Instructions to use Jason248/raw_controlnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jason248/raw_controlnet with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Jason248/raw_controlnet", 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-1500/optimizer.bin from Jason248/raw_controlnet: direct link, hf CLI and curl.
- Browser
- Download file 2.89 GB
-
https://huggingface.co/Jason248/raw_controlnet/resolve/main/checkpoint-1500/optimizer.bin
- Command line
-
hf download hf://Jason248/raw_controlnet/checkpoint-1500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/Jason248/raw_controlnet/resolve/main/checkpoint-1500/optimizer.bin
2.89 GB
- Xet hash:
- 60c6d135e6c59c365915be37ceabde49f3b5be7fe33ab79c9ea799a72a8c63ac
- Size of remote file:
- 2.89 GB
- SHA256:
- 69ae4dad33e6cce3450797c6078cc1d32dd954b8b51ff082db75297a46bc78a5
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