to diffusers", default_factory=time.time
@@ -248,7 +249,6 @@ class VAECheckpointConfig(CheckpointConfigBase):
     """Model config for standalone VAE models."""
 
     type: Literal[ModelType.VAE] = ModelType.VAE
-    format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
 
     @staticmethod
     def get_tag() -> Tag:
@@ -287,7 +287,6 @@ class ControlNetCheckpointConfig(CheckpointConfigBase, ControlAdapterConfigBase)
     """Model config for ControlNet models (diffusers version)."""
 
     type: Literal[ModelType.ControlNet] = ModelType.ControlNet
-    format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
 
     @staticmethod
     def get_tag() -> Tag:
@@ -336,6 +335,21 @@ class MainCheckpointConfig(CheckpointConfigBase, MainConfigBase):
         return Tag(f"{ModelType.Main.value}.{ModelFormat.Checkpoint.value}")
 
 
+class MainBnbQuantized4bCheckpointConfig(CheckpointConfigBase, MainConfigBase):
+    """Model config for main checkpoint models."""
+
+    prediction_type: SchedulerPredictionType = SchedulerPredictionType.Epsilon
+    upcast_attention: bool = False
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.format = ModelFormat.BnbQuantizednf4b
+
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.Main.value}.{ModelFormat.BnbQuantizednf4b.value}")
+
+
 class MainDiffusersConfig(DiffusersConfigBase, MainConfigBase):
     """Model config for main diffusers models."""
 
@@ -438,6 +452,7 @@ AnyModelConfig = Annotated[
     Union[
         Annotated[MainDiffusersConfig, MainDiffusersConfig.get_tag()],
         Annotated[MainCheckpointConfig, MainCheckpointConfig.get_tag()],
+        Annotated[MainBnbQuantized4bCheckpointConfig, MainBnbQuantized4bCheckpointConfig.get_tag()],
         Annotated[VAEDiffusersConfig, VAEDiffusersConfig.get_tag()],
         Annotated[VAECheckpointConfig, VAECheckpointConfig.get_tag()],
         Annotated[ControlNetDiffusersConfig, ControlNetDiffusersConfig.get_tag()],

invokeai/backend/model_manager/probe.py

@@ -162,7 +162,7 @@ class ModelProbe(object):
         fields["description"] = (
             fields.get("description") or f"{fields['base'].value} {model_type.value} model {fields['name']}"
         )
-        fields["format"] = ModelFormat(fields.get("format")) or probe.get_format()
+        fields["format"] = ModelFormat(fields.get("format")) if "format" in fields else probe.get_format()
         fields["hash"] = fields.get("hash") or ModelHash(algorithm=hash_algo).hash(model_path)
 
         fields["default_settings"] = fields.get("default_settings")
@@ -179,7 +179,7 @@ class ModelProbe(object):
         # additional fields needed for main and controlnet models
         if (
             fields["type"] in [ModelType.Main, ModelType.ControlNet, ModelType.VAE]
-            and fields["format"] is ModelFormat.Checkpoint
+            and fields["format"] in [ModelFormat.Checkpoint, ModelFormat.BnbQuantizednf4b]
         ):
             ckpt_config_path = cls._get_checkpoint_config_path(
                 model_path,
@@ -323,6 +323,7 @@ class ModelProbe(object):
 
         if model_type is ModelType.Main:
             if base_type == BaseModelType.Flux:
+                # TODO: Decide between dev/schnell
                 config_file = "flux/flux1-schnell.yaml"
             else:
                 config_file = LEGACY_CONFIGS[base_type][variant_type]
@@ -422,6 +423,9 @@ class CheckpointProbeBase(ProbeBase):
         self.checkpoint = ModelProbe._scan_and_load_checkpoint(model_path)
 
     def get_format(self) -> ModelFormat:
+        state_dict = self.checkpoint.get("state_dict") or self.checkpoint
+        if "double_blocks.0.img_attn.proj.weight.quant_state.bitsandbytes__nf4" in state_dict:
+            return ModelFormat.BnbQuantizednf4b
         return ModelFormat("checkpoint")
 
     def get_variant_type(self) -> ModelVariantType: