gradute comfyui-refiners get_dtype to refiners

src/comfyui-refiners/grounding_dino.py

@@ -3,9 +3,9 @@ from typing import Any, Sequence
 import torch
 from transformers import GroundingDinoForObjectDetection, GroundingDinoProcessor  # type: ignore
 
-from refiners.fluxion.utils import no_grad, tensor_to_image
+from refiners.fluxion.utils import no_grad, str_to_dtype, tensor_to_image
 
-from .utils import BoundingBox, get_dtype
+from .utils import BoundingBox
 
 
 class LoadGroundingDino:
@@ -54,7 +54,7 @@ class LoadGroundingDino:
         processor = GroundingDinoProcessor.from_pretrained(checkpoint)  # type: ignore
         assert isinstance(processor, GroundingDinoProcessor)
 
-        model = GroundingDinoForObjectDetection.from_pretrained(checkpoint, torch_dtype=get_dtype(dtype))  # type: ignore
+        model = GroundingDinoForObjectDetection.from_pretrained(checkpoint, torch_dtype=str_to_dtype(dtype))  # type: ignore
         model = model.to(device=device)  # type: ignore
         assert isinstance(model, GroundingDinoForObjectDetection)
 

src/comfyui-refiners/utils.py

@@ -48,39 +48,6 @@ class DrawBoundingBox:
         return (image,)
 
 
-def get_dtype(dtype: str) -> torch.dtype:
-    """Converts a string dtype to a torch.dtype.
-
-    See also https://pytorch.org/docs/stable/tensor_attributes.html#torch-dtype"""
-    match dtype:
-        case "float32" | "float":
-            return torch.float32
-        case "float64" | "double":
-            return torch.float64
-        case "complex64" | "cfloat":
-            return torch.complex64
-        case "complex128" | "cdouble":
-            return torch.complex128
-        case "float16" | "half":
-            return torch.float16
-        case "bfloat16":
-            return torch.bfloat16
-        case "uint8":
-            return torch.uint8
-        case "int8":
-            return torch.int8
-        case "int16" | "short":
-            return torch.int16
-        case "int32" | "int":
-            return torch.int32
-        case "int64" | "long":
-            return torch.int64
-        case "bool":
-            return torch.bool
-        case _:
-            raise ValueError(f"Unknown dtype: {dtype}")
-
-
 NODE_CLASS_MAPPINGS: dict[str, Any] = {
     "DrawBoundingBox": DrawBoundingBox,
 }

src/refiners/fluxion/utils.py

@@ -270,3 +270,37 @@ def summarize_tensor(tensor: torch.Tensor, /) -> str:
     )
 
     return "Tensor(" + ", ".join(info_list) + ")"
+
+
+def str_to_dtype(dtype: str) -> torch.dtype:
+    """Converts a string dtype to a torch.dtype.
+
+    See also https://pytorch.org/docs/stable/tensor_attributes.html#torch-dtype
+    """
+    match dtype.lower():
+        case "float32" | "float":
+            return torch.float32
+        case "float64" | "double":
+            return torch.float64
+        case "complex64" | "cfloat":
+            return torch.complex64
+        case "complex128" | "cdouble":
+            return torch.complex128
+        case "float16" | "half":
+            return torch.float16
+        case "bfloat16":
+            return torch.bfloat16
+        case "uint8":
+            return torch.uint8
+        case "int8":
+            return torch.int8
+        case "int16" | "short":
+            return torch.int16
+        case "int32" | "int":
+            return torch.int32
+        case "int64" | "long":
+            return torch.int64
+        case "bool":
+            return torch.bool
+        case _:
+            raise ValueError(f"Unknown dtype: {dtype}")