cjwbw/lavie 🔢📝✓❓ → 🖼️

▶️ 13.9K runs 📅 Nov 2023 ⚙️ Cog 0.8.3 🔗 GitHub 📄 Paper ⚖️ License
text-to-video

About

High-Quality Video Generation with Cascaded Latent Diffusion Models

Example Output

Prompt:

"a Corgi walking in the park at sunrise, oil painting style"

Output

Performance Metrics

12.40s Prediction Time
57.10s Total Time
All Input Parameters 
{
  "width": 512,
  "height": 320,
  "prompt": "a Corgi walking in the park at sunrise, oil painting style",
  "quality": 9,
  "video_fps": 8,
  "interpolation": false,
  "sample_method": "ddpm",
  "guidance_scale": 7,
  "super_resolution": false,
  "num_inference_steps": 50
}
Input Parameters
seed Type: integer
Random seed. Leave blank to randomize the seed
width Type: integerDefault: 512
Width of output video.
height Type: integerDefault: 320
Height of output video
prompt Type: stringDefault: a Corgi walking in the park at sunrise, oil painting style
Prompt for video generation.
quality Type: integerDefault: 9Range: 0 - 10
Quality of the output vide0
video_fps Type: integerDefault: 8
Number of frames per second in the output video
interpolation Type: booleanDefault: false
Default output has 16 frames. Set interpolation to True to get 61 frames output.
sample_method Default: ddpm
Choose a scheduler for sampling base video output.
guidance_scale Type: numberDefault: 7
Scale for classifier-free guidance
super_resolution Type: booleanDefault: false
Super resolution 4x when set to True.
num_inference_steps Type: integerDefault: 250
Number of denoising steps
Output Schema

Output

Type: string • Format: uri

Example Execution Logs 
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Version Details
Version ID
0bca850c4928b6c30052541fa002f24cbb4b677259c461dd041d271ba9d3c517
Version Created
November 21, 2023
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