Text-to-Image
Diffusers
Safetensors
stable-diffusion-xl
stable-diffusion-xl-diffusers
controlnet
diffusers-training
Instructions to use Nawatix/out_multihmr_normal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Nawatix/out_multihmr_normal with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("Nawatix/out_multihmr_normal") pipe = StableDiffusionControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from Nawatix/out_multihmr_normal: direct link, hf CLI and curl.
- Browser
- Download file 1.37 kB
-
https://huggingface.co/Nawatix/out_multihmr_normal/resolve/main/README.md
- Command line
-
hf download hf://Nawatix/out_multihmr_normal/README.md
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curl -L -o README.md https://huggingface.co/Nawatix/out_multihmr_normal/resolve/main/README.md
1.37 kB
metadata
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
inference: true
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
controlnet-Nawatix/out_multihmr_normal
These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with new type of conditioning. You can find some example images below.
prompt: a woman on a trapeze in the air
prompt: a woman in red hair and white dress sitting on fire
prompt: two dancers in white dress and black shoes on stage
prompt: two men in period clothing stand in front of a table
prompt: two men are walking down a hallway in a building

Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]