no speed up ! i installed forge on gtx1060 , 6 vram , forge image generation speed is same as A1111 :(

[mostafablender]

Hi i did what your github page said as i expected and it was wrote on the page i expect "30~45% speed " but nothing actually and me and all my friends wonder what is wrong with it ? and what should i do ?
i tested on A1111 with xformer and medvram for 512 x 512 in 20 sample i could generate 1 image in 22 second that was same on forge
i tried just to do 1024 x 1024 same with 20 sample on A1111 it was like near 2 minutes but in forge it was longer like 3 minute
what should i do ?

[lllyasviel]

Hi the speedup on the readme is Compared to original WebUI (for SDXL inference at 1024px) and you need to use SDXL at 1024px to see the speedup

for sd1.5 the speedup is smaller but it should still be faster

Nevertheless if you use sd1.5 and find webui 2 minutes but in forge it was longer like 3 minute this should be a bug and if you give a log and screenshot in issue we will take a look

[mostafablender]

yse i am using sd model , what you mean by origin webui ? i try to test sdxl model now . also which sampling method is better on forge if it change anything ?

[mostafablender]

i wish i had your discord or you were on sebastian kamph discord server so we could talk there

[mostafablender]

if you asking that im running A1111 and forge at same time , no my poor laptop cant handle that lol

[mostafablender]

it took me 13 min and 40 second to generate 1 sdxl image on forge ( 20 sample and 1024 x 1024 )
the console was like this

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To load target model SDXLClipModel
Begin to load 1 model
Moving model(s) has taken 3.93 seconds
To load target model SDXL
Begin to load 1 model
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loading in lowvram mode 2771.0952520370483
Moving model(s) has taken 241.11 seconds
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [08:48<00:00, 26.41s/it]
To load target model AutoencoderKL████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [08:36<00:00, 46.66s/it]
Begin to load 1 model
Moving model(s) has taken 4.95 seconds
Total progress: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [09:08<00:00, 27.45s/it]
Total progress: 100%|███████████████████████████████████████████████████████████████████████

[mostafablender]

tried sdxl turbo find out half of my lora are not on list , i wanted to add lichking_alpha_v02 and LCM_LORA_SDXL but i could not find them in lora tab also i tried to do 1 sample step to 8 sample stap , i saw other ppl do on 4 sample step and get good looking frog but i had to use 8 sample until i get a cat and it was not even good enough . i tried several images only a few were good

[TheRealPhooney]

Something else may have happened in a recent change...

I have been getting 29-31it/s on sd1.5 512x512 Euler A 20 steps

Today, it's back down to 20-22it/s, which was exactly what I was getting in A1111

I thought it might have somethig to do with the Nvidia driver I updated today, but I downgraded that again and it didn't make a difference to the speed.

[TheRealPhooney]

Here's startup and a few generations at 512x512, Euler a, 20 steps, no prompt:

Python 3.10.6 (tags/v3.10.6:9c7b4bd, Aug 1 2022, 21:53:49) [MSC v.1932 64 bit (AMD64)]
Version: f0.0.15v1.8.0rc-latest-224-gcc5f7735
Commit hash: cc5f773
no module 'xformers'. Processing without...
no module 'xformers'. Processing without...
No module 'xformers'. Proceeding without it.
CUDA 12.1
Launching Web UI with arguments: --api
Total VRAM 24564 MB, total RAM 65405 MB
Set vram state to: NORMAL_VRAM
Device: cuda:0 NVIDIA GeForce RTX 4090 : native
VAE dtype: torch.bfloat16
Using pytorch cross attention
ControlNet preprocessor location: C:\xxx\webui\models\ControlNetPreprocessor
[-] ADetailer initialized. version: 24.1.2, num models: 15
10:59:48 - ReActor - STATUS - Running v0.7.0-a2 on Device: CUDA
Loading weights [8635af1c8c] from C:\xxx\webui\models\Stable-diffusion\epicphotogasm_x.safetensors
2024-02-24 10:59:49,494 - ControlNet - INFO - ControlNet UI callback registered.
model_type EPS
UNet ADM Dimension 0
git: 'submodule' is not a git command. See 'git --help'.
[openOutpaint-extension-submodule] failed to download update, check network
Using pytorch attention in VAE
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
Using pytorch attention in VAE
Running on local URL: http://127.0.0.1:7860

