Instructions to use abhinavr/path-to-save-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use abhinavr/path-to-save-model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("abhinavr/path-to-save-model") prompt = "a photo of xyzabcd guy hd, 4k, ultra clear" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Ctrl+K
- checkpoint-100
- checkpoint-1000
- checkpoint-1100
- checkpoint-1200
- checkpoint-1300
- checkpoint-1400
- checkpoint-1500
- checkpoint-1600
- checkpoint-1700
- checkpoint-1800
- checkpoint-1900
- checkpoint-200
- checkpoint-2000
- checkpoint-2100
- checkpoint-2200
- checkpoint-2300
- checkpoint-2400
- checkpoint-2500
- checkpoint-2600
- checkpoint-2700
- checkpoint-2800
- checkpoint-2900
- checkpoint-300
- checkpoint-3000
- checkpoint-400
- checkpoint-500
- checkpoint-600
- checkpoint-700
- checkpoint-800
- checkpoint-900
- 1.52 kB
- 689 Bytes
- 477 kB
- 562 kB
- 443 kB
- 540 kB
- 3.23 MB xet