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Zero To Hero Generative AI - Become A Master

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  1. Lecture 1 : How To Install Python, Setup Virtual Environment VENV, Set Default Python System Path & Install Git
  2. Lecture 3 : Zero to Hero ControlNet Tutorial: Stable Diffusion Web UI Extension | Complete Feature Guide
  3. Lecture 4 : How To Find Best Stable Diffusion Generated Images By Using DeepFace AI - DreamBooth / LoRA Training
  4. Lecture 5 : Generate Studio Quality Realistic Photos By Kohya LoRA Stable Diffusion Training - Full Tutorial
  5. Lecture 6 : The END of Photography - Use AI to Make Your Own Studio Photos, FREE Via DreamBooth Training
  6. Lecture 7 : How To Use Stable Diffusion X-Large (SDXL) On Google Colab For Free
  7. Lecture 8 : Stable Diffusion XL (SDXL) Locally On Your PC - 8GB VRAM - Easy Tutorial With Automatic Installer
  8. Lecture 9 : Ultimate RunPod Tutorial For Stable Diffusion - Automatic1111 - Data Transfers, Extensions, CivitAI
  9. Lecture 10 : How To Use SDXL On RunPod Tutorial. Auto Installer & Refiner & Amazing Native Diffusers Based Gradio
  10. Lecture 11 : ComfyUI Tutorial - How to Install ComfyUI on Windows, RunPod & Google Colab | Stable Diffusion SDXL
  11. Lecture 12 : First Ever SDXL Training With Kohya LoRA - Stable Diffusion XL Training Will Replace Older Models
  12. Lecture 13 : How To Use SDXL in Automatic1111 Web UI - SD Web UI vs ComfyUI - Easy Local Install Tutorial / Guide
  13. Lecture 14 : Mind-Blowing Deepfake Tutorial: Turn Anyone into Your Favorite Movie Star! PC & Google Colab - roop
  14. Lecture 15 : How to use Stable Diffusion X-Large (SDXL) with Automatic1111 Web UI on RunPod - Easy Tutorial
  15. Lecture 16 : Become A Master Of SDXL Training With Kohya SS LoRAs - Combine Power Of Automatic1111 & SDXL LoRAs
  16. Lecture 17 : How To Do SDXL LoRA Training On RunPod With Kohya SS GUI Trainer & Use LoRAs With Automatic1111 UI
  17. Lecture 18 : How to Do SDXL Training For FREE with Kohya LoRA - Kaggle - NO GPU Required - Pwns Google Colab
  18. Lecture 19 : How Use Stable Diffusion, SDXL, ControlNet, LoRAs For FREE Without A GPU On Kaggle Like Google Colab
  19. Lecture 20 : Turn Videos Into Animation With Just 1 Click - ReRender A Video Tutorial
  20. Lecture 21 : Turn Videos Into Animation / 3D Just 1 Click - ReRender A Video Tutorial - Installer For RunPod
  21. Lecture 22 : Double Your Stable Diffusion Inference Speed with RTX Acceleration TensorRT: A Comprehensive Guide
  22. Lecture 23 : How to Install & Run TensorRT on RunPod, Unix, Linux for 2x Faster Stable Diffusion Inference Speed
  23. Lecture 24 : SOTA Image PreProcessing Scripts For Stable Diffusion Training - Auto Subject Crop & Face Focus
  24. Lecture 25 : Fooocus Stable Diffusion Web UI - Use SDXL Like You Are Using Midjourney - Easy To Use High Quality
  25. Lecture 26 : How To Do Stable Diffusion XL (SDXL) DreamBooth Training For Free - Utilizing Kaggle - Easy Tutorial
  26. Lecture 2 : Essential AI Tools and Libraries: A Guide to Python, Git, C++ Compile Tools, FFmpeg, CUDA, PyTorch
  27. Lecture 27 : Essential AI Tools and Libraries: A Guide to Python, Git, C++ Compile Tools, FFmpeg, CUDA, PyTorch
Lesson 7 of 27
In Progress

Lecture 8 : Stable Diffusion XL (SDXL) Locally On Your PC – 8GB VRAM – Easy Tutorial With Automatic Installer

icarus November 10, 2023

#SDXL is currently in beta and in this video I will show you how to use it install it on your PC. This tutorial should work on all devices including Windows, Unix, Mac even may work with AMD but I couldn’t test it. I also have shown settings for 8GB VRAM so don’t forget to watch that chapter.

