Stable Diffusion Web UI Forge

Stable Diffusion Web UI Forge is a platform on top of Stable Diffusion WebUI to make development easier, optimize resource management, and speed up inference.

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

Compared to original WebUI (for SDXL inference at 1024px), you can expect the below speed-ups:

1. If you use common GPU like 8GB vram, you are expected to get about 30~45% speed up in inference speed (it/s), the GPU memory peak (in task manager) will drop about 700MB to 1.3GB, the maximum diffusion resolution (that will not OOM) will increase about 2x to 3x, and the maximum diffusion batch size (that will not OOM) will increase about 4x to 6x.

2. If you use less powerful GPU like 6GB vram, you are expected to get about 60~75% speed up in inference speed (it/s), the GPU memory peak (in task manager) will drop about 800MB to 1.5GB, the maximum diffusion resolution (that will not OOM) will increase about 3x, and the maximum diffusion batch size (that will not OOM) will increase about 4x.

3. If you use powerful GPU like 4090 with 24GB vram, you are expected to get about 3~6% speed up in inference speed (it/s), the GPU memory peak (in task manager) will drop about 1GB to 1.4GB, the maximum diffusion resolution (that will not OOM) will increase about 1.6x, and the maximum diffusion batch size (that will not OOM) will increase about 2x.

4. If you use ControlNet for SDXL, the maximum ControlNet count (that will not OOM) will increase about 2x, the speed with SDXL+ControlNet will speed up about 30~45%.

Another very important change that Forge brings is Unet Patcher. Using Unet Patcher, methods like Self-Attention Guidance, Kohya High Res Fix, FreeU, StyleAlign, Hypertile can all be implemented in about 100 lines of codes.

Thanks to Unet Patcher, many new things are possible now and supported in Forge, including SVD, Z123, masked Ip-adapter, masked controlnet, photomaker, etc.

No need to monkey patch UNet and conflict other extensions anymore!

Installing Forge

You can install Forge using same method as SD-WebUI. (Install Git, Python, Git Clone this repo and then run webui-user.bat).

Or you can just use this one-click installation package (with git and python included).

>>> Click Here to Download One-Click Package<<<

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

Screenshots of Comparison

I tested with several devices, and this is a typical result from 8GB VRAM (3070ti laptop) with SDXL.

This is original WebUI:

(average about 7.4GB/8GB, peak at about 7.9GB/8GB)

This is WebUI Forge:

(average and peak are all 6.3GB/8GB)

You can see that Forge does not change WebUI results. Installing Forge is not a seed breaking change.

Forge can perfectly keep WebUI unchanged even for most complicated prompts like fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5].

All your previous works still work in Forge!

Also, Forge promise that we will only do our jobs. We will not add unnecessary opinioned changes to UI. You are still using 100% Automatic1111 WebUI.

Forge Backend

Forge backend removes all WebUI's codes related to resource management and reworked everything. All previous CMD flags like medvram, lowvram, medvram-sdxl, precision full, no half, no half vae, attention_xxx, upcast unet, ... are all REMOVED. Adding these flags will not cause error but they will not do anything now. We highly encourage Forge users to remove all cmd flags and let Forge to decide how to load models.

Without any cmd flag, Forge can run SDXL with 4GB vram and SD1.5 with 2GB vram.

The only one flag that you may still need is --always-offload-from-vram (This flag will make things slower). This option will let Forge always unload models from VRAM. This can be useful if you use multiple software together and want Forge to use less VRAM and give some vram to other software, or when you are using some old extensions that will compete vram with Forge, or (very rarely) when you get OOM.

If you really want to play with cmd flags, you can additionally control the GPU with:

(extreme VRAM cases)

--always-gpu
--always-cpu

(rare attention cases)

--attention-split
--attention-quad
--attention-pytorch
--disable-xformers
--disable-attention-upcast

(float point type)

--all-in-fp32
--all-in-fp16
--unet-in-bf16
--unet-in-fp16
--unet-in-fp8-e4m3fn
--unet-in-fp8-e5m2
--vae-in-fp16
--vae-in-fp32
--vae-in-bf16
--clip-in-fp8-e4m3fn
--clip-in-fp8-e5m2
--clip-in-fp16
--clip-in-fp32

(rare platforms)

--directml
--disable-ipex-hijack
--pytorch-deterministic

Again, Forge do not recommend users to use any cmd flags unless you are very sure that you really need these.

