README.md: fix demo code.
Browse files
README.md
CHANGED
@@ -136,14 +136,20 @@ pipe.to("cuda")
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pipe_upsample.to("cuda")
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pipe.vae.enable_tiling()
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prompt = "The video depicts a winding mountain road covered in snow, with a single vehicle traveling along it. The road is flanked by steep, rocky cliffs and sparse vegetation. The landscape is characterized by rugged terrain and a river visible in the distance. The scene captures the solitude and beauty of a winter drive through a mountainous region."
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negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
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expected_height, expected_width =
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downscale_factor = 2 / 3
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num_frames = 121
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# Part 1. Generate video at smaller resolution
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downscaled_height, downscaled_width = int(expected_height * downscale_factor), int(expected_width * downscale_factor)
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latents = pipe(
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conditions=None,
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prompt=prompt,
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@@ -200,6 +206,11 @@ pipe.to("cuda")
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pipe_upsample.to("cuda")
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pipe.vae.enable_tiling()
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image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/penguin.png")
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video = [image]
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condition1 = LTXVideoCondition(video=video, frame_index=0)
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@@ -254,7 +265,6 @@ video = pipe(
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video = [frame.resize((expected_width, expected_height)) for frame in video]
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export_to_video(video, "output.mp4", fps=24)
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```
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### For video-to-video:
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@@ -272,8 +282,8 @@ pipe_upsample.to("cuda")
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pipe.vae.enable_tiling()
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def round_to_nearest_resolution_acceptable_by_vae(height, width):
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height = height - (height % pipe.
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width = width - (width % pipe.
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return height, width
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video = load_video(
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@@ -331,7 +341,6 @@ video = pipe(
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video = [frame.resize((expected_width, expected_height)) for frame in video]
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export_to_video(video, "output.mp4", fps=24)
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-
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```
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pipe_upsample.to("cuda")
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pipe.vae.enable_tiling()
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def round_to_nearest_resolution_acceptable_by_vae(height, width):
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height = height - (height % pipe.vae_spatial_compression_ratio)
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width = width - (width % pipe.vae_spatial_compression_ratio)
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return height, width
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prompt = "The video depicts a winding mountain road covered in snow, with a single vehicle traveling along it. The road is flanked by steep, rocky cliffs and sparse vegetation. The landscape is characterized by rugged terrain and a river visible in the distance. The scene captures the solitude and beauty of a winter drive through a mountainous region."
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negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
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expected_height, expected_width = 512, 704
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downscale_factor = 2 / 3
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num_frames = 121
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# Part 1. Generate video at smaller resolution
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downscaled_height, downscaled_width = int(expected_height * downscale_factor), int(expected_width * downscale_factor)
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downscaled_height, downscaled_width = round_to_nearest_resolution_acceptable_by_vae(downscaled_height, downscaled_width)
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latents = pipe(
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conditions=None,
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prompt=prompt,
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pipe_upsample.to("cuda")
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pipe.vae.enable_tiling()
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def round_to_nearest_resolution_acceptable_by_vae(height, width):
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height = height - (height % pipe.vae_spatial_compression_ratio)
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width = width - (width % pipe.vae_spatial_compression_ratio)
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return height, width
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image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/penguin.png")
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video = [image]
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condition1 = LTXVideoCondition(video=video, frame_index=0)
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video = [frame.resize((expected_width, expected_height)) for frame in video]
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export_to_video(video, "output.mp4", fps=24)
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```
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### For video-to-video:
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pipe.vae.enable_tiling()
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def round_to_nearest_resolution_acceptable_by_vae(height, width):
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height = height - (height % pipe.vae_spatial_compression_ratio)
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width = width - (width % pipe.vae_spatial_compression_ratio)
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return height, width
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video = load_video(
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video = [frame.resize((expected_width, expected_height)) for frame in video]
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export_to_video(video, "output.mp4", fps=24)
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```
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