Instructions to use Deltadahl/backup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Deltadahl/backup with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Deltadahl/backup", 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
Ctrl+K
- 0-bk-sdm-tiny
- 0-step-model
- 100-CytoData_zoom_0
- 100-CytoData_zoom_1
- 100-CytoData_zoom_2
- 100-CytoData_zoom_3
- 100-CytoData_zoom_4
- 100000-PBC
- 100000-Raabin-WBC_HPC
- 1000000-Bodzas_cont
- 300000-CytoData_to_PBC_1
- 300000-CytoData_to_PBC_2
- 300000-CytoData_to_PBC_3
- 500000-CytoData_anomaly_artifact
- 500000-CytoData_anomaly_erythroblast
- 500000-CytoData_updated
- 500000-CytoData_zoom15-2.2zoom_5e-6_bs5*20
- 500000-CytoData_zoom16-2.2zoom_5e-6_bs5*20
- 500000-CytoData_zoom17-2.2zoom_5e-6_bs6*80
- 500000-CytoData_zoom18-2.2zoom_5e-6_bs6*80_from_230k
- 500000-CytoData_zoom19-2.2zoom_5e-6_bs6*80_from_500k
- EXAMPLE
- efficiency
- efficiency_Bodzas
- feature_extractor
- other
- scheduler
- text_encoder
- tokenizer
- unet
- vae
- 1.52 kB
- 624 Bytes