Instructions to use williamberman/controlnet-model-3-12-learning-rates with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use williamberman/controlnet-model-3-12-learning-rates with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("williamberman/controlnet-model-3-12-learning-rates", 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
Download checkpoint-2000/optimizer.bin from williamberman/controlnet-model-3-12-learning-rates: direct link, hf CLI and curl.
- Browser
- Download file 2.89 GB
-
https://huggingface.co/williamberman/controlnet-model-3-12-learning-rates/resolve/main/checkpoint-2000/optimizer.bin
- Command line
-
hf download hf://williamberman/controlnet-model-3-12-learning-rates/checkpoint-2000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/williamberman/controlnet-model-3-12-learning-rates/resolve/main/checkpoint-2000/optimizer.bin
2.89 GB
- Xet hash:
- 0558193e3bcff3d34f9c66d5c5734e02e3d3cf30b9f4a51c3c6ac7f2cdc85647
- Size of remote file:
- 2.89 GB
- SHA256:
- 33c7077c616d6a343a4e3111b55666f42e626376d3e8e32ec04db7f166ac94e3
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