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Description

youtu.be/dpM02YMj8FY
You want to use Stable Diffusion, use image generative #AI models for free, but you can’t pay online services or you don’t have a strong computer. Then this is the tutorial you were looking for. By watching this tutorial, you will learn how to use Kaggle free cloud service with famous Stable Diffusion #Automatic1111 SD Web UI as easy as it is running on your local computer. I have prepared an amazing Kaggle notebook that even supports SDXL and ControlNet of SDXL and LoRAs and custom models of #SDXL . Of course it supports all of the Stable Diffusion SD 1.5 based models too.
Kaggle Automatic1111 Notebook File ⤵️
www.patreon.com/posts/run-on-f…
Tutorial GitHub Readme File ⤵️
github.com/FurkanGozukara/Stab…
Introduction to the power of image-generation AI, particularly “Stable Diffusion.”
Addressing challenges with computer limitations or budget constraints.
A comprehensive guide using a Kaggle notebook.
Mention of custom models, specifically “Realistic Vision.”
Details on “X-Large” (possibly a model or a feature) and its acronym “SDXL.”
Reference to other tools or models such as “Pixel Art XL” and “LoRAs.”
Introduction of “Stable Diffusion ControlNet” and its benefits.
Discussion on new updates/features: “Web UI.”
Emphasis on the ease of use, such as generating images with one click.
Mention of platforms like Kaggle.
To craft a detailed summary, I’ll use this breakdown and expand on each point.
Detailed Summary of the Tutorial Video:
Introduction to Image-Generation AI:
The tutorial kicks off by addressing the allure and capabilities of modern image-generation AI, placing emphasis on the “Stable Diffusion” technique.
Overcoming Limitations:
For those limited by their hardware or financial constraints, the tutorial promises a viable solution, ensuring everyone can access advanced AI capabilities.
Kaggle Notebook as a Solution:
A significant portion of the tutorial is devoted to guiding viewers through a Kaggle notebook, which serves as a practical demonstration and hands-on approach to the topic at hand.
Diving into Custom Models:
The tutorial doesn’t stop at generic solutions. It delves deep into custom models, with a special highlight on the “Realistic Vision” model. This model appears to offer cutting-edge features for image generation.
The Power of X-Large (SDXL):
“X-Large”, also referred to as “SDXL”, is introduced as either a powerful model or a feature within the image-generation AI spectrum. It’s presented as a significant topic, suggesting it offers enhanced capabilities or functionalities.
Exploring Other Tools and Models:
Various tools and models like “Pixel Art XL” and “LoRAs” are discussed. Each seems to offer unique features, with “LoRAs” being highlighted as compatible with SDXL, hinting at a synergy between different tools.
Stable Diffusion ControlNet:
A segment is dedicated to introducing “Stable Diffusion ControlNet”. This could be a new model or a control mechanism for Stable Diffusion. It’s presented as a bridge between base models and the more advanced SDXL models.
Updates and New Features:
The tutorial informs viewers about the arrival of a “Web UI”, suggesting updates or new functionalities that can enhance the user experience.
Ease of Use:
Emphasizing user-friendliness, the tutorial showcases how one can generate images with a single click, likely using the discussed models and tools.
Platform Highlights:
Kaggle is mentioned multiple times, suggesting it’s the primary platform used for demonstrations and hands-on exercises in the tutorial.
In essence, this tutorial provides a comprehensive guide on image-generation AI, from the basics to the advanced, ensuring viewers are equipped with the knowledge and tools to harness the full power of modern AI techniques.
