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ntc-ai/SDXL-LoRA-slider.color-grading
SDXL-LoRA-slider.color-grading is a text-to-image model from ntc-ai. Use it when you need an image from a text prompt. It is set up for diffusers. The card lists the license as mit.
Weights for this model are available in Safetensors format.
Downloads · 30 days
64
6% of all-time downloads
All-time downloads
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From the Hugging Face model README
| Strength: -3 | Strength: 0 | Strength: 3 |
|---|---|---|
| <img src="images/color grading_17_-3.0.png" width=256 height=256 /> | <img src="images/color grading_17_0.0.png" width=256 height=256 /> | <img src="images/color grading_17_3.0.png" width=256 height=256 /> |
| <img src="images/color grading_19_-3.0.png" width=256 height=256 /> | <img src="images/color grading_19_0.0.png" width=256 height=256 /> | <img src="images/color grading_19_3.0.png" width=256 height=256 /> |
| <img src="images/color grading_20_-3.0.png" width=256 height=256 /> | <img src="images/color grading_20_0.0.png" width=256 height=256 /> | <img src="images/color grading_20_3.0.png" width=256 height=256 /> |
Weights for this model are available in Safetensors format.
You can apply this LoRA with trigger words for additional effect:
color grading
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.color-grading', weight_name='color grading.safetensors', adapter_name="color grading")
# Activate the LoRA
pipe.set_adapters(["color grading"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, color grading"
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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