language:
- en
thumbnail: images/trending on artstation_17_3.0.png
widget:
- text: trending on artstation
output:
url: images/trending on artstation_17_3.0.png
- text: trending on artstation
output:
url: images/trending on artstation_19_3.0.png
- text: trending on artstation
output:
url: images/trending on artstation_20_3.0.png
- text: trending on artstation
output:
url: images/trending on artstation_21_3.0.png
- text: trending on artstation
output:
url: images/trending on artstation_22_3.0.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
instance_prompt: trending on artstation
base_model: stabilityai/stable-diffusion-xl-base-1.0
ntcai.xyz slider - trending on artstation (SDXL LoRA)
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See more at https://sliders.ntcai.xyz/sliders/app/loras/91e360ed-5283-46d2-8b3f-78274e0dbb79
Download
Weights for this model are available in Safetensors format.
Trigger words
You can apply this LoRA with trigger words for additional effect:
trending on artstation
Use 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.trending-on-artstation', weight_name='trending on artstation.safetensors', adapter_name="trending on artstation")
# Activate the LoRA
pipe.set_adapters(["trending on artstation"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, trending on artstation"
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')
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