MonsterMMORPG (Furkan Gözükara)

posted an update 6 days ago

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The zip file contains installers for Windows, RunPod, Massed Compute and a free Kaggle account notebook

It generates a VENV and install everything inside it. Works with Python 3.10.x - I suggest 3.10.11

Also you need C++ tools and Git. You can follow this tutorial to install all : https://youtu.be/-NjNy7afOQ0

Updated 27 May 2024 : https://www.patreon.com/posts/95759342

21 January 2024 Update
SDXL model upgraded to ip-adapter-faceid-plusv2_sd15
Kaggle Notebook upgraded to V3 and supports SDXL now

First of all I want to thank you so much for this amazing model.

I have spent over 1 week to code the Gradio and prepare the video. I hope you let this thread remain and even add to the Readme file.

After video has been published I even added face embedding caching mechanism. So now it will calculate face embedding vector only 1 time for each image, thus super speed up the image generation.

Instantly Transfer Face By Using IP-Adapter-FaceID: Full Tutorial & GUI For Windows, RunPod & Kaggle : https://youtu.be/rjXsJ24kQQg

chapters are like below

0:00 Introduction to IP-Adapter-FaceID full tutorial
2:19 Requirements to use IP-Adapter-FaceID gradio Web APP
2:45 Where the Hugging Face models are downloaded by default on Windows
3:12 How to change folder path where the Hugging Face models are downloaded and cached
3:39 How to install IP-Adapter-FaceID Gradio Web APP and use on Windows
5:35 How to start the IP-Adapter-FaceID Web UI after the installation
5:46 How to use Stable Diffusion XL (SDXL) models with IP-Adapter-FaceID
5:56 How to select your input face and start generating 0-shot face transferred new amazing images
6:06 What does each option on the Web UI do explanations

replied to their post 9 days ago

It works, @MonsterMMORPG !

https://huggingface.co/spaces/Fabrice-TIERCELIN/SUPIR

SUPIR is now available on HuggingFace 🙂 I have disabled LLaVa because there is still an error with it. I will try to fix it in the future. Add links everywhere!

congrats

replied to their post 12 days ago

OK so I have created a template space. Of course it's not working itself because it runs on a CPU but people can duplicate it on a GPU. It should work but I can only test the interface. I say that they need 60 GB VRAM. Correct me if it's wrong. I will wait for feedback.

Our apps works with 29gb ram on kaggle

Can't tell others

replied to their post 14 days ago

@Fabrice-TIERCELIN we have a working Kaggle notebook
Also we have installers for runpod and massed compute

posted an update 16 days ago

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Stable Cascade Full Tutorial for Windows, Massed Compute, RunPod & Kaggle — Predecessor of SD3 — 1-Click Install Amazing Gradio APP

Stable Cascade is another amazing model for Stability AI

Weights are published

Stable Cascade Full Tutorial for Windows — Predecessor of SD3–1-Click Install Amazing Gradio APP : https://youtu.be/q0cYhalUUsc

Stable Cascade Full Tutorial for Cloud — Predecessor of SD3 — Massed Compute, RunPod & Kaggle : https://youtu.be/PKDeMdEObNo

replied to their post 17 days ago

sadly i can't for this. I also don't know and this requires good GPU

posted an update 29 days ago

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The IDM-VTON (Improving Diffusion Models for Authentic Virtual Try-on in the Wild) is so powerful that it can even transfer beard or hair as well.

I have prepared installer scripts and full tutorials for Windows (requires min 8 GB VRAM GPU), Massed Compute (I suggest this if you don’t have a strong GPU), RunPod and a free Kaggle account (works perfect as well but slow).

Windows Tutorial : https://youtu.be/m4pcIeAVQD0

Cloud (Massed Compute, RunPod & Kaggle) Tutorial : https://youtu.be/LeHfgq_lAXU

posted an update about 1 month ago

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Complete Guide to SUPIR Enhancing and Upscaling Images Like in Sci-Fi Movies on Your PC : https://youtu.be/OYxVEvDf284

In this video, I explain how to 1 click install and use the most advanced image upscaler / enhancer in the world that is both commercially and open source available. The upscaler that I am going to introduce you is open source #SUPIR and the model is free to use. SUPIR upscaler is many times better than both paid Topaz AI and Magnific AI and you can use this upscaler on your computer for free forever. The difference of SUPIR vs #Topaz and #Magnific is like ages. So in this tutorial you are going to learn everything about how to install, update and use SUPIR upscaler on your personal computer. The video shows Windows but it works perfectly fine on Linux as well.