UNet Patcher

Now developing an extension is super simple. We finally have a patchable UNet.

Below is using one single file with 80 lines of codes to support FreeU:

extensions-builtin/sd_forge_freeu/scripts/forge_freeu.py

```python import torch import gradio as gr from modules import scripts

def Fourierfilter(x, threshold, scale): xfreq = torch.fft.fftn(x.float(), dim=(-2, -1)) xfreq = torch.fft.fftshift(xfreq, dim=(-2, -1)) B, C, H, W = xfreq.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 xfreq = xfreq * mask xfreq = torch.fft.ifftshift(xfreq, dim=(-2, -1)) xfiltered = torch.fft.ifftn(xfreq, dim=(-2, -1)).real return xfiltered.to(x.dtype)

def setfreeuv2patch(model, b1, b2, s1, s2): modelchannels = model.model.modelconfig.unetconfig["modelchannels"] scaledict = {modelchannels * 4: (b1, s1), modelchannels * 2: (b2, s2)}

def output_block_patch(h, hsp, *args, **kwargs):
    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)
        hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
    return h, hsp

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

class FreeUForForge(scripts.Script): 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 = set_freeu_v2_patch(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

```

It looks like this:

Similar components like HyperTile, KohyaHighResFix, SAG, can all be implemented within 100 lines of codes (see also the codes).

ControlNets can finally be called by different extensions.

Implementing Stable Video Diffusion and Zero123 are also super simple now (see also the codes).

Stable Video Diffusion:

