Instructions to use HelpingAI/PixelGen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use HelpingAI/PixelGen with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("HelpingAI/PixelGen", 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
| pipeline_tag: text-to-image | |
| license_name: hsul | |
| license_link: https://huggingface.co/OEvortex/vortex-3b/raw/main/LICENSE.md | |
| language: | |
| - en | |
| tags: | |
| - PixelGen | |
| - HelpingAI | |
| license: other | |
| ## PixelGen: Text-to-Image Generation Model | |
| ### Overview | |
| PixelGen, developed by HelpingAI, is a Text-to-Image generation model. It enables the generation of high-quality images from textual descriptions, offering a versatile tool for various creative and practical applications. | |
| ### Features | |
| - **Text-to-Image Generation**: PixelGen translates textual descriptions into visually appealing images. | |
| - **Large Model Size**: With 3.47 billion parameters, PixelGen can capture intricate details in generated images. |