Source GitHub Readme File ⤵️

Automatic Installer Script File ⤵️

Our Discord server ⤵️

If I have been of assistance to you and you would like to show your support for my work, please consider becoming a patron on 🥰 ⤵️

Technology & Science: News, Tips, Tutorials, Tricks, Best Applications, Guides, Reviews ⤵️

Playlist of #StableDiffusion Tutorials, Automatic1111 and Google Colab Guides, DreamBooth, Textual Inversion / Embedding, LoRA, AI Upscaling, Pix2Pix, Img2Img ⤵️

0:00 How to use SDXL locally on your PC
1:01 How to install via Automatic installer script
1:35 Beginning manual installation
1:47 How to accept terms and conditions to access SDXL weights and model files (instantly approved)
2:08 How agreement page looks like and how to fill form for instant access
2:38 How to generate Hugging Face access token
2:53 Continuing the manual installation
3:36 Automatic installation is completed. How to start using SDXL
4:00 How to add your Hugging Face token so that Gradio will work
4:45 Continuing the manual installation
5:19 Manual installation is completed. How to start using SDXL
6:17 How to delete cached model and weight files
6:44 How the app will download weight files showing live
7:20 Advanced settings of the Gradio APP of SDXL
8:11 Speed of image generation with RTX 3090 TI
8:39 Where are the generated images are saved
9:44 8 GB VRAM settings – min VRAM settings for SDXL
10:06 How to see file extensions on Windows

Paper :

SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

We present SDXL, a latent diffusion model for text-to-image synthesis. Compared
to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet
backbone: The increase of model parameters is mainly due to more attention blocks
and a larger cross-attention context as SDXL uses a second text encoder. We design
multiple novel conditioning schemes and train SDXL on multiple aspect ratios.
We also introduce a refinement model which is used to improve the visual fidelity
of samples generated by SDXL using a post-hoc image-to-image technique. We
demonstrate that SDXL shows drastically improved performance compared the
previous versions of Stable Diffusion and achieves results competitive with those
of black-box state-of-the-art image generators. In the spirit of promoting open
research and fostering transparency in large model training and evaluation, we
provide access to code and model weights.

The last year has brought enormous leaps in deep generative modeling across various data domains,
such as natural language [50], audio [17], and visual media [38, 37, 40, 44, 15, 3, 7]. In this report,
we focus on the latter and unveil SDXL, a drastically improved version of Stable Diffusion. Stable
Diffusion is a latent text-to-image diffusion model (DM), which serves as the foundation for an
array of recent advancements in, e.g., 3D classification [43], controllable image editing [54], image
personalization [10], synthetic data augmentation [48], graphical user interface prototyping [51], etc.
Remarkably, the scope of applications has been extraordinarily extensive, encompassing fields as
diverse as music generation [9] and reconstructing images from fMRI brain scans [49].
User studies demonstrate that SDXL consistently surpasses all previous versions of Stable Diffusion
by a significant margin (see Fig. 1). In this report, we present the design choices which lead to this
boost in performance encompassing i) a 3× larger UNet-backbone compared to previous Stable
Diffusion models (Sec. 2.1), ii) two simple yet effective additional conditioning techniques (Sec. 2.2)
which do not require any form of additional supervision, and iii) a separate diffusion-based refinement
model which applies a noising-denoising process [28] to the latents produced by SDXL to improve
the visual quality of its samples (Sec. 2.5).
A major concern in the field of visual media creation is that while black-box-models are often
recognized as state-of-the-art, the opacity of their architecture prevents faithfully assessing and
validating their performance.

thumb photo taken from twitter : stonekaiju