--- 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 - text: ' ' output: url: >- 4560739.jpeg --- # Doctor Diffusion's Xray XL LoRA ## 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](/DoctorDiffusion/doctor-diffusion-s-xray-xl-lora/tree/main) them in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py 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`').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)