metadata

license: other
license_name: bespoke-lora-trained-license
license_link: >-
  https://multimodal.art/civitai-licenses?allowNoCredit=True&allowCommercialUse=Rent&allowDerivatives=True&allowDifferentLicense=False
tags:
  - text-to-image
  - stable-diffusion
  - lora
  - diffusers
  - template:sd-lora
  - skull
  - x-ray
  - style
  - bones
  - xray
  - medical
  - x ray
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: xray
widget:
  - text: xray crow in a tree
    output:
      url: 4560006.jpeg
  - text: xray pirate ship
    output:
      url: 4560092.jpeg
  - text: xray cat in a racecar
    output:
      url: 4560036.jpeg
  - text: xray daft punk
    output:
      url: 4560046.jpeg
  - text: xray eye of providence enclosed in a triangle
    output:
      url: 4560051.jpeg
  - text: xray mountain sunset
    output:
      url: 4560056.jpeg
  - text: xray mushroom cloud nuke explosion
    output:
      url: 4560063.jpeg
  - text: xray rocket ship flying to mars
    output:
      url: 4560112.jpeg
  - text: xray busy Tokyo city
    output:
      url: 4560026.jpeg

Doctor Diffusion's Xray XL LoRA

Prompt
xray eye of providence enclosed in a triangle

Prompt
xray mushroom cloud nuke explosion

Model description

Use "xray" in the front of your prompt.

Experimental: This is the first version of this LoRA and it may not always work as desired. If you get poor results please share in the comments so I can improve v2.

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The history of x-rays:
X-ray scans were first developed by Wilhelm Conrad Röntgen in 1895 and are widely used in medical diagnostics. They work by passing electromagnetic radiation through the body, capturing an image of internal structures like bones and organs based on their density. Over time, advancements have led to methods such as CT scans and MRI for more detailed imaging without ionizing radiation.

Trigger words

You should use xray to trigger the image generation.

Download model

Weights for this model are available in Safetensors format.

Download them in the Files & versions tab.

Use it with the 🧨 diffusers library

from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained('runwayml/stable-diffusion-v1-5', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('DoctorDiffusion/doctor-diffusion-s-xray-xl-lora', weight_name='DD-xray-v1.safetensors')
image = pipeline('xray busy Tokyo city').images[0]

For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers