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 Settings
- Draw Things
- DiffusionBee
| 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 |