ComfyUI_UltraFlux

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4K原生生成多宽高比支持图生图(i2i)LoRA与显存调优

在ComfyUI集成UltraFlux，实现跨多宽高比的高质量原生4K文本到图像生成，支持图生图、LoRA兼容与显存调优。

💡 在ComfyUI中生成高质量原生4K图像或进行图生图增强

🍴 1 Forks💻 Python🔄 2025-11-27

🔗 GitHub 原文

📦

网盘下载

复制链接后前往夸克网盘下载

https://pan.quark.cn/s/c1eafc754fbb

📦 requirements.txt

torch
transformers
accelerate
diffusers

📄 README

ComfyUI_UltraFlux

UltraFlux:Data-Model Co-Design for High-quality Native 4K Text-to-Image Generation across Diverse Aspect Ratios,try it in comfyUI

Update

图生图模式上线，加噪伪超分 / i2i is done。

因为基于flux ，如果出现人物，推荐使用修手lora，风格lora因为微调图片精度不够，可能会劣化输出，8G显存block number适当从10下调，4G显存，你就从1开始往上测试吧

Because based on flux, if a character appears, it is recommended to use a hand fix Lora. The style Lora may degrade the output due to insufficient fine-tuning of the image accuracy. The block number of 8GB VRAM should be appropriately reduced from 10, and 4G VRAM should be tested from 1 onwards

1.Installation

In the ./ComfyUI/custom_nodes directory, run the following:

git clone https://github.com/smthemex/ComfyUI_UltraFlux

2.requirements i

不装也行，没什么需求

pip install -r requirements.txt

3.Model

gguf or transformer smthem/UltraFlux-v1-gguf optional/推荐用fp16 ,因为块卸载,只要内存大

vae rnamae it v1

diffusers transformer v1 or v1.1 optional/备选填repo的方式，一般不用

comfyUI normal T5 and clip-l

lora, any turbo and style flux lora #任意flux加速和风格lora，部分Lora的精度不够可能会劣化输出

├── ComfyUI/models/gguf # or transformer
|     ├── UltraFlux-v1-1-BF16.gguf # or Q8
├── ComfyUI/models/diffusion_models # or gguf
|     ├── UltraFlux-v1-1-BF16..safetensors # or e4m3fn
├── ComfyUI/models/vae
|        ├─diffusion_pytorch_model.safetensors  # rename it 换个名字
├── ComfyUI/models/clip
|        ├──t5xxl_fp8_e4m3fn.safetensors
|        ├──clip_l.safetensors 
├── ComfyUI/models/loras 
|        ├──any turbo lora
|        ├──any style lora

4.Example

5.Citation

@misc{ye2025ultrafluxdatamodelcodesignhighquality,
      title={UltraFlux: Data-Model Co-Design for High-quality Native 4K Text-to-Image Generation across Diverse Aspect Ratios}, 
      author={Tian Ye and Song Fei and Lei Zhu},
      year={2025},
      eprint={2511.18050},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2511.18050}, 
}