Image-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
controlnet
jax-diffusers-event
Instructions to use SAMControlNet/sd-controlnet-sam-seg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SAMControlNet/sd-controlnet-sam-seg with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("SAMControlNet/sd-controlnet-sam-seg") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
metadata
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- image-to-image
- diffusers
- controlnet
- jax-diffusers-event
inference: true
controlnet- SAMControlNet/sd-controlnet-sam-seg
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images in the following.
prompt: a dolphin jumping out of the water
prompt: a antelope standing in a field with birds
