Instructions to use clem/friedeberg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use clem/friedeberg with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("clem/friedeberg", 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
metadata
tags:
- autotrain
- stable-diffusion
- text-to-image
datasets:
- clem/autotrain-data-friedeberg-0OIYU5UZXE
co2_eq_emissions:
emissions: 23.966933198902527
Model Trained Using AutoTrain
- Problem type: Dreambooth
- Model ID: 2603979056
- CO2 Emissions (in grams): 23.9669