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
File size: 702 Bytes
f1299e7 793f5b3 2fa7f3d 793f5b3 4110404 793f5b3 be346c7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ---
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. |