Scripts Download Link ⤵️
https://www.patreon.com/posts/99176057

Samplers and Text CFG (Text Guidance Scale) Comparison Link ⤵️
https://imgsli.com/MjU2ODQz/2/1

How to install accurate Python, Git and FFmpeg on Windows Tutorial ⤵️
https://youtu.be/-NjNy7afOQ0

Full DreamBooth / Fine-tuning Tutorial ⤵️
https://youtu.be/0t5l6CP9eBg

Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild : https://arxiv.org/abs/2401.13627

Authors introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up. Leveraging multi-modal techniques and advanced generative prior, SUPIR marks a significant advance in intelligent and realistic image restoration. As a pivotal catalyst within SUPIR, model scaling dramatically enhances its capabilities and demonstrates new potential for image restoration. Authors collect a dataset comprising 20 million high-resolution, high-quality images for model training, each enriched with descriptive text annotations. SUPIR provides the capability to restore images guided by textual prompts, broadening its application scope and potential

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posted an update about 2 months ago

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Watch the full tutorial here : https://youtu.be/0t5l6CP9eBg

The tutorial is over 2 hours literally with manually fixed captions and perfect video chapters.

Most Awaited Full Fine Tuning (with DreamBooth effect) Tutorial Generated Images - Full Workflow Shared In The Comments - NO Paywall This Time - Explained OneTrainer - Cumulative Experience of 16 Months Stable Diffusion

In this tutorial, I am going to show you how to install OneTrainer from scratch on your computer and do a Stable Diffusion SDXL (Full Fine-Tuning 10.3 GB VRAM) and SD 1.5 (Full Fine-Tuning 7GB VRAM) based models training on your computer and also do the same training on a very cheap cloud machine from MassedCompute if you don't have such computer.

Tutorial Readme File ⤵️
https://github.com/FurkanGozukara/Stable-Diffusion/blob/main/Tutorials/OneTrainer-Master-SD-1_5-SDXL-Windows-Cloud-Tutorial.md

Register Massed Compute From Below Link (could be necessary to use our Special Coupon for A6000 GPU for 31 cents per hour) ⤵️
https://bit.ly/Furkan-Gözükara

Coupon Code for A6000 GPU is : SECourses

0:00 Introduction to Zero-to-Hero Stable Diffusion (SD) Fine-Tuning with OneTrainer (OT) tutorial
3:54 Intro to instructions GitHub readme
4:32 How to register Massed Compute (MC) and start virtual machine (VM)
5:48 Which template to choose on MC
6:36 How to apply MC coupon
8:41 How to install OT on your computer to train
9:15 How to verify your Python, Git, FFmpeg and Git installation
12:00 How to install ThinLinc and start using your MC VM
12:26 How to setup folder synchronization and file sharing between your computer and MC VM
13:56 End existing session in ThinClient
14:06 How to turn off MC VM
14:24 How to connect and start using VM
14:41 When use end existing session
16:38 How to download very best OT preset training configuration for SD 1.5 & SDXL models
18:00 How to load configuration preset
18:38 Full explanation of OT configuration and best hyper parameters for SDXL
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replied to their post 2 months ago

thanks a lot

posted an update 2 months ago

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Compared Effect Of Image Captioning For SDXL Fine-tuning / DreamBooth Training for a Single Person, 10.3 GB VRAM via OneTrainer

Sadly the post character count is limited so please read full info on Medium here

https://medium.com/@furkangozukara/compared-effect-of-image-captioning-for-sdxl-fine-tuning-dreambooth-training-for-a-single-person-961087e42334

replied to their post 2 months ago

I did over 100 trainings empirically to find best hyper parameters. And training U-NET + Text Encoder 1 yields better results that only U-NET @researcher171473

posted an update 2 months ago

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Now You Can Full Fine Tune / DreamBooth Stable Diffusion XL (SDXL) with only 10.3 GB VRAM via OneTrainer — Both U-NET and Text Encoder 1 is trained — Compared 14 GB config vs slower 10.3 GB Config

Full config and instructions are shared here : https://www.patreon.com/posts/96028218

Used SG161222/RealVisXL_V4.0 as a base model and OneTrainer to train on Windows 10 : https://github.com/Nerogar/OneTrainer

The posted example x/y/z checkpoint comparison images are not cherry picked. So I can get perfect images with multiple tries.

