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OmPrakashSingh1704
commited on
Commit
•
ad2ab39
1
Parent(s):
af4a9f2
options/Video.py
CHANGED
@@ -1,4 +1,4 @@
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from .Video_model import Model
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def Video(image):
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return Model.Video(image)
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from .Video_model import Model
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def Video(image):
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return Model.Video(image)[0]
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options/Video_model/Model.py
CHANGED
@@ -53,8 +53,9 @@ def Video(
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base_count = len(glob(os.path.join(output_folder, "*.mp4")))
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video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
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#
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if device == "cuda":
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with torch.autocast(device_type='cuda', dtype=torch.float16):
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frames = pipeline(
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image, height=height, width=width,
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@@ -67,20 +68,21 @@ def Video(
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generator=generator,
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).frames[0]
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else:
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#
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# Save the generated video
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save_video(frames, video_path, fps=fps, quality=5.0)
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torch.manual_seed(seed)
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return video_path, seed
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base_count = len(glob(os.path.join(output_folder, "*.mp4")))
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video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
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# Perform computation with appropriate dtype based on device
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if device == "cuda":
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# Use float16 for GPU
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with torch.autocast(device_type='cuda', dtype=torch.float16):
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frames = pipeline(
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image, height=height, width=width,
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generator=generator,
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).frames[0]
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else:
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# Use float32 for CPU to avoid issues with half precision
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with torch.autocast(device_type='cpu', dtype=torch.float32):
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frames = pipeline(
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image, height=height, width=width,
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num_inference_steps=num_inference_steps,
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min_guidance_scale=min_guidance_scale,
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max_guidance_scale=max_guidance_scale,
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num_frames=num_frames, fps=fps, motion_bucket_id=motion_bucket_id,
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decode_chunk_size=8,
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noise_aug_strength=0.02,
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generator=generator,
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).frames[0]
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# Save the generated video
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save_video(frames, video_path, fps=fps, quality=5.0)
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torch.manual_seed(seed)
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return video_path, seed
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options/Video_model/__pycache__/Model.cpython-310.pyc
CHANGED
Binary files a/options/Video_model/__pycache__/Model.cpython-310.pyc and b/options/Video_model/__pycache__/Model.cpython-310.pyc differ
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