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
| 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 | |
|  | |