Text-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
controlnet
diffusers-training
Instructions to use kmaksatk/experiments with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kmaksatk/experiments with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("kmaksatk/experiments") pipe = StableDiffusionControlNetPipeline.from_pretrained( "SG161222/Realistic_Vision_V6.0_B1_noVAE", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
controlnet-kmaksatk/experiments
These are controlnet weights trained on SG161222/Realistic_Vision_V6.0_B1_noVAE with new type of conditioning. You can find some example images below.
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]
- Downloads last month
- 1
Model tree for kmaksatk/experiments
Base model
SG161222/Realistic_Vision_V6.0_B1_noVAE
