remove duplicate checks

src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py

@@ -662,12 +662,6 @@ class AnimateDiffVideoToVideoPipeline(
                 self.vae.to(dtype=torch.float32)
 
             if isinstance(generator, list):
-                if len(generator) != batch_size:
-                    raise ValueError(
-                        f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                        f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-                    )
-
                 init_latents = [
                     self.encode_video(video[i], generator[i], decode_chunk_size).unsqueeze(0)
                     for i in range(batch_size)

src/diffusers/pipelines/animatediff/pipeline_animatediff_video2video_controlnet.py

@@ -794,12 +794,6 @@ class AnimateDiffVideoToVideoControlNetPipeline(
                 self.vae.to(dtype=torch.float32)
 
             if isinstance(generator, list):
-                if len(generator) != batch_size:
-                    raise ValueError(
-                        f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                        f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-                    )
-
                 init_latents = [
                     self.encode_video(video[i], generator[i], decode_chunk_size).unsqueeze(0)
                     for i in range(batch_size)

src/diffusers/pipelines/cogvideo/pipeline_cogvideox_video2video.py

@@ -373,12 +373,6 @@ class CogVideoXVideoToVideoPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin)
 
         if latents is None:
             if isinstance(generator, list):
-                if len(generator) != batch_size:
-                    raise ValueError(
-                        f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                        f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-                    )
-
                 init_latents = [
                     retrieve_latents(self.vae.encode(video[i].unsqueeze(0)), generator[i]) for i in range(batch_size)
                 ]