Instructions to use ZihengWang/ControlGlaucoma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ZihengWang/ControlGlaucoma with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("ZihengWang/ControlGlaucoma") pipe = StableDiffusionControlNetPipeline.from_pretrained( "fill-in-base-model", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
tags:
- controlnet
- stable-diffusion
- fundus
- glaucoma
- medical-imaging
ControlGlaucoma — Pre-trained Weights
Pre-trained weights for ControlGlaucoma — vCDR-controllable ControlNet on Stable Diffusion 2.1 for SLO fundus generation.
| Folder | Description |
|---|---|
controlnet_vpred/ |
Main weight. ControlNet on SD 2.1 (v-prediction). |
controlnet_eps/ |
ε-prediction variant (compatible with SD 2.1-base, 512 px). |
segman_b/ |
SegMAN-B segmentation model used as the reward signal during training. |
huggingface-cli download ZihengWang/ControlGlaucoma \
--local-dir ./checkpoints \
--local-dir-use-symlinks False
See the GitHub repository for code and training / inference commands.
License
Apache-2.0.