Instructions to use astrobytem/unimodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use astrobytem/unimodel with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("astrobytem/unimodel", 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
Unimodel is a state-of-the-art text-to-image generative model designed to create high-quality, photorealistic images from text prompts. Leveraging a vast 16.6 billion parameter architecture, the model can generate images with stunning accuracy and creativity, making it suitable for various applications such as content creation, advertising, and media generation. This model supports both short prompts and long prompts. Prompts can be as short as 1-2 words and as long as 3oo words. *We tested it on longer prompts and got the model to support a 500 words prompt however, further testing is needed.
Prompt= "a women holding a sign that says hello world with hello in cursive"
Key Features:
-16.6 Billion Parameters: Unimodel is built on a large-scale architecture, making it capable of understanding complex prompts and producing highly detailed images. -Text-to-Image Generation: The model accepts natural language prompts and transforms them into images using advanced diffusion-based techniques. -Fine-Tuned for Image Quality: Unimodel has been trained on a high-resolution dataset to ensure the images produced are clear, realistic, and aligned with user input. -Versatile Applications: Unimodel can generate a wide range of visuals, from artistic styles to highly realistic representations, catering to industries such as media, marketing, and design.
If you have permission to review this model you can test it out with follow prompt:
from diffusers import DiffusionPipeline import torch
pipe = DiffusionPipeline.from_pretrained("astrobytem/unimodel", torch_dtype=torch.float16) pipe.to("cuda")
prompt = "A futuristic city skyline at sunset" image = pipe(prompt).images[0]
image.save("output_image.png")
Unimodel can also be found at www.astrosystem.com generate images and explore our project. Currently our team is workin on a 3 min video model that will generate movie like shows,movies, music videos and marketing material.
If you are intrested in becoming part of our team you can contact us at info@astrobytemarketing.com
- Downloads last month
- -