extensions-builtin/sd_forge_svd/scripts/forge_svd.py

```python import torch import gradio as gr import os import pathlib

from modules import scriptcallbacks from modules.paths import modelspath from modules.uicommon import ToolButton, refreshsymbol from modules import shared

from modulesforge.forgeutil import numpytopytorch, pytorchtonumpy from ldmpatched.modules.sd import loadcheckpointguessconfig from ldmpatched.contrib.externalvideomodel import VideoLinearCFGGuidance, SVDimg2vidConditioning from ldmpatched.contrib.external import KSampler, VAEDecode

opVideoLinearCFGGuidance = VideoLinearCFGGuidance() opSVDimg2vidConditioning = SVDimg2vidConditioning() opKSampler = KSampler() opVAEDecode = VAEDecode()

svdroot = os.path.join(modelspath, 'svd') os.makedirs(svdroot, existok=True) svd_filenames = []

def updatesvdfilenames(): global svdfilenames svdfilenames = [ pathlib.Path(x).name for x in shared.walkfiles(svdroot, allowedextensions=[".pt", ".ckpt", ".safetensors"]) ] return svdfilenames

@torch.inferencemode() @torch.nograd() def predict(filename, width, height, videoframes, motionbucketid, fps, augmentationlevel, samplingseed, samplingsteps, samplingcfg, samplingsamplername, samplingscheduler, samplingdenoise, guidancemincfg, inputimage): filename = os.path.join(svdroot, filename) modelraw, , vae, clipvision = \ loadcheckpointguessconfig(filename, outputvae=True, outputclip=False, outputclipvision=True) model = opVideoLinearCFGGuidance.patch(modelraw, guidancemincfg)[0] initimage = numpytopytorch(inputimage) positive, negative, latentimage = opSVDimg2vidConditioning.encode( clipvision, initimage, vae, width, height, videoframes, motionbucketid, fps, augmentationlevel) outputlatent = opKSampler.sample(model, samplingseed, samplingsteps, samplingcfg, samplingsamplername, samplingscheduler, positive, negative, latentimage, samplingdenoise)[0] outputpixels = opVAEDecode.decode(vae, outputlatent)[0] outputs = pytorchtonumpy(outputpixels) return outputs

def onuitabs(): with gr.Blocks() as svdblock: with gr.Row(): with gr.Column(): inputimage = gr.Image(label='Input Image', source='upload', type='numpy', height=400)

            with gr.Row():
                filename = gr.Dropdown(label="SVD Checkpoint Filename",
                                       choices=svd_filenames,
                                       value=svd_filenames[0] if len(svd_filenames) > 0 else None)
                refresh_button = ToolButton(value=refresh_symbol, tooltip="Refresh")
                refresh_button.click(
                    fn=lambda: gr.update(choices=update_svd_filenames),
                    inputs=[], outputs=filename)

            width = gr.Slider(label='Width', minimum=16, maximum=8192, step=8, value=1024)
            height = gr.Slider(label='Height', minimum=16, maximum=8192, step=8, value=576)
            video_frames = gr.Slider(label='Video Frames', minimum=1, maximum=4096, step=1, value=14)
            motion_bucket_id = gr.Slider(label='Motion Bucket Id', minimum=1, maximum=1023, step=1, value=127)
            fps = gr.Slider(label='Fps', minimum=1, maximum=1024, step=1, value=6)
            augmentation_level = gr.Slider(label='Augmentation Level', minimum=0.0, maximum=10.0, step=0.01,
                                           value=0.0)
            sampling_steps = gr.Slider(label='Sampling Steps', minimum=1, maximum=200, step=1, value=20)
            sampling_cfg = gr.Slider(label='CFG Scale', minimum=0.0, maximum=50.0, step=0.1, value=2.5)
            sampling_denoise = gr.Slider(label='Sampling Denoise', minimum=0.0, maximum=1.0, step=0.01, value=1.0)
            guidance_min_cfg = gr.Slider(label='Guidance Min Cfg', minimum=0.0, maximum=100.0, step=0.5, value=1.0)
            sampling_sampler_name = gr.Radio(label='Sampler Name',
                                             choices=['euler', 'euler_ancestral', 'heun', 'heunpp2', 'dpm_2',
                                                      'dpm_2_ancestral', 'lms', 'dpm_fast', 'dpm_adaptive',
                                                      'dpmpp_2s_ancestral', 'dpmpp_sde', 'dpmpp_sde_gpu',
                                                      'dpmpp_2m', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu',
                                                      'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu', 'ddpm', 'lcm', 'ddim',
                                                      'uni_pc', 'uni_pc_bh2'], value='euler')
            sampling_scheduler = gr.Radio(label='Scheduler',
                                          choices=['normal', 'karras', 'exponential', 'sgm_uniform', 'simple',
                                                   'ddim_uniform'], value='karras')
            sampling_seed = gr.Number(label='Seed', value=12345, precision=0)

            generate_button = gr.Button(value="Generate")

            ctrls = [filename, width, height, video_frames, motion_bucket_id, fps, augmentation_level,
                     sampling_seed, sampling_steps, sampling_cfg, sampling_sampler_name, sampling_scheduler,
                     sampling_denoise, guidance_min_cfg, input_image]

        with gr.Column():
            output_gallery = gr.Gallery(label='Gallery', show_label=False, object_fit='contain',
                                        visible=True, height=1024, columns=4)

    generate_button.click(predict, inputs=ctrls, outputs=[output_gallery])
return [(svd_block, "SVD", "svd")]

updatesvdfilenames() scriptcallbacks.onuitabs(onui_tabs) ```

Note that although the above codes look like independent codes, they actually will automatically offload/unload any other models. For example, below is me opening webui, load SDXL, generated an image, then go to SVD, then generated image frames. You can see that the GPU memory is perfectly managed and the SDXL is moved to RAM then SVD is moved to GPU.

Note that this management is fully automatic. This makes writing extensions super simple.

Similarly, Zero123:

Write a simple ControlNet:

Below is a simple extension to have a completely independent pass of ControlNet that never conflicts any other extensions:

extensions-builtin/sd_forge_controlnet_example/scripts/sd_forge_controlnet_example.py

Note that this extension is hidden because it is only for developers. To see it in UI, use --show-controlnet-example.

The memory optimization in this example is fully automatic. You do not need to care about memory and inference speed, but you may want to cache objects if you wish.