0:00 Introduction to how to use Stable Diffusion for free without a computer or a GPU
2:44 How to register a Free Kaggle Account and activate it
3:28 How to create a new Notebook on Kaggle
3:42 The two very important steps that you need to make before start using Kaggle Notebook
3:52 How to set accelerator (select GPUs) and enable Internet
4:28 What is persistence and should you use it
4:45 How to write code on a Kaggle notebook and use the code shared in the GitHub readme file
4:55 How to manually setup your Stable Diffusion Automatic1111 Web UI notebook if you are not my Patreon supporter
5:08 How to download and import the Automatic1111 SD Web UI notebook used in this tutorial
6:20 Suggested resources before following this tutorial
7:28 How to start your Free Kaggle Dual GPU session
7:39 How to see how many resources you are using in your current session in a Kaggle notebook
8:04 How to install Automatic1111 on a Kaggle notebook
8:43 Explanation of the right section of the Kaggle notebook, working directory
9:00 How to clear the outputs of a Kaggle notebook session
9:10 How to download Stable Diffusion SD 1.5, LoRAs and SDXL models into the correct Kaggle directory
9:39 How to download models manually if you are not my Patreon supporter
10:14 An example of how to download a LoRA model from CivitAI
11:11 An example of how to download a full model checkpoint from CivitAI
11:48 How to start downloading all the model files
13:41 How to install ControlNet extension and download ControlNet models
15:50 How to use custom LoRAs from CivitAI or Hugging Face
16:29 How to start Automatic1111 Web UI instance with correct parameters and settings
18:42 How to understand installation of Automatic1111 Web UI has been completed and ready to use
19:09 First time model loading may be very slow
19:26 How to enable quick VAE selection drop down list
19:50 How to set correct folder scan path for ControlNet
20:29 How to reload UI for extensions and setting changes to be effective
20:51 Automatic1111 Web UI is ready to use on a Free Kaggle notebook
21:22 How to use embedded VAE of the models
21:48 Which image generation sampler is the best one
21:58 Why and how much first image generation is slower
22:57 How to install extensions (e.g. After Detailer) of Automatic1111 on a Free Kaggle notebook
23:35 You need to reload Web UI to see new extensions
23:55 How to use your downloaded LoRA models in Automatic1111 Web UI
25:17 First image generation with SDXL model
26:16 First time image generation speed vs consequent images generation on SDXL
26:54 First image generation with the custom LoRA model from CivitAI
27:20 Image generation speed of SDXL when using LoRA
27:41 How to use your own trained LoRAs, models or LoRAs, models from your computer
29:03 How to import your files, datasets in to your current session on Kaggle
29:21 How to use files imported as data set such as LoRAs or models that you have uploaded as dataset
30:23 How to restart Automatic1111 Web UI on a Kaggle notebook
31:06 How to prompt your own trained LoRA
31:32 How to enable After Detailer (adetailer) extension to improve faces of Stable Diffusion generated images
33:01 Where are the SD generated images are saved on a Kaggle notebook
33:45 SDXL with LoRA image generation speed
34:20 How to use Stable Diffusion XL (SDXL) ControlNet models in Automatic1111 Web UI on a free Kaggle
35:05 Where to download SDXL ControlNet models if you are not my Patreon supporter
36:13 Notebook crashes due to insufficient RAM when first time using SDXL ControlNet and how I fix it
37:39 First image generation results of SDXL ControlNet
39:21 SDXL ControlNet development discussion topic
40:22 Possible bug with ControlNet
41:10 How to use Stable Diffusion 1.5 version based ControlNet instead of SDXL ControlNet
43:29 What happens if you exceed your assigned disk space on a free Kaggle notebook
44:35 First time using SD 1.5 ControlNet model on a SD 1.5 based Realistic Vision 5.1 model
44:55 How to fix runtime error when changing SDXL to SD 1.5 based model
46:42 How to use PNG info to get the prompt
47:57 How to download all generated images

Description

youtu.be/dpM02YMj8FY
You want to use Stable Diffusion, use image generative #AI models for free, but you can’t pay online services or you don’t have a strong computer. Then this is the tutorial you were looking for. By watching this tutorial, you will learn how to use Kaggle free cloud service with famous Stable Diffusion #Automatic1111 SD Web UI as easy as it is running on your local computer. I have prepared an amazing Kaggle notebook that even supports SDXL and ControlNet of SDXL and LoRAs and custom models of #SDXL . Of course it supports all of the Stable Diffusion SD 1.5 based models too.
Kaggle Automatic1111 Notebook File ⤵️
www.patreon.com/posts/run-on-f…
Tutorial GitHub Readme File ⤵️
github.com/FurkanGozukara/Stab…
Introduction to the power of image-generation AI, particularly “Stable Diffusion.”
Addressing challenges with computer limitations or budget constraints.
A comprehensive guide using a Kaggle notebook.
Mention of custom models, specifically “Realistic Vision.”
Details on “X-Large” (possibly a model or a feature) and its acronym “SDXL.”
Reference to other tools or models such as “Pixel Art XL” and “LoRAs.”
Introduction of “Stable Diffusion ControlNet” and its benefits.
Discussion on new updates/features: “Web UI.”
Emphasis on the ease of use, such as generating images with one click.
Mention of platforms like Kaggle.
To craft a detailed summary, I’ll use this breakdown and expand on each point.
Detailed Summary of the Tutorial Video:
Introduction to Image-Generation AI:
The tutorial kicks off by addressing the allure and capabilities of modern image-generation AI, placing emphasis on the “Stable Diffusion” technique.
Overcoming Limitations:
For those limited by their hardware or financial constraints, the tutorial promises a viable solution, ensuring everyone can access advanced AI capabilities.
Kaggle Notebook as a Solution:
A significant portion of the tutorial is devoted to guiding viewers through a Kaggle notebook, which serves as a practical demonstration and hands-on approach to the topic at hand.
Diving into Custom Models:
The tutorial doesn’t stop at generic solutions. It delves deep into custom models, with a special highlight on the “Realistic Vision” model. This model appears to offer cutting-edge features for image generation.
The Power of X-Large (SDXL):
“X-Large”, also referred to as “SDXL”, is introduced as either a powerful model or a feature within the image-generation AI spectrum. It’s presented as a significant topic, suggesting it offers enhanced capabilities or functionalities.