Trained 150 epochs, 15 images and used my ground truth 5200 regularization images : https://www.patreon.com/posts/massive-4k-woman-87700469

In each epoch only 15 of regularization images used to make DreamBooth training affect

As a caption only “ohwx man” is used, for regularization images just “man”
You can download configs and full instructions here : https://www.patreon.com/posts/96028218

Hopefully full public tutorial coming within 2 weeks. I will show all configuration as well

The tutorial will be on our channel : https://www.youtube.com/SECourses
Training speeds are as below thus durations:

RTX 3060 — slow preset : 3.72 second / it thus 15 train images 150 epoch 2 (reg images concept) : 4500 steps = 4500 3.72 / 3600 = 4.6 hours

RTX 3090 TI — slow preset : 1.58 second / it thus : 4500 * 1.58 / 3600 = 2 hours

RTX 3090 TI — fast preset : 1.45 second / it thus : 4500 * 1.45 / 3600 = 1.8 hours

A quick tutorial for how to use concepts in OneTrainer : https://youtu.be/yPOadldf6bI

5 replies

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replied to their post 3 months ago

100% this is next level. thanks for comment @ajibawa-2023

replied to their post 3 months ago

@ameerazam08 100%. I am talking with original developers for CPU Offloading too if they hopefully add.

posted an update 3 months ago

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I have dedicated several days, working over 12 hours each day, on SUPIR (Scaling-UP Image Restoration), a cutting-edge image enhancement and upscaling model introduced in the paper Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild.

This model is simply mind-blowing. At the bottom of this post, you will see side-by-side comparisons of SUPIR versus the extremely expensive online service, Magnific AI. Magnific is known to be the best among the community. However, SUPIR is by far superior. SUPIR also significantly outperforms Topaz AI upscale. SUPIR manages to remain faithful to the original image almost 100% while adding details and achieving super upscaling with the best realism.

You can read the full blog post here : https://huggingface.co/blog/MonsterMMORPG/supir-sota-image-upscale-better-than-magnific-ai

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posted an update 3 months ago

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Compared Stable Diffusion 3 with Dall-E3 and results are mind blowing.

SD 3 can follow prompts many times better than SD 1.5 or SDXL. It is even better than Dall-E3 in following text / spelling prompts.

The realism of the SD 3 can't be even compared with Dall-E3, since every Dall-E3 output is like a digital render.

Can't wait to get approved of Stability AI early preview program to do more intensive testing.

Some people says be skeptical about cherry picking. I agree but I hope that these Stability AI released images are not that heavy cherry picking.

You can see SD3 vs Dall-E3 comparison here : https://youtu.be/DJxodszsERo

posted an update 4 months ago

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Today my RunPod pod was broken and I didn't notice until I fully did setup it. So I have written the following tutorial for how to deploy a Pod and also verify it is not broken.

You can read on:

Patreon (public) : https://www.patreon.com/posts/how-to-deploy-on-97919576

Medium (public) : https://medium.com/@furkangozukara/how-to-deploy-a-pod-on-runpod-and-verify-it-is-working-20e47031c0b5

CivitAI (public) : https://civitai.com/articles/3994/how-to-deploy-a-pod-on-runpod-and-verify-it-is-working

LinkedIn (public) : https://www.linkedin.com/pulse/how-deploy-pod-runpod-verify-working-furkan-g%2525C3%2525B6z%2525C3%2525BCkara-lgplf%3FtrackingId=EuNOjpKCSQ%252BVfpiQV3D6KQ%253D%253D/?trackingId=EuNOjpKCSQ%2BVfpiQV3D6KQ%3D%3D

Dev . to (public) : https://dev.to/furkangozukara/how-to-deploy-a-pod-on-runpod-and-verify-it-is-working-3pop

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