Stable Diffusion WebUI Forge - Neo

^{[ Neo | Classic ]}

Stable Diffusion WebUI Forge is a platform on top of the original Stable Diffusion WebUI by AUTOMATIC1111, to make development easier, optimize resource management, speed up inference, and study experimental features.
The name "Forge" is inspired by "Minecraft Forge". This project aims to become the Forge of Stable Diffusion WebUI.

- lllyasviel
^{(paraphrased)}

"Neo" mainly serves as an continuation for the "latest" version of Forge, which was built on Gradio 4.40.0 before lllyasviel became too busy... Additionally, this fork is focused on optimization and usability, with the main goal of being able to run the latest popular models via an easy-to-use GUI.

[!Tip] How to Install

Features [May.]

Most base features of the original Automatic1111 Webui should still function

New Features

Support Anima
Support Flux.2-Klein
- 4B / 9B (not FLUX.2-Dev)

[!Important] To use Flux.2-Klein for regular img2img, toggle the functionality in Settings/Stable Diffusion

Support Ernie-Image
- ernie-image / ernie-image-turbo
Support Z-Image
- z-image / z-image-turbo
Support Wan 2.2
- use Refiner to achieve High Noise / Low Noise switching
  - enable Refiner in Settings/Refiner

[!Important] To export a video, you need to have FFmpeg installed

Support Mugen
- display the Shift slider for xl preset in Settings/Presets/XL
Support advanced SDXL models

[!Note]

v-prediction: state_dict must include "v_pred"

Zero Terminal SNR: state_dict must include "ztsnr"

Rectified Flow: the model must include "rectified" in its path (e.g. file name or folder name)

Support Qwen-Image / Qwen-Image-Edit

[!Note] To be detected as an Edit model, the model must include "qwen" and "edit" in its path (e.g. file name or folder name)

Support Flux Kontext

[!Note] To be detected as a Kontext model, the model must include "kontext" in its path (e.g. file name or folder name)

Implement ImageStitch Integrated
- support Multi-Image Inputs for flux.2-klein / flux-kontext / qwen-image-edit
- support FirstLastFrameToVideo for wan 2.2
Support Nunchaku (SVDQ) Models
- flux-dev, flux-krea, flux-kontext, qwen-image, qwen-image-edit, z-image-turbo
- only Flux and Qwen support LoRA currently
- see Commandline
Support Lumina-Image-2.0
- Neta-Lumina / NetaYume-Lumina
Support Chroma1-HD
Support MixedPrecision Models
- fp4mixed / fp8mixed / mxfp8 / nvfp4 / fp8_scaled
Support Flux.2-Small-Decoder & Qwen2D VAE

[!Tip] Check out Download Models for where to get each model and the accompanying modules

[!Tip] Check out Inference References for how to use each model and the recommended parameters

Removed Features

Optimizations

[!Important] Put every upscaler (.pth / .safetensors) inside the ESRGAN folder

[!Tip] Check out OpenModelDB for where to get upscalers

[!Note] If your GPU does not support the latest PyTorch, manually install older version of PyTorch

Update some packages to newer versions
Update recommended Python to 3.13.12
many more... :tm:

Commandline

These flags can be added after the set COMMANDLINE_ARGS= line in the webui-user.bat (in the same line ; separate each flag with space)

[!Tip] Use python launch.py --help to see all available flags

--xformers: Install the xformers package to speed up generation

[!Warning] xformers does not support RTX 50s

--port: Specify a server port to use
- defaults to 7860
--api: Enable API access

by. Neo

--cuda-malloc: Improve memory allocation
--cuda-stream: Enable async weight offloading
--pin-shared-memory: Improve RAM utilization
--expandable-segments: Enable experimental PyTorch allocator (may prevent OutOfMemory errors on certain platforms)

--uv: Replace the python -m pip calls with uv pip to massively speed up package installation
- requires uv to be installed first (see Installation)
--uv-symlink: Same as above; but additionally pass --link-mode symlink to the commands
- significantly reduces installation size (~7 GB to ~100 MB)

[!Important] Using symlink means it will directly access the packages from the cache folders; refrain from clearing the cache if using this option

--model-ref: Points to a central models folder that contains all your models
- said folder should contain subfolders like Stable-diffusion, Lora, VAE, ESRGAN, etc.

