README.md

---
license: openrail
language:
- en
base_model:
- CompVis/stable-diffusion-v1-4
pipeline_tag: text-to-image
library_name: diffusers
tags:
- medical
- X-ray
- Diffusion
- Generation
- Text-to-image
- stable-diffusion
- lora
- fine_tune

widget:
  - text: >-
      Hey doc, I've been feeling really out of breath lately, 
      especially when I'm walking up a flight of stairs or doing some light exercise. 
      It's like my chest gets tight and I can't catch my breath. 
      I've also been coughing up some stuff that's not quite right, it's been a few weeks now.
      And I've noticed a bit of weight loss, I'm not sure if that's related but it's been on my mind.
      I've been to a few doctors already, but they haven't been able to figure out what's going on. 
      I'm hoping you can help.
    output:
      url: example.png

---

#  Symptom-to-Medical-Image Generator

This project introduces a **text-to-image diffusion model** fine-tuned using **LoRA (Low-Rank Adaptation)** on top of [`CompVis/stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) for the task of **medical image generation**. The model generates **X-ray**, **CT**, or **MRI** scans based on **natural language descriptions of patient symptoms**, offering a novel way to visualize potential diagnostic outcomes.

---

##  What Is This Model?

This is a **domain-adapted diffusion model** tailored to generate realistic medical scans conditioned on symptom prompts. The model was fine-tuned using LoRA, which allowed for:

* Efficient training without modifying the original model weights.
* Adaptation to a smaller, highly-specialized medical dataset.
* Retention of high-quality generative capabilities from the base model.

### Key Features

* **Symptom-to-scan generation**: Input symptoms in plain English and receive a plausible X-ray, CT, or MRI image.
* **Multi-modality support**: Generate different types of scans (e.g., chest X-rays, brain MRIs) depending on the prompt context.
* **High realism**: Outputs are visually realistic and follow anatomical structure, trained using real medical datasets.

---

##  When Can You Use This Model?

###  Use Cases

| Application Area                | Description                                                                                                                              |
| ------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------- |
|  **Medical Research**         | Generate datasets for hypothesis testing or model training without using real patient data.                                              |
|  **Education & Training**     | Teach students about correlations between symptoms and imaging in an interactive way.                                                    |
|  **AI-Aided Prototyping**     | Test downstream diagnostic pipelines on synthetic but realistic image data.                                                              |
|  **Data Augmentation**       | Enrich datasets for training classification/segmentation models.                                                                         |
|  **Prompt-Based Exploration** | Investigate how changes in symptoms affect image generation (e.g., how “fever + cough” differs from “chest pain + shortness of breath”). |

### Not for Use In:

* Real-world **clinical diagnosis or decision-making**
* Generating scans for real patients or influencing treatment
* Bypassing ethical or regulatory controls in medical AI

---

## Example Usage

### Input Prompt:

> "I've been feeling really out of breath lately, especially when I'm walking up a flight of stairs or doing some light exercise. It's like my chest gets tight and I can't catch my breath. "

### Output:

<img src="example.png" alt="Generated Chest X-ray" width="512"/>

> The model generates a chest X-ray image that corresponds to symptoms of a potential pulmonary issue.

---

##  Under the Hood

* **Base Model**: `CompVis/stable-diffusion-v1-4`
* **Fine-tuning Method**: LoRA (efficient, parameter-light adaptation)
* **Dataset**: Custom dataset of symptom-to-image pairs, curated for medical imaging consistency
* **Framework**: PyTorch + 🤗 Diffusers + Hugging Face Spaces

---

##  Ethical & Legal Disclaimer

This model is strictly intended for **research and educational** use. It is **not a substitute for professional medical judgment**. Use of synthetic medical images should follow all local regulatory and ethical guidelines.

---