# FurkanGozukara/Stable-Diffusion: what the SECourses repository actually contains > The repository with the SECourses Stable Diffusion tutorials is a collection of guides, Colab notebooks and scripts, not an application. Here is what you get, how to navigate it, and where it stops being the right tool. **FurkanGozukara/Stable-Diffusion** — FLUX, Stable Diffusion, SDXL, SD3, LoRA, Fine Tuning, DreamBooth, Training, Automatic1111, Forge WebUI, SwarmUI, DeepFake, TTS, Animation, Text To Video, Tutorials, Guides, Lectures, Courses, ComfyUI, Google Colab, RunPod, Kaggle, NoteBooks, ControlNet, TTS, Voice Cloning, AI, AI News, ML, ML News, News, Tech, Tech News, Kohya, Midjourney, RunPod - Repository: https://github.com/FurkanGozukara/Stable-Diffusion - Website: https://www.youtube.com/SECourses - Stars: 2,769 · Forks: 371 - Language: Jupyter Notebook - License: GPL-3.0 - Published: 2026-09-10 · Updated: 2026-09-10 · Language: en - Canonical page: https://hysenlabs.com/projects/furkangozukara-stable-diffusion ## This is a tutorial index, not a Stable Diffusion distribution The repository is a Jupyter Notebook project that collects written guides, video links and helper files around generative image and video models. The README opens with a short biography of the author, Dr. Furkan Gozukara, describing his work as an assistant professor and full time generative AI researcher, and then lists the SECourses channels: YouTube, Patreon, Discord, LinkedIn, Twitter, Medium, DeviantArt, CivitAI and Udemy. Everything below that is a table of tutorial videos with thumbnails, each linking to a YouTube video. The topics list is the clearest statement of scope: ai-art, deepfake-generation, dreambooth, flux-dev, flux-lora, kohya-webui, lora-training, stable-diffusion, text-to-image, text-to-video, tts, tutorials. That is a wide net. It covers training a LoRA, running Automatic1111 or Forge WebUI, using ComfyUI and SwarmUI, upscaling images and video, voice cloning, and running the same workflows on Colab, RunPod, Kaggle or a local Windows machine. The practical consequence is that you will not find a diffusion model or a sampler in this repository. If you are looking for stable-diffusion.cpp or an inference engine, this is the wrong address. What you get is a map of where those things are explained, plus a set of scripts and notebooks that the author maintains alongside the videos. ## How the repository is laid out, and what each directory is for The top level mixes markdown indexes with a few code directories. The markdown files are the bulk of it: Amazing-Generative-AI-Scripts.md, Amazing-Prompts-List-For-Stable-Diffusion.md, Generateive-AI-Tutorials-News-Developments-Apps-Tech.md, Generative-AI-Updates-And-News.md, Patreon-Posts-Index.md, SECourses_YouTube_Videos_List.md, SECourses_Rumble_Videos_List.md, SECourses_VKVIDEO_Videos_List.md, SD_WebUI_Forge_NEO_Support_List.md, Technology-Science-AI-ML-ChatGPTs-Tutorials-Guides-Tips-News.md, Useful-Commands.md and Useful-Hugging-Face-Spaces.md. The code and configuration sit in four directories: ColabNotebooks/, CustomPythonScripts/, DreamBooth/, Generative-AI/ and Tutorials/. App_Updates_Change_Logs/ holds release notes for the tools the author ships separately. Links/ is a link collection. .github/ holds repository automation. Two files are worth flagging for anyone deciding whether to clone this. Useful-Commands.md reads as a command reference rather than prose, which makes it the fastest way to find an install line without watching a video. SD_WebUI_Forge_NEO_Support_List.md is a compatibility list for Forge NEO, which is the kind of artifact that goes stale quickly and therefore needs a date check. The repository contains no build system, no package manifest at the top level, and no test suite, because it is not building a single artifact. ## Installing the repository and running your first script There is no installation step for the repository as a whole, because there is no application to install. The README points readers to the video How To Install Python, Setup Virtual Environment VENV, Set Default Python System Path & Install Git, which is the author's own prerequisite walkthrough. The realistic first move is to clone the repository and read the command reference. ```bash git clone https://github.com/FurkanGozukara/Stable-Diffusion.git cd Stable-Diffusion ls ColabNotebooks CustomPythonScripts ``` After that listing you should see the notebook and script filenames that the repository actually ships. Those names, not the README, tell you which workflows are covered in code form. If a directory is empty or holds only a placeholder, that workflow exists only as a video. For the notebooks, the intended path is a hosted runtime. The description names Google Colab, RunPod and Kaggle as targets, and ColabNotebooks/ is where those files live. Opening one in Colab and running its cells top to bottom is the documented pattern; the videos that accompany each notebook explain the settings. For local Windows use, the README's second video covers the Automatic1111 installer, which is a separate project that this repository only links to. ```bash python -m venv venv source venv/bin/activate ``` The virtual environment commands above are the standard VENV setup the first video teaches, and they are what the CustomPythonScripts/ helpers assume when they import packages. Nothing in the repository pins those packages, so versions come from whatever the linked tutorial specifies at the time it was recorded. ## The maintenance model: dated markdown, external code The last push to the default branch was on 2026-09-04, so the repository is being updated. What that update consists of matters more than the fact of it. The README describes the file as a list the author keeps up to date, and the presence of an App_Updates_Change_Logs/ directory plus several news-and-updates markdown files confirms that a large share of the commits are index maintenance: new video rows, new links, new entries in the news files. That is a legitimate model for a tutorial collection. It is a poor model if you were hoping for semantic versioning, a changelog you can diff against an API, or a release artifact. The repository has no retrieved releases, and the README does not document a versioning scheme, a support window, or a deprecation policy for the notebooks. A Colab notebook that worked against one model release may need edits after the next one, and nothing