URSA-1.7B-FSQ320

URSA-1.7B-FSQ320 Model Card#

Model Details#

• Developed by: BAAI
• Model type: Text-to-Video Generation Model
• Model size: 1.7B
• Model precision: torch.float16 (FP16)
• Model resolution: 512x320
• Model paper: Uniform Discrete Diffusion with Metric Path for Video Generation
• Model family: BAAI-Vision-URSA
• Model Tokenizer: Cosmos-Tokenize1-DV4x8x8-360p
• Model Description: This is a model that can be used to generate and modify videos based on text prompts.

Examples#

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

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

Running the pipeline:

import os, torch, numpy
from diffnext.pipelines import URSAPipeline
from diffnext.utils import export_to_video
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"

model_id, height, width = "BAAI/URSA-1.7B-FSQ320", 320, 512
model_args = {"torch_dtype": torch.float16, "trust_remote_code": True}
pipe = URSAPipeline.from_pretrained(model_id, **model_args)
pipe = pipe.to(torch.device("cuda"))

text_prompt = "a lone grizzly bear walks through a misty forest at dawn, sunlight catching its fur."
negative_prompt = "worst quality, low quality, inconsistent motion, static, still, blurry, jittery, distorted, ugly"

# Text-to-Image
prompt = text_prompt
num_frames, num_inference_steps = 1, 25
image = pipe(**locals()).frames[0]
image.save("ursa.jpg")

# Image-to-Video
prompt = f"motion=9.0, {text_prompt}"
num_frames, num_inference_steps = 49, 50
video = pipe(**locals()).frames[0]
export_to_video(video, "ursa_1+48f.mp4", fps=12)

# Text-to-Video
image, video = None, None
prompt = f"motion=9.0, {text_prompt}"
num_frames, num_inference_steps = 49, 50
video = pipe(**locals()).frames[0]
export_to_video(video, "ursa_49f.mp4", fps=12)

# Video-to-Video
prompt = f"motion=5.0, {text_prompt}"
num_frames, num_inference_steps = 49, 50
num_cond_frames, cond_noise_scale = 13, 0.1
for i in range(12):
    video, start_video = video[-num_cond_frames:], video
    video = pipe(**locals()).frames[0]
    video = numpy.concatenate([start_video, video[num_cond_frames:]])
    export_to_video(video, "ursa_{}f.mp4".format(video.shape[0]), 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.