Instructions to use rj1ALINT/day-time with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rj1ALINT/day-time with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("rj1ALINT/day-time", 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
day_time on Stable Diffusion via Dreambooth
model by rj1ALINT
This your the Stable Diffusion model fine-tuned the day_time concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the instance_prompt: of a car driving at day time
You can also train your own concepts and upload them to the library by using this notebook.
And you can run your new concept via diffusers: Colab Notebook for Inference, Spaces with the Public Concepts loaded
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