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BAAI/
nova-d48w1024-osp480

Name: nova-d48w1024-osp480
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NOVA (d48w1024-osp480) Model Card

Model Details

Developed by: BAAI
Model type: Non-quantized Autoregressive Text-to-Video Generation Model
Model size: 645M
Model precision: torch.float16 (FP16)
Model resolution: 768x480
Model Description: This is a model that can be used to generate and modify videos based on text prompts. It is a Non-quantized Video Autoregressive (NOVA) diffusion model that uses a pretrained text encoder (Phi-2) and one VAE video tokenizer (OpenSoraPlanV1.2-VAE).
Model License: Apache 2.0 License
Resources for more information: GitHub Repository.

Examples

Using the 🤗's Diffusers library to run NOVA in a simple and efficient manner.

bash

pip install diffusers transformers accelerate imageio[ffmpeg]
pip install git+ssh://git@github.com/baaivision/NOVA.git

Running the pipeline:

python

import torch
from diffnext.pipelines import NOVAPipeline
from diffnext.utils import export_to_image, export_to_video

model_id = "BAAI/nova-d48w1024-osp480"
model_args = {"torch_dtype": torch.float16, "trust_remote_code": True}
pipe = NOVAPipeline.from_pretrained(model_id, **model_args)
pipe = pipe.to("cuda")

prompt = "Many spotted jellyfish pulsating under water."

image = pipe(prompt, max_latent_length=1).frames[0, 0]
export_to_image(image, "jellyfish.jpg")

video = pipe(prompt, max_latent_length=9).frames[0]
export_to_video(video, "jellyfish.mp4", fps=12)

# Increase AR and diffusion steps for better video quality.
video = pipe(
  prompt,
  max_latent_length=9,
  num_inference_steps=128,  # default: 64
  num_diffusion_steps=100,  # default: 25
).frames[0]
export_to_video(video, "jellyfish_v2.mp4", fps=12)

Uses

Direct Use

The model is intended for research purposes only. Possible research areas and tasks include

Research on generative models.
Applications in educational or creative tools.
Generation of artworks and use in design and other artistic processes.
Probing and understanding the limitations and biases of generative models.
Safe deployment of models which have the potential to generate harmful content.

Excluded uses are described below.

Out-of-Scope Use

The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.

Misuse and Malicious Use

Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:

Mis- and disinformation.
Representations of egregious violence and gore.
Impersonating individuals without their consent.
Sexual content without consent of the people who might see it.
Sharing of copyrighted or licensed material in violation of its terms of use.
Intentionally promoting or propagating discriminatory content or harmful stereotypes.
Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.

Limitations and Bias

Limitations

The autoencoding part of the model is lossy.
The model cannot render complex legible text.
The model does not achieve perfect photorealism.
The fingers, .etc in general may not be generated properly.
The model was trained on a subset of the web datasets LAION-5B and COYO-700M, which contains adult, violent and sexual content.

Bias

While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.