Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
textual_inversion
Instructions to use hcarrion/reactive_lymphoid_hyperplasia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hcarrion/reactive_lymphoid_hyperplasia with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base", dtype=torch.bfloat16, device_map="cuda") pipe.load_textual_inversion("hcarrion/reactive_lymphoid_hyperplasia") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
metadata
base_model: stabilityai/stable-diffusion-2-1-base
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable-diffusion
- stable-diffusion-diffusers
- textual_inversion
inference: true
Textual inversion text2image fine-tuning - hcarrion/reactive_lymphoid_hyperplasia
These are textual inversion adaptation weights for stabilityai/stable-diffusion-2-1-base to generate images representing reactive lymphoid hyperplasia.
This model was introduced as part of the paper Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification (MICCAI 2026).
- Repository: GitHub - ControllableGenDDI
- Dataset: Hugging Face - ControllableGenDDI Dataset
Citation
@inproceedings{carrion2026cgddi,
title = {Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification},
author = {Carri{\'o}n, H{\\'e}ctor and Norouzi, Narges},
booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
year = {2026},
publisher = {Springer},
series = {Lecture Notes in Computer Science}
}