Instructions to use cuio/URSA-0.6B-IBQ1024 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cuio/URSA-0.6B-IBQ1024 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cuio/URSA-0.6B-IBQ1024", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
URSA-0.6B-IBQ1024 Model Card
Model Details
- Developed by: BAAI
- Model type: Text-to-Image Generation Model
- Model size: 0.6B
- Model precision: torch.float16 (FP16)
- Model resolution: 1024x1024
- Model paper: Uniform Discrete Diffusion with Metric Path for Video Generation
- Model family: BAAI-Vision-URSA
- Model Tokenizer: Emu3.5-Vision-Tokenizer
- Model Description: This is a model that can be used to generate and modify images 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 torch
from diffnext.pipelines import URSAPipeline
model_id, height, width = "BAAI/URSA-0.6B-IBQ1024", 1024, 1024
model_args = {"torch_dtype": torch.float16, "trust_remote_code": True}
pipe = URSAPipeline.from_pretrained(model_id, **model_args)
pipe = pipe.to(torch.device("cuda"))
prompt = "The bear, calm and still, gazes upward as if lost in contemplation of the cosmos."
negative_prompt = "worst quality, low quality, inconsistent motion, static, still, blurry, jittery, distorted, ugly"
image = pipe(**locals()).frames[0]
image.save("ursa.jpg")
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.
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Paper for cuio/URSA-0.6B-IBQ1024
Paper • 2510.24717 • Published • 43