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-3000/optimizer.bin from asvin-kumar/cnh: direct link, hf CLI and curl.
- Browser
- Download file 6.6 MB
-
https://huggingface.co/asvin-kumar/cnh/resolve/main/checkpoint-3000/optimizer.bin
- Command line
-
hf download hf://asvin-kumar/cnh/checkpoint-3000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/asvin-kumar/cnh/resolve/main/checkpoint-3000/optimizer.bin
6.6 MB
- Xet hash:
- 5a5f3e593593ccba67fc79230613e58d476b3381dee8523779669042b83fa8db
- Size of remote file:
- 6.6 MB
- SHA256:
- 9d5ee3e8470a03737a9b661af916f74e458e34f871f8e68f1a0a88f005a34eb7
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