Text-to-Image
Diffusers
TensorBoard
Safetensors
stable-diffusion
stable-diffusion-diffusers
controlnet
diffusers-training
Instructions to use justacoderwhocodes/flattener with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use justacoderwhocodes/flattener with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("justacoderwhocodes/flattener") pipe = StableDiffusionControlNetPipeline.from_pretrained( "Yntec/mistoonRuby3", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
controlnet-justacoderwhocodes/flattener
These are controlnet weights trained on Yntec/mistoonRuby3 with new type of conditioning. You can find some example images below.
prompt: toon style, flat colors, clean thick outlines
prompt: toon style, flat colors, clean thick outlines
prompt: toon style, flat colors, clean thick outlines
prompt: toon style, flat colors, clean thick outlines

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
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Model tree for justacoderwhocodes/flattener
Base model
Yntec/mistoonRuby3