Text-to-Image
Diffusers
TensorBoard
Safetensors
stable-diffusion
stable-diffusion-diffusers
controlnet
diffusers-training
Instructions to use shunki555/model_out2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use shunki555/model_out2 with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("shunki555/model_out2") pipe = StableDiffusionControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-2-1-base", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from shunki555/model_out2: direct link, hf CLI and curl.
- Browser
- Download file 1.25 kB
-
https://huggingface.co/shunki555/model_out2/resolve/main/README.md
- Command line
-
hf download hf://shunki555/model_out2/README.md
-
curl -L -o README.md https://huggingface.co/shunki555/model_out2/resolve/main/README.md
1.25 kB
metadata
base_model: stabilityai/stable-diffusion-2-1-base
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
controlnet-shunki555/model_out2
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 lymph node without cancer metastasis, lymph, center of the image,
prompt: A lymph node without cancer metastasis, lymph, center of the image,
prompt: A lymph node without cancer metastasis, lymph, center of the image,

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]