Load clipvision model to GPU for faster performance.

comfy/clip_vision.py

@@ -2,14 +2,27 @@ from transformers import CLIPVisionModelWithProjection, CLIPVisionConfig, CLIPIm
 from .utils import load_torch_file, transformers_convert
 import os
 import torch
+import contextlib
+
 import comfy.ops
+import comfy.model_patcher
+import comfy.model_management
 
 class ClipVisionModel():
     def __init__(self, json_config):
         config = CLIPVisionConfig.from_json_file(json_config)
-        with comfy.ops.use_comfy_ops():
+        self.load_device = comfy.model_management.text_encoder_device()
+        offload_device = comfy.model_management.text_encoder_offload_device()
+        self.dtype = torch.float32
+        if comfy.model_management.should_use_fp16(self.load_device, prioritize_performance=False):
+            self.dtype = torch.float16
+
+        with comfy.ops.use_comfy_ops(offload_device, self.dtype):
             with modeling_utils.no_init_weights():
                 self.model = CLIPVisionModelWithProjection(config)
+        self.model.to(self.dtype)
+
+        self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
         self.processor = CLIPImageProcessor(crop_size=224,
                                             do_center_crop=True,
                                             do_convert_rgb=True,
@@ -27,7 +40,16 @@ class ClipVisionModel():
         img = torch.clip((255. * image), 0, 255).round().int()
         img = list(map(lambda a: a, img))
         inputs = self.processor(images=img, return_tensors="pt")
-        outputs = self.model(**inputs)
+        comfy.model_management.load_model_gpu(self.patcher)
+        pixel_values = inputs['pixel_values'].to(self.load_device)
+
+        if self.dtype != torch.float32:
+            precision_scope = torch.autocast
+        else:
+            precision_scope = lambda a, b: contextlib.nullcontext(a)
+
+        with precision_scope(comfy.model_management.get_autocast_device(self.load_device), torch.float32):
+            outputs = self.model(pixel_values=pixel_values)
         return outputs
 
 def convert_to_transformers(sd, prefix):