wren93/consisti2v 🔢🖼️📝 → 🖼️

▶️ 3.2K runs 📅 Mar 2024 ⚙️ Cog 0.9.4 🔗 GitHub 📄 Paper ⚖️ License
image-to-video visual-consistency

About

A diffusion-based method to enhance visual consistency for I2V generation

Example Output

Prompt:

"timelapse at the snow land with aurora in the sky"

Output

Performance Metrics

37.08s Prediction Time
81.43s Total Time
All Input Parameters
{
  "image": "https://replicate.delivery/pbxt/KVX68CBiatzs0asnIyqWZJORA4g5K5TWjts1WSmonroU9kAv/example_01.png",
  "prompt": "timelapse at the snow land with aurora in the sky",
  "num_inference_steps": 50,
  "text_guidance_scale": 7.5,
  "image_guidance_scale": 1
}
Input Parameters
seed Type: integer
Random seed. Leave blank to randomize the seed
image (required) Type: string
Input image as the first frame of the video.
prompt Type: stringDefault: An astronaut riding a rainbow unicorn
Input prompt
negative_prompt Type: stringDefault:
Input Negative Prompt
num_inference_steps Type: integerDefault: 50Range: 1 - 500
Number of denoising steps
text_guidance_scale Type: numberDefault: 7.5Range: 1 - 50
Scale for classifier-free guidance from the text
image_guidance_scale Type: numberDefault: 1
Scale for classifier-free guidance from the image
Output Schema

Output

Type: stringFormat: uri

Example Execution Logs
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Version Details
Version ID
ce1f2912887d0e1926988004122f9049ff69f4b749b6221ed1dfcfc9487dc71b
Version Created
March 3, 2024
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