首先强烈建议安装个汉化扩展,便于理解参数,不然全是英文看着头疼,当然必须克服英文,因为文生图所用到的关键词全是英文的:(((lll￢ω￢),启动Stable Diffusion(如果不知道是什么,可以看该系类的第一篇)后,找到Extensions,点击Available,然后找到一个插件叫做 sd-webui-bilingual-localization 下载安装它,如下图所示,...
下载此模型可在C站内搜索“Smiling slider LoRA (for anime models)”此lora模型名称找到此模型进行下载使用。 我们首先输入正向提示词“petite, 1girl, solo, white hair,hair intakes, floating hair,head wreath,tiger tooth,galaxy,choker,center frills,torn clothes,embarrassed ,{{{by famous artist}}}, bea...
Diffusion 本意指的是分子从高浓度区域向低浓度区域的转移过程。Stable Diffusion 有两种扩散，前向扩散和...
Many (but not all) models are suitable as your foundational/base model. Pick a model that is good at what you’re trying to do. More model suggestions below. Here are some ideas: For anime/cartoon style, choose aNAI family model: NAI Diffusion, AnythingV3.0, AnythingV4.0, AnythingV4.5,...
Anything V3 is one of the most popular Stable Diffusion anime models, and for good reason. It's a huge improvement over its predecessor, NAI Diffusion (akaNovelAIakaanimefull), and is used to create every major anime model today.
(Anime model): Clip skip 1 or 2, VAE: 'kl-f8-anime2.clowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurrykpt' from Waifu Diffusion (https:/...
NAI / Anything- for anime models Comes with the NAI model by default when you put it into the models folder SD 1.5- for realistic models Download a VAE Followthisquick section of the guide to set up VAEs in the WebUI Make sure to put them instable-diffusion-webui\models\VAE\ ...
from modelscope.pipelines import pipeline # 内嵌风格中的正面提示词定义prompt_dict= { "None": "{prompt}", "Enhance": "breathtaking {prompt} . award-winning, professional, highly detailed", "Anime": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed",...
收录于文集 stablediffusion · 1篇"sd_model_checkpoint": "anyhentai_19.safetensors [011cdb0b18]","sd_vae": "anything-v4.0.vae.pt","lora":"samdoesartsSamYang_offse", ...
An example of training a diffusion model for modeling a 2D swiss roll data. (Image source:Sohl-Dickstein et al., 2015) 虽然我们无法得到逆转后的分布,但是如果知道,是可以通过贝叶斯公式得到为: 过程: 遵循标准高斯密度函数,均值和方差可以参数化如下: ...