Implement support for t2i style model.

It needs the CLIPVision model so I added CLIPVisionLoader and CLIPVisionEncode.

Put the clip vision model in models/clip_vision
Put the t2i style model in models/style_models

StyleModelLoader to load it, StyleModelApply to apply it
ConditioningAppend to append the conditioning it outputs to a positive one.

comfy_extras/clip_vision.py

@@ -0,0 +1,32 @@
+from transformers import CLIPVisionModel, CLIPVisionConfig, CLIPImageProcessor
+from comfy.sd import load_torch_file
+import os
+
+class ClipVisionModel():
+    def __init__(self):
+        json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config.json")
+        config = CLIPVisionConfig.from_json_file(json_config)
+        self.model = CLIPVisionModel(config)
+        self.processor = CLIPImageProcessor(crop_size=224,
+                                            do_center_crop=True,
+                                            do_convert_rgb=True,
+                                            do_normalize=True,
+                                            do_resize=True,
+                                            image_mean=[ 0.48145466,0.4578275,0.40821073],
+                                            image_std=[0.26862954,0.26130258,0.27577711],
+                                            resample=3, #bicubic
+                                            size=224)
+
+    def load_sd(self, sd):
+        self.model.load_state_dict(sd, strict=False)
+
+    def encode_image(self, image):
+        inputs = self.processor(images=[image[0]], return_tensors="pt")
+        outputs = self.model(**inputs)
+        return outputs
+
+def load(ckpt_path):
+    clip_data = load_torch_file(ckpt_path)
+    clip = ClipVisionModel()
+    clip.load_sd(clip_data)
+    return clip