Update handler.py

handler.py

@@ -6,6 +6,8 @@ import logging
 import random
 import traceback
 import torch
+
+# note: there is no HunyuanImageToVideoPipeline yet in Diffusers
 from diffusers import HunyuanVideoPipeline, HunyuanVideoTransformer3DModel
 from varnish import Varnish
 from varnish.utils import is_truthy, process_input_image
@@ -95,18 +97,19 @@ class EndpointHandler:
         )
  
         if support_image_prompt:
-            # Initialize image-to-video pipeline
-            self.image_to_video = HunyuanImageToVideoPipeline.from_pretrained(
-                path,
-                transformer=transformer,
-                torch_dtype=torch.float16,
-            ).to(self.device)
-
-            # Initialize components in appropriate precision
-            self.image_to_video.text_encoder = self.image_to_video.text_encoder.half()
-            self.image_to_video.text_encoder_2 = self.image_to_video.text_encoder_2.half()
-            self.image_to_video.transformer = self.image_to_video.transformer.to(torch.bfloat16)
-            self.image_to_video.vae = self.image_to_video.vae.half()
+            raise Exception("Please use a version of Diffusers that supports HunyuanImageToVideoPipeline")
+            # # Initialize image-to-video pipeline
+            # self.image_to_video = HunyuanImageToVideoPipeline.from_pretrained(
+            #     path,
+            #     transformer=transformer,
+            #     torch_dtype=torch.float16,
+            # ).to(self.device)
+            #
+            # # Initialize components in appropriate precision
+            # self.image_to_video.text_encoder = self.image_to_video.text_encoder.half()
+            # self.image_to_video.text_encoder_2 = self.image_to_video.text_encoder_2.half()
+            # self.image_to_video.transformer = self.image_to_video.transformer.to(torch.bfloat16)
+            # self.image_to_video.vae = self.image_to_video.vae.half()
         else:
             # Initialize text-to-video pipeline
             self.text_to_video = HunyuanVideoPipeline.from_pretrained(