Rename column name
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@@ -75,7 +75,7 @@ class PreprocessPipeline_I2V(BasePreprocessPipeline):
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num_frames - 1, height, width)
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],
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dim=2)
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video_condition = video_condition.to(device=fastvideo_args.device,
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video_condition = video_condition.to(device=get_torch_device(),
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dtype=torch.float32)
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video_conditions.append(video_condition)
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@@ -120,7 +120,7 @@ class PreprocessPipeline_I2V(BasePreprocessPipeline):
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image_latent = torch.concat([mask_lat_size, latent_condition],
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dim=1)
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features["encoded_first_frame"] = image_latent
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features["first_frame_latent"] = image_latent
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return features
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@@ -156,18 +156,18 @@ class PreprocessPipeline_I2V(BasePreprocessPipeline):
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"clip_feature_dtype": "",
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})
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if extra_features and "encoded_first_frame" in extra_features:
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encoded_first_frame = extra_features["encoded_first_frame"]
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if extra_features and "first_frame_latent" in extra_features:
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first_frame_latent = extra_features["first_frame_latent"]
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record.update({
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"encoded_first_frame_bytes": encoded_first_frame.tobytes(),
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"encoded_first_frame_shape": list(encoded_first_frame.shape),
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"encoded_first_frame_dtype": str(encoded_first_frame.dtype),
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"first_frame_latent_bytes": first_frame_latent.tobytes(),
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"first_frame_latent_shape": list(first_frame_latent.shape),
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"first_frame_latent_dtype": str(first_frame_latent.dtype),
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})
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else:
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record.update({
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"encoded_first_frame_bytes": b"",
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"encoded_first_frame_shape": [],
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"encoded_first_frame_dtype": "",
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"first_frame_latent_bytes": b"",
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"first_frame_latent_shape": [],
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"first_frame_latent_dtype": "",
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})
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return record
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