Update pipeline.py

pipeline.py

@@ -33,7 +33,7 @@ EXAMPLE_DOC_STRING = """
 class InstantCharacterFluxPipeline(FluxPipeline):
 
 
-    @torch.inference_mode()
+    @torch.no_grad()
     def encode_siglip_image_emb(self, siglip_image, device, dtype):
         siglip_image = siglip_image.to(device, dtype=dtype)
         res = self.siglip_image_encoder(siglip_image, output_hidden_states=True)
@@ -45,7 +45,7 @@ class InstantCharacterFluxPipeline(FluxPipeline):
         return siglip_image_embeds, siglip_image_shallow_embeds
 
 
-    @torch.inference_mode()
+    @torch.no_grad()
     def encode_dinov2_image_emb(self, dinov2_image, device, dtype):
         dinov2_image = dinov2_image.to(device, dtype=dtype)
         res = self.dino_image_encoder_2(dinov2_image, output_hidden_states=True)
@@ -57,7 +57,7 @@ class InstantCharacterFluxPipeline(FluxPipeline):
         return dinov2_image_embeds, dinov2_image_shallow_embeds
 
 
-    @torch.inference_mode()
+    @torch.no_grad()
     def encode_image_emb(self, siglip_image, device, dtype):
         object_image_pil = siglip_image
         object_image_pil_low_res = [object_image_pil.resize((384, 384))]
@@ -104,7 +104,7 @@ class InstantCharacterFluxPipeline(FluxPipeline):
         return image_embeds_dict
 
 
-    @torch.inference_mode()
+    @torch.no_grad()
     def init_ccp_and_attn_processor(self, *args, **kwargs):
         subject_ip_adapter_path = kwargs['subject_ip_adapter_path']
         nb_token = kwargs['nb_token']
@@ -150,7 +150,7 @@ class InstantCharacterFluxPipeline(FluxPipeline):
         self.subject_image_proj_model = image_proj_model
 
 
-    @torch.inference_mode()
+    @torch.no_grad()
     def init_adapter(
         self, 
         image_encoder_path=None,