language:
- en
thumbnail: images/the doge from dogecoin_17_3.0.png
widget:
- text: the doge from dogecoin
output:
url: images/the doge from dogecoin_17_3.0.png
- text: the doge from dogecoin
output:
url: images/the doge from dogecoin_19_3.0.png
- text: the doge from dogecoin
output:
url: images/the doge from dogecoin_20_3.0.png
- text: the doge from dogecoin
output:
url: images/the doge from dogecoin_21_3.0.png
- text: the doge from dogecoin
output:
url: images/the doge from dogecoin_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: the doge from dogecoin
base_model: stabilityai/stable-diffusion-xl-base-1.0
ntcai.xyz slider - the doge from dogecoin (SDXL LoRA)
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See more at https://sliders.ntcai.xyz/sliders/app/loras/1c0eaa74-a269-44bf-9952-c2cd377992d5
Download
Weights for this model are available in Safetensors format.
Trigger words
You can apply this LoRA with trigger words for additional effect:
the doge from dogecoin
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.the-doge-from-dogecoin', weight_name='the doge from dogecoin.safetensors', adapter_name="the doge from dogecoin")
# Activate the LoRA
pipe.set_adapters(["the doge from dogecoin"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, the doge from dogecoin"
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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