Text-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
controlnet
diffusers-training
Instructions to use manhattan23/output_train_colormap_coconut with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use manhattan23/output_train_colormap_coconut with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("manhattan23/output_train_colormap_coconut") pipe = StableDiffusionControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-2-1-base", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
controlnet-manhattan23/output_train_colormap_coconut
These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning. You can find some example images below.
prompt: A beautiful woman taking a picture with her smart phone.,People underneath an arched bridge near the water.
prompt: A young man bending next to a toilet.,A man is kneeling and holding on to a toilet.
prompt: Two people are sitting on chairs talking on at a corner.,Two men sitting on the street in front of 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]
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Model tree for manhattan23/output_train_colormap_coconut
Base model
stabilityai/stable-diffusion-2-1-base