stable-diffusion-webui-forge README

The project's README from github.com/lllyasviel/stable-diffusion-webui-forge. As one markdown file: README.md.

Stable Diffusion WebUI Forge

Stable Diffusion WebUI Forge is a platform on top of Stable Diffusion WebUI (based on Gradio ) to make development easier, optimize resource management, speed up inference, and study experimental features.

The name "Forge" is inspired from "Minecraft Forge". This project is aimed at becoming SD WebUI's Forge.

Forge is currently based on SD-WebUI 1.10.1 at this commit. (Because original SD-WebUI is almost static now, Forge will sync with original WebUI every 90 days, or when important fixes.)

News are moved to this link: Click here to see the News section

Quick List

Gradio 4 UI Must Read (TLDR: You need to use RIGHT MOUSE BUTTON to move canvas!)

Flux Tutorial (BitsandBytes Models, NF4, "GPU Weight", "Offload Location", "Offload Method", etc)

Flux Tutorial 2 (Seperated Full Models, GGUF, Technically Correct Comparison between GGUF and NF4, etc)

Forge Extension List and Extension Replacement List (Temporary)

How to make LoRAs more precise on low-bit models; How to Skip" Patching LoRAs"; How to only load LoRA one time rather than each generation; How to report LoRAs that do not work

Report Flux Performance Problems (TLDR: DO NOT set "GPU Weight" too high! Lower "GPU Weight" solves 99% problems!)

How to solve "Connection errored out" / "Press anykey to continue ..." / etc

(Save Flux BitsandBytes UNet/Checkpoint)

LayerDiffuse Transparent Image Editing

Tell us what is missing in ControlNet Integrated

(Policy) Soft Advertisement Removal Policy

(Flux BNB NF4 / GGUF Q8_0/Q5_0/Q5_1/Q4_0/Q4_1 are all natively supported with GPU weight slider and Quene/Async Swap toggle and swap location toggle. All Flux BNB NF4 / GGUF Q8_0/Q5_0/Q4_0 have LoRA support.)

Installing Forge

Just use this one-click installation package (with git and python included).

>>> Click Here to Download One-Click Package (CUDA 12.1 + Pytorch 2.3.1) <<<

Some other CUDA/Torch Versions:

Forge with CUDA 12.1 + Pytorch 2.3.1 <- Recommended

Forge with CUDA 12.4 + Pytorch 2.4 <- Fastest, but MSVC may be broken, xformers may not work

Forge with CUDA 12.1 + Pytorch 2.1 <- the previously used old environments

After you download, you uncompress, use update.bat to update, and use run.bat to run.

Note that running update.bat is important, otherwise you may be using a previous version with potential bugs unfixed.

image

Advanced Install

If you are proficient in Git and you want to install Forge as another branch of SD-WebUI, please see here. In this way, you can reuse all SD checkpoints and all extensions you installed previously in your OG SD-WebUI, but you should know what you are doing.

If you know what you are doing, you can also install Forge using same method as SD-WebUI. (Install Git, Python, Git Clone the forge repo https://github.com/lllyasviel/stable-diffusion-webui-forge.git and then run webui-user.bat).

Previous Versions

You can download previous versions here.

Forge Status

Based on manual test one-by-one:

Component Status Last Test
Basic Diffusion Normal 2024 Aug 26
GPU Memory Management System Normal 2024 Aug 26
LoRAs Normal 2024 Aug 26
All Preprocessors Normal 2024 Aug 26
All ControlNets Normal 2024 Aug 26
All IP-Adapters Normal 2024 Aug 26
All Instant-IDs Normal 2024 July 27
All Reference-only Methods Normal 2024 July 27
All Integrated Extensions Normal 2024 July 27
Popular Extensions (Adetailer, etc) Normal 2024 July 27
Gradio 4 UIs Normal 2024 July 27
Gradio 4 Forge Canvas Normal 2024 Aug 26
LoRA/Checkpoint Selection UI for Gradio 4 Normal 2024 July 27
Photopea/OpenposeEditor/etc for ControlNet Normal 2024 July 27
Wacom 128 level touch pressure support for Canvas Normal 2024 July 15
Microsoft Surface touch pressure support for Canvas Broken, pending fix 2024 July 29
ControlNets (Union) Not implemented yet, pending implementation 2024 Aug 26
ControlNets (Flux) Not implemented yet, pending implementation 2024 Aug 26
API endpoints (txt2img, img2img, etc) Normal, but pending improved Flux support 2024 Aug 29
OFT LoRAs Broken, pending fix 2024 Sep 9

Feel free to open issue if anything is broken and I will take a look every several days. If I do not update this "Forge Status" then it means I cannot reproduce any problem. In that case, fresh re-install should help most.

UnetPatcher

Below are self-supported single file of all codes to implement FreeU V2.

