Instructions to use asvin-kumar/cnh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use asvin-kumar/cnh 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("asvin-kumar/cnh") 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 checkpoint-7000/pytorch_model.bin from asvin-kumar/cnh: direct link, hf CLI and curl.
- Browser
- Download file 3.29 MB
-
https://huggingface.co/asvin-kumar/cnh/resolve/main/checkpoint-7000/pytorch_model.bin
- Command line
-
hf download hf://asvin-kumar/cnh/checkpoint-7000/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/asvin-kumar/cnh/resolve/main/checkpoint-7000/pytorch_model.bin
3.29 MB
- Xet hash:
- 5aae2305a6eff50fcd196d9acaf9c03a833ce86dbe6550a551e332e32f0c2f07
- Size of remote file:
- 3.29 MB
- SHA256:
- 9aa11bac67f99741008910dd8bd3327a98c99326d075a8d6a05a4542648cb57b
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