Files changed (1) hide show
  1. README.md +5 -5
README.md CHANGED
@@ -143,7 +143,7 @@ def round_to_nearest_resolution_acceptable_by_vae(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 = 704, 512
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  downscale_factor = 2 / 3
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  num_frames = 121
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@@ -202,7 +202,7 @@ export_to_video(video, "output.mp4", fps=24)
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  import torch
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  from diffusers import LTXConditionPipeline, LTXLatentUpsamplePipeline
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  from diffusers.pipelines.ltx.pipeline_ltx_condition import LTXVideoCondition
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- from diffusers.utils import export_to_video, load_image
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  pipe = LTXConditionPipeline.from_pretrained("Lightricks/LTX-Video-0.9.7-distilled", torch_dtype=torch.bfloat16)
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  pipe_upsample = LTXLatentUpsamplePipeline.from_pretrained("Lightricks/ltxv-spatial-upscaler-0.9.7", vae=pipe.vae, torch_dtype=torch.bfloat16)
@@ -216,12 +216,12 @@ def round_to_nearest_resolution_acceptable_by_vae(height, width):
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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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- 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 = 832, 480
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  downscale_factor = 2 / 3
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  num_frames = 96
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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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  import torch
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  from diffusers import LTXConditionPipeline, LTXLatentUpsamplePipeline
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  from diffusers.pipelines.ltx.pipeline_ltx_condition import LTXVideoCondition
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+ from diffusers.utils import export_to_video, load_image, load_video
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  pipe = LTXConditionPipeline.from_pretrained("Lightricks/LTX-Video-0.9.7-distilled", torch_dtype=torch.bfloat16)
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  pipe_upsample = LTXLatentUpsamplePipeline.from_pretrained("Lightricks/ltxv-spatial-upscaler-0.9.7", vae=pipe.vae, torch_dtype=torch.bfloat16)
 
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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 = load_video(export_to_video([image])) # compress the image using video compression as the model was trained on videos
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  condition1 = LTXVideoCondition(video=video, frame_index=0)
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+ prompt = "A cute little penguin takes out a book and starts reading it"
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  negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
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+ expected_height, expected_width = 480, 832
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  downscale_factor = 2 / 3
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  num_frames = 96
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