chenxwh/nova-t2v 🔢🖼️📝 → 🖼️
About
Autoregressive Video Generation without Vector Quantization

Example Output
"The camera slowly rotates around a massive stack of vintage televisions that are placed within a large New York museum gallery. Each of the televisions is showing a different program. There are 1950s sci-fi movies with their distinctive visuals, horror movies with their creepy scenes, news broadcasts with moving images and words, static on some screens, and a 1970s sitcom with its characteristic look. The televisions are of various sizes and designs, some with rounded edges and others with more angular shapes. The gallery is well-lit, with light falling on the stack of televisions and highlighting the different programs being shown. There are no people visible in the immediate vicinity, only the stack of televisions and the surrounding gallery space."
Output
Performance Metrics
All Input Parameters
{ "fps": 12, "prompt": "The camera slowly rotates around a massive stack of vintage televisions that are placed within a large New York museum gallery. Each of the televisions is showing a different program. There are 1950s sci-fi movies with their distinctive visuals, horror movies with their creepy scenes, news broadcasts with moving images and words, static on some screens, and a 1970s sitcom with its characteristic look. The televisions are of various sizes and designs, some with rounded edges and others with more angular shapes. The gallery is well-lit, with light falling on the stack of televisions and highlighting the different programs being shown. There are no people visible in the immediate vicinity, only the stack of televisions and the surrounding gallery space.", "motion_flow": 5, "guidance_scale": 7, "negative_prompt": "low quality, deformed, distorted, disfigured, fused fingers, bad anatomy, weird hand", "num_diffusion_steps": 100, "num_inference_steps": 128 }
Input Parameters
- fps
- fps for the output video
- seed
- Random seed. Leave blank to randomize the seed
- image
- Input image prompt, optional
- prompt
- Input prompt
- motion_flow
- Motion Flow
- guidance_scale
- Scale for classifier-free guidance
- negative_prompt
- Specify things to not see in the output
- num_diffusion_steps
- Number of diffusion steps
- num_inference_steps
- Number of inference steps
Output Schema
Output
Example Execution Logs
Using seed: 22625 0%| | 0/9 [00:00<?, ?it/s] 11%|█ | 1/9 [00:13<01:47, 13.42s/it] 22%|██▏ | 2/9 [00:26<01:33, 13.42s/it] 33%|███▎ | 3/9 [00:40<01:20, 13.34s/it] 44%|████▍ | 4/9 [00:53<01:06, 13.32s/it] 56%|█████▌ | 5/9 [01:06<00:53, 13.31s/it] 67%|██████▋ | 6/9 [01:20<00:39, 13.32s/it] 78%|███████▊ | 7/9 [01:33<00:26, 13.34s/it] 89%|████████▉ | 8/9 [01:46<00:13, 13.32s/it] 100%|██████████| 9/9 [01:59<00:00, 13.31s/it] 100%|██████████| 9/9 [01:59<00:00, 13.33s/it] <class 'diffnext.pipelines.pipeline_nova.NOVAPipelineOutput'>
Version Details
- Version ID
efe91027f017e9b32e1d458c59139dc5ab783955d111a2c72c8e7063e6f38261
- Version Created
- December 27, 2024