Instructions to use Taimoor-R/model_out with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Taimoor-R/model_out with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("Taimoor-R/model_out") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-3000/optimizer.bin from Taimoor-R/model_out: direct link, hf CLI and curl.
- Browser
- Download file 2.89 GB
-
https://huggingface.co/Taimoor-R/model_out/resolve/main/checkpoint-3000/optimizer.bin
- Command line
-
hf download hf://Taimoor-R/model_out/checkpoint-3000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/Taimoor-R/model_out/resolve/main/checkpoint-3000/optimizer.bin
2.89 GB
- Xet hash:
- 63761cce4ecf06bbeb16aaf9e414dd735ad3da7ceac8acd0493d40aa9acec906
- Size of remote file:
- 2.89 GB
- SHA256:
- 1c2b8b39738b5cb70dd1d718317a94207917c17702dbfd01b38427ac0afde076
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.