How to use from the
Use from the
Diffusers library
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline

# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("P-RAJIV/cxr_stable_diffusion_sdxl_lora")

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]

🩻 CXR SDXL LoRA

πŸ“Œ Overview

This repository provides LoRA weights for SDXL fine-tuned on chest X-ray data.

Base model required: stabilityai/stable-diffusion-xl-base-1.0.


πŸš€ Usage

import torch
from diffusers import StableDiffusionXLPipeline

pipe = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16
).to("cuda")

pipe.load_lora_weights("P-RAJIV/cxr_stable_diffusion_sdxl_lora")

prompt = "High resolution chest X-ray with lung opacity"

image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5).images[0]
image.save("output.png")

πŸ§ͺ Example Prompts
"Normal chest X-ray"
"Chest X-ray showing cardiomegaly"
"Lung opacity in right lower lobe"
"Severe pneumonia chest radiograph"

⚠️ Limitations
Generated images are synthetic and not for clinical use
May produce anatomically inconsistent outputs
Performance depends heavily on prompt quality

πŸ“š Training Details
Base Model: Stable Diffusion v1.5
Domain: Chest X-ray imaging
Fine-tuning: Text-to-image diffusion training

🧠 Intended Use
Research in medical imaging
Data augmentation
Diffusion model experimentation

❗ Disclaimer
This model is not intended for medical diagnosis or clinical decision-making.
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