Instructions to use BlueTriangles/SDXL_Koihime_Nami with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BlueTriangles/SDXL_Koihime_Nami with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("OnomaAIResearch/Illustrious-XL-v2.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("BlueTriangles/SDXL_Koihime_Nami") prompt = "\u0000\u0000<\u0000l\u0000o\u0000r\u0000a\u0000:\u0000S\u0000D\u0000X\u0000L\u0000_\u0000K\u0000o\u0000i\u0000h\u0000i\u0000m\u0000e\u0000_\u0000N\u0000a\u0000m\u0000i\u0000_\u0000v\u00001\u0000:\u00000\u0000.\u00007\u0000>\u0000 \u0000s\u0000d\u0000x\u0000l\u0000-\u0000k\u0000o\u0000i\u0000h\u0000i\u0000m\u0000e\u0000-\u0000n\u0000a\u0000m\u0000i\u0000,\u0000 \u0000b\u0000l\u0000a\u0000c\u0000k\u0000 \u0000h\u0000a\u0000i\u0000r\u0000,\u0000 \u0000l\u0000o\u0000n\u0000g\u0000 \u0000h\u0000a\u0000i\u0000r\u0000,\u0000 \u0000b\u0000l\u0000u\u0000e\u0000 \u0000e\u0000y\u0000e\u0000s\u0000,\u0000 \u0000m\u0000a\u0000t\u0000u\u0000r\u0000e\u0000 \u0000f\u0000e\u0000m\u0000a\u0000l\u0000e\u0000)\u0000,\u0000 \u0000(\u0000g\u0000e\u0000n\u0000e\u0000r\u0000a\u0000l\u0000,\u0000 \u00001\u0000g\u0000i\u0000r\u0000l\u0000,\u0000 \u0000s\u0000o\u0000l\u0000o\u0000,\u0000 \u0000a\u0000l\u0000o\u0000n\u0000e\u0000,\u0000 \u0000c\u0000o\u0000w\u0000b\u0000o\u0000y\u0000 \u0000s\u0000h\u0000o\u0000t\u0000)\u0000,\u0000 \u0000<\u0000l\u0000o\u0000r\u0000a\u0000:\u0000s\u0000h\u0000o\u0000u\u0000l\u0000d\u0000e\u0000r\u0000_\u0000t\u0000o\u0000_\u0000c\u0000h\u0000e\u0000e\u0000k\u0000-\u00000\u00000\u00000\u00000\u00000\u00005\u0000:\u00000\u0000.\u00007\u0000>\u0000 \u0000(\u0000s\u0000h\u0000o\u0000u\u0000l\u0000d\u0000e\u0000r\u0000 \u0000t\u0000o\u0000 \u0000c\u0000h\u0000e\u0000e\u0000k\u0000)\u0000,\u0000 \u0000(\u0000l\u0000a\u0000u\u0000g\u0000h\u0000i\u0000n\u0000g\u0000)\u0000,\u0000 \u0000(\u0000y\u0000u\u0000k\u0000a\u0000t\u0000a\u0000,\u0000 \u0000w\u0000h\u0000i\u0000t\u0000e\u0000 \u0000c\u0000l\u0000o\u0000t\u0000h\u0000e\u0000s\u0000,\u0000 \u0000r\u0000e\u0000d\u0000 \u0000o\u0000b\u0000i\u0000,\u0000 \u0000g\u0000e\u0000t\u0000a\u0000)\u0000,\u0000 \u0000(\u0000s\u0000i\u0000m\u0000p\u0000l\u0000e\u0000 \u0000b\u0000a\u0000c\u0000k\u0000g\u0000r\u0000o\u0000u\u0000n\u0000d\u0000,\u0000 \u0000b\u0000r\u0000o\u0000w\u0000n\u0000 \u0000b\u0000a\u0000c\u0000k\u0000g\u0000r\u0000o\u0000u\u0000n\u0000d\u0000}\u0000:\u00001\u0000.\u00005\u0000)\u0000,\u0000 \u0000a\u0000n\u0000i\u0000m\u0000e\u0000 \u0000c\u0000o\u0000l\u0000o\u0000r\u0000i\u0000n\u0000g\u0000,\u0000 \u0000a\u0000n\u0000i\u0000m\u0000e\u0000 \u0000s\u0000c\u0000r\u0000e\u0000e\u0000n\u0000c\u0000a\u0000p\u0000,\u0000 \u0000a\u0000n\u0000i\u0000m\u0000e\u0000 \u0000s\u0000t\u0000y\u0000l\u0000e\u0000,\u0000 \u0000s\u0000o\u0000u\u0000r\u0000c\u0000e\u0000 \u0000a\u0000n\u0000i\u0000m\u0000e\u0000,\u0000 \u0000f\u0000l\u0000a\u0000t\u0000 \u0000c\u0000o\u0000l\u0000o\u0000r\u0000,\u0000 \u0000m\u0000a\u0000s\u0000t\u0000e\u0000r\u0000p\u0000i\u0000e\u0000c\u0000e\u0000,\u0000 \u0000b\u0000e\u0000s\u0000t\u0000 \u0000q\u0000u\u0000a\u0000l\u0000i\u0000t\u0000y\u0000,\u0000 \u0000a\u0000b\u0000s\u0000u\u0000r\u0000d\u0000r\u0000e\u0000s\u0000,\u0000 \u0000h\u0000i\u0000g\u0000h\u0000r\u0000e\u0000s\u0000,\u0000 \u0000v\u0000i\u0000b\u0000r\u0000a\u0000n\u0000t\u0000 \u0000c\u0000o\u0000l\u0000o\u0000r\u0000s\u0000,\u0000 \u0000c\u0000o\u0000l\u0000o\u0000r\u0000f\u0000u\u0000l\u0000,\u0000 \u0000c\u0000l\u0000e\u0000a\u0000r\u0000,\u0000 \u0000f\u0000i\u0000n\u0000e\u0000l\u0000y\u0000 \u0000d\u0000e\u0000t\u0000a\u0000i\u0000l\u0000e\u0000d\u0000)\u0000 \u0000<\u0000l\u0000o\u0000r\u0000a\u0000:\u0000d\u0000m\u0000d\u00002\u0000_\u0000s\u0000d\u0000x\u0000l\u0000_\u00004\u0000s\u0000t\u0000e\u0000p\u0000_\u0000l\u0000o\u0000r\u0000a\u0000:\u00000\u0000.\u00007\u0000>\u0000" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Nami / Mystic Princess(那水 in 恋姫 K・O・I・H・I・M・E)