Exploring Other Tools and Models:
Various tools and models like “Pixel Art XL” and “LoRAs” are discussed. Each seems to offer unique features, with “LoRAs” being highlighted as compatible with SDXL, hinting at a synergy between different tools.
Stable Diffusion ControlNet:
A segment is dedicated to introducing “Stable Diffusion ControlNet”. This could be a new model or a control mechanism for Stable Diffusion. It’s presented as a bridge between base models and the more advanced SDXL models.
Updates and New Features:
The tutorial informs viewers about the arrival of a “Web UI”, suggesting updates or new functionalities that can enhance the user experience.
Ease of Use:
Emphasizing user-friendliness, the tutorial showcases how one can generate images with a single click, likely using the discussed models and tools.
Platform Highlights:
Kaggle is mentioned multiple times, suggesting it’s the primary platform used for demonstrations and hands-on exercises in the tutorial.
In essence, this tutorial provides a comprehensive guide on image-generation AI, from the basics to the advanced, ensuring viewers are equipped with the knowledge and tools to harness the full power of modern AI techniques.
0:00 Introduction to how to use Stable Diffusion for free without a computer or a GPU
2:44 How to register a Free Kaggle Account and activate it
3:28 How to create a new Notebook on Kaggle
3:42 The two very important steps that you need to make before start using Kaggle Notebook
3:52 How to set accelerator (select GPUs) and enable Internet
4:28 What is persistence and should you use it
4:45 How to write code on a Kaggle notebook and use the code shared in the GitHub readme file
4:55 How to manually setup your Stable Diffusion Automatic1111 Web UI notebook if you are not my Patreon supporter
5:08 How to download and import the Automatic1111 SD Web UI notebook used in this tutorial
6:20 Suggested resources before following this tutorial
7:28 How to start your Free Kaggle Dual GPU session
7:39 How to see how many resources you are using in your current session in a Kaggle notebook
8:04 How to install Automatic1111 on a Kaggle notebook
8:43 Explanation of the right section of the Kaggle notebook, working directory
9:00 How to clear the outputs of a Kaggle notebook session
9:10 How to download Stable Diffusion SD 1.5, LoRAs and SDXL models into the correct Kaggle directory
9:39 How to download models manually if you are not my Patreon supporter
10:14 An example of how to download a LoRA model from CivitAI
11:11 An example of how to download a full model checkpoint from CivitAI
11:48 How to start downloading all the model files
13:41 How to install ControlNet extension and download ControlNet models
15:50 How to use custom LoRAs from CivitAI or Hugging Face
16:29 How to start Automatic1111 Web UI instance with correct parameters and settings
18:42 How to understand installation of Automatic1111 Web UI has been completed and ready to use
19:09 First time model loading may be very slow
19:26 How to enable quick VAE selection drop down list
19:50 How to set correct folder scan path for ControlNet
20:29 How to reload UI for extensions and setting changes to be effective
20:51 Automatic1111 Web UI is ready to use on a Free Kaggle notebook
21:22 How to use embedded VAE of the models
21:48 Which image generation sampler is the best one
21:58 Why and how much first image generation is slower
22:57 How to install extensions (e.g. After Detailer) of Automatic1111 on a Free Kaggle notebook
23:35 You need to reload Web UI to see new extensions
23:55 How to use your downloaded LoRA models in Automatic1111 Web UI
25:17 First image generation with SDXL model
26:16 First time image generation speed vs consequent images generation on SDXL
26:54 First image generation with the custom LoRA model from CivitAI
27:20 Image generation speed of SDXL when using LoRA
27:41 How to use your own trained LoRAs, models or LoRAs, models from your computer
29:03 How to import your files, datasets in to your current session on Kaggle
29:21 How to use files imported as data set such as LoRAs or models that you have uploaded as dataset
30:23 How to restart Automatic1111 Web UI on a Kaggle notebook
31:06 How to prompt your own trained LoRA
31:32 How to enable After Detailer (adetailer) extension to improve faces of Stable Diffusion generated images
33:01 Where are the SD generated images are saved on a Kaggle notebook
33:45 SDXL with LoRA image generation speed
34:20 How to use Stable Diffusion XL (SDXL) ControlNet models in Automatic1111 Web UI on a free Kaggle
35:05 Where to download SDXL ControlNet models if you are not my Patreon supporter
36:13 Notebook crashes due to insufficient RAM when first time using SDXL ControlNet and how I fix it
37:39 First image generation results of SDXL ControlNet
39:21 SDXL ControlNet development discussion topic
40:22 Possible bug with ControlNet
41:10 How to use Stable Diffusion 1.5 version based ControlNet instead of SDXL ControlNet
43:29 What happens if you exceed your assigned disk space on a free Kaggle notebook
44:35 First time using SD 1.5 ControlNet model on a SD 1.5 based Realistic Vision 5.1 model
44:55 How to fix runtime error when changing SDXL to SD 1.5 based model
46:42 How to use PNG info to get the prompt
47:57 How to download all generated images