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Stable Diffusion WebUI Forge

Stable Diffusion WebUI Forge is a platform on top of Stable Diffusion WebUI (based on Gradio <a href='https://github.com/gradio-app/gradio'><img src='https://img.shields.io/github/stars/gradio-app/gradio'></a>) 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.

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:

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.