Text-to-Image
Diffusers
Safetensors
stable-diffusion-xl
stable-diffusion-xl-diffusers
controlnet
diffusers-training
Instructions to use msy78/model_out with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use msy78/model_out with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("msy78/model_out") pipe = StableDiffusionControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-48000/scheduler.bin from msy78/model_out: direct link, hf CLI and curl.
- Browser
- Download file 563 Bytes
-
https://huggingface.co/msy78/model_out/resolve/main/checkpoint-48000/scheduler.bin
- Command line
-
hf download hf://msy78/model_out/checkpoint-48000/scheduler.bin
-
curl -L -o scheduler.bin https://huggingface.co/msy78/model_out/resolve/main/checkpoint-48000/scheduler.bin
563 Bytes
- Xet hash:
- f9ff7c9fe8c93a141eee37c37f056aad39c042b0663574ea6de9dccd9bb02d44
- Size of remote file:
- 563 Bytes
- SHA256:
- 2f1e7689b89611abde800262c763cf9b096ef3350cc770c20d695aadc350ef44
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.