Text-to-Image
Diffusers
TensorBoard
Safetensors
stable-diffusion
stable-diffusion-diffusers
controlnet
Instructions to use jxAIbot/init_train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jxAIbot/init_train with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("jxAIbot/init_train") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from jxAIbot/init_train: direct link, hf CLI and curl.
- Browser
- Download file 522 Bytes
-
https://huggingface.co/jxAIbot/init_train/resolve/main/README.md
- Command line
-
hf download hf://jxAIbot/init_train/README.md
-
curl -L -o README.md https://huggingface.co/jxAIbot/init_train/resolve/main/README.md
522 Bytes
metadata
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
inference: true
controlnet-jxAIbot/init_train
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
You can find some example images below.
prompt: red circle with blue background
prompt: cyan circle with brown floral background