```python

Use --show-controlnet-example to see this extension.

import cv2 import gradio as gr import torch

from modules import scripts from modules.sharedcmdoptions import cmdopts from modulesforge.shared import supportedpreprocessors from modules.modelloader import loadfilefromurl from ldmpatched.modules.controlnet import loadcontrolnet from modulesforge.controlnet import applycontrolnetadvanced from modulesforge.forgeutil import numpytopytorch from modulesforge.shared import controlnet_dir

class ControlNetExampleForge(scripts.Script): model = None

def title(self):
    return "ControlNet Example for Developers"

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()):
        gr.HTML('This is an example controlnet extension for developers.')
        gr.HTML('You see this extension because you used --show-controlnet-example')
        input_image = gr.Image(source='upload', type='numpy')
        funny_slider = gr.Slider(label='This slider does nothing. It just shows you how to transfer parameters.',
                                 minimum=0.0, maximum=1.0, value=0.5)

    return input_image, funny_slider

def process(self, p, *script_args, **kwargs):
    input_image, funny_slider = script_args

    # This slider does nothing. It just shows you how to transfer parameters.
    del funny_slider

    if input_image is None:
        return

    # controlnet_canny_path = load_file_from_url(
    #     url='https://huggingface.co/lllyasviel/sd_control_collection/resolve/main/sai_xl_canny_256lora.safetensors',
    #     model_dir=model_dir,
    #     file_name='sai_xl_canny_256lora.safetensors'
    # )
    controlnet_canny_path = load_file_from_url(
        url='https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/control_v11p_sd15_canny_fp16.safetensors',
        model_dir=controlnet_dir,
        file_name='control_v11p_sd15_canny_fp16.safetensors'
    )
    print('The model [control_v11p_sd15_canny_fp16.safetensors] download finished.')

    self.model = load_controlnet(controlnet_canny_path)
    print('Controlnet loaded.')

    return

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.

    input_image, funny_slider = script_args

    if input_image is None or self.model is None:
        return

    B, C, H, W = kwargs['noise'].shape  # latent_shape
    height = H * 8
    width = W * 8
    batch_size = p.batch_size

    preprocessor = supported_preprocessors['canny']

    # detect control at certain resolution
    control_image = preprocessor(
        input_image, resolution=512, slider_1=100, slider_2=200, slider_3=None)

    # here we just use nearest neighbour to align input shape.
    # You may want crop and resize, or crop and fill, or others.
    control_image = cv2.resize(
        control_image, (width, height), interpolation=cv2.INTER_NEAREST)

    # Output preprocessor result. Now called every sampling. Cache in your own way.
    p.extra_result_images.append(control_image)

    print('Preprocessor Canny finished.')

    control_image_bchw = numpy_to_pytorch(control_image).movedim(-1, 1)

    unet = p.sd_model.forge_objects.unet

    # Unet has input, middle, output blocks, and we can give different weights
    # to each layers in all blocks.
    # Below is an example for stronger control in middle block.
    # This is helpful for some high-res fix passes. (p.is_hr_pass)
    positive_advanced_weighting = {
        'input': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2],
        'middle': [1.0],
        'output': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2]
    }
    negative_advanced_weighting = {
        'input': [0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25],
        'middle': [1.05],
        'output': [0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25]
    }

    # The advanced_frame_weighting is a weight applied to each image in a batch.
    # The length of this list must be same with batch size
    # For example, if batch size is 5, the below list is [0.2, 0.4, 0.6, 0.8, 1.0]
    # If you view the 5 images as 5 frames in a video, this will lead to
    # progressively stronger control over time.
    advanced_frame_weighting = [float(i + 1) / float(batch_size) for i in range(batch_size)]

    # The advanced_sigma_weighting allows you to dynamically compute control
    # weights given diffusion timestep (sigma).
    # For example below code can softly make beginning steps stronger than ending steps.
    sigma_max = unet.model.model_sampling.sigma_max
    sigma_min = unet.model.model_sampling.sigma_min
    advanced_sigma_weighting = lambda s: (s - sigma_min) / (sigma_max - sigma_min)

    # You can even input a tensor to mask all control injections
    # The mask will be automatically resized during inference in UNet.
    # The size should be B 1 H W and the H and W are not important
    # because they will be resized automatically
    advanced_mask_weighting = torch.ones(size=(1, 1, 512, 512))

    # But in this simple example we do not use them
    positive_advanced_weighting = None
    negative_advanced_weighting = None
    advanced_frame_weighting = None
    advanced_sigma_weighting = None
    advanced_mask_weighting = None

    unet = apply_controlnet_advanced(unet=unet, controlnet=self.model, image_bchw=control_image_bchw,
                                     strength=0.6, start_percent=0.0, end_percent=0.8,
                                     positive_advanced_weighting=positive_advanced_weighting,
                                     negative_advanced_weighting=negative_advanced_weighting,
                                     advanced_frame_weighting=advanced_frame_weighting,
                                     advanced_sigma_weighting=advanced_sigma_weighting,
                                     advanced_mask_weighting=advanced_mask_weighting)

