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# ComfyUI_StoryDiffusion
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本节点主要方法来源于[StoryDiffusion](https://github.com/HVision-NKU/StoryDiffusion) ,部分内容也来源于[MS-Diffusion](https://github.com/MS-Diffusion/MS-Diffusion),[StoryMaker](https://github.com/RedAIGC/StoryMaker),[consistory](https://github.com/NVlabs/consistory),[kolor](https://github.com/Kwai-Kolors/Kolors),[pulid](https://github.com/ToTheBeginning/PuLID),[flux](https://github.com/black-forest-labs/flux),[photomaker](https://github.com/TencentARC/PhotoMaker),[IP-Adapter](https://github.com/tencent-ailab/IP-Adapter) 感谢他们的开源!
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本节点主要方法来源于[StoryDiffusion](https://github.com/HVision-NKU/StoryDiffusion) ,部分内容也来源于[MS-Diffusion](https://github.com/MS-Diffusion/MS-Diffusion),[StoryMaker](https://github.com/RedAIGC/StoryMaker),[consistory](https://github.com/NVlabs/consistory),[kolor](https://github.com/Kwai-Kolors/Kolors),[pulid](https://github.com/ToTheBeginning/PuLID),[flux](https://github.com/black-forest-labs/flux),[photomaker](https://github.com/TencentARC/PhotoMaker),[IP-Adapter](https://github.com/tencent-ailab/IP-Adapter) [InfiniteYou](https://github.com/bytedance/InfiniteYou) 感谢他们的开源!
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## 更新:
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**2024/11/15**
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* 加入了consistory的支持,你可以在easyfunction输入consi开启此功能(cache,inject2个附属功能,显存大的可以试试)
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* consistory模式只支持单主体,当然你也可以使用(cat)或者(boy)或者(hat),来创造2个主体,比如在角色栏输入 a cure [girl],wearing a (hat).可查看示例图片
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* lora功能已加入
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**2025/03/25**
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* 加入了InfiniteYou的支持,类IP方法人脸特征迁移,用于flux模型,可用lora,所有模型地址在[此](https://huggingface.co/ByteDance/InfiniteYou),有两种风格的cn模型
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## 特色功能
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**Flux and PULID-FLUX**
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* flux支持图生图和文生图,量化fp8和nf4运行,推荐用最快的nf4,但是这两模式都需要本地flux diffuse模型并在ckpt name选模型,在填写 'repo_id'格式"X:/xxx/xxx/black-forest-labs/FLUX.1-dev";开启nf4,需要在easy-function输入nf4,如果你想保存自己的量化fp8模型,可以只用repo跑,然后在easy-function输入save,会保存一个fp8量化模型到你的checkpoints目录;
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* PULID-FLUX 改成散装式的,clip接comfyUI标准的双clip(1个l 1个T5),ckpt_name选类kj大佬的flux Unet模型(模型名字里必须有flux),clip-vision选pulid的'EVA02_CLIP_L_336_psz14_s6B.pt“模型,vae选择flux标准的 'ae.safetensors'模型,然后在easy-function输入 'pulid, fp8, cpu' 就可以跑,当然你是4090可以试试去掉cpu;
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**InfiniteYou**
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* 模型加载节点填写本地或者在线flux dev 的repo地址,easyfunction填写infinite,注意不能有大写,前置model连节点easyfunction_lite(插件自带),easyfunction_lite的repo填写InfiniteYou的cn模型地址(见示例及说明)。
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* 因为模型显存要求较大,默认对flux模型使用nf4的量化,cpu加载,迟点再改成可选,12G目前可用,8G未测试
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1.安装
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-----
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@@ -265,6 +266,39 @@ RMBG-1.4 下载至 [link](https://huggingface.co/briaai/RMBG-1.4/tree/main)#自
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| ├── w600k_r50.onnx
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```
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**3.4 使用字节的InfiniteYou**
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* 3.4.1 flux-dev 的diffusers 模型地址 [here](https://huggingface.co/black-forest-labs/FLUX.1-dev)
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* 3.4.2 infinite 的cn和IP模型地址 from [here ](https://huggingface.co/ByteDance/InfiniteYou) ,sim_stage1 , aes_stage2两个可选,
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```
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├── 任意地址/sim_stage1/
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| ├── image_proj_model.bin
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| ├── InfuseNetModel/
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| ├── diffusion_pytorch_model-00001-of-00002.safetensors
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| ├── diffusion_pytorch_model-00002-of-00002.safetensors
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| ├── diffusion_pytorch_model.safetensors.index.json
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| ├── config.json
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```
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或者
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```
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├── 任意地址/aes_stage2/
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| ├── image_proj_model.bin
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| ├── InfuseNetModel/
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| ├── diffusion_pytorch_model-00001-of-00002.safetensors
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| ├── diffusion_pytorch_model-00002-of-00002.safetensors
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| ├── diffusion_pytorch_model.safetensors.index.json
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| ├── config.json
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```
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* 3.4.3 lora 可选 from [here](https://huggingface.co/ByteDance/InfiniteYou)
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* 3.4.4 insightface
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```
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├── ComfyUI/models/antelopev2/
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| ├──1k3d68.onnx
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| ├──2d106det.onnx
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| ├──genderage.onnx
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| ├──glintr100.onnx
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| ├──scrfd_10g_bnkps.onnx
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```
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* 3.4.5 recognition_arcface_ir_se50.pth from [here](https://github.com/xinntao/facexlib/releases/download/v0.1.0/recognition_arcface_ir_se50.pth) 自带下载,如果网络不通,手动下载放在控制台提示的地址,一般是C盘XXX
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4 Example
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----
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@@ -497,5 +531,16 @@ Consistory
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publisher={ACM New York, NY, USA}
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}
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```
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```
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@article{jiang2025infiniteyou,
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title={{InfiniteYou}: Flexible Photo Recrafting While Preserving Your Identity},
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author={Jiang, Liming and Yan, Qing and Jia, Yumin and Liu, Zichuan and Kang, Hao and Lu, Xin},
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journal={arXiv preprint},
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volume={arXiv:2503.16418},
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year={2025}
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}
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````
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FLUX
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