- Prompt
- <lora:SDXL_Koihime_Nami_v1:0.7> sdxl-koihime-nami, black hair, long hair, blue eyes, mature female), (general, 1girl, solo, alone, cowboy shot), <lora:shoulder_to_cheek-000005:0.7> (shoulder to cheek), (laughing), (yukata, white clothes, red obi, geta), (simple background, brown background}:1.5), anime coloring, anime screencap, anime style, source anime, flat color, masterpiece, best quality, absurdres, highres, vibrant colors, colorful, clear, finely detailed) <lora:dmd2_sdxl_4step_lora:0.7>

- Prompt
- <lora:SDXL_Koihime_Nami_v1:0.7> sdxl-koihime-nami, black hair, long hair, blue eyes, mature female), (general, 1girl, solo, alone, cowboy shot), <lora:shoulder_to_cheek-000005:0.7> (shoulder to cheek), (innocent), (yukata, white clothes, red obi, geta), (simple background, green background}:1.5), anime coloring, anime screencap, anime style, source anime, flat color, masterpiece, best quality, absurdres, highres, vibrant colors, colorful, clear, finely detailed) <lora:dmd2_sdxl_4step_lora:0.7>

- Prompt
- <lora:SDXL_Koihime_Nami_v1:0.7> sdxl-koihime-nami, black hair, long hair, blue eyes, mature female), (general, 1girl, solo, alone, cowboy shot), <lora:Exaggerated_Dynamic_Fashion_Pose-000010:0.7> (exagg3rat3dp0s3, exaggerated pose, dynamic pose), (innocent), (yukata, white clothes, red obi, geta), (simple background, yellow background}:1.5), anime coloring, anime screencap, anime style, source anime, flat color, masterpiece, best quality, absurdres, highres, vibrant colors, colorful, clear, finely detailed) <lora:dmd2_sdxl_4step_lora:0.7>
Model description
sdxl-koihime-nami, black hair, long hair, blue eyes, mature female, yukata, white clothes, red obi, geta
Trigger words
You should use sdxl-koihime-nami to trigger the image generation.
Download model
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