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metadata
language:
  - en
thumbnail: images/evaluate/cinematic lighting.../cinematic lighting_17_3.0.png
widget:
  - text: cinematic lighting
    output:
      url: images/cinematic lighting_17_3.0.png
  - text: cinematic lighting
    output:
      url: images/cinematic lighting_19_3.0.png
  - text: cinematic lighting
    output:
      url: images/cinematic lighting_20_3.0.png
  - text: cinematic lighting
    output:
      url: images/cinematic lighting_21_3.0.png
  - text: cinematic lighting
    output:
      url: images/cinematic lighting_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: cinematic lighting
base_model: stabilityai/stable-diffusion-xl-base-1.0

ntcai.xyz slider - cinematic lighting (SDXL LoRA)

Strength: -3 Strength: 0 Strength: 3

Download

Weights for this model are available in Safetensors format.

Trigger words

You can apply this LoRA with trigger words for additional effect:

cinematic lighting

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.cinematic-lighting', weight_name='cinematic lighting.safetensors', adapter_name="cinematic lighting")

# Activate the LoRA
pipe.set_adapters(["cinematic lighting"], adapter_weights=[2.0])

prompt = "medieval rich kingpin sitting in a tavern, cinematic lighting"
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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Other resources

  • CivitAI - Follow ntc on Civit for even more LoRAs
  • ntcai.xyz - See ntcai.xyz to find more articles and LoRAs