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