Text-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
controlnet
diffusers-training
Instructions to use Amitz244/output_dir_controlnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Amitz244/output_dir_controlnet with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("Amitz244/output_dir_controlnet") pipe = StableDiffusionControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-2-1-base", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-5000/optimizer.bin from Amitz244/output_dir_controlnet: direct link, hf CLI and curl.
- Browser
- Download file 2.91 GB
-
https://huggingface.co/Amitz244/output_dir_controlnet/resolve/main/checkpoint-5000/optimizer.bin
- Command line
-
hf download hf://Amitz244/output_dir_controlnet/checkpoint-5000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/Amitz244/output_dir_controlnet/resolve/main/checkpoint-5000/optimizer.bin
2.91 GB
- Xet hash:
- 7366e655bf763788af450e5533c7c5309ca73fdcb4962c81f7429c6561d4bbe1
- Size of remote file:
- 2.91 GB
- SHA256:
- 641b7330c769bb3e3800b16d4f2cfdb0eb1f6c8607b2b388b6958b302cb78342
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.