wcarle/text2video-zero-openjourney 🔢📝 → 🖼️

▶️ 13.3K runs 📅 Apr 2023 ⚙️ Cog 0.6.1 🔗 GitHub 📄 Paper ⚖️ License

text-to-video

About

The Picsart Text2Video-Zero model leverages the power of existing text-to-image synthesis methods (e.g., Stable Diffusion), making them suitable for the video domain.

Example Output

Prompt:

"A panda is dancing in times square"

Output

Performance Metrics

82.32s Prediction Time

82.44s Total Time

All Input Parameters

{
  "t0": 44,
  "t1": 45,
  "fps": 8,
  "prompt": "A panda is dancing in times square",
  "chunk_size": 8,
  "resolution": 512,
  "video_length": 30,
  "motion_field_strength_x": 12,
  "motion_field_strength_y": 12
}

Input Parameters

t0 Type: integerDefault: 44Range: 1 - 50: Timestep t0: Perform DDPM steps from t0 to t1. The larger the gap between t0 and t1, the more variance between the frames. Ensure t0 < t1
t1 Type: integerDefault: 47Range: 1 - 50: Timestep t1: Perform DDPM steps from t0 to t1. The larger the gap between t0 and t1, the more variance between the frames. Ensure t0 < t1
fps Type: integerDefault: 15Range: 5 - 60: Frame rate for the video.
seed Type: string: Leave blank to randomize the seed.
prompt Type: stringDefault: a cat: Input prompt.
chunk_size Type: integerDefault: 8Range: 1 - 10: Chunk size: Number of frames processed at once. Reduce for lower memory usage.
resolution Type: integerDefault: 512: Resolution of the video (square)
video_length Type: integerDefault: 8: Number of frames in the video
merging_ratio Type: numberDefault: 0Range: 0 - 0.9: Ratio of how many tokens are merged. The higher the more compression (less memory and faster inference).
negative_prompt Type: stringDefault:: Negative prompt.
motion_field_strength_x Type: integerDefault: 12Range: -20 - 20: Global Translation $\delta_{x}$
motion_field_strength_y Type: integerDefault: 12Range: -20 - 20: Global Translation $\delta_{y}$

Output Schema

Output

Type: string • Format: uri

Example Execution Logs

Setting Random seed.
Module Text2Video
Processing chunk 1 / 5
t0 = 881 t1 = 901
Continue DDIM with i = 0, t = 981, latent = torch.Size([1, 4, 64, 64]), device = cuda:0, type = torch.float16
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Continue DDIM with i = 4, t = 901, latent = torch.Size([8, 4, 64, 64]), device = cuda:0, type = torch.float16
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Processing chunk 2 / 5
t0 = 881 t1 = 901
Continue DDIM with i = 0, t = 981, latent = torch.Size([1, 4, 64, 64]), device = cuda:0, type = torch.float16
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latent t1 found at i=3, t = 921
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Continue DDIM with i = 4, t = 901, latent = torch.Size([8, 4, 64, 64]), device = cuda:0, type = torch.float16
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Processing chunk 3 / 5
t0 = 881 t1 = 901
Continue DDIM with i = 0, t = 981, latent = torch.Size([1, 4, 64, 64]), device = cuda:0, type = torch.float16
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Continue DDIM with i = 4, t = 901, latent = torch.Size([8, 4, 64, 64]), device = cuda:0, type = torch.float16
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Processing chunk 4 / 5
t0 = 881 t1 = 901
Continue DDIM with i = 0, t = 981, latent = torch.Size([1, 4, 64, 64]), device = cuda:0, type = torch.float16
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Continue DDIM with i = 4, t = 901, latent = torch.Size([8, 4, 64, 64]), device = cuda:0, type = torch.float16
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Processing chunk 5 / 5
t0 = 881 t1 = 901
Continue DDIM with i = 0, t = 981, latent = torch.Size([1, 4, 64, 64]), device = cuda:0, type = torch.float16
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latent t1 found at i=3, t = 921
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latent t0 found at i = 4, t = 901
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Continue DDIM with i = 4, t = 901, latent = torch.Size([3, 4, 64, 64]), device = cuda:0, type = torch.float16
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Version Details

Version ID: 2bf28cacd1f02765bd557294ec53f743b42be123675773c810bb3e0f8e3ce6f6
Version Created: April 11, 2023

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