    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(
        controlnet_info='You should see these texts below output images!',
    ))

    return

Use --show-controlnet-example to see this extension.

if not cmdopts.showcontrolnet_example: del ControlNetExampleForge

```

Add a preprocessor

Below is the full codes to add a normalbae preprocessor with perfect memory managements.

You can use arbitrary independent extensions to add a preprocessor.

Your preprocessor will be read by all other extensions using modules_forge.shared.preprocessors

Below codes are in extensions-builtin\forge_preprocessor_normalbae\scripts\preprocessor_normalbae.py

```python from modulesforge.supportedpreprocessor import Preprocessor, PreprocessorParameter from modulesforge.shared import preprocessordir, addsupportedpreprocessor from modulesforge.forgeutil import resizeimagewithpad from modules.modelloader import loadfilefromurl

import types import torch import numpy as np

from einops import rearrange from annotator.normalbae.models.NNET import NNET from annotator.normalbae import load_checkpoint from torchvision import transforms

class PreprocessorNormalBae(Preprocessor): def init(self): super().init() self.name = 'normalbae' self.tags = ['NormalMap'] self.modelfilenamefilters = ['normal'] self.sliderresolution = PreprocessorParameter( label='Resolution', minimum=128, maximum=2048, value=512, step=8, visible=True) self.slider1 = PreprocessorParameter(visible=False) self.slider2 = PreprocessorParameter(visible=False) self.slider3 = PreprocessorParameter(visible=False) self.showcontrolmode = True self.donotneedmodel = False self.sortingpriority = 100 # higher goes to top in the list

def load_model(self):
    if self.model_patcher is not None:
        return

    model_path = load_file_from_url(
        "https://huggingface.co/lllyasviel/Annotators/resolve/main/scannet.pt",
        model_dir=preprocessor_dir)

    args = types.SimpleNamespace()
    args.mode = 'client'
    args.architecture = 'BN'
    args.pretrained = 'scannet'
    args.sampling_ratio = 0.4
    args.importance_ratio = 0.7
    model = NNET(args)
    model = load_checkpoint(model_path, model)
    self.norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

    self.model_patcher = self.setup_model_patcher(model)

def __call__(self, input_image, resolution, slider_1=None, slider_2=None, slider_3=None, **kwargs):
    input_image, remove_pad = resize_image_with_pad(input_image, resolution)

    self.load_model()

    self.move_all_model_patchers_to_gpu()

    assert input_image.ndim == 3
    image_normal = input_image

    with torch.no_grad():
        image_normal = self.send_tensor_to_model_device(torch.from_numpy(image_normal))
        image_normal = image_normal / 255.0
        image_normal = rearrange(image_normal, 'h w c -> 1 c h w')
        image_normal = self.norm(image_normal)

        normal = self.model_patcher.model(image_normal)
        normal = normal[0][-1][:, :3]
        normal = ((normal + 1) * 0.5).clip(0, 1)

        normal = rearrange(normal[0], 'c h w -> h w c').cpu().numpy()
        normal_image = (normal * 255.0).clip(0, 255).astype(np.uint8)

    return remove_pad(normal_image)

addsupportedpreprocessor(PreprocessorNormalBae())

```

New features (that are not available in original WebUI)

Thanks to Unet Patcher, many new things are possible now and supported in Forge, including SVD, Z123, masked Ip-adapter, masked controlnet, photomaker, etc.

Masked Ip-Adapter

Masked ControlNet

PhotoMaker

Marigold Depth

About Extensions

ControlNet and TiledVAE are integrated, and you should uninstall these two extensions:

sd-webui-controlnet
multidiffusion-upscaler-for-automatic1111

Other extensions should work without problems, like:

canvas-zoom
translations/localizations
Dynamic Prompts
Adetailer
Ultimate SD Upscale
Reactor

However, if newer extensions use Forge, their codes can be much shorter.

Usually if an old extension rework using Forge's unet patcher, 80% codes can be removed, especially when they need to call controlnet.