README.md

---
title: stable-diffusion-webui-forge
app_file: spaces.py
sdk: gradio
sdk_version: 4.40.0
---
# Stable Diffusion WebUI Forge

Stable Diffusion WebUI Forge is a platform on top of [Stable Diffusion WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) (based on [Gradio](https://www.gradio.app/) <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](https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/82a973c04367123ae98bd9abdf80d9eda9b910e2). (Because original SD-WebUI is almost static now, Forge will sync with original WebUI every 90 days, or when important fixes.)

# Quick List

[Gradio 4 UI Must Read (TLDR: You need to use RIGHT MOUSE BUTTON to move canvas!)](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/853)

[Flux Tutorial (BitsandBytes Models, NF4, VRAM management UI, etc)](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/981)

[Flux Tutorial 2 (Seperated Full Models, GGUF, Technically Correct Comparison between GGUF and NF4, etc)](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/1050)

[(Save Flux BitsandBytes UNet/Checkpoint)](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/1224#discussioncomment-10384104)

[LayerDiffuse Transparent Image Editing](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/854)

[(Policy) Soft Advertisement Removal Policy](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/1286)

(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) <<<](https://github.com/lllyasviel/stable-diffusion-webui-forge/releases/download/latest/webui_forge_cu121_torch231.7z)

Some other CUDA/Torch Versions:

[Forge with CUDA 12.1 + Pytorch 2.3.1](https://github.com/lllyasviel/stable-diffusion-webui-forge/releases/download/latest/webui_forge_cu121_torch231.7z) <- **Recommended**

[Forge with CUDA 12.4 + Pytorch 2.4](https://github.com/lllyasviel/stable-diffusion-webui-forge/releases/download/latest/webui_forge_cu124_torch24.7z) <- **Fastest**, but MSVC may be broken, xformers may not work

[Forge with CUDA 12.1 + Pytorch 2.1](https://github.com/lllyasviel/stable-diffusion-webui-forge/releases/download/latest/webui_forge_cu121_torch21.7z) <- 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](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/c49bd60d-82bd-4086-9859-88d472582b94)

### Advanced Install

If you are proficient in Git and you want to install Forge as another branch of SD-WebUI, please see [here](https://github.com/continue-revolution/sd-webui-animatediff/blob/forge/master/docs/how-to-use.md#you-have-a1111-and-you-know-git). 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](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/849).

# Forge Status

Based on manual test one-by-one:

| Component                                         | Status  | Last Test    |
|---------------------------------------------------|---------|--------------|
| Basic Diffusion                                   | Normal  | 2024 July 27 |
| GPU Memory Management System                      | Normal  | 2024 July 27 |
| LoRAs                                             | Normal  | 2024 July 27 |
| All Preprocessors                                 | Normal  | 2024 July 27 |
| All ControlNets                                   | Normal  | 2024 July 27 |
| All IP-Adapters                                   | Normal  | 2024 July 27 |
| 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 July 27 |
| 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 |

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`:

```python
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](https://github.com/lllyasviel/stable-diffusion-webui-forge/blob/main/backend/nn/unet.py).

# Under Construction

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