Instructions to use ArtbyArbi/picbixex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ArtbyArbi/picbixex with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ArtbyArbi/picbixex", 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
- Draw Things
- DiffusionBee
PicBixex on Stable Diffusion via Dreambooth trained on the fast-DreamBooth.ipynb by TheLastBen notebook
model by ArbiCreatesArt
This your the Stable Diffusion model fine-tuned the PicBixex concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the instance_prompt(s): PicBixex
You can also train your own concepts and upload them to the library by using the fast-DremaBooth.ipynb by TheLastBen.
And you can run your new concept via diffusers: Colab Notebook for Inference, Spaces with the Public Concepts loaded
Here are the images used for training this concept:
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