in the repository structure guarantees a fix. The upside is that the markdown files are plain text. You can grep them, fork them, and keep your own trimmed copy without fighting a build. The downside is that accuracy depends on one maintainer's attention across a topic list that spans image generation, video generation, TTS and voice cloning. ## Where the repository stops being the right tool If you need to call Stable Diffusion from your own service, this repository will not help you. There is no importable package, no documented HTTP API, and no client library. The topics list mentions Gradio, which is the interface layer used by several of the web UIs the tutorials cover, but the repository does not expose a Gradio app of its own. A second boundary is reproducibility. A tutorial video records one working configuration on one machine at one point in time. The repository does not ship lockfiles, container images or pinned dependency sets at the top level, so a reader following a two-year-old notebook should expect to resolve version conflicts. The README does not document rollback or a tested baseline for any workflow. A third boundary is the licence. The repository is GPL-3.0. That is a copyleft licence, and it applies to the repository's own contents, which are mostly documentation and scripts. Anyone planning to lift a script from CustomPythonScripts/ into a closed product should read the licence text in LICENSE rather than assume that a guide is freely reusable in any context. This is a description of the licence, not legal advice. The final boundary is audience. Someone who already knows which sampler and scheduler they want and just needs the flags will find the video format slow. The written files are the better entry point for that reader, and Useful-Commands.md is the one to open first. ## How it differs from the projects it teaches The natural comparison is with the software the tutorials cover. Automatic1111's Stable Diffusion Web UI is an application: you install it, it serves a browser interface on a local port, and it manages models, extensions and generation settings. ComfyUI is a node graph editor where you wire a pipeline together and save it as a workflow file. SwarmUI is another front end with its own session and model management. This repository is none of those. It is documentation, plus notebooks and scripts that drive those tools. The difference in approach shows up the moment something breaks. With a web UI you file an issue against the code that failed. Here you check whether the video or markdown still matches the tool's current behaviour, and if it does not, the fix is an edit to a guide rather than a patch. That distinction also explains why the repository can cover so many tools at once. It does not have to keep them compatible with each other, only to describe them. The cost is that a reader gets breadth of coverage and no guarantee of depth on any single tool. If you want one tool documented exhaustively, its own repository is the better source; if you want to know which tools exist and roughly how they compare, the index files here do that job. ## Licence and the cost of keeping up The repository is licensed GPL-3.0, and the LICENSE file at the top level is the authoritative text. Documentation and scripts under that licence can be copied and modified, but derivative distribution carries the same licence obligations. For an internal team reading guides, this is not a practical constraint. For anyone vendoring a script into a product, it is the first thing to check, and the second thing is whether the script depends on a model whose own licence differs. Upgrade cost is the more likely ongoing expense. The repository tracks fast-moving subjects: FLUX, SDXL, SD3, Qwen Image, Wan 2.2, SeedVR2. Model releases change defaults, and a notebook that pins nothing will drift. The maintenance work falls on whoever runs the notebook, not on the repository. Budget for reading the App_Updates_Change_Logs/ entries and the news markdown files before reusing an old workflow, and treat the video dates as the version marker, since there is no other one. ## Conclusion Adopt this repository if you want a curated, dated index of generative AI tutorials and Colab notebooks, and you accept that the code lives in other projects. Do not adopt it if you need a library to import, a CLI to script, or a supported API. Before relying on it, open ColabNotebooks/ and CustomPythonScripts/ to see what is actually runnable, and check the last push date against the models you plan to use. ## FAQ ### Is Stable Diffusion still free? The repository does not address pricing for Stable Diffusion itself. It links to free hosting options including Google Colab, Kaggle and RunPod, and to the Automatic1111 installer, but the README does not state terms for any model or service. ### How can I use Stable Diffusion? According to the README, the path is to follow the tutorial videos and the ColabNotebooks/ or CustomPythonScripts/ files. The repository teaches Automatic1111, Forge WebUI, ComfyUI and SwarmUI rather than shipping a generator of its own. ### How do I install Stable Diffusion on Windows? The README's second video covers installing and running the Stable Diffusion Web UI on PC with an open source automatic installer, and the first video covers installing Python, Git and a VENV. This repository itself has no installer. ### How do I use Stable Diffusion WebUI? The README links a dedicated tutorial playlist for Automatic1111 Web UI and Google Colab, and the SD_WebUI_Forge_NEO_Support_List.md file tracks Forge NEO compatibility. The Web UI itself is installed from its own project, not from this repository. ### How do I use Stable Diffusion in ComfyUI? ComfyUI appears in the topics and the channel description, and the repository's tutorial lists cover it. The README does not describe a ComfyUI workflow file shipped here, so the coverage is through the linked videos and guides. ## Sources - [FurkanGozukara/Stable-Diffusion on GitHub](https://github.com/FurkanGozukara/Stable-Diffusion) - [Issues](https://github.com/FurkanGozukara/Stable-Diffusion/issues) - [License: GPL-3.0](https://github.com/FurkanGozukara/Stable-Diffusion/blob/main/LICENSE) - [Project website](https://www.youtube.com/SECourses) - [README](https://github.com/FurkanGozukara/Stable-Diffusion/blob/main/README.md) --- Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/furkangozukara-stable-diffusion