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Stable Diffusion Web UI Forge
Stable Diffusion Web UI Forge is a platform on top of Stable Diffusion WebUI to make development easier, and optimize the speed and resource consumption.
The name "Forge" is inspired from "Minecraft Forge". This project will become SD WebUI's Forge.
Forge will give you:
- Improved optimization. (Fastest speed and minimal memory use among all alternative software.)
- Patchable UNet and CLIP objects. (Developer-friendly platform.)
Improved Optimization
I tested with several devices, and this is a typical result from 8GB VRAM (3070ti laptop) with SDXL.
This is WebUI:
(average about 7.4GB/8GB, peak at about 7.9GB/8GB)
This is WebUI Forge:
(average and peak are all 6.3GB/8GB)
Also, you can see that Forge does not change WebUI results. Installing Forge is not a seed breaking change.
We do not change any UI. But you will see the version of Forge here
"f0.0.1v1.7.0" means WebUI 1.7.0 with Forge 0.0.1
Changes
Forge removes all WebUI's codes related to speed and memory optimization 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 is 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 main UI, 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.
Patchable UNet
Now developing an extension is super simple. We finally have 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
import torch
import gradio as gr
from modules import scripts
def Fourier_filter(x, threshold, scale):
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
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 set_freeu_v2_patch(model, b1, b2, s1, s2):
model_channels = model.model.model_config.unet_config["model_channels"]
scale_dict = {model_channels * 4: (b1, s1), model_channels * 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. (80% codes of ControlNet can be removed now, will start soon)
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
import torch
import gradio as gr
import os
import pathlib
from modules import script_callbacks
from modules.paths import models_path
from modules.ui_common import ToolButton, refresh_symbol
from modules import shared
from modules_forge.forge_util import numpy_to_pytorch, pytorch_to_numpy
from ldm_patched.modules.sd import load_checkpoint_guess_config
from ldm_patched.contrib.external_video_model import VideoLinearCFGGuidance, SVD_img2vid_Conditioning
from ldm_patched.contrib.external import KSampler, VAEDecode
opVideoLinearCFGGuidance = VideoLinearCFGGuidance()
opSVD_img2vid_Conditioning = SVD_img2vid_Conditioning()
opKSampler = KSampler()
opVAEDecode = VAEDecode()
svd_root = os.path.join(models_path, 'svd')
os.makedirs(svd_root, exist_ok=True)
svd_filenames = []
def update_svd_filenames():
global svd_filenames
svd_filenames = [
pathlib.Path(x).name for x in
shared.walk_files(svd_root, allowed_extensions=[".pt", ".ckpt", ".safetensors"])
]
return svd_filenames
@torch.inference_mode()
@torch.no_grad()
def predict(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):
filename = os.path.join(svd_root, filename)
model_raw, _, vae, clip_vision = \
load_checkpoint_guess_config(filename, output_vae=True, output_clip=False, output_clipvision=True)
model = opVideoLinearCFGGuidance.patch(model_raw, guidance_min_cfg)[0]
init_image = numpy_to_pytorch(input_image)
positive, negative, latent_image = opSVD_img2vid_Conditioning.encode(
clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level)
output_latent = opKSampler.sample(model, sampling_seed, sampling_steps, sampling_cfg,
sampling_sampler_name, sampling_scheduler, positive,
negative, latent_image, sampling_denoise)[0]
output_pixels = opVAEDecode.decode(vae, output_latent)[0]
outputs = pytorch_to_numpy(output_pixels)
return outputs
def on_ui_tabs():
with gr.Blocks() as svd_block:
with gr.Row():
with gr.Column():
input_image = 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")]
update_svd_filenames()
script_callbacks.on_ui_tabs(on_ui_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.
# Use --show-controlnet-example to see this extension.
import os
import cv2
import gradio as gr
import numpy as np
from modules import scripts
from modules.shared_cmd_options import cmd_opts
from modules.paths import models_path
from modules.modelloader import load_file_from_url
from ldm_patched.modules.controlnet import load_controlnet
from modules_forge.controlnet import apply_controlnet_advanced
from modules_forge.forge_util import pytorch_to_numpy, numpy_to_pytorch
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
model_dir = os.path.join(models_path, 'ControlNet')
os.makedirs(model_dir, exist_ok=True)
# 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=model_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
input_image = cv2.resize(input_image, (width, height))
# Below are two methods to preprocess images.
# Method 1: do it in your own way
canny_image_1 = cv2.cvtColor(cv2.Canny(input_image, 100, 200), cv2.COLOR_GRAY2RGB)
# Method 2: use built-in preprocessor
from modules_forge.shared import shared_preprocessors
canny_image_2 = shared_preprocessors['canny'](input_image, 100, 200)
# The two methods will give your same result
assert np.allclose(canny_image_1, canny_image_2)
canny_image = canny_image_1
# Output preprocessor result. Now called every sampling. Cache in your own way.
p.extra_result_images.append(canny_image)
print('Preprocessor Canny finished.')
control_image = numpy_to_pytorch(canny_image)
unet = p.sd_model.forge_objects.unet
unet = apply_controlnet_advanced(unet=unet, controlnet=self.model, image_bhwc=control_image,
strength=0.6, start_percent=0.0, end_percent=0.8,
positive_advanced_weighting=None, negative_advanced_weighting=None,
advanced_frame_weighting=None)
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 cmd_opts.show_controlnet_example:
del ControlNetExampleForge
About Extensions
All UI related extensions should work without problems, like:
canvas-zoom
different translations
etc
Below extensions are tested and worked well:
Dynamic Prompts
Adetailer
Ultimate SD Upscale
Reactor
Below extensions will be tested soon:
Regional Prompter (I have not figure out how to use that UI yet.. will test later)
Below extensions will be given up but they may still work
MultiDiffusion / Tiled Diffusison
Deform
Roop
(Tiled diffusion is integrated now and no need to install extra extensions. Also the current smart unet offload is much better than multi-diffusion and people can directly generate 4k images without using multi-diffusion, by automatically offload unet to RAM. If bigger than 4k, use Ultimate SD Upscale.) (But if you want to use some special features in MultiDiffusion like inversion or region prompt, probably you can still use it, but it can be very rare.)
Below extensions will be reworked soon
sd-webui-controlnet
controlnet will not be replaced by another extension. We will have 10+ extensions for different preprocessors, and 10+ extensions for different control. Each extension will have less than 100 lines of codes. Everyone will be able to add preprocessor/control model by adding new extensions. And adding those extensions will be super easy. Those extensions will share a UI managed by Forge.