[!Important] This simply replaces the models folder rather than adding on top of it

--forge-ref-a1111-home: Point to an Automatic1111 installation to load its models folders
- i.e. Stable-diffusion, text_encoder, etc.
--forge-ref-comfy-home: Point to a ComfyUI installation to load its models folders
- i.e. diffusion_models, clip, etc.
--forge-ref-comfy-yaml: Point to the ComfyUI extra_model_paths.yaml to load its configurations
- i.e. base_path, checkpoints, etc.

--sage: Install the sageattention package to speed up generation
- will also attempt to install triton automatically
--flash: Install the flash_attn package to speed up generation
--nunchaku: Install the nunchaku package to inference SVDQ models
--bnb: Install the bitsandbytes package to do low-bits (nf4) inference
--onnxruntime-gpu: Install the onnxruntime with the latest GPU support

--fast-fp8: Use the torch._scaled_mm function when the model type is float8_e4m3fn
--fast-fp16: Enable the allow_fp16_accumulation option
--autotune: Enable the torch.backends.cudnn.benchmark option
- this is slower in my experience...
--tiled-conv2d: Replace Conv2d ops with tiled variants
- has greater reduction for SD1 and SDXL VAE; less for Wan VAE
- 64 / 128 / 256 / 512

Installation

Install git

Clone the Repo

git clone https://github.com/Haoming02/sd-webui-forge-classic sd-webui-forge-neo --branch neo

Setup Python

Recommended Method

Install uv

Set up venv

cd sd-webui-forge-neo
uv venv venv --python 3.13 --seed

Add the --uv flag to webui-user.bat

Deprecated Method

Install Python 3.13.12
- Remember to enable Add Python to PATH

(Optional) Configure Commandline
Launch the WebUI via webui-user.bat
During the first launch, it will automatically install all the requirements
Once the installation is finished, the WebUI will start in a browser automatically

[!Tip]

For Linux and macOS, refer to Wiki

For Docker (Nvidia), refer to Docker

[!Tip] Check out Extra Installations for how to install git, uv, and FFmpeg

Attention Functions

[!Important] The --xformers, --flash, and --sage args are only responsible for installing the packages, not whether its respective attention is used (this also means you can remove them once the packages are successfully installed)

[!Caution] Do not just blindly install all of them
Nowadays the native PyTorch scaled_dot_product_attention is usually as fast, and also more stable

Forge Neo tries to import the packages and automatically choose the first available attention function in the following order:

SageAttention
FlashAttention
xformers
PyTorch
Basic

[!Note] To skip a specific attention, add the respective disable arg such as --disable-sage

Issues & Requests

Issues about removed features will simply be ignored
Issues that is obviously user-error will simply be ignored
Issues regarding AMD GPU will simply be ignored
Issues running non-official models will simply be ignored
- do not just randomly download every single finetune/quant you find
Issues about 3rd-party Extensions will simply be ignored
- extension should support the UI, not the other way around
Issues caused by StabilityMatrix will simply be ignored
- only open an Issue if you can reproduce it on a clean install following the official Installation instruction

[!Caution]

If you post NSFW images/videos, you will immediately be banned

the sole discretion is on me ; if you are unsure, just generate cats and dogs...

[!Tip] Check out the Wiki & FAQ

Special thanks to AUTOMATIC1111, lllyasviel, and comfyanonymous, kijai, city96,
along with the rest of the contributors,
for their invaluable efforts in the open-source image generation community

_{Buy me a Coffee ☕~}
_{PayPal me 💳~}