Instructions to use alperiox/cctv-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use alperiox/cctv-lora 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("alperiox/cctv-lora") 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
Download pytorch_lora_weights.safetensors from alperiox/cctv-lora: direct link, hf CLI and curl.
- Browser
- Download file 38.3 MB
-
https://huggingface.co/alperiox/cctv-lora/resolve/main/pytorch_lora_weights.safetensors
- Command line
-
hf download hf://alperiox/cctv-lora/pytorch_lora_weights.safetensors
-
curl -L -o pytorch_lora_weights.safetensors https://huggingface.co/alperiox/cctv-lora/resolve/main/pytorch_lora_weights.safetensors
38.3 MB
- Xet hash:
- dbdbea5ddae0dc8a1f900d7cef4c249196da784e2d5c13f17bc78498401032c7
- Size of remote file:
- 38.3 MB
- SHA256:
- 6557ec24b2cd7cebab4af6eaf716cc19f60627d05917ece1d5281d8a47a7a900
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