Update pipeline.py

pipeline.py

@@ -920,7 +920,7 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
                 returned, otherwise a `tuple` is returned where the first element is a list with the generated frames.
         """
         
-        if controlnet != None:
+        if self.controlnet != None:
             controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
 
             # align format for control guidance
@@ -957,7 +957,7 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
 
         device = self._execution_device
         
-        if controlnet != None:
+        if self.controlnet != None:
             if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
                 controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
 
@@ -1003,7 +1003,7 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
             if do_classifier_free_guidance:
                 image_embeds = torch.cat([negative_image_embeds, image_embeds])
 
-        if controlnet != None:
+        if self.controlnet != None:
             if isinstance(controlnet, ControlNetModel):
                 conditioning_frames = self.prepare_image(
                     image=conditioning_frames,
@@ -1125,7 +1125,7 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
         added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
         
         # 7.1 Create tensor stating which controlnets to keep
-        if controlnet != None:
+        if self.controlnet != None:
             controlnet_keep = []
             for i in range(len(timesteps)):
                 keeps = [
@@ -1192,7 +1192,7 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
                     
                     
                     
-                    if controlnet != None:
+                    if self.controlnet != None:
                         if guess_mode and self.do_classifier_free_guidance:
                             # Infer ControlNet only for the conditional batch.
                             control_model_input = latents