svjack/GenshinImpact_XL_Base
This model is derived from CivitAI.
Acknowledgments
Special thanks to mobeimunan for their contributions to the development of this model.
Supported Characters
The model currently supports the following 73 characters from Genshin Impact:
name_dict = {
'旅行者女': 'lumine',
'旅行者男': 'aether',
'派蒙': 'PAIMON',
'迪奥娜': 'DIONA',
'菲米尼': 'FREMINET',
'甘雨': 'GANYU',
'凯亚': 'KAEYA',
'莱依拉': 'LAYLA',
'罗莎莉亚': 'ROSARIA',
'七七': 'QIQI',
'申鹤': 'SHENHE',
'神里绫华': 'KAMISATO AYAKA',
'优菈': 'EULA',
'重云': 'CHONGYUN',
'夏洛蒂': 'charlotte',
'莱欧斯利': 'WRIOTHESLEY',
'艾尔海森': 'ALHAITHAM',
'柯莱': 'COLLEI',
'纳西妲': 'NAHIDA',
'绮良良': 'KIRARA',
'提纳里': 'TIGHNARI',
'瑶瑶': 'YAOYAO',
'珐露珊': 'FARUZAN',
'枫原万叶': 'KAEDEHARA KAZUHA',
'琳妮特': 'LYNETTE',
'流浪者 散兵': 'scaramouche',
'鹿野院平藏': 'SHIKANOIN HEIZOU',
'琴': 'JEAN',
'砂糖': 'SUCROSE',
'温迪': 'VENTI',
'魈': 'XIAO',
'早柚': 'SAYU',
'安柏': 'AMBER',
'班尼特': 'BENNETT',
'迪卢克': 'DILUC',
'迪西娅': 'DEHYA',
'胡桃': 'HU TAO',
'可莉': 'KLEE',
'林尼': 'LYNEY',
'托马': 'THOMA',
'香菱': 'XIANG LING',
'宵宫': 'YOIMIYA',
'辛焱': 'XINYAN',
'烟绯': 'YANFEI',
'八重神子': 'YAE MIKO',
'北斗': 'BEIDOU',
'菲谢尔': 'FISCHL',
'九条裟罗': 'KUJO SARA',
'久岐忍': 'KUKI SHINOBU',
'刻晴': 'KEQING',
'雷电将军': 'RAIDEN SHOGUN',
'雷泽': 'RAZOR',
'丽莎': 'LISA',
'赛诺': 'CYNO',
'芙宁娜': 'FURINA',
'芭芭拉': 'BARBARA',
'公子 达达利亚': 'TARTAGLIA',
'坎蒂丝': 'CANDACE',
'莫娜': 'MONA',
'妮露': 'NILOU',
'珊瑚宫心海': 'SANGONOMIYA KOKOMI',
'神里绫人': 'KAMISATO AYATO',
'行秋': 'XINGQIU',
'夜兰': 'YELAN',
'那维莱特': 'NEUVILLETTE',
'娜维娅': 'NAVIA',
'阿贝多': 'ALBEDO',
'荒泷一斗': 'ARATAKI ITTO',
'凝光': 'NING GUANG',
'诺艾尔': 'NOELLE',
'五郎': 'GOROU',
'云堇': 'YUN JIN',
'钟离': 'ZHONGLI'
}
Installation
To use this model, you need to install the following dependencies:
pip install -U diffusers transformers sentencepiece peft controlnet-aux
Example Usage
Generating an Image of Zhongli
Here's an example of how to generate an image of Zhongli using this model:
from diffusers import StableDiffusionXLPipeline
import torch
pipeline = StableDiffusionXLPipeline.from_pretrained(
"svjack/GenshinImpact_XL_Base",
torch_dtype=torch.float16
).to("cuda")
prompt = "solo,ZHONGLI\(genshin impact\),1boy,portrait,upper_body,highres,"
negative_prompt = "nsfw,lowres,(bad),text,error,fewer,extra,missing,worst quality,jpeg artifacts,low quality,watermark,unfinished,displeasing,oldest,early,chromatic aberration,signature,extra digits,artistic error,username,scan,[abstract],"
image = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
generator=torch.manual_seed(0),
).images[0]
image
image.save("zhongli_1024x1024.png")
钟离
Using Canny ControlNet to Restore 2D Images from 3D Toy Photos
Here's an example of how to use Canny ControlNet to restore 2D images from 3D toy photos:
Genshin Impact 3D Toys
钟离
派蒙
from diffusers import AutoPipelineForText2Image, ControlNetModel
from diffusers.utils import load_image
import torch
from PIL import Image
from controlnet_aux import CannyDetector
controlnet = ControlNetModel.from_pretrained(
"diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16
)
pipeline = AutoPipelineForText2Image.from_pretrained(
"svjack/GenshinImpact_XL_Base",
controlnet=controlnet,
torch_dtype=torch.float16
).to("cuda")
#pipeline.enable_model_cpu_offload()
canny = CannyDetector()
canny(Image.open("zhongli-cb.jpg")).save("zhongli-cb-canny.jpg")
canny_image = load_image(
"zhongli-cb-canny.jpg"
)
controlnet_conditioning_scale = 0.5
generator = torch.Generator(device="cpu").manual_seed(1)
images = pipeline(
prompt="solo,ZHONGLI\(genshin impact\),1boy,portrait,highres",
controlnet_conditioning_scale=controlnet_conditioning_scale,
image=canny_image,
num_inference_steps=50,
guidance_scale=7.0,
generator=generator,
).images
images[0]
images[0].save("zhongli_trans.png")
canny = CannyDetector()
canny(Image.open("paimon-cb-crop.jpg")).save("paimon-cb-canny.jpg")
canny_image = load_image(
"paimon-cb-canny.jpg"
)
controlnet_conditioning_scale = 0.7
generator = torch.Generator(device="cpu").manual_seed(3)
images = pipeline(
prompt="solo,PAIMON\(genshin impact\),1girl,portrait,highres, bright, shiny, high detail, anime",
controlnet_conditioning_scale=controlnet_conditioning_scale,
image=canny_image,
num_inference_steps=50,
guidance_scale=8.0,
generator=generator,
).images
images[0]
images[0].save("paimon_trans.png")
Creating a Grid Image
You can also create a grid image from a list of PIL Image objects:
from PIL import Image
def create_grid_image(image_list, rows, cols, cell_width, cell_height):
"""
Create a grid image from a list of PIL Image objects.
:param image_list: A list of PIL Image objects
:param rows: Number of rows in the grid
:param cols: Number of columns in the grid
:param cell_width: Width of each cell in the grid
:param cell_height: Height of each cell in the grid
:return: The resulting grid image
"""
total_width = cols * cell_width
total_height = rows * cell_height
grid_image = Image.new('RGB', (total_width, total_height))
for i, img in enumerate(image_list):
row = i // cols
col = i % cols
img = img.resize((cell_width, cell_height))
x_offset = col * cell_width
y_offset = row * cell_height
grid_image.paste(img, (x_offset, y_offset))
return grid_image
create_grid_image([Image.open("zhongli-cb.jpg") ,Image.open("zhongli-cb-canny.jpg"), Image.open("zhongli_trans.png")], 1, 3, 512, 768)
create_grid_image([Image.open("paimon-cb-crop.jpg") ,Image.open("paimon-cb-canny.jpg"), Image.open("paimon_trans.png")], 1, 3, 512, 768)
This will create a grid image showing the original, Canny edge detection, and transformed images side by side.
Below image list in : (Genshin Impact Toy/ Canny Image / Gemshin Impact Restore 2D Image)
钟离
派蒙