See also extension-builtin/sd_forge_freeu/scripts/forge_freeu.py:

import torch
import gradio as gr

from modules import scripts


def Fourier_filter(x, threshold, scale):
    # FFT
    x_freq = torch.fft.fftn(x.float(), dim=(-2, -1))
    x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1))

    B, C, H, W = x_freq.shape
    mask = torch.ones((B, C, H, W), device=x.device)

    crow, ccol = H // 2, W // 2
    mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale
    x_freq = x_freq * mask

    # IFFT
    x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1))
    x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real

    return x_filtered.to(x.dtype)


def patch_freeu_v2(unet_patcher, b1, b2, s1, s2):
    model_channels = unet_patcher.model.diffusion_model.config["model_channels"]
    scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}
    on_cpu_devices = {}

    def output_block_patch(h, hsp, transformer_options):
        scale = scale_dict.get(h.shape[1], None)
        if scale is not None:
            hidden_mean = h.mean(1).unsqueeze(1)
            B = hidden_mean.shape[0]
            hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True)
            hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True)
            hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3)

            h[:, :h.shape[1] // 2] = h[:, :h.shape[1] // 2] * ((scale[0] - 1) * hidden_mean + 1)

            if hsp.device not in on_cpu_devices:
                try:
                    hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
                except:
                    print("Device", hsp.device, "does not support the torch.fft.")
                    on_cpu_devices[hsp.device] = True
                    hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
            else:
                hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)

        return h, hsp

    m = unet_patcher.clone()
    m.set_model_output_block_patch(output_block_patch)
    return m


class FreeUForForge(scripts.Script):
    sorting_priority = 12  # It will be the 12th item on UI.

    def title(self):
        return "FreeU Integrated"

    def show(self, is_img2img):
        # make this extension visible in both txt2img and img2img tab.
        return scripts.AlwaysVisible

    def ui(self, *args, **kwargs):
        with gr.Accordion(open=False, label=self.title()):
            freeu_enabled = gr.Checkbox(label='Enabled', value=False)
            freeu_b1 = gr.Slider(label='B1', minimum=0, maximum=2, step=0.01, value=1.01)
            freeu_b2 = gr.Slider(label='B2', minimum=0, maximum=2, step=0.01, value=1.02)
            freeu_s1 = gr.Slider(label='S1', minimum=0, maximum=4, step=0.01, value=0.99)
            freeu_s2 = gr.Slider(label='S2', minimum=0, maximum=4, step=0.01, value=0.95)

        return freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2

    def process_before_every_sampling(self, p, *script_args, **kwargs):
        # This will be called before every sampling.
        # If you use highres fix, this will be called twice.

        freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2 = script_args

        if not freeu_enabled:
            return

        unet = p.sd_model.forge_objects.unet

        unet = patch_freeu_v2(unet, freeu_b1, freeu_b2, freeu_s1, freeu_s2)

        p.sd_model.forge_objects.unet = unet

        # Below codes will add some logs to the texts below the image outputs on UI.
        # The extra_generation_params does not influence results.
        p.extra_generation_params.update(dict(
            freeu_enabled=freeu_enabled,
            freeu_b1=freeu_b1,
            freeu_b2=freeu_b2,
            freeu_s1=freeu_s1,
            freeu_s2=freeu_s2,
        ))

        return

See also Forge's Unet Implementation.

Under Construction

WebUI Forge is now under some constructions, and docs / UI / functionality may change with updates.

stable-diffusion-webui-forge (lllyasviel/stable-diffusion-webui-forge)

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13.1K ·GitHub
Stable-diffusion-webui-forge
这是什么？Stable Diffusion WebUI Forge 是基于 Stable Diffusion WebUI（使用 Gradio）构建的平台，增加了显存管理和性能优化，用于在本地运行 Stable Diffusion 图像生成。
它是独立应用，还是原版 SD WebUI 的分支？它的设计目的是作为 SD-WebUI 的另一个分支来安装，因此你可以复用原版 WebUI 中已有的全部 SD 模型文件和已安装的扩展。
我该怎么把它跑起来？下载一键整合包（内置 CUDA 12.1 和 PyTorch 2.3.1），解压后先运行 update.bat 进行更新，再运行 run.bat 启动；高级用户也可以改为安装 Git 和 Python，克隆仓库后运行 webui-user.bat。
我需要怎样的 GPU 环境？默认的一键整合包内置 CUDA 12.1 和 PyTorch 2.3.1，另外也提供 CUDA 12.4 / Pytorch 2.4 和 CUDA 12.1 / Pytorch 2.1 版本；基于 Git 的安装方式还需要 Git 和 Python。
Forge 里的功能现在都能正常用吗？README 中有一个手动维护的状态表：LoRA、ControlNet 和扩展等核心功能标记为“Normal”，但 Union/Flux 模型的 ControlNet 尚未实现，OFT LoRA 已失效，画布的 Surface 触控压力感应支持也已失效。
这东西适合谁？是老用户还是新手？主要面向现有的 Stable Diffusion WebUI（AUTOMATIC1111）用户，他们可以保留模型文件和扩展，同时获得更快、更省显存的推理速度；不过一键安装程序也让新手更容易上手。
UnetPatcher 是什么？它是一种自成体系、受官方支持的方式，可直接在 Forge 自带的 Unet 实现中添加 FreeU V2 等功能，示例位于 extension-builtin/sdforgefreeu。
它开箱即用，支持 ControlNet 和 LoRA 吗？支持。状态表显示，所有 ControlNet、IP-Adapter、Instant-ID 和 LoRA 的测试结果均为“Normal”，同时也支持 Adetailer 等热门扩展。
为什么 README 说界面可能会变？该项目明确仍处于开发阶段，因此文档、界面和功能都可能随后续更新而调整。
What are similar projects?stable-diffusion-webui, stable-diffusion-webui-colab, kohya_ss, ai-toolkit, stable-diffusion.cpp, PhotoMaker, krita-ai-diffusion, ControlNet
Which topics is it in?unet, img2img, controlnet, diffusion, extension, stable-diffusion, gradio, text-to-image, lora, webui, gpu-acceleration, image-generation
Creator
lllyasviel
Size
46.4 MB
Stars
13.1K
Updated
CommitJun 26ReleaselatestFeb 05
Age
2y
Commits · 90 Days