To create a public link, set share=True in launch().
Startup time: 31.4s (prepare environment: 15.6s, import torch: 3.1s, import gradio: 0.8s, setup paths: 0.7s, initialize shared: 0.1s, other imports: 1.4s, load scripts: 5.4s, create ui: 1.3s, gradio launch: 0.4s, add APIs: 2.4s).
extra {'cond_stage_model.clip_l.logit_scale', 'cond_stage_model.clip_l.text_projection'}
left over keys: dict_keys(['model_ema.decay', 'model_ema.num_updates'])
To load target model SD1ClipModel
Begin to load 1 model
Reuse 0 loaded models
[Memory Management] Current Free Memory (MB) = 22995.9990234375
[Memory Management] Model Memory (MB) = 454.2076225280762
[Memory Management] Estimated Inference Memory (MB) = 1024.0
[Memory Management] Estimated Remaining Memory (MB) = 21517.791400909424
Moving model(s) has taken 0.19 seconds
Model loaded in 5.0s (load weights from disk: 0.4s, forge load real models: 3.7s, load VAE: 0.2s, calculate empty prompt: 0.6s).
Moving model(s) skipped. Cleanup has taken 0.00 seconds
Moving model(s) skipped. Cleanup has taken 0.00 seconds
To load target model BaseModel
Begin to load 1 model
Reuse 0 loaded models
[Memory Management] Current Free Memory (MB) = 22422.23046875
[Memory Management] Model Memory (MB) = 1639.4137649536133
[Memory Management] Estimated Inference Memory (MB) = 1024.0
[Memory Management] Estimated Remaining Memory (MB) = 19758.816703796387
Moving model(s) has taken 0.49 seconds
100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:01<00:00, 12.10it/s]
To load target model AutoencoderKL██████████████████████████████████████████████▍ | 18/20 [00:01<00:00, 19.06it/s]
Begin to load 1 model
Reuse 0 loaded models
[Memory Management] Current Free Memory (MB) = 20907.33544921875
[Memory Management] Model Memory (MB) = 159.55708122253418
[Memory Management] Estimated Inference Memory (MB) = 1089.0
[Memory Management] Estimated Remaining Memory (MB) = 19658.778367996216
Moving model(s) has taken 0.15 seconds
Total progress: 100%|██████████████████████████████████████████████████████████████████| 20/20 [00:01<00:00, 12.36it/s]
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100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:01<00:00, 17.00it/s]
Moving model(s) skipped. Cleanup has taken 0.00 seconds████████████████████████████▋ | 19/20 [00:00<00:00, 21.24it/s]
Total progress: 100%|██████████████████████████████████████████████████████████████████| 20/20 [00:01<00:00, 18.15it/s]
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100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:01<00:00, 17.13it/s]
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Total progress: 100%|██████████████████████████████████████████████████████████████████| 20/20 [00:01<00:00, 16.77it/s]
Total progress: 100%|██████████████████████████████████████████████████████████████████| 20/20 [00:01<00:00, 20.55it/s]

[lllyasviel]

Your log looks a bit strange because it says no module xformers. Processing without...
Forge never log this, even if user do not have or use xformers or even when xformers does not exist

The only possible reason for this line is that Forge is hijacked by some other extension to use CUDA context in some other way and that hijacking extension is competing CUDA context with Forge.

I recommend try a fresh install of forge and see if the problem still

[lllyasviel]

your replyed to anther wong post but i saw that

from that log i cannot detect anything wrong now. Perhaps just the it/s not accurate enough. The real generation time should not be longer, if you have the time record on the left-bottom corner of the output canvas

let us know if it is real slower even on the real generation time

[TheRealPhooney]

It's definitely not slower than A1111, but the it/s is not as fast as it was yesterday

As for the time in the bottom left of the canvas, I can't really compare it to yesterday as I never really looked at that itme for some reason, it's only the it/s that stuck in my mind.

Comparing installations now...

Forge takes about 1.3-1.5s for SD1.5 512x512 images, A1111 was 1.5-1.6s.
Forge Takes 5.6-5.8s for SDXL 1024x1024, A1111 was 6.3-6.8s

I guess these speeds are about what you'd expect as the difference between A1111 and Forge on a 4090. Sorry I don't have the real-time outputs to compare with yesterday. It definitely doesn't feel as fast...

[TheRealPhooney]

Well... I did the classic IT trick. Turned it off and on again and the speed is back up, even on the latest NVidia driver, and on my installation with all the xformers messages.

I dont know wtf.

[magpie514]

Mine is also much slower than sd-webui.
I'm not using XL, just 1.5. Same setup takes 1.70it/s on low-res step and 1.5s/it on hires.
With forge it's 1.6it/s on low res and 3s/it on hires, double the time and higher VRAM usage.
I'm using an nVidia model, a 1080, on Ubuntu 23.10, Python 11, xformers is installed and loaded and I'm using the exact same configs. No command line arguments or any extensions (other than cutoff) being used.
I disabled all built-in forge extensions since they were making things even slower (3s/it on lowres) and I'm not using controlnet or anything extra for testing.

There are no warnings or errors in the console output, it's all info and progress outputs. It also isn't reproducing the same image with the same seed, despite what the README says.

EDIT: disabled cutoff, same speeds.

[lllyasviel]

@magpie514 you need to disable token merging on original webui to stop your original webui from sacrificing and decreasing result quality for faster images, and let the original webui to generate full quality images.

Then you will see the real speed on original webui and the speed up by Forge

[magpie514]

The images I get with original webui even with token merging are of good quality already, I came here for a speed up.

[freecoderwaifu]

Copying the config from A1111 might have copied settings that aren't exactly the same in Forge, like some extensions. Try backing up your ui-config.json and config.json, then delete them on both A1111 and Forge and test on both with default settings.

[magpie514]

I clearly indicated I copied the settings AFTER testing the speed.

[EaglePPP]

try not using xformers? i cant remember it correctly but i think using xformers got me the same speed, try --disable-xformers.(not a tech-y advcie)

[EaglePPP]

my crappy 1060 was 1.6~1.7it/s best, now fastest ~1.1it/s with forge (also SD 1.5 models). It actually depends on ur batch count and sizes, I didnt benchmark forge yet, but in my own a1111 benchmark, I noticed batch count x sizes more than 4x4 have better speed per img.

[mostafablender]

i did when i installed forge

…

On Thu, Feb 29, 2024 at 12:15 AM EagleP ***@***.***> wrote: try not using xformers? i cant remember it correctly but i think using xformers got me the same speed, try --disable-xformers.(not a tech-y advcie) — Reply to this email directly, view it on GitHub <#373 (reply in thread)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ARKYD7O3HRJLQFBZTWWGBILYV6JNHAVCNFSM6AAAAABDWQJL72VHI2DSMVQWIX3LMV43SRDJONRXK43TNFXW4Q3PNVWWK3TUHM4DMMRTGAZTE> . You are receiving this because you authored the thread.Message ID: <lllyasviel/stable-diffusion-webui-forge/repo-discussions/373/comments/8623032 @github.com>