ntc-ai/SDXL-LoRA-slider.holding-a-gun-at-the-camera
language:
- en thumbnail: "images/holding a gun at the camera173.0.png" widget:
- text: holding a gun at the camera output: url: images/holding a gun at the camera173.0.png
- text: holding a gun at the camera output: url: images/holding a gun at the camera193.0.png
- text: holding a gun at the camera output: url: images/holding a gun at the camera203.0.png
- text: holding a gun at the camera output: url: images/holding a gun at the camera213.0.png
- text: holding a gun at the camera output: url: images/holding a gun at the camera223.0.png tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers license: "mit" inference: false instanceprompt: "holding a gun at the camera" basemodel: "stabilityai/stable-diffusion-xl-base-1.0" ---
ntcai.xyz slider - holding a gun at the camera (SDXL LoRA)
See more at https://sliders.ntcai.xyz/sliders/app/loras/cdfc2e8f-7de2-41f7-94b1-4513984200fd
Download
Weights for this model are available in Safetensors format.
Trigger words
You can apply this LoRA with trigger words for additional effect:
holding a gun at the cameraUse in diffusers
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.holding-a-gun-at-the-camera', weight_name='holding a gun at the camera.safetensors', adapter_name="holding a gun at the camera")
# Activate the LoRA
pipe.set_adapters(["holding a gun at the camera"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, holding a gun at the camera"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')Support the Patreon
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