Make supported_dtypes a priority list.

comfy/model_management.py

@@ -562,12 +562,22 @@ def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, tor
         if model_params * 2 > free_model_memory:
             return fp8_dtype
 
-    if should_use_fp16(device=device, model_params=model_params, manual_cast=True):
-        if torch.float16 in supported_dtypes:
-            return torch.float16
-    if should_use_bf16(device, model_params=model_params, manual_cast=True):
-        if torch.bfloat16 in supported_dtypes:
-            return torch.bfloat16
+    for dt in supported_dtypes:
+        if dt == torch.float16 and should_use_fp16(device=device, model_params=model_params):
+            if torch.float16 in supported_dtypes:
+                return torch.float16
+        if dt == torch.bfloat16 and should_use_bf16(device, model_params=model_params):
+            if torch.bfloat16 in supported_dtypes:
+                return torch.bfloat16
+
+    for dt in supported_dtypes:
+        if dt == torch.float16 and should_use_fp16(device=device, model_params=model_params, manual_cast=True):
+            if torch.float16 in supported_dtypes:
+                return torch.float16
+        if dt == torch.bfloat16 and should_use_bf16(device, model_params=model_params, manual_cast=True):
+            if torch.bfloat16 in supported_dtypes:
+                return torch.bfloat16
+
     return torch.float32
 
 # None means no manual cast
@@ -583,13 +593,13 @@ def unet_manual_cast(weight_dtype, inference_device, supported_dtypes=[torch.flo
     if bf16_supported and weight_dtype == torch.bfloat16:
         return None
 
-    if fp16_supported and torch.float16 in supported_dtypes:
-        return torch.float16
+    for dt in supported_dtypes:
+        if dt == torch.float16 and fp16_supported:
+            return torch.float16
+        if dt == torch.bfloat16 and bf16_supported:
+            return torch.bfloat16
 
-    elif bf16_supported and torch.bfloat16 in supported_dtypes:
-        return torch.bfloat16
-    else:
-        return torch.float32
+    return torch.float32
 
 def text_encoder_offload_device():
     if args.gpu_only: