DEV Community: Furkan Gözükara

Ultimate Image Processing Toolkit by SECourses

Furkan Gözükara — Tue, 28 Jul 2026 17:02:10 +0000

Ultimate Image Processing APP Premium by SECourses : Batch Cropping, Zooming In, Resizing, Duplicate Image Removing, Face Extraction, SAM 2, SAM 3.1 and Yolo Segmentation, Masking for Windows, RunPod, Massed Compute

Installers and APP is shared here with instructions : https://www.patreon.com/posts/120352012

APP : https://www.patreon.com/posts/120352012

Video Tutorial : Hopefully coming soon

Batch Image Cropping, Zooming Subject, Resizing, Segmenting, Masking, Duplicate Removing APP that utilizes YOLO V26, YOLO Face V12, SAM 2, SAM 3.1 with 1-click installers for Windows, RunPod, SimplePod and Massed Compute (Linux users use this)

28 July 2026 V1.0

We have completey remade the app and read below to understand how it works and full features
Make a fresh install and read the below carefully please
We use latest pre-compiled wheels and Torch 2.13 and CUDA 13

Windows Requirements

Python 3.12.10, FFmpeg, CUDA 13, cuDNN 9.17 or above, Visual Studio Community Edition with all C++ options selected
- Don't worry CUDA 13 works with all GPUs - make sure you have updated NVIDIA driver
- Follow this requirements tutorial video exactly : https://youtu.be/DrhUHnYfwC0
- Follow its updated post with links and screenshots exactly : https://www.patreon.com/SECourses/posts/requirements-written-tutorial-111553210

Turn a folder of raw photos into a clean, correctly framed, ready-to-train dataset. The nine tabs are designed to feed each other: the output of one is exactly what the next expects to read.

One architectural detail matters more than any single feature: the Gradio process never has to import torch. Tick Run every task in a subprocess on the Performance tab and every job runs in a throw-away process that loads the model, does the work and exits, returning 100% of its VRAM, RAM and CUDA context the moment it finishes. A long session never creeps upwards, and the app sits at zero idle VRAM between runs. It ships off, so jobs run in-process by default and keep their models resident for faster repeated runs — turn it on when you would rather have the memory back.

Requirements
Installation
Launching the app
Presets
Tab 1 — Image Cropper
Tab 2 — Segmentation
Tab 3 — Extract Faces & Objects
Tab 4 — Image Resizer
Tab 5 — Generate Tiled Images
Tab 6 — Find Duplicates
Tab 7 — Move Low-Res Files
Tab 8 — Rename Files
Tab 9 — Performance & VRAM
Recommended workflow
Output folder layout
Measured benchmarks

Requirements

Python 3.12.10 (3.10.x / 3.11.x / 3.13.x may also work)
CUDA 13.0 and cuDNN 9.17+
Git, FFmpeg
Visual Studio Community Edition with all C++ options (needed for torch.compile)
An NVIDIA GPU — any card works, more VRAM is better. CPU-only is supported but slow.

Setup walkthrough for the prerequisites: https://youtu.be/DrhUHnYfwC0

Installation

Windows — automatic

Run Windows_Install_or_Update.bat. It clones this repository, creates a Python 3.12 venv, installs the dependency set with uv, and then downloads every model via HF_model_downloader_img_process.py.

Re-run the same script later to update — it does a git reset --hard + git pull before reinstalling.

Manual

git clone --depth 1 https://github.com/FurkanGozukara/Ultimate_Image_Processor_Pro
cd Ultimate_Image_Processor_Pro
py -3.12 -m venv venv
call .\venv\Scripts\activate.bat
python -m pip install --upgrade pip
pip install uv
cd ..
uv pip install -r requirements_image_process.txt --index-strategy unsafe-best-match
python HF_model_downloader_img_process.py

The requirements file pins torch==2.13.0+cu130 plus prebuilt wheels for xformers, FlashAttention, SageAttention, torchao, InsightFace and SAM 2 — so no compilation is needed on a supported platform.

Cloud (RunPod / SimplePod / Massed Compute)

See the instructions bundled with the Patreon release.

Launching the app

Use Windows_Start_App.bat, or activate the venv and run:

python app.py

Flag	Effect
`--share`	create a public Gradio link
`--server-port PORT`	serve on a specific port (default: auto)
`--server-name HOST`	host to bind to (default: `127.0.0.1`)
`--no-browser`	do not open a browser window on launch
`--quiet`	reduce Gradio's start-up output

Presets

A preset captures every control in the entire app, across all nine tabs — around 150 of them. The preset you last saved or loaded is restored automatically the next time the app starts, so your working configuration survives a restart.

Selecting a preset in the dropdown changes nothing until you press Load, so a mis-click can never wipe the settings you are in the middle of editing. The built-in Default set lives in code rather than on disk, so it can never be overwritten or deleted.

Tab 1 — Image Cropper

Finds the subject in every photo of a folder and crops the tightest box that matches each aspect ratio you ask for. It detects the subject, adds your padding, then grows the box outwards until the ratio is exact — so the subject is never squashed and no part of it is ever cut off.

Output goes to one sub-folder per aspect ratio (1024x1024/, 1280x720/, 1080x1350/ …), which is exactly the layout the Image Resizer tab reads back.

Live folder scan — image count, extensions and total size shown before you press start
Three detection engines, each showing only its own thresholds
All instances (union) — spans every match so nobody is cropped out of a group photo
Padding in percent or pixels, applied before the ratio so the ratio stays exact; negative values crop in tighter
Annotated previews — optionally writes a copy with detections drawn on it, to check a prompt before a big run
Safe re-runs — with overwrite off, images whose crops already exist are skipped

The three detection engines

Engine	What it is	Its knobs	Use when
SAM 3.1 (default)	Detects and segments in one model from open-vocabulary text. Pixel-accurate masks, no fixed class list.	confidence, max instances	your subject is not a COCO class, or you need a real mask
YOLO	Fastest, no prompt wording to get wrong. 80 COCO classes plus a dedicated face model (`yolov12l-face.pt` swaps it in automatically).	confidence, NMS IoU, inference size, max detections	your subject is a COCO class and you want throughput
SAM 2.1 + GroundingDINO	The older two-stage pipeline: GroundingDINO proposes boxes, SAM 2.1 turns them into masks.	box threshold, text threshold	reproducing results from before SAM 3.1

How the cropping logic works

Detect → pad → grow outwards to the ratio. And the detail that surprises most people: nothing is ever upscaled. The aspect ratio you type is used as a ratio, not as a target size, so a 6000 px wide photo yields a 6000 px wide 16:9 crop, not a 1280 px one. Tick Resize crops to the exact target size if you want the Cropper to scale too — otherwise crop here and resize in the Image Resizer tab, where you choose the filter. That order throws away the least detail.

The run panel

Every batch tab shares the same panel: a live status card with progress, images/sec and ETA, a copyable log naming each file and how many outputs it produced, and a Stop button that really stops the worker and keeps whatever was already written.

Real results

The annotated detection preview alongside the 1:1, 16:9 and 4:5 crops of the same photos. Every crop is a plain rectangle of original pixels — the app never resamples during cropping.

Tab 2 — Segmentation

SAM 3.1 masks from plain English, with an interactive single-image tester and a folder batch that share one set of settings.

Dial your prompt in on one image first — it takes a few seconds and shows the exact mask, the confidence score and the mask area in pixels. The batch section further down the page then uses those identical settings; there is no second set of controls to keep in sync.

You get the segmented overlay, the mask itself (white inside, black outside — ready for inpainting, ControlNet or masked training), and a copyable detections table with label, score, bounding box and mask pixel count per instance.

Negative prompts

Subtract one concept from another in a single pass. Below, the prompt is face. and the negative prompt is eye. lips. — the eyes and mouth are carved straight out of the face mask. The negative mask is saved separately so you can reuse or invert it.

This is how you build skin-only masks, or segment a car while cutting out the person standing in front of it.

Settings and the YOLO backend

Concepts are separated by periods and the vocabulary is open. Confidence drops weak instances; max instances caps both the result count and how much VRAM the mask upsample needs on a busy scene; overlay opacity affects only the preview tint (the saved mask is always pure black and white).

On the YOLO backend you multi-select any number of the 80 COCO classes instead of writing a prompt, with a separate exclude list. YOLO mask source decides where masks come from: SAM 2.1 refined turns each box into a pixel-accurate mask (loads a second model, still fast), while filled boxes is instant and plenty for rectangular crops or inpainting blocks.

Batch processing

One mask per image, plus optional annotated overlays and negative masks. Leaving the overlay folder empty makes a run noticeably faster, because the masks then never need upsampling to full resolution for display. Skip images that already have a mask makes an interrupted run cheap to resume.

The prompt below was a single word: face. The bottom row is a red-carpet shot with a crowd in the background — SAM 3.1 found 16 separate faces in it and every one is in the mask.

Tab 3 — Extract Faces & Objects

Cuts the detected region out of each photo and saves it as its own image — the fastest way to build a face dataset. Unlike the Image Cropper this does not force an aspect ratio: you get the detection box plus whatever padding you ask for.

Save every detected instance as its own file — a group photo of five people yields five crops (photo_1.png … photo_5.png). Off gives exactly one crop per photo, chosen by confidence or largest area.
Padding — faces almost always look better with 15–30% of breathing room. Percent scales with the face, so close-ups and wide shots come out consistent.
Minimum crop size — one slider that drops background faces too small to be useful.
Not limited to faces: hand. licence plate. dog. all work.

Tab 4 — Image Resizer

Writes every input image at one or more exact pixel sizes. Each input is written once per resolution into <output>/<WxH>/, so several dataset buckets come out of a single run.

Run it after the Image Cropper: crop first at native resolution, then resize once with the filter you want.

It works out what you pointed it at

Point at an Image Cropper output folder and it reports Image Cropper layout detected, listing how many files each WxH bucket contributes and naming any resolution with no matching sub-folder. Point at a plain folder of pictures and it says Plain folder detected and does the multiplication for you: 12 images × 3 resolutions = 36 output files.

Framing choices

Crop to fill keeps the aspect ratio and trims the overflow — no bars, some content lost, and the default. Fit inside keeps every pixel and pads with your chosen background colour. Stretch distorts the image and is almost never what you want.

Separately, Centre the crop on decides where the crop is taken. Image centre is instant and loads no model, but a geometric centre crop can push the subject out of frame. Detected face / subject runs a detector first so the subject stays in frame — slower, since each worker loads its own model, so keep the worker count low in that mode.

Tab 5 — Generate Tiled Images

Slices each photo into an overlapping N×N grid of fixed-size tiles. Because the tiles are cut at native resolution and never upscaled, this is a very good way to turn a handful of very large photos into a high-detail training set.

Tiles per image must be a perfect square — 9 means a 3×3 grid — and tiles overlap automatically so the whole frame is covered. If an image is smaller than one tile you choose the policy: Skip protects quality, Upscale guarantees output but invents pixels, Shrink the tile keeps the grid but writes smaller tiles for those images only.

Tab 6 — Find Duplicates

Groups visually identical or near-identical images and moves the extras out of the way, keeping one copy per group. Every comparison uses a true perceptual distance — earlier versions compared hashes as big integers, which is not a similarity measure at all and both missed real duplicates and invented fake ones.

Nothing is deleted. Duplicates are moved to a destination folder keeping their relative structure, and a full report is written next to them, so the step is reversible. You choose which copy survives: highest resolution, largest file, oldest, or first alphabetically.

Choosing an algorithm

The app ships a reference table for aHash, dHash, pHash, wHash, ColorHash, KAZE local features and a combined multi-hash mode — what each measures, what it is good at, where it breaks down, and the cutoff to start from.

Cutoffs are Hamming distances: how many bits differ out of hash size², so smaller is stricter. Each algorithm gets its own threshold, because a distance of 5 means something completely different for aHash than for pHash. KAZE is the odd one out — it survives rotation, perspective and heavy cropping, but it is quadratic in image count, so keep it for small folders.

There is also a safety cap on image count, because comparison is quadratic and a 100,000-image folder would otherwise run for hours. Anything dropped by the cap is reported explicitly rather than silently ignored.

Tab 7 — Move Low-Res Files

Quality control for a crop run. Any processed image smaller than your threshold is moved out of the way together with the original it came from, so your two folders stay in sync — you never end up with a source photo whose result has been thrown away.

Set the minimum width and height, then choose whether to reject when either dimension is under the limit (strict) or only when both are. Files are paired by name, and Same name, any extension handles the everyday case where the crop is a .png and the original a .jpg. Preview mode is on by default and lists exactly what would move without touching disk.

Tab 8 — Rename Files

Renames a folder to a clean prefix + number sequence. The rename happens in two passes through temporary names, so a target name already taken by another file in the same folder can never clobber it. Extensions are always preserved.

Set the prefix, the starting number, the zero padding (4 or 5 so files sort correctly everywhere) and the numbering order — by name, by date, or by size. Only rename image files leaves captions and JSON sidecars alone. Preview mode is on by default and shows the first 50 planned renames without touching disk.

Tab 9 — Performance & VRAM

Everything on this tab applies to every other tab and is saved with your presets. The shipped defaults are already the fastest configuration that still matches the reference model output — you only need this tab to trade quality for memory, or to try torch.compile.

Run every task in a subprocess (off by default, tick it if you want it) — each run starts a fresh Python process, works, then exits, returning 100% of its VRAM and RAM, CUDA context and all. Left off, models stay resident between runs: faster repeated runs, but the VRAM stays held.
Switching model frees the old one first. Only one model set is ever resident. Change the backend, the precision, the resolution, the GPU or the YOLO class that swaps in the face weights, and the previous model is unloaded — collected out of RAM and released from VRAM — before the new one is read from disk, so a switch never has to hold both. Measured on an RTX 5090: SAM 3.1 (1861 MB) → YOLO leaves 262 MB held, and eight switches back and forth drift by 0 MB. The console names what it dropped: [models] released SAM 3.1 (concept prompts) [bfloat16 @ cuda:0, res 1008]. If a second task happens to be running when you switch, its model is left alone until the process is quiet again.
Unload models and release VRAM — the diagnostics button below drops every loaded model on demand and reports the before/after figures, e.g. model weights 1893 MB → 0 MB, allocator reserve 2670 MB → 0 MB. What is left after that is the CUDA context itself, which only a process exit returns.
GPU IDs — comma-separated CUDA indices. With several GPUs the image list is split across them, but each device loads its own copy of the model, so a second GPU only pays for itself on folders of a few hundred images or more. Enter cpu to force CPU.
Model precision — the shipped SAM 3.1 checkpoint is stored in bfloat16, so keeping the weights in bfloat16 is bit-identical to the published values. Measured against the float32 path: 0.9992 mask IoU, 46% less VRAM (1.9 GB vs 3.5 GB), ~7× faster model loading and ~5% faster per image.
Load weights straight into VRAM — builds the model on the GPU and reads the safetensors file directly into VRAM, skipping the ~3.4 GB float32 copy in system RAM. Roughly 2.5× faster model loading.
Chunked mask upsampling — upsamples instance masks a few at a time. On a 24 MP photo with 20 instances this is the difference between a ~1 GB spike and a few tens of MB, with pixel-identical results.
Attention kernel, TF32, cuDNN autotuner, channels-last, matmul precision — all exposed and documented inline.
torch.compile the vision backbone — a one-off compile cost of roughly one to three minutes (cached under .cache/uipp) for faster steady-state inference. Worth it for large batches, not for a handful of images; falls back to eager mode if compilation fails.
SAM 3.1 inference resolution — 1008 is what the model was trained at. Lower is faster but loses small objects; higher costs quadratically more and adds no detail the model was trained to use.

Runtime probe

One click imports torch — in a throw-away subprocess if Run every task in a subprocess is ticked, otherwise in this process — and reports what your machine can actually do: Python and platform, torch and CUDA versions, every GPU with its compute capability and VRAM, the status of Triton / FlashAttention / xformers / SageAttention / torchao, whether torch.compile is genuinely usable on this box (CUDA root, host compiler, ninja, cache path), and whether each model backend is ready. In subprocess mode the process then exits and the memory is gone.

Recommended workflow

Find Duplicates — clean first, so you never spend GPU time on near-identical shots
Image Cropper — frame the subject, one sub-folder per ratio, at native resolution
Move Low-Res Files — quality control, results and originals moved together
Image Resizer — point it at the Cropper's output folder for exact pixel sizes
Rename Files — a clean, correctly sorting sequence

Side branches whenever you need them: Segmentation for masks, Extract Faces for a face dataset, Generate Tiled Images for more data out of very large photos, and Performance & VRAM set once for everything.

The one rule that matters most: crop before you resize. Cropping keeps the original pixels, so resizing afterwards starts from the largest possible image and only has to downscale once. The other way round throws away detail you can never get back, and can force an upscale.

Output folder layout

The Image Cropper's <width>x<height>/ sub-folder layout is not cosmetic — the Image Resizer reads it directly, matching each bucket to the resolution of the same name. That is exactly why the recommended order is crop → resize.

Quick reference

Every tab, the one setting that matters most, and the mistake to avoid.

Measured benchmarks

Single RTX 5090, 12 source photos at up to 6714 × 5274 px, SAM 3.1 at its default settings.

Job	Work done	Time
Image Cropper	12 photos → 36 crops across 3 aspect ratios	47.7 s (0.25 img/s)
Segmentation (batch)	12 photos, 33 instances masked	28.8 s (0.42 img/s)
Extract Faces	33 face crops from 12 photos	9.6 s (1.25 img/s)
Image Resizer	36 files at exact pixel sizes	2.0 s (17.7 img/s, CPU)
Generate Tiled Images	108 tiles from 12 photos	1.2 s
Find Duplicates	4 groups across 17 images	0.4 s

Built by SECourses · Patreon · YouTube

Ideogram 4 Is Here: The Ultimate JSON Prompting Masterclass

Furkan Gözükara — Sun, 19 Jul 2026 13:30:16 +0000

Learn how to run Ideogram 4 locally with SwarmUI and ComfyUI, download the required model bundle, use the ready Turbo, Balanced, and Highest Quality presets, and create accurate structured JSON prompts with Ultimate Image Captioner Pro.

The workflow covers image recreation, reliable text rendering, bounding-box editing, batch captioning, and training-dataset preparation.

Full Tutorial

Watch the complete step-by-step tutorial on YouTube:

Ideogram 4: The Ultimate JSON Prompting Masterclass

Tutorial Resources

Resource	Link
Ultimate Image Captioner Pro download and installer	Patreon post
SwarmUI installer, model downloader, and presets	Patreon post
ComfyUI installer	Patreon post
Windows requirements tutorial	YouTube
Requirements guide with links and screenshots	Patreon post
Community support	SECourses Discord
More tutorials, installers, and resources	SECourses GitHub

Supported Models

Ultimate Image Captioner Pro supports the following models with robust torch.compile integration.

Qwen Vision Models

Qwen3-VL 8B Instruct (default)
Huihui Qwen3-VL 8B Instruct Abliterated
Qwen3-VL 4B Instruct
Qwen3-VL 2B Instruct
Qwen3-VL 30B-A3B Instruct
Qwen3.6 27B
Huihui Qwen3.6 27B Abliterated

Joy Caption Models

Joy Caption Beta 1
Joy Caption Alpha 2
Joy Caption Alpha 1
Joy Caption Pre Alpha

Torch 2.13 Runtime

The application uses Torch 2.13 with the latest project-tested, precompiled supporting libraries.

Application Preview

Click the image to open the full-size screenshot.

Torch Compile Performance

The fully compiled captioning path delivers an 84% speed improvement in the demonstrated benchmark.

Installers

Installer workflows are available for Windows, RunPod, SimplePod, Massed Compute, and local Linux systems.

Video Chapters

Show all tutorial chapters

00:00:00 - Ideogram 4 overview: JSON prompting, SwarmUI presets, ComfyUI workflows, and model bundle
00:00:53 - Ultimate Image Captioner Pro for turning reference images into Ideogram JSON prompts
00:01:10 - Editing JSON elements, bounding boxes, wanted text fields, captions, and prompt layout
00:02:02 - Regeneration examples showing structure, objects, scene layout, and image text matching
00:03:18 - Captioner Pro feature tour: Qwen, JoyCaption, saved outputs, and JSON builder
00:04:30 - Dataset workflow: prompt presets, batch folder captioning, and automatic VRAM presets
00:05:13 - Tutorial roadmap: ComfyUI update, SwarmUI update, model download, app installation, and usage
00:05:40 - Updating ComfyUI by extracting the latest installer ZIP and overwriting old files
00:05:56 - Optional fresh ComfyUI virtual environment rebuild for outdated or broken installations
00:06:15 - Running the ComfyUI update script, Python choice, UV speed, and quantization support
00:07:05 - Installing recommended custom nodes bundle 100 for ComfyUI and SwarmUI compatibility
00:07:50 - Launching fresh ComfyUI and testing the Ideogram Turbo preset workflow
00:08:47 - Setting width, height, resolution, and matching the prompt aspect ratio
00:09:07 - Updating SwarmUI with the latest ZIP, overwrite method, and safe folder paths
00:09:48 - Automatic .NET SDK 10 installation and why SwarmUI needs the correct SDK version
00:10:51 - SwarmUI backend setup: ComfyUI backend, Triton, Sage Attention cautions, and extra arguments
00:11:44 - Downloading the Ideogram 4 core bundle with hash verification
00:12:28 - 16-connection parallel downloads, target folders, ComfyUI mode, and URL downloader
00:13:20 - Merging model parts and sharing SwarmUI models through extra_model_paths.yaml
00:13:51 - Setting the SwarmUI model root to reuse another model folder and avoid duplicates
00:14:12 - Updating SwarmUI presets with delete import, normal import, overwrite, and backup
00:14:58 - Refreshing presets and confirming Ideogram Turbo, Balanced, and Highest Quality
00:15:14 - First simple Ideogram prompt, false safety-filter block, and weak plain prompting
00:15:34 - Using Realism Engine Ideogram 5 LoRA to fix the blocked car prompt
00:15:57 - Why detailed JSON prompts are needed and downloading Captioner Pro
00:16:23 - Installing Captioner Pro with the Windows install/update app, virtual environment, and model downloads
00:16:34 - Windows requirements: Python, CUDA, cuDNN, C++ tools, FFmpeg, Git, and setup guide
00:17:03 - Cloud and Linux notes plus the Massed Compute interface, creator image, GPU, and coupon
00:17:34 - Captioner installer downloader: 16 connections, hash checks, and accurate setup
00:17:57 - Starting Ultimate Image Captioner Pro and saving custom user presets
00:18:14 - Loading the Bugatti reference image and generating official Ideogram JSON
00:18:39 - Prompt generation speed, copying the prompt, and understanding VRAM usage
00:19:09 - Subprocess mode to release all VRAM and RAM after each captioning run
00:19:54 - Reviewing generated JSON: high-level description, visible text, boxes, and details
00:20:21 - Pasting JSON into SwarmUI and matching the custom 5:3 aspect ratio
00:20:43 - Aspect-ratio calculator, side-length control, and high-resolution generation
00:21:36 - Comparing results with and without aspect-ratio metadata and avoiding false safety blocks
00:21:58 - Realism Engine LoRA strength, when to use it, and output comparison
00:22:34 - Choosing Turbo, Balanced, or Highest Quality and testing Turbo speed
00:22:54 - Ideogram 4 image-to-image, inpainting, image creativity, and image prompts
00:23:19 - Captioner Pro batch-folder processing: subfolders, overwrite, and append modes
00:23:35 - Post-processing captions with prefixes, suffixes, replacements, and sensitivity
00:24:07 - Final options, automatic quantization by GPU VRAM, support channels, and closing

Covered in the Tutorial

Local Ideogram 4 installation
SwarmUI and ComfyUI preset usage
Automatic model downloads and hash verification
Structured JSON prompt creation
Bounding-box and visible-text editing
Reference-image recreation
Safety-filter troubleshooting and LoRA realism settings
Folder-based batch captioning
VRAM-friendly caption generation

ACE-Step XL 1.5 Premium + Facebook / META SAM Audio + Auto-Editor Trim + Audio Tools Enhancement Full Tutorial

Furkan Gözükara — Sun, 28 Jun 2026 00:59:23 +0000

ACE-Step XL 1.5 Premium + Facebook / META SAM Audio + Auto-Editor Trim + Audio Tools Enhancement Full Tutorial

Video Tutorial

https://youtu.be/9C_6qNKjgpA

Source Video, Links, And Chapters

The public video description presents this as a full ACE-Step XL 1.5 Premium guide for local AI music generation, remix, repaint, stem extraction, wildcard prompt variation, audio processing, SAM Audio segmentation, Windows installation, RunPod, Massed Compute, SimplePod, and Linux/cloud workflows.

Tutorial video: Forget Suno: Run the Ultimate AI Music Studio LOCALLY (100% Free)
Channel: SECourses
App/latest zip and Patreon post: Patreon ACESTEP XL Premium SAM Audio post
Windows requirements guide: Windows requirements guide
Discord/help/community: SECourses Discord server
Duration: 1:15:09
Published: June 17, 2026

Video Chapters

0:00 — Intro: ACESTEP XL 1.5 Premium local music, segmentation and processing tutorial
0:52 — Fast song generation examples across styles in under one minute
1:55 — Output manifest proof, 40-second generation time and supported models
2:29 — Turbo/SFT/Base models, LoRA support, GPU presets and Torch Compile boost
3:10 — Remix feature preview, same-lyrics requirement and responsible usage note
4:16 — Repaint mode: regenerate and merge only a selected song section
5:38 — Extract mode: stems, silence trimming, all-stems and batch folders
6:30 — Lego mode: add an instrument stem such as guitar into existing audio
7:25 — Audio Processing presets and manual enhancement controls for AI songs
8:35 — Auto-Editor silent trim for tutorials, videos, audio and workflow export
9:48 — DaVinci/Premiere/Final Cut/ShotCut/Kdenlive timeline export demo
11:01 — SAM Audio Segment: BF16 models, VRAM presets and advanced segmentation
11:47 — SAM outputs demo: vocals, drums, bass, remaining audio and saved files
12:47 — Custom SAM prompts, semicolon batch segmenting and speech cleanup example
14:19 — Batch processing, load metadata, manifests, saved settings and presets
15:09 — Why local open-source models matter and where to run ACESTEP
15:55 — Windows install begins: Patreon zip, changelog, attachments and download
16:53 — Windows requirements tutorial before Python/CUDA/C++/FFmpeg setup
17:29 — Extract zip safely, avoid bad paths and run Windows_Install_or_Update.bat
18:24 — Automatic VENV, FFmpeg, UV install, model downloads and hash verification
19:24 — Turbo default vs all-model download for SFT/Base and BF16 safetensors
20:32 — First Windows launch, default Generate Song test and CMD progress
21:44 — Model recommendations, VRAM tiers, languages, vocals and MP4 image output
23:29 — Torch Compile setup for faster repeated generations
24:05 — Outputs folder, model switching and full remix setup workflow
25:24 — Practical remix loop: adapted lyrics, strength, reference audio and seed lock
28:03 — Repaint workflow with source range preview, generated result and comparison
29:13 — Recap: extraction, Lego, audio processing and SAM text-prompt usage
30:20 — Windows wrap-up, LoRA training teaser and move to cloud installs
31:16 — RunPod setup: credits, template, CUDA filters, GPU choice and storage
34:53 — Upload zip in Jupyter Lab, extract, run instructions and handle installs
35:43 — RunPod errors, resume behavior, model downloads and hash verification
38:04 — Start ACESTEP on RunPod with Gradio Live, proxy ports and persistence
40:18 — Add 7860/7861 ports, verify storage reuse and rerun installer after resume
42:10 — RunPod connection troubleshooting and Gradio Live recommendation
44:12 — Fix corrupted VENV/stale handle errors, reinstall safely and retest
47:24 — Successful RunPod relaunch, default generation, nvitop and loading tips
49:26 — RunPod first load vs fast inference, 15-second second generation example
51:02 — Download outputs and delete RunPod pods/storage to stop spending
53:30 — Massed Compute setup: coupon, Creator image, GPU prices and ThinLinc
57:13 — Massed install from extracted folder, Linux notes and ultra-fast downloads
59:18 — Start app on Massed Compute via localhost or Gradio Live
1:00:23 — Default Massed generation, nvitop, faster loading and speed test
1:02:03 — Sync/download outputs and delete Massed Compute instance safely
1:03:25 — SimplePod setup: template, persistent volume, pricing and GPU choice
1:06:39 — Jupyter upload, direct file browser, install command and model downloads
1:08:21 — Start SimplePod, Gradio Live, default generation and one-time load errors
1:09:31 — nvitop monitoring, newer driver/CUDA details and generation completion
1:10:42 — Direct output/model downloads through SimplePod file browser
1:11:42 — Delete instance, keep storage, relaunch GPU and verify install
1:13:15 — Discord, subreddit, changelog, update guidance and support links
1:14:30 — Final cleanup: terminate servers, delete storage and LoRA training outro

Few Updates Made Since Video Tutorial So They Are Listed Here

18 June 2026 V5.5 Update

Full tutorial video published finally for inference : https://youtu.be/9C_6qNKjgpA
I started working on LoRA training tutorial as well hopefully soon
With 5.5 optimizer specific parameters are now shown that you can set, I am also working on to make them auto default best hopefully
There was a visual bug that hidden Remix Melody Retention and Direct Source Latents (no_fsq) on Remix songs page and this bug fixed and app scanned entirely and all visuals verified
- Default value set to 0.97 one of our expert remixer recommended that
Just run Windows_Install_or_Update.bat to update, the zip file not changed

18 June 2026 V5.4 Update

Now batch folder processing for ACESTEP XL 1.5 and SAM Audio has this extra option Save only output
- This is useful to get only processed files and no other stuff like remaining part of the songs or metadata files, etc.

18 June 2026 V5.3 Update

Wildcard feature implemented
It works both for Style / Captions and Lyrics with syntax verification as well
It will work in batch folder processing as well so you can write that way in txt files
- If you enable Auto improve lyrics or Auto improve style they may break your syntax so don't enable when using wildcards
Also full inference tutorial published that covers every topic in details including how to install on Windows, RunPod, Massed Compute and SimplePod : https://youtu.be/9C_6qNKjgpA

1. What ACE-Step XL 1.5 Premium Is

ACE-Step XL 1.5 Premium is a local-first music generation and audio utility suite. The video presents it as more than a song generator: it also includes Wildcards for prompt variation, advanced generation modes, remix/repaint workflows, stem extraction, LEGO-style stem addition, SAM Audio segmentation, Auto-Editor trimming, mastering-style audio processing, dataset tools, and LoRA/LoKr training pages.

Responsible-use note: the source tutorial says to use the application respectfully and for research/education. For remix, repaint, extraction, and pitch work, use material you own, have permission to process, or are otherwise allowed to use.

Video introduction

Feature overview

Core jobs covered in the tutorial:

Generate complete songs from a style prompt, structured lyrics, model choice, duration, language, and seed settings.
Use Wildcards in style, Music Caption, and Lyrics fields to randomly pick bracketed options at generation time.
Remix, repaint, extract, LEGO-add, complete, retake, edit, and reuse LM code hints when the selected model supports the workflow.
Trim silence, export audio/video, enhance or pre-master generated songs, and optionally run pitch correction with DiffPitcher.
Use SAM Audio with quick prompts, custom prompts, explicit spans, or batch prompt lists to extract target audio and save the remaining audio.
Use Library, Load Metadata, Results, and the preset system to restore, inspect, score, save, and reuse generation runs.

2. Install And Start On Windows

The Windows workflow uses the included batch files. Extract the ZIP, keep the folder structure intact, run the installer/update script, optionally download all models, then start the app with the Windows launcher.

Windows installer

Extract the ACE-Step Premium ZIP to a path with enough free disk space for the virtual environment, model files, outputs, and FFmpeg runtime.
Run Windows_Install_or_Update.bat. The installer creates the Python virtual environment, downloads or uses shared FFmpeg, installs packages with UV, and prepares the app.
Run Windows_Download_All_Models.bat if you want SFT and Base models in addition to the automatically available Turbo path.
Run Windows_Start_App.bat. In this workspace the launcher started ACE-Step at http://127.0.0.1:7862 because other Gradio apps were already using 7860 and 7861.
Watch the command window for model download, model load, generation, and error details. The video recommends trusting the terminal status more than only the browser UI.

Model availability: Turbo is the quick default. SFT and Base require additional model files. Remix is recommended with SFT in the video; some modes are marked Base-only or unavailable until the matching model is selected.

Windows first generation

3. Quick Song Generation

The Generate Song tab is the fast path. It exposes the controls most users need: style, lyrics, Wildcards, model, LoRA, GPU preset, quantization, language, vocal type, instrumental toggle, duration, count, seed, optional MP4 image, and video resolution.

Generate Song overview

Generate Song filled

Wildcards For Prompt Variation

ACE-Step XL 1.5 Premium v5.3 adds Wildcards. Write bracketed choices separated by pipes, such as [option A|option B|option C], and one option is picked when you generate. Wildcards can be used in the quick Generate Song Style field, the Advanced Music Caption field, and Lyrics.

Wildcards in Generate Song

Wildcards in Advanced caption and lyrics

Basic syntax: [piano|guitar|synth] picks one option when the job starts.
Nested syntax is supported. Example: cinematic [piano|guitar [clean|crunchy]] hook.
Lyrics can use the same pattern, for example: I feel [alive|ready|free] tonight.
Lyric tags without a pipe, such as [Verse], [Chorus], and [Instrumental], stay unchanged.
Do not enable Auto/Enhance Style or Auto/Enhance Lyrics when you need exact wildcard behavior; those improvement tools may rewrite the text and overwrite or remove wildcard expressions.
Batch folder processing uses the same Wildcards behavior, so batch jobs can vary instruments, moods, hooks, or lyric phrases across outputs without manually editing every run.
For repeatable comparisons, save the manifest/settings and lock other variables such as model, duration, and seed while testing wildcard choices.

Write a concise Style prompt that describes genre, vocal character, instrumentation, production quality, tempo or mood, and mix target.
Write Lyrics with section tags such as [Verse] and [Chorus]. The included ACE_Step_Lyric_Generation_Instructions_For_LLMs.txt file can be given to an LLM to format lyrics or style prompts.
Optionally add Wildcards to Style or Lyrics when you want the app to choose between prompt variants automatically.
Select the Model. Start with ACE-Step XL 1.5 Turbo to verify the machine and workflow quickly.
Leave GPU Optimization Preset and DiT Quantization at safe defaults unless you are solving VRAM pressure or repeating a known workflow.
Set Song Duration and Songs. The demo run used 20 seconds and 1 song.
Use Random Seed while exploring. When a promising result appears, uncheck Random Seed and keep the seed so future edits stay comparable.
Click Generate Song and monitor the Status field plus the terminal window.

Demo generation result

Useful quick-tab buttons:

Random Style creates a starting style prompt.
Enhance Style improves the style prompt.
Enhance Lyrics improves lyric structure or phrasing before generation.
Cancel Generation stops a run from the UI.
Open Outputs Folder opens the app’s outputs directory where audio, manifests, lyrics, captions, sessions, and metadata are saved.

4. Results, Seeds, And Reuse

The tutorial stresses generating repeatedly until you have a good base result, then locking the seed and making controlled edits. This is especially important for remix and repaint work, where small prompt or range changes can be tested against the same underlying random state.

Seed and remix discussion

Results after generation

Send To Remix loads the generated song as Source Audio and prepares the advanced Remix workflow.
Send To Repaint loads the generated song and prepares a repaint range workflow.
Convert To Codes reuses the musical plan as LM Codes Hints in compatible Custom workflows.
Get Score and Get LRC create quality-score and lyric-timestamp artifacts.
Save and All Generated Files expose generated files for download or reuse.

Seed workflow:

Keep Random Seed on while searching for a usable base result.
When the result is close, copy or keep the seed shown by the UI.
Turn Random Seed off.
Change one word, one range, or one strength setting at a time.
Compare outputs against the locked seed.

5. Advanced Generation Modes

The ACESTEP Advanced tab is the full workstation. It exposes generation mode, runtime settings, source/reference audio, LM code utilities, advanced prompts, Wildcards in Music Caption/Lyrics, metadata, sampler settings, output settings, and batch processing.

Advanced overview

Generation modes:

Simple: plain-language generation when you want the app to fill many details.
Custom: precise manual control over caption, lyrics, BPM, key, time signature, language, duration, and advanced settings.
Remix: create a new version from source audio. The video recommends SFT for Remix and keeping the same lyrics for best results.
Repaint: replace or modify a selected time range while preserving the rest of the source audio.
Extract: use ACE-Step extraction or stem workflows where the selected model supports it.
LEGO: add a new stem to existing audio, such as adding a guitar stem to a track.
Complete: continue or complete source audio when the selected model supports it.

Advanced source audio

Advanced generation controls

Important advanced controls:

Source Audio is required for Remix, Repaint, Extract, LEGO, and Complete.
Reference Audio guides timbre, mix, performance feel, and atmosphere; it is not meant to copy exact melody, rhythm, or lyrics.
Analyze can fill or update caption/lyrics/metadata from source audio.
BPM Auto, Key Auto, TimeSig Auto, Language Auto, and Duration Auto let the model infer metadata.
Think enables LM planning. Turn it off only when deliberately using pasted LM Codes Hints.
Wildcards in Music Caption and Lyrics are expanded at generation time and also work when the same prompt fields are used for batch folder processing.
Retake creates controlled variation from the same seed/settings.
Edit changes the whole uploaded source using source and target prompts.
Auto Score, AutoGen, and Auto LRC can create score, metadata, and lyric timing outputs during generation.

Engine settings

Engine settings include GPU tier, checkpoint file, main model path, device, VAE, 5Hz LM model/backend, Flash Attention, CPU offload, compile, DiT quantization, LoRA path/folder, LoRA scale, inference steps, sampler, DCW, ADG, MP3 bitrate/sample rate, normalization, fades, LM temperature, top-k/top-p, negative prompt, and LM code settings. Leave these at defaults until you have verified a basic generation.

6. Remix, Repaint, Extract, LEGO, And Auto-Editor Features

The first part of the video demonstrates feature outcomes before the installation section. These are not separate apps; they are modes and panels inside the same ACE-Step interface.

Remix demo

Extract and LEGO demo

Auto-Editor demo

For Remix, upload the source song, keep the same lyrics, use SFT when available, and start with the default remix strength before changing one variable at a time.
For Repaint, set Repainting Start and End carefully, preview the selected range, then choose repaint mode and strength.
For Extract, choose Track Name or Extract All Stems. Batch folder processing can extract from multiple files.
For LEGO, choose or describe the new stem to add and use source audio as the foundation.
For Auto-Editor, set threshold, margin, mincut, and minclip. Workflow export can produce an editor timeline instead of rendering media.

7. Audio Processing

Audio Processing is used on uploaded or local audio/video and can also be applied automatically to generated songs. It includes format output, Auto-Editor trimming, video re-encode controls, audio enhancement stages, pre-mastering stages, DiffPitcher, and batch folder processing.

Audio Processing overview

Generated song loaded for processing

Audio Processing result

Core Audio Processing controls:

Apply automatically to generated songs runs the processing chain after generation.
Save original plus processed song keeps an untouched copy beside the processed copy.
Processed Output selects WAV/MP3 or another output format.
Processing Preset sets a preset chain before you tune individual stages.
Run as subprocess isolates processing so cancellation and memory cleanup are safer.
Export Only Audio extracts processed audio from video inputs.
Auto-Editor trim silent sections removes quiet/silent segments using threshold, margin, mincut, and minclip.
Auto-Editor workflow export exports an editing timeline/workflow instead of only rendering media.
Disable upload preview helps with very large MKV or multi-GB media.

Audio Enhancement and Pre-Mastering

Audio Enhancement includes Stereo Depth, Stereo Width, HF Refinement, Harmonic Enrichment, Timing Humanizer, and Ambience Shaping. Pre-Mastering includes Multiband Compressor, Tape Saturation, Glue Compressor, Mid/Side EQ, Soft Clipper, and LUFS Normalization.

DiffPitcher controls

DiffPitcher is for isolated vocals that sing the wrong notes. Use a guide vocal or MIDI score for the same phrase. The tutorial text in the UI warns that this is not for copying another singer or another song.

8. SAM Audio Segment

SAM Audio Segment is a heavier but more flexible segmentation system. It can extract target audio from a prompt, save the residual/remaining audio, process video inputs, use explicit span anchors, and run batch prompt lists separated by semicolons.

SAM Audio source-video demo

SAM Audio overview

SAM prompt runtime controls

Upload an audio or video file. Optionally upload a visual mask video for video-guided workflows.
Choose Mode and Quick Prompt, or type a Custom Prompt such as vocals, guitar, bass, drums, applause, or another target.
Enable Batch Segment when you want several prompts in one run; separate prompts with semicolons.
Use Predict spans when you want SAM Audio to estimate target time ranges from text.
Use explicit span anchor only when you can provide positive/negative time anchors as JSON.
Choose a VRAM preset and candidate count that match the GPU. Higher candidate counts can improve quality but cost runtime and VRAM.
Enable Save remaining audio when you need both the extracted target and the residual track.

9. Library, Metadata, Presets, Dataset, And Training Pages

The remaining app tabs are operational pages. They help you find previous generations, restore metadata, manage presets, inspect datasets, and train adapters.

Library

Load Metadata

Custom Preset System

Dataset browser

LoRA Dataset Builder

Train LoRA

Use Library when you want to find a past song by day and inspect its lyrics/metadata.
Use Load Metadata when you have a generation_manifest.json and want to restore a generation into the UI.
Use Custom Preset System to persist frequently used model, GPU, LoRA, audio, and generation defaults across sessions.
Use Dataset Builder to scan audio, auto-label captions/lyrics/BPM/key/time signature, review samples, and save a dataset JSON.
Use Preprocess before training; it creates tensor files for faster LoRA or LoKr training.
Use Train LoRA or Train LoKr only after preparing a clean dataset. The video states LoRA training is intended for a separate deeper tutorial.

10. RunPod Deployment

The RunPod chapter focuses on persistent network storage, GPU/region selection, unreliable installs, Gradio live URLs, nvitop monitoring, output downloads, and safe termination.

RunPod storage

RunPod install

RunPod Gradio services

RunPod monitoring

Create persistent network storage in the same region as the GPU you intend to rent.
Deploy the pod/template with the storage mounted. Choose a GPU with enough VRAM for the selected model and quality target.
Run the installer. If RunPod throws an OS/server error, run the installer again; it should resume from completed work.
If installation stalls from excessive parallelism, delete the virtual environment, lower installer thread count as shown in the video, and rerun.
Start the app and prefer the Gradio live link when the RunPod proxy is unreliable. If port 7860 does not open, try the port shown by the terminal, sometimes 7861.
Use nvitop to monitor GPU memory and load. First model load can be slow on RunPod storage; later generations are faster.
Download outputs from JupyterLab by right-clicking the outputs folder and downloading it as an archive.
Stop or terminate the pod deliberately. Delete storage too if you no longer want monthly storage charges.

11. Massed Compute Deployment

The Massed Compute chapter is similar to the Linux/cloud workflow, but the tutorial emphasizes faster disk performance and lower friction compared with RunPod. The tradeoff called out in the video is the lack of the same persistent network storage flow.

Massed Compute GPU selection

Massed Compute install

Choose the creator category and the SECourses image when following the video workflow.
Select a GPU appropriate for ACE-Step XL 1.5. The tutorial mentions RTX Pro 6000 and RTX 5090 class GPUs.
Upload the ACE-Step ZIP to Downloads, extract it, open Massed_Compute_Instructions_READ.txt, and copy the install command.
Open a terminal inside the extracted ACE-Step folder and run the command from that location.
Start ACE-Step and use the Gradio live URL. If Gradio live shows a transient error, refresh the page.
Back up large outputs or model/data folders to Hugging Face, Google Drive, OneDrive, or another storage service if you need to recreate the machine later.

12. SimplePod Deployment

The SimplePod chapter uses the RunPod/SimplePod instruction file and shows a persistent-storage flow that resembles RunPod. The tutorial demonstrates starting, generating, monitoring, stopping, and resuming from the same storage volume.

SimplePod setup

SimplePod generation

SimplePod resume

Register, add credits, and create/use persistent storage as shown in the instruction file.
Open the template link, attach the storage volume, choose a GPU, and run the machine.
Use the JupyterLab or console link to run the installer/start commands from the workspace.
If the Gradio live page throws a first-click error, refresh or click again after the page is fully loaded.
Install nvitop when you want GPU/VRAM visibility: pip install nvitop, then run nvitop.
To resume, reuse the template link, attach the same volume, select a GPU, start the machine, and run the app start command again.
Stop or terminate compute and remove storage when finished to avoid unwanted billing.

13. Troubleshooting And Best Practices

If Gradio errors after opening, refresh the browser or click again after the page finishes loading.
If RunPod installer errors, rerun the installer. If the virtual environment is corrupt, delete only the virtual environment and run the installer again.
If first generation is slow, wait for model load and monitor the terminal or nvitop.
For Remix quality, use SFT when available, keep lyrics aligned with the source, generate until you get a good base, then lock the seed.
For very large videos, use Disable upload preview in Audio Processing or supply a local path when available.
For prompt variation in many runs, use Wildcards in Style, Music Caption, or Lyrics. Batch folder processing can use the same wildcard syntax to vary each batch output. Keep Auto/Enhance Style and Auto/Enhance Lyrics disabled if you want exact wildcard expressions preserved.
Save presets and keep generation_manifest.json with outputs.
For VRAM pressure, use GPU Optimization Preset, quantization, offload controls, smaller duration/count, lower SAM candidates, or a larger GPU.
Stop/terminate cloud compute and delete storage volumes when the tutorial work is finished.

ACESTEP XL 1.5 Remix Mode Full Tutorial

Furkan Gözükara — Sun, 28 Jun 2026 00:57:50 +0000

ACESTEP XL 1.5 Remix Mode Full Tutorial

The ULTIMATE Local AI Update Just Dropped! ACE-Step 1.5, Paints-Undo & Whisper Premium Will WOW You

Full tutorial link > https://www.youtube.com/watch?v=hzKSt5WUAm0

Info

In this tutorial I show the newer ACE-Step XL 1.5 Premium features, especially the corrected remix workflow that was missing from the previous video. You will see how to remix songs properly, convert lyrics and language, tune remix strength and melody retention, regenerate only selected parts, use the Library metadata system, install the upgraded Paints-Undo pipeline, reduce VRAM usage, and fix repeating lines in Whisper Premium transcriptions.

Important links:

Download ACESTEP XL Premium files: [ https://www.patreon.com/posts/ace-step-1-5-xl-premium-157675060 ]
Discord: https://discord.com/invite/software-engineering-courses-secourses-772774097734074388
Google AI Studio: https://aistudio.google.com/
Previous ACE-Step tutorial: [ https://youtu.be/9C_6qNKjgpA ]
Windows requirements/setup tutorial: [ https://youtu.be/DrhUHnYfwC0 ]
Whisper Premium tutorial: [ https://youtu.be/4lAk6sf1qF8 ]
Download Whisper Premium App Files: [ https://www.patreon.com/posts/whisper-webui-premium-145395299 ]

Video chapters:

00:00 ACE-Step XL 1.5 Premium update, remix focus, and Paints-Undo preview
00:49 Billie Jean remix example: checking style preservation and vocal change
01:16 Lower remix strength idea and Gangnam Style Korean-to-English demo
01:36 Hearing the Korean-to-English result and why extreme remixes sound strange
01:53 Stronger remix settings with higher strength and melody retention values
02:12 How to update ACE-Step XL 1.5: download ZIP, extract, and overwrite
02:37 Run Windows install/update.bat and rebuild the virtual environment if needed
02:56 Library tab overview: daily categories, saved generations, and metadata
03:18 Loading old songs from Library with lyrics, parameters, and JSON restored
03:33 Starting a proper remix: select the SFT model and open Advanced -) Remix
03:45 Uploading the source song and learning the two key remix parameters
04:04 Remix presets explained: different lyrics, same lyrics, medium and big change
04:19 Why lower melody retention lets the model generate completely new lyrics
04:30 Torch compile speed tip and preparing the target style caption and lyrics
04:42 Using Gemini in Google AI Studio with the ACE-Step lyric instruction file
04:59 Editing or writing lyrics and matching the vocal language accurately
05:15 Launching a live remix generation and measuring local generation speed
05:27 Live timing result: around 33 seconds for a complete remix generation
05:49 Use generated result as source to repair or improve selected song parts
06:03 Selecting remix start/stop points and regenerating only the chosen section
06:17 How section patching works: full remix generated, only selected part replaced
06:36 When to keep lyrics/style the same and when to change them for a section
06:47 Comparing the full output against the newly generated section preview
06:58 Iterative remix workflow for perfecting each part of the composition
07:14 LoRA training progress, future voice accuracy, and language-swap limits
07:27 Fast local iterations, multiple attempts, no watermark, and usable outputs
07:55 Final remix reminders: SFT model, proper Windows setup, and compile mode
08:11 Tuning dramatic changes with percentage values and fixed seed comparisons
08:41 Paints-Undo upgraded intro: new pipeline, faster speed, and better results
08:52 Download, install, and start Paints-Undo with windows_startup.bat
09:08 First launch model downloads plus new xFormers Triton attention support
09:21 GPU compatibility, Torch 2.12.1, CUDA 13, and upgrades over the original
09:34 Upload an image, generate the prompt, and tag it with the WD14 tagger
09:52 Operating steps, keyframes, Tiled VAE options, and low VRAM preparation
10:04 24GB vs 7GB VRAM usage and how the new memory-saving options help
10:27 How keyframes become the drawing video before final video generation
10:52 CUDA 13, Torch 2.12, Triton attention, diagnostics, and speed improvements
11:10 Automatic attention fallback plus Linux/cloud installer compatibility notes
11:27 Full generation time, possible torch compile addition, and result preview
11:46 Why keyframes matter, why results vary, and why not every image works
11:57 Fixing out-of-VRAM errors by lowering resolution and using supported ratios
12:14 Whisper Premium update: transcribing videos with all Whisper model options
12:31 Faster Whisper quality mode and the repeated sentence problem
12:42 Using large-v1 and repetition penalty to prevent repeated subtitle lines
12:54 How to tune repetition penalty carefully so transcription is not skipped
13:06 Example result: highly accurate subtitles generated from the new video
13:19 29-minute video transcribed in 1.5 minutes, 20x real-time, and closing

Conclusion

This video is for users who want fast local AI music remixing, better generation iteration, image-to-drawing animation, and high quality subtitle transcription. Follow the timestamps to jump directly to ACE-Step remix settings, Paints-Undo installation, low VRAM options, or Whisper repetition penalty tuning.

App Installer Zip File Content

Some App Screenshots

Remix Page

Iterative Remix

Full Page

ACE-Step XL 1.5 Premium + Facebook / META Sam Audio + Auto-Editor Trim + Audio Tools Enhancement Full Tutorial

Furkan Gözükara — Fri, 19 Jun 2026 01:42:11 +0000

Video Tutorial

https://youtu.be/9C_6qNKjgpA

Source Video, Links, And Chapters

Tutorial video: Forget Suno: Run the Ultimate AI Music Studio LOCALLY (100% Free)
Channel: SECourses
App/latest zip and Patreon post: Patreon ACESTEP XL Premium SAM Audio post
Windows requirements guide: Windows requirements guide
Discord/help/community: SECourses Discord server
Duration: 1:15:09
Published: June 17, 2026

Video Chapters

0:00 — Intro: ACESTEP XL 1.5 Premium local music, segmentation and processing tutorial
0:52 — Fast song generation examples across styles in under one minute
1:55 — Output manifest proof, 40-second generation time and supported models
2:29 — Turbo/SFT/Base models, LoRA support, GPU presets and Torch Compile boost
3:10 — Remix feature preview, same-lyrics requirement and responsible usage note
4:16 — Repaint mode: regenerate and merge only a selected song section
5:38 — Extract mode: stems, silence trimming, all-stems and batch folders
6:30 — Lego mode: add an instrument stem such as guitar into existing audio
7:25 — Audio Processing presets and manual enhancement controls for AI songs
8:35 — Auto-Editor silent trim for tutorials, videos, audio and workflow export
9:48 — DaVinci/Premiere/Final Cut/ShotCut/Kdenlive timeline export demo
11:01 — SAM Audio Segment: BF16 models, VRAM presets and advanced segmentation
11:47 — SAM outputs demo: vocals, drums, bass, remaining audio and saved files
12:47 — Custom SAM prompts, semicolon batch segmenting and speech cleanup example
14:19 — Batch processing, load metadata, manifests, saved settings and presets
15:09 — Why local open-source models matter and where to run ACESTEP
15:55 — Windows install begins: Patreon zip, changelog, attachments and download
16:53 — Windows requirements tutorial before Python/CUDA/C++/FFmpeg setup
17:29 — Extract zip safely, avoid bad paths and run Windows_Install_or_Update.bat
18:24 — Automatic VENV, FFmpeg, UV install, model downloads and hash verification
19:24 — Turbo default vs all-model download for SFT/Base and BF16 safetensors
20:32 — First Windows launch, default Generate Song test and CMD progress
21:44 — Model recommendations, VRAM tiers, languages, vocals and MP4 image output
23:29 — Torch Compile setup for faster repeated generations
24:05 — Outputs folder, model switching and full remix setup workflow
25:24 — Practical remix loop: adapted lyrics, strength, reference audio and seed lock
28:03 — Repaint workflow with source range preview, generated result and comparison
29:13 — Recap: extraction, Lego, audio processing and SAM text-prompt usage
30:20 — Windows wrap-up, LoRA training teaser and move to cloud installs
31:16 — RunPod setup: credits, template, CUDA filters, GPU choice and storage
34:53 — Upload zip in Jupyter Lab, extract, run instructions and handle installs
35:43 — RunPod errors, resume behavior, model downloads and hash verification
38:04 — Start ACESTEP on RunPod with Gradio Live, proxy ports and persistence
40:18 — Add 7860/7861 ports, verify storage reuse and rerun installer after resume
42:10 — RunPod connection troubleshooting and Gradio Live recommendation
44:12 — Fix corrupted VENV/stale handle errors, reinstall safely and retest
47:24 — Successful RunPod relaunch, default generation, nvitop and loading tips
49:26 — RunPod first load vs fast inference, 15-second second generation example
51:02 — Download outputs and delete RunPod pods/storage to stop spending
53:30 — Massed Compute setup: coupon, Creator image, GPU prices and ThinLinc
57:13 — Massed install from extracted folder, Linux notes and ultra-fast downloads
59:18 — Start app on Massed Compute via localhost or Gradio Live
1:00:23 — Default Massed generation, nvitop, faster loading and speed test
1:02:03 — Sync/download outputs and delete Massed Compute instance safely
1:03:25 — SimplePod setup: template, persistent volume, pricing and GPU choice
1:06:39 — Jupyter upload, direct file browser, install command and model downloads
1:08:21 — Start SimplePod, Gradio Live, default generation and one-time load errors
1:09:31 — nvitop monitoring, newer driver/CUDA details and generation completion
1:10:42 — Direct output/model downloads through SimplePod file browser
1:11:42 — Delete instance, keep storage, relaunch GPU and verify install
1:13:15 — Discord, subreddit, changelog, update guidance and support links
1:14:30 — Final cleanup: terminate servers, delete storage and LoRA training outro

1. What ACE-Step XL 1.5 Premium Is

Video introduction

Feature overview

Core jobs covered in the tutorial:

Generate complete songs from a style prompt, structured lyrics, model choice, duration, language, and seed settings.
Use Wildcards in style, Music Caption, and Lyrics fields to randomly pick bracketed options at generation time.
Remix, repaint, extract, LEGO-add, complete, retake, edit, and reuse LM code hints when the selected model supports the workflow.
Trim silence, export audio/video, enhance or pre-master generated songs, and optionally run pitch correction with DiffPitcher.
Use SAM Audio with quick prompts, custom prompts, explicit spans, or batch prompt lists to extract target audio and save the remaining audio.
Use Library, Load Metadata, Results, and the preset system to restore, inspect, score, save, and reuse generation runs.

2. Install And Start On Windows

Windows installer

Extract the ACE-Step Premium ZIP to a path with enough free disk space for the virtual environment, model files, outputs, and FFmpeg runtime.
Run Windows_Install_or_Update.bat. The installer creates the Python virtual environment, downloads or uses shared FFmpeg, installs packages with UV, and prepares the app.
Run Windows_Download_All_Models.bat if you want SFT and Base models in addition to the automatically available Turbo path.
Run Windows_Start_App.bat. In this workspace the launcher started ACE-Step at http://127.0.0.1:7862 because other Gradio apps were already using 7860 and 7861.
Watch the command window for model download, model load, generation, and error details. The video recommends trusting the terminal status more than only the browser UI.

Windows first generation

3. Quick Song Generation

Generate Song overview

Generate Song filled

Wildcards For Prompt Variation

Wildcards in Generate Song

Wildcards in Advanced caption and lyrics

Basic syntax: [piano|guitar|synth] picks one option when the job starts.
Nested syntax is supported. Example: cinematic [piano|guitar [clean|crunchy]] hook.
Lyrics can use the same pattern, for example: I feel [alive|ready|free] tonight.
Lyric tags without a pipe, such as [Verse], [Chorus], and [Instrumental], stay unchanged.
Do not enable Auto/Enhance Style or Auto/Enhance Lyrics when you need exact wildcard behavior; those improvement tools may rewrite the text and overwrite or remove wildcard expressions.
Batch folder processing uses the same Wildcards behavior, so batch jobs can vary instruments, moods, hooks, or lyric phrases across outputs without manually editing every run.
For repeatable comparisons, save the manifest/settings and lock other variables such as model, duration, and seed while testing wildcard choices.

Write a concise Style prompt that describes genre, vocal character, instrumentation, production quality, tempo or mood, and mix target.
Write Lyrics with section tags such as [Verse] and [Chorus]. The included ACE_Step_Lyric_Generation_Instructions_For_LLMs.txt file can be given to an LLM to format lyrics or style prompts.
Optionally add Wildcards to Style or Lyrics when you want the app to choose between prompt variants automatically.
Select the Model. Start with ACE-Step XL 1.5 Turbo to verify the machine and workflow quickly.
Leave GPU Optimization Preset and DiT Quantization at safe defaults unless you are solving VRAM pressure or repeating a known workflow.
Set Song Duration and Songs. The demo run used 20 seconds and 1 song.
Use Random Seed while exploring. When a promising result appears, uncheck Random Seed and keep the seed so future edits stay comparable.
Click Generate Song and monitor the Status field plus the terminal window.

Demo generation result

Useful quick-tab buttons:

Random Style creates a starting style prompt.
Enhance Style improves the style prompt.
Enhance Lyrics improves lyric structure or phrasing before generation.
Cancel Generation stops a run from the UI.
Open Outputs Folder opens the app’s outputs directory where audio, manifests, lyrics, captions, sessions, and metadata are saved.

4. Results, Seeds, And Reuse

Seed and remix discussion

Results after generation

Send To Remix loads the generated song as Source Audio and prepares the advanced Remix workflow.
Send To Repaint loads the generated song and prepares a repaint range workflow.
Convert To Codes reuses the musical plan as LM Codes Hints in compatible Custom workflows.
Get Score and Get LRC create quality-score and lyric-timestamp artifacts.
Save and All Generated Files expose generated files for download or reuse.

Seed workflow:

Keep Random Seed on while searching for a usable base result.
When the result is close, copy or keep the seed shown by the UI.
Turn Random Seed off.
Change one word, one range, or one strength setting at a time.
Compare outputs against the locked seed.

5. Advanced Generation Modes

Advanced overview

Generation modes:

Simple: plain-language generation when you want the app to fill many details.
Custom: precise manual control over caption, lyrics, BPM, key, time signature, language, duration, and advanced settings.
Remix: create a new version from source audio. The video recommends SFT for Remix and keeping the same lyrics for best results.
Repaint: replace or modify a selected time range while preserving the rest of the source audio.
Extract: use ACE-Step extraction or stem workflows where the selected model supports it.
LEGO: add a new stem to existing audio, such as adding a guitar stem to a track.
Complete: continue or complete source audio when the selected model supports it.

Advanced source audio

Advanced generation controls

Important advanced controls:

Source Audio is required for Remix, Repaint, Extract, LEGO, and Complete.
Reference Audio guides timbre, mix, performance feel, and atmosphere; it is not meant to copy exact melody, rhythm, or lyrics.
Analyze can fill or update caption/lyrics/metadata from source audio.
BPM Auto, Key Auto, TimeSig Auto, Language Auto, and Duration Auto let the model infer metadata.
Think enables LM planning. Turn it off only when deliberately using pasted LM Codes Hints.
Wildcards in Music Caption and Lyrics are expanded at generation time and also work when the same prompt fields are used for batch folder processing.
Retake creates controlled variation from the same seed/settings.
Edit changes the whole uploaded source using source and target prompts.
Auto Score, AutoGen, and Auto LRC can create score, metadata, and lyric timing outputs during generation.

Engine settings

6. Remix, Repaint, Extract, LEGO, And Auto-Editor Features

The first part of the video demonstrates feature outcomes before the installation section. These are not separate apps; they are modes and panels inside the same ACE-Step interface.

Remix demo

Extract and LEGO demo

Auto-Editor demo

For Remix, upload the source song, keep the same lyrics, use SFT when available, and start with the default remix strength before changing one variable at a time.
For Repaint, set Repainting Start and End carefully, preview the selected range, then choose repaint mode and strength.
For Extract, choose Track Name or Extract All Stems. Batch folder processing can extract from multiple files.
For LEGO, choose or describe the new stem to add and use source audio as the foundation.
For Auto-Editor, set threshold, margin, mincut, and minclip. Workflow export can produce an editor timeline instead of rendering media.

7. Audio Processing

Audio Processing overview

Generated song loaded for processing

Audio Processing result

Core Audio Processing controls:

Apply automatically to generated songs runs the processing chain after generation.
Save original plus processed song keeps an untouched copy beside the processed copy.
Processed Output selects WAV/MP3 or another output format.
Processing Preset sets a preset chain before you tune individual stages.
Run as subprocess isolates processing so cancellation and memory cleanup are safer.
Export Only Audio extracts processed audio from video inputs.
Auto-Editor trim silent sections removes quiet/silent segments using threshold, margin, mincut, and minclip.
Auto-Editor workflow export exports an editing timeline/workflow instead of only rendering media.
Disable upload preview helps with very large MKV or multi-GB media.

Audio Enhancement and Pre-Mastering

DiffPitcher controls

8. SAM Audio Segment

SAM Audio source-video demo

SAM Audio overview

SAM prompt runtime controls

Upload an audio or video file. Optionally upload a visual mask video for video-guided workflows.
Choose Mode and Quick Prompt, or type a Custom Prompt such as vocals, guitar, bass, drums, applause, or another target.
Enable Batch Segment when you want several prompts in one run; separate prompts with semicolons.
Use Predict spans when you want SAM Audio to estimate target time ranges from text.
Use explicit span anchor only when you can provide positive/negative time anchors as JSON.
Choose a VRAM preset and candidate count that match the GPU. Higher candidate counts can improve quality but cost runtime and VRAM.
Enable Save remaining audio when you need both the extracted target and the residual track.

9. Library, Metadata, Presets, Dataset, And Training Pages

The remaining app tabs are operational pages. They help you find previous generations, restore metadata, manage presets, inspect datasets, and train adapters.

Library

Load Metadata

Custom Preset System

Dataset browser

LoRA Dataset Builder

Train LoRA

Use Library when you want to find a past song by day and inspect its lyrics/metadata.
Use Load Metadata when you have a generation_manifest.json and want to restore a generation into the UI.
Use Custom Preset System to persist frequently used model, GPU, LoRA, audio, and generation defaults across sessions.
Use Dataset Builder to scan audio, auto-label captions/lyrics/BPM/key/time signature, review samples, and save a dataset JSON.
Use Preprocess before training; it creates tensor files for faster LoRA or LoKr training.
Use Train LoRA or Train LoKr only after preparing a clean dataset. The video states LoRA training is intended for a separate deeper tutorial.

10. RunPod Deployment

The RunPod chapter focuses on persistent network storage, GPU/region selection, unreliable installs, Gradio live URLs, nvitop monitoring, output downloads, and safe termination.

RunPod storage

RunPod install

RunPod Gradio services

RunPod monitoring

Create persistent network storage in the same region as the GPU you intend to rent.
Deploy the pod/template with the storage mounted. Choose a GPU with enough VRAM for the selected model and quality target.
Run the installer. If RunPod throws an OS/server error, run the installer again; it should resume from completed work.
If installation stalls from excessive parallelism, delete the virtual environment, lower installer thread count as shown in the video, and rerun.
Start the app and prefer the Gradio live link when the RunPod proxy is unreliable. If port 7860 does not open, try the port shown by the terminal, sometimes 7861.
Use nvitop to monitor GPU memory and load. First model load can be slow on RunPod storage; later generations are faster.
Download outputs from JupyterLab by right-clicking the outputs folder and downloading it as an archive.
Stop or terminate the pod deliberately. Delete storage too if you no longer want monthly storage charges.

11. Massed Compute Deployment

Massed Compute GPU selection

Massed Compute install

Choose the creator category and the SECourses image when following the video workflow.
Select a GPU appropriate for ACE-Step XL 1.5. The tutorial mentions RTX Pro 6000 and RTX 5090 class GPUs.
Upload the ACE-Step ZIP to Downloads, extract it, open Massed_Compute_Instructions_READ.txt, and copy the install command.
Open a terminal inside the extracted ACE-Step folder and run the command from that location.
Start ACE-Step and use the Gradio live URL. If Gradio live shows a transient error, refresh the page.
Back up large outputs or model/data folders to Hugging Face, Google Drive, OneDrive, or another storage service if you need to recreate the machine later.

12. SimplePod Deployment

SimplePod setup

SimplePod generation

SimplePod resume

Register, add credits, and create/use persistent storage as shown in the instruction file.
Open the template link, attach the storage volume, choose a GPU, and run the machine.
Use the JupyterLab or console link to run the installer/start commands from the workspace.
If the Gradio live page throws a first-click error, refresh or click again after the page is fully loaded.
Install nvitop when you want GPU/VRAM visibility: pip install nvitop, then run nvitop.
To resume, reuse the template link, attach the same volume, select a GPU, start the machine, and run the app start command again.
Stop or terminate compute and remove storage when finished to avoid unwanted billing.

13. Troubleshooting And Best Practices

If Gradio errors after opening, refresh the browser or click again after the page finishes loading.
If RunPod installer errors, rerun the installer. If the virtual environment is corrupt, delete only the virtual environment and run the installer again.
If first generation is slow, wait for model load and monitor the terminal or nvitop.
For Remix quality, use SFT when available, keep lyrics aligned with the source, generate until you get a good base, then lock the seed.
For very large videos, use Disable upload preview in Audio Processing or supply a local path when available.
For prompt variation in many runs, use Wildcards in Style, Music Caption, or Lyrics. Batch folder processing can use the same wildcard syntax to vary each batch output. Keep Auto/Enhance Style and Auto/Enhance Lyrics disabled if you want exact wildcard expressions preserved.
Save presets and keep generation_manifest.json with outputs.
For VRAM pressure, use GPU Optimization Preset, quantization, offload controls, smaller duration/count, lower SAM candidates, or a larger GPU.
Stop/terminate cloud compute and delete storage volumes when the tutorial work is finished.

Best Open Source Subtitle Generator? Canary Qwen 2.5B + Whisper Full Guide

Furkan Gözükara — Sat, 02 May 2026 02:25:17 +0000

Best Open Source Subtitle Generator? Canary Qwen 2.5B + Whisper Full Guide

Full tutorial link > https://www.youtube.com/watch?v=4lAk6sf1qF8

Info

NVIDIA NeMo Canary-Qwen-2.5B is an English speech recognition model that achieves state-of-the art performance on multiple English speech benchmarks. Canary model is the new king that dethroned famous Whisper.

Full tutorial for the Whisper TTS Premium speech-to-text app by SECourses with new NVIDIA Canary Qwen 2.5B support. In this video, I demo local subtitle generation, compare Canary Qwen 2.5B against Whisper Large V3, show output formats, batch processing, presets, YouTube URL and live microphone options, then install the app from scratch on Windows.

You will also see RunPod and Massed Compute notes, first-run model download, RTX 5000/CUDA 13 driver requirements, subprocess mode for preventing VRAM/RAM leaks, and when to use Whisper instead of Canary.

Links:

Download App and the source post: [ https://www.patreon.com/posts/whisper-webui-to-145395299 ]

Discord: [ https://discord.com/channels/772774097734074388/1079506787734134844 ]

Patreon app index: [ https://github.com/FurkanGozukara/Stable-Diffusion/blob/main/Patreon-Posts-Index.md ]

Related RunPod/Massed Compute setup tutorial: [ https://youtu.be/ZRrzvD4wNys ]

In my tutorial-video tests, Canary Qwen 2.5B achieved 5.91% global WER and reached up to 46x faster than real-time transcription, making it my new recommended default for English speech-to-text. Whisper remains useful when you need broader spoken-language support or word-level timestamps.

Chapters:

0:00 Intro to the local open-source speech-to-text app and new Canary support
0:20 Quick demo setup with NVIDIA Canary Qwen 2.5B
0:33 Maximum-quality defaults and starting subtitle generation
0:48 Live transcription speed and accuracy preview
1:03 Chunk length settings for smaller or larger subtitle segments
1:18 Fast generation, supported exports, and restarting with all formats
1:31 Multiple subtitle file formats explained
1:47 Batch processing folders, output paths, subfolders, and overwrite mode
1:58 YouTube URLs, microphone/live transcription, translation, and BGM separation
2:09 Saving presets and using advanced parameters
2:24 Auto-optimized defaults for Whisper and Canary models
2:39 Canary Qwen 2.5B vs Whisper Large V3 comparison begins
2:54 Real-world WER benchmark and 5.91% Canary result
3:10 Why non-native English speech is harder to transcribe accurately
3:24 Canary speed advantage and 46x real-time transcription explained
3:43 Test averages across long and short tutorial videos
3:59 Cases where Whisper slightly wins and final Canary recommendation
4:14 Opening the output folder after transcription completes
4:27 VTT output, matching filenames, capitalization, and punctuation
4:44 Accuracy examples inside the generated transcript
4:58 TXT, TSV, SRT, LRC exports and word-level timestamp note
5:20 Download page, latest ZIP, and installation overview
5:31 Windows requirements: Python 3.11, Git, CUDA, and C++ notes
5:51 Choosing install location and keeping the app isolated in venv
6:04 Extracting the ZIP and running Windows install/update BAT
6:23 Automatic model downloads on first run
6:34 RunPod, Massed Compute, and Linux installation files
6:50 Where to learn RunPod and Massed Compute setup in the related guide
7:29 UV-powered Windows installation completes quickly
7:41 Starting the app with Windows start app BAT
7:58 Selecting video/audio input and generating subtitles on a fresh install
8:10 First-run Canary model download and 5GB model size
8:35 Easy setup goal and automatic fresh-install workflow
8:53 Discord, Patreon index, and 100+ SECourses applications
9:13 RTX 5000 support and updated NVIDIA driver requirement
9:35 Fresh-install transcription starts successfully
9:47 Automatic downloads for Canary, Whisper, diarization, and extra tools
10:16 Canary becomes the new default model recommendation
10:36 Subprocess mode to prevent VRAM and RAM leaks
10:51 Why running transcription as a subprocess is recommended
11:04 Switching back to Whisper models when needed
11:20 Whisper language coverage vs Canary and audio/video support
11:42 Real recording benchmark: 27 minutes transcribed in about 2 minutes
11:56 Model loading overhead and clean RAM/VRAM release
12:08 Final notes, subscribe reminder, and downloading the full transcript ZIP

Download Installers and App

https://www.patreon.com/posts/145395299

30 April 2026 - Version 10.0

This is a quite big upgrade to our application
We now fully support NVIDIA NeMo Canary-Qwen-2.5B is an English speech recognition model : https://huggingface.co/nvidia/canary-qwen-2.5b
This model is currently State Of The Art (SOTA) Speech to Text model for English language
I have done extensive research and testing and it is set to best default parameters
Fully supporting all of the features our Whisper app were already supporting
Get the zip file, overwrite all previous files and run installer for update / upgrade
The model will be auto downloaded when you first time run

I also have compared with Whisper best configurations are here the comparison results - best results of Whisper taken

As you can see NVIDIA NeMo Canary-Qwen-2.5B is not only significantly better but also faster

15 April 2026 - Version 8.0

Diarization had some error and this is fixed
Mic tab completey remade and now both live transcription from microphone and offline transcription from microphone working
- Live transcription quality is not that great
- Both live transcription and offline transcription recordings from microphone will be saved in outputs folder
- Live transcription will auto run but for offline transcription first record voice with microphone and then click Generate Subtitles button
Don't forget to select your working microphone and give permission for app to use your microphone from your browser
For update / install get the latest zip file, overwrite older files and run Windows_Install_Update.bat

14 April 2026 - Version 7.0

Now auto downloads Diarization files and thus you don't need to enter Hugging Face token and get permission
Now you can copy paste any YouTube link and generate subtitles
- This was broken and now fixed
- It will save generated files with same name as the video title
Now you can batch generate subtitles for YouTube video channels
Paste the video channel, enable batch and it will generate subtitles for every video
- Set how many videos you want (scans latest ones)
- You may get rate limited by YouTube
For update / install get the latest zip file, overwrite older files and run Windows_Install_Update.bat

8 April 2026 - Version 5.0

This is a massive update with so many new features
- Get the latest zip file and make a fresh install please > https://www.patreon.com/posts/145395299
- 1-Click to install on Windows, RunPod, SimplePod, Massed Compute, Linux
New preset save and load system with extremely well tested best_quality and fast pre-made presets
- Presets are automatically loaded as you change them and also last used preset is remembered when you restart the app
- Word Timestamps is enabled by default to improve quality but it also generates regular version as well automatically
Download transcription button
Open outputs folder button (all transcriptions automatically saved)
Load video / audio file directly from path (useful for platforms like RunPod where Gradio upload is slow)

The fast preset uses new custom in house implemented batch size 32 feature and it is literally blazing fast compared to all other existing Whisper apps and repos
Fully supporting all kind of video and audio formats upload with full preview
Batch folder processing process given folder all files automatically
Live transcription Window that shows latest transcription live while processing
At batch size 1 with best quality, 11x real time transcription speed (depends on GPU)
At batch size 32 fast preset 15x to 30x real time transcription speed (depends on GPU)
New feature Repeat Initial Prompt Every Window

Supports all Whisper models like Large V1, Large V3, Turbo, Distill Large, Tiny, etc
Supports following format outputs you can have checked all so all generated at the same time : SRT, WebVTT, txt, LRC,JSON, TSV
- All outputs will have the same name as your input file name
With sub process working system, you can cancel any processing immediately with 0 RAM or VRAM leak
Fully supports Windows and Linux (use Massed Compute installer)
Based on Python 3.11 VENV and CUDA 13 and Torch 2.9.1 with pre-compiled libraries like Flash Attention
If you don't like output, try to enable / disable Condition On Previous Text it makes big difference

The app supports 100 languages and 32 models

Lots of Advanced Parameters and all set to best quality
Built in Background Music Remover Filter
Built in Voice Detection Filter
Fully detailed CMD output to watch entire progress
Extremely optimized VRAM usage as low as 6 GB GPUs

Some other utility features like YouTube, record from a Mic, T2T Translation, BGM Seperation

Full Page Screenshot

The ULTIMATE Guide to AI Voice Cloning: RVC WebUI (Zero to Hero)

Furkan Gözükara — Thu, 23 Apr 2026 02:42:04 +0000

Full tutorial link > https://www.youtube.com/watch?v=ZRrzvD4wNys

Info

Ultimate AI Voice Changer Tutorial: SECourses Premium RVC Web UI (Windows, RunPod & Massed Compute). This video is only for educational and responsible usage purposes. With V3: Multiple voice merge to generate custom voice feature implemented.
Welcome to the complete tutorial for the SECourses Premium RVC Web UI! In this video, I will show you how to easily transform your speaking voice or song vocals using our highly optimized AI voice conversion application.
Whether you want to sound like a famous celebrity (like Donald Trump or Tupac), replace vocals in AI-generated music, or change your voice live in real-time, this tool has everything you need. The installer automatically downloads 30+ pre-trained demo voices, and you can easily add hundreds more from Hugging Face!
I will walk you through the entire 1-click installation process for local Windows PCs, as well as step-by-step cloud deployment guides for RunPod, SimplePod, and our highly recommended cloud provider, Massed Compute.

✨ Key Features Covered in this Video:

• 1-Click Install with automatic model & dependency downloading
• 30+ Pre-trained demo voices included automatically
• Auto-separation of vocals and music for song processing
• Custom Preset System to save your favorite inference settings
• Batch Inference to process multiple audio files instantly
• Real-Time Live Voice Changing desktop application included
• Full Custom Voice Training support (VRAM usage below 4GB!)
• Fully optimized for Cloud GPUs (RunPod, SimplePod, Massed Compute)

🔗 Important Links & Resources:

📥 Download Installer & Full Instructions: [ https://www.patreon.com/posts/rvc-web-ui-app-installer-zip-file-149104996 ]
⚙️ Windows Requirements Tutorial: [ https://youtu.be/DrhUHnYfwC0 ]
☁️ Massed Compute Registration (Use coupon code SECourses for a discount!): [ https://vm.massedcompute.com/signup?linkId=lp_034338&sourceId=secourses&tenantId=massed-compute ]
☁️ RunPod Registration & Templates: [ https://get.runpod.io/955rkuppqv4h ]
☁️ SimplePod Registration & Templates: [ https://simplepod.ai/ref?user=secourses ]

⏱️ Video Chapters:

0:00 Introduction to SECourses Premium RVC Web UI
0:18 Pre-trained demo voices & Hugging Face repositories
0:54 Live demonstration of speech-to-speech voice changing
1:17 Processing songs and AI-generated music vocals
1:50 Overview of features: batch inference, real-time, and training
2:36 Downloading the installer zip file and reading requirements
3:06 Installing SECourses Premium RVC on Windows
3:41 Automatic model downloading process and verification
4:26 Where to place custom demo voices, weights, and indices
4:55 Starting the Web UI application and loading models
5:24 Auto separate vocals and music feature explained
5:38 Using the inference preset system to save settings
6:08 Uploading audio files and testing default voice conversion
6:37 Converting speech to Donald Trump demo voice
7:18 Adjusting parameters to improve voice conversion quality
7:37 Testing voice conversion on an AI-generated song
8:12 Converting the song vocals to Tupac demo voice
9:24 How automatic music and vocal separation works
9:56 Custom voice model training overview and FAQ
10:28 How to use the Batch Inference feature for multiple files
11:37 Accessing generated output files and their metadata
12:04 Real-time live voice changing desktop application setup
12:47 Live real-time voice conversion demonstration
13:13 Cloud deployment introduction: RunPod, SimplePod, Massed Compute
13:46 SimplePod and RunPod interface and pricing comparison
14:00 Setting up RunPod template and persistent volume
14:53 Important: Selecting the correct CUDA version on RunPod
15:47 Deploying the RunPod pod and waiting for initialization
16:13 Connecting to Jupyter Lab and uploading the installer zip
16:32 Running the installation commands on RunPod terminal
17:08 Starting the application on RunPod with Gradio Live link
17:33 Testing voice conversion and downloading files on RunPod
18:53 How to use RVC Premium on Massed Compute cloud provider
19:22 Deploying a machine on Massed Compute with SECourses image
20:41 Connecting to Massed Compute using the ThinLinc client
21:28 Transferring files using the shared thin drives folder
22:03 Extracting and installing the application on Massed Compute
23:15 Starting the application and launching Gradio Live on Massed Compute
23:54 Testing voice conversion and downloading outputs on Massed Compute
25:08 Deleting cloud instances to save credits and money
25:37 How to install and use the application on SimplePod
26:33 Speed comparison between SimplePod and RunPod installations
27:17 Conclusion and final thoughts

SECourses Upscaler Pro Beating Topaz AI by Far With Specalized FlashVSR+ & SeedVR2.5 - Local Windows

Furkan Gözükara — Wed, 25 Feb 2026 00:16:07 +0000

SECourses Upscaler Pro Beating Topaz AI by Far With Specalized FlashVSR+ & SeedVR2.5 - Local Windows

Full tutorial link > https://www.youtube.com/watch?v=_WT4C78j5-c

Download SECourses Upscaler Pro : https://www.patreon.com/posts/secourses-upscaler-pro-150202809

Tutorial Info

🚀 Welcome to the Ultimate SECourses Upscaler Pro & Trellis 3D Tutorial!

Greetings everyone! Today, I am incredibly excited to showcase the massive new improvements and brand-new features we have added to the SECourses Upscaler Pro application. I have been working non-stop to bring you a studio-level AI video and image enhancement tool that completely redefines what is possible running locally on your own PC.

In this video, we dive deep into side-by-side comparisons between our custom FlashVSR+ upscaler, original viral social media videos, and Topaz AI. As you will see in our live slider comparisons, the SECourses Upscaler Pro is adding 10x more detail than Topaz AI, generating breathtaking, high-definition results while running highly optimized on GPUs with as little as 8GB of VRAM!

We also explore the immensely powerful SeedVR2 model for flawless 4x image upscaling, and I give you an exclusive sneak peek at our upcoming Trellis Image-to-3D application featuring fully automated UniRig 3D character rigging!

🔗 Important Links & Resources:

📥 Download the Latest SECourses Upscaler Pro Installer: [ https://www.patreon.com/posts/secourses-upscaler-pro-150202809 ]

📥 Download Trellis Image-to-3D App: [ https://www.patreon.com/posts/trellis2-app-147686623 ]

Some Important Comparison Screenshots From Actual Upscaled Videos

Below videos are upscaled with FlashVSR+ and it is ultra optimized, fast and low VRAM (As low as 8 GB)

⚡ Key Features of SECourses Upscaler Pro Covered in This Video:

•• Unmatched AI Upscaling: Outperforms Topaz AI and other public repositories by adding massive amounts of true-to-life details to low-resolution inputs.

•• Optimized for Low VRAM: Run advanced models like FlashVSR+ and SeedVR2 on GPUs with 8GB VRAM using GGUF and FP8 models.

•• Scene-Based & Chunk-Based Processing: Automatically detects scene changes and processes videos in chunks. This maximizes quality and prevents VRAM crashes!

•• Full Resume Capability

•• Built-In Comparison Tools: Automatically generate custom, animated side-by-side comparison slider videos with custom text labels to showcase your upscales.

•• Advanced Video Output: Supports global RIFE frame interpolation, Face Restoration, H265 10-bit encoding, and custom audio codecs.

•• Easy Installation: 1-click install and update .bat files. No complicated coding required!

⏱️ Video Chapters & Timestamps:

00:00:00 Introduction to SECourses Upscaler Pro & Amazing New Features
00:00:29 Comparison: Original Video vs SECourses Pro Upscaled Video
00:00:50 Slider Tool Comparison: Original vs SECourses Upscaler
00:01:06 Comparison: Original Video vs Topaz AI Video Enhance
00:01:19 Slider Tool Comparison: Original vs Topaz AI Quality
00:01:35 Ultimate Comparison: SECourses Upscaler vs Topaz AI
00:02:07 Overview of FlashVSR+ Version and Custom Improvements
00:02:20 Live Studio Level Video Comparison Slider Tool Demonstration
00:03:02 Topaz AI vs Original Video Slider Demonstration in App
00:03:15 SECourses vs Topaz AI Massive Quality Difference Revealed
00:03:35 Running Locally vs Cloud GPUs (RunPod, MassedCompute)
00:03:56 Test Case 2: Viral AI Video Original vs SECourses Upscaled
00:04:17 How to Generate Custom Comparison Videos with Custom Titles
00:04:50 Test Case 2 Slider: Topaz AI vs SECourses FlashVSR+
00:05:25 Chunk-Based & Scene-Based Upscaling System Explained
00:05:50 How to Download, Install, and Update the Application Easily
00:06:34 SeedVR2 Model Tricks: Batch Size & Blocks to Swap Setup
00:07:00 Live Demonstration: 4x Image Upscaling with SeedVR2
00:07:36 Optimizing VRAM Usage with Max Blocks to Swap & GGUF Models
00:08:34 FlashVSR+ Video Upscaling Setup & Scene Detection Features
00:09:17 Downscale and 4x Upscale Resolution Configuration Explained
00:09:54 How to Use the Full Resume Capability for Long Video Upscales
00:10:15 Custom Target Resolution Settings & Target Pixel Dimensions
00:10:46 Frame Chunk Size Optimization for Maximum Speed & Quality
00:11:20 Enable DiT Tiling vs VAE Tiling for VRAM Management
00:11:58 Live FlashVSR+ Upscaling Process & Input Chunk Splitting
00:12:43 VRAM Usage Analysis: Upscaling to 4K on a 16GB GPU
00:13:18 Additional SeedVR2 Tricks: Sparse Ratio & Tile Size Limits
00:13:41 Canceling Upscale Jobs & Monitoring RAM/VRAM Release Live
00:13:53 App Features: Health Check, Global Settings & Light/Dark Theme
00:14:15 Queue System, Face Restoration & RIFE Frame Interpolation
00:14:50 Video Output Formats, Codecs & H265 10-bit Encoding Setup
00:15:17 Automatic Comparison Display & Run Metadata Saving Feature
00:16:03 Direct Video Compare Tool & Animated Slider Generation
00:16:34 Multi-Video Comparison Slider (Original vs Topaz vs SECourses)
00:17:32 Testing Default Settings for VRAM Memory Efficiency
00:18:15 FlashVSR Default Settings Setup & Upscale Demonstration
00:19:07 Verifying Low VRAM Usage Under 8GB for HD Video Upscaling
00:19:38 Upcoming Trellis Image to 3D Application Features Preview
00:19:51 UniRig 3D Rigging Integration for Character Skeletons
00:20:15 Compiling Custom 3D Libraries for Modern CUDA 13 GPUs
00:20:53 Trellis Output Formats: Skeleton FBX & Preview GLB Saves
00:21:14 Importing & Previewing 3D Models, Bones, and Textures
00:22:20 Future Updates: Multi-Stage Upscale Queue & SUPIR Integration

SECourses Upscaler Pro

Furkan Gözükara — Fri, 20 Feb 2026 14:35:49 +0000

SECourses Upscaler Pro

Download link : https://www.patreon.com/posts/150202809

SECourses Ultimate Video and Image Upscaler Pro - SeedVR2 - FlashVSR+ - Face Restoration - Gan Upscalers - Queue System - Fully Automated Movie Restoration and Upscale Studio - with so many amazing new features and full VRAM optimizations, can upscale a whole 120 minutes video fully properly with full resume capability

Fully working locally with SOTA public Open Source AI Models

Quick tutorial video 4K : https://youtu.be/bPWsg8DREiM?si=cMz5avk_etPlwlKY

Get the app from here : https://www.patreon.com/posts/150202809

Version 2.0 of the most awaited Pro Upscaler App published
Almost all of the features completed please test all and let me know if any broken or what new features you need
It has so many features
Fully automated installation and model download on Windows, RunPod, SimplePod, Massed Compute, Linux
It fully supports SeedVR2, FlashVSR+, Image Based Upscalers like Gan upscalers etc
Make sure to follow requirements tutorial fully for torch compile for SeedVR2 it is very requirements depended MSVC C++ Visual Studio CL
Watch demo video above I have uploaded and full tutorial hopefully coming soon

Check Below Screenshots to See All of The Features of The App So Far

🎬 SeedVR2

Features List

⚡ FlashVSR+

📐 Resolution & Scene Split

🖼️ Image-Based (GAN)

⏱️ Output & Comparison - Global RIFE

⏱️ Output & Comparison - Video Output

⏱️ Output & Comparison - Videos Comparison Slider

⏱️ Output & Comparison - Images Comparison Slider

⏱️ RIFE / FPS / Edit Videos

⏱️ Face Restoration

⏱️ Fully Automatic Queue System

⏱️ System App Health Check

⏱️ Automatic 1-Click Installers for Windows, RunPod, Massed Compute, Linux, SimplePod

SeedVR2 and FlashVSR+ Studio Level Image and Video Upscaler Pro Released

Furkan Gözükara — Wed, 11 Feb 2026 00:48:31 +0000

SeedVR2 and FlashVSR+ Studio Level Image and Video Upscaler Pro Released

Full tutorial link > https://www.youtube.com/watch?v=bPWsg8DREiM

It has been long waited to have a studio level video and image upscaler app. Today we have publishing the version 1.0 of SECourses Ultimate Video and Image Upscaler Pro. It is supporting SeedVR2, FlashVSR+, Gan based upscalers, RIFE frame interpolation, full queue system, full batch folder processing, scene / chunked based processing and many more. It is fully working on every cloud and consumer GPUs like RTX 2000, 3000, 4000, 5000 series and H100, H200, B200, RTX PRO 6000. We are installing app with latest Torch and CUDA versions atm all fully automatic with pre-compiled libraries. Even Torch compile is fully and automatically working.

📂 Resources & Links:

💻 SECourses Ultimate Video and Image Upscaler Pro Download Link : [ https://www.patreon.com/posts/Upscaler-Studio-Pro-150202809 ]

🚆 Requirements Tutorial : https://youtu.be/DrhUHnYfwC0

🛠️ Requirements Written Post : [ https://www.patreon.com/posts/Windows-AI-Requirements-Setup-Guide-111553210 ]

👋 SECourses Discord Channel for 7/24 Support: [ https://bit.ly/SECoursesDiscord ]

ℹ️ SimplePod Register : https://simplepod.ai/ref?user=secourses

ℹ️ SimplePod Template : https://dash.simplepod.ai/account/explore/100/ref-secourses/

ℹ️ RunPod Register : https://get.runpod.io/955rkuppqv4h

ℹ️ RunPod Template : https://get.runpod.io/SECourses_CU13

ℹ️ Massed Compute Register : https://bit.ly/SECoursesMassedCompute

⏱️ TIMESTAMPS

00:00:00 Announcing SECourses Ultimate Video & Image Upscaler Pro V1
00:00:22 Ultra-Fast High-Quality Image Upscaling Demo (1024 to 4096px)
00:00:40 Low GPU Usage & Prompt-Free Workflow Explained
00:01:00 Single Step Upscaling & Advanced Optimization (Torch Compile)
00:01:20 Introduction to Video Upscaling & Custom Live Comparison Tool
00:01:45 Video Quality Comparison vs Topaz Labs & Open Source Models
00:02:08 Cloud GPU Support (RunPod, Massed Compute) & Local Execution
00:02:20 Dynamic UI Features: Real-Time Input Analysis & Settings
00:02:59 Scene-Based Upscaling Workflow with PySceneDetect Integration
00:03:18 Advantages of Scene-Based Processing & Resume Functionality
00:03:48 Visualizing Processed Scene Chunks & Adjusting Split Settings
00:04:09 Automating Full-Length Movie Upscaling (120+ Minutes)
00:04:26 Analyzing Scene Detection Statistics & Frame Count Distribution
00:04:51 RIFE FPS Interpolation Support & Run Logs Interface
00:05:10 Visual Demonstration of Upscaled Video Details & Future Models
00:05:30 Extensive GPU Compatibility (RTX 2000-5000, H200, B200, CUDA 13)
00:05:52 VRAM Error Handling, Health Check & Recovery Tools
00:06:19 Comprehensive Queue System & Changing Parameters Live
00:06:48 Canceling Processes & Error Recovery Without App Restart
00:07:11 Output Folder Structure, Subfolders & Saving Processed Data
00:07:56 Debugging with Executed Command Logs & Run Summaries
00:08:18 One-Click Windows Installation, Auto-Updates & Model Hash Check
00:08:38 Critical Windows Requirements for Torch Compile & Python 3.10
00:08:57 Installation Instructions for RunPod & Massed Compute Cloud
00:09:21 Detailed Feature List: Face Restore, GANs, FlashVSR+ & More
00:09:55 System Diagnostics, Reliability, Safety & UX Features
00:10:28 Batch Processing Support for Image & Video Folders

Some Core Features - Hopefully More Features Coming Soon

Core features: Multi-tab, multi-pipeline app (SeedVR2 / GAN / RIFE / FlashVSR+ / Face Restore) with unified resolution strategy + global face restoration controls.
SeedVR2: Video/image/folder inputs, first-frame preview, smart model defaults/constraints, and advanced VRAM/performance options (offload/BlockSwap/VAE tiling/compile/attention backend/cache toggles).
GAN / RIFE / FlashVSR+: GAN upscaling for images/videos/folders w/ metadata-based model detection; RIFE interpolation (model, FPS modes, scale/precision, static-skip, recursion); FlashVSR+ diffusion upscaling (version/mode/scale + VRAM-aware tiling/unload options).
Output & comparison: MP4/PNG/sequence outputs, full ffmpeg encoding controls (codec/CRF/preset/pixfmt), audio copy/re-encode/remove, 2-pass, FPS override, global RIFE post-process (incl. chunk-safe), and rich comparison tools (slider/SxS/overlay + HTML slider + auto comparison videos + “compare any 2 videos”).
Long runs & batching: PySceneDetect chunking + fallback, lossless vs fast splitting, resume partial runs, per-chunk cleanup, batch processing across pipelines, and an app-wide FIFO queue with snapshot-isolated settings.
Presets & reliability: Universal presets + per-tab presets, migration/guardrails, persistent global settings/profiles, safe cancel/cleanup, OOM detection + guidance, partial output salvage, collision-safe naming, telemetry/command logs, and validation/fallbacks (ffmpeg/audio utilities).
Diagnostics & UX: Health checks (ffmpeg/CUDA/tools/disk), GPU-safe detection, model registries, env var integration, file upload/path entry, previews, auto input detection, estimators, quick links, progress/ETA, CUDA cache clear, and Gradio --share support.

LTX 2 & Z Image Base Full Tutorial + Audio to Video Lip Sync + ComfyUI + SwarmUI + Windows + Cloud

Furkan Gözükara — Sat, 31 Jan 2026 00:56:26 +0000

LTX 2 & Z Image Base Full Tutorial + Audio to Video Lip Sync + ComfyUI + SwarmUI + Windows + Cloud

Full tutorial link > https://www.youtube.com/watch?v=SkXrYezeEDc

Info

LTX 2 is the newest state of the art (SOTA) Open Source video generation model and tutorial will show you how to use it with very best and most performant way in ComfyUI and also in SwarmUI. Moreover, Z Image Base model published and I will show how to use Z Image Base with most amazing preset and workflow as well. Furthermore, this tutorial will show you how to install, update, setup, download ComfyUI and SwarmUI and models and presets and workflows both on Windows and on RunPod, Massed Compute and SimplePod. Linux users can use Massed Compute scripts and installers directly. This is a masterpiece entire lecture level complete tutorial. This video will kickstart your AI journey 100x. Both local Windows and Cloud.

📂 Resources & Links:

🤖 ComfyUI Installer and Presets Zip File With CUDA 13: [ https://www.patreon.com/posts/ComfyUI-Installers-105023709 ]
💻 SwarmUI Installer and Presets Zip File: [ https://www.patreon.com/posts/SwarmUI-Install-Presets-114517862 ]
🚀 Model Downloader Zip File: [ https://www.patreon.com/posts/Model-Downloader-114517862 ]
🚆 SECourses Musubi Trainer (Model Quantize and Train App): [ https://www.patreon.com/posts/SECourses-Musubi-Trainer-137551634 ]
🛠️ Image Comparison Slider Tool: [ https://www.patreon.com/posts/image-video-comparison-slider-app-133935178 ]
👋 SECourses Discord Channel for 7/24 Support: [ https://bit.ly/SECoursesDiscord ]
ℹ️ SimplePod Register : https://simplepod.ai/ref?user=secourses
ℹ️ SimplePod Template : https://dash.simplepod.ai/account/explore/100/ref-secourses/
ℹ️ RunPod Register : https://get.runpod.io/955rkuppqv4h
ℹ️ RunPod Template : https://get.runpod.io/SECourses_CU13
ℹ️ Massed Compute Register : https://bit.ly/SECoursesMassedCompute

⏱️ TIMESTAMPS

00:00:00 Intro: ComfyUI + SwarmUI presets, Z-Image, model downloader, cloud installs
00:00:28 Free prompt enhancement with Google AI Studio (prepared prompt file)
00:01:26 Demo: 45s audio-driven lip-sync image→video (LTX 2)
00:02:25 Quick demos: ComfyUI image→video, text→video, Z-Image base
00:03:43 Quick demos: SwarmUI LTX 2 image→video + Z-Image base
00:04:46 Install/update presets zip (v78+): extract & overwrite everything
00:05:11 Upgrade to CUDA 13 safely: delete venv then run ComfyUI update/install
00:06:00 Windows prereqs + the always-updated setup guide referenced in description
00:06:42 Install required node bundles: (1) SwarmUI extra nodes + (100) LTX audio
00:07:29 VRAM-optimized launcher: no-VRAM / cache / smart-memory / precision choices
00:09:36 Share one model library: configure extra_model_paths.yaml (no duplicates)
00:10:25 Model Downloader overview: set base path + one-click bundles for SwarmUI/ComfyUI
00:11:43 Download LTX 2 core bundle: multi-connection download + merge + hash verify
00:12:12 Low-VRAM path: GGUF distilled models vs recommended FP8-scaled defaults
00:14:16 URL Downloader: Civitai/HF links, folder targeting, and optional API keys
00:14:54 ComfyUI preset pack tour: LTX2 (I2V/T2V/audio) + Z-Image (base/2× upscale)
00:15:16 ComfyUI audio lip-sync preset: image + resolution + audio setup
00:16:58 Frames & prompting: 24fps math, run/stop frame count, lyrics/subtitles prompts
00:17:59 Quality/perf knobs: CRF, VRAM monitoring, and low-VRAM args recap
00:20:22 Review result + move to LTX 2 image→video preset workflow
00:21:50 Prompt enhancer workflow: drag prompt file into AI Studio (optionally add image)
00:24:11 Z-Image troubleshooting: disable Sage Attention and restart
00:24:32 Z-Image base + 2× upscale preset: when to use it and what to expect
00:26:47 Outputs & reproducibility: where renders save + drag PNG to reload metadata
00:27:57 Update SwarmUI via zip: get latest presets + utilities
00:28:27 SwarmUI setup: ComfyUI backend, passing args, and pointing to model folders
00:30:21 SwarmUI image→video: direct apply + init image workflow
00:31:17 Fix model load/caching issues: add --use-cache-none when needed
00:32:51 SwarmUI text→video + upscale: duration/frames, half-res then upscale rules
00:33:52 SwarmUI outputs: output_local/raw + metadata saved with generations
00:35:05 SwarmUI Z-Image: base vs 2× upscale comparison + speed notes
00:36:58 Image comparison slider tool: quick before/after inspection
00:37:51 RunPod start: template choice, CUDA/driver constraints, optional storage volume
00:43:30 RunPod Jupyter: upload ComfyUI zip, extract, install bundles (1+100)
00:46:28 RunPod: run Model Downloader, start ComfyUI, connect via exposed port
00:52:05 RunPod: switch to SwarmUI, map folders (case-sensitive), import presets
00:55:43 RunPod: download outputs as archive + stop vs terminate cost control
00:57:55 SimplePod: cheaper/faster alternative + persistent volume setup highlights
01:03:52 Massed Compute: deploy with coupon + connect via ThinLinc (shared folder setup)
01:08:17 Massed Compute: install bundles + download models (disk speed advantage)
01:10:48 Massed Compute: start ComfyUI, connect from PC, run lip-sync preset
01:14:26 Massed Compute: hook SwarmUI to ComfyUI backend + text→video demo
01:17:02 Wrap-up: recap providers + next steps

BF16 vs GGUF, FP8 Scaled, NVFP4 Speed & Quality Compared + ComfyUI CUDA 13 Gains + FLUX 2 Klein 9B

Furkan Gözükara — Sat, 17 Jan 2026 20:40:03 +0000

BF16 vs GGUF, FP8 Scaled, NVFP4 Speed & Quality Compared + ComfyUI CUDA 13 Gains + FLUX 2 Klein 9B

Full tutorial link > https://www.youtube.com/watch?v=XDzspWgnzxI

Compared Quality and Speed Difference (with CUDA 13 & Sage Attention) of BF16 vs GGUF Q8 vs FP8 Scaled vs NVFP4 for Z Image Turbo, FLUX Dev, FLUX SRPO, FLUX Kontext, FLUX 2

Video Info

It was always wondered how much quality and speed difference exists between BF16, GGUF, FP8 Scaled and NVFP4 precisions. In this tutorial I have compared all these precision and quantization variants for both speed and quality. The results are pretty surprising. Moreover, we have developed and published NVFP4 model quant generator app and FP8 Scaled quant generator apps. The links of the apps are below if you want to use them. Furthermore, upgrading ComfyUI to CUDA 13 with properly compiled libraries is now very much recommended. We have observed some noticeable performance gains with CUDA 13. So for both SwarmUI and ComfyUI solo users, CUDA 13 ComfyUI is now recommended.

📂 Resources & Links:

📥 Download ComfyUI CUDA 13 Installer: [ https://www.patreon.com/posts/ComfyUI-Installers-105023709 ]
📥 SwarmUI & ComfyUI Unified Model Downloader: [ https://www.patreon.com/posts/SwarmUI-Install-Download-Models-Presets-114517862 ]
🤖 NVFP4 Model Quantizer App: [ https://www.patreon.com/posts/nvfp4-quantizer-app-148217625 ]
🤖 SECourses Musubi Trainer (FP8 Scaled Quantization App): [ https://www.patreon.com/posts/nvfp4-quantizer-app-148217625 ]
🛠️ Image Comparison Slider Tool: [ https://www.patreon.com/posts/image-video-comparison-slider-app-133935178 ]
☁️ SimplePod AI: [ https://simplepod.ai/ref?user=secourses ]
New Model FLUX 2 Klein 9B: [ https://huggingface.co/black-forest-labs/FLUX.2-klein-9B ]
🎥 FLUX 1 Kontext Dev Tutorial (inpaint - outpaint - image fix): [ https://youtu.be/XWzZ2wnzNuQ ]
🎥 Previous ComfyUI Installation Tutorial: [ https://youtu.be/yOj9PYq3XYM ]
How to Use SwarmUI Presets & Workflows in ComfyUI + Custom Model Paths Setup for ComfyUI & SwarmUI Tutorial: [ https://youtu.be/EqFilBM3i7s ]
SECourses Discord Channel for 7/24 Support: [ https://discord.com/invite/software-engineering-courses-secourses-772774097734074388 ]
SECourses Musubi Tuner Tutorial: [ https://youtu.be/DPX3eBTuO_Y ]
NVIDIA NVFP4 Blog Post to learn More: [ https://developer.nvidia.com/blog/introducing-nvfp4-for-efficient-and-accurate-low-precision-inference/ ]

⏱️ Video Chapters:

00:00:00 Introduction: GGUF Q8 vs NVFP4 vs BF16 vs FP8 Precision Comparison
00:00:38 FP8 Quantization & New NVFP4 Model Quantizer App in Musubi Trainer
00:01:08 The New FLUX SRPO Mixed NVFP4 Model & FLUX 2 Klein 9B Announcement
00:01:56 Speed Comparison Setup: ComfyUI CUDA 13 & Compiled Libraries
00:02:41 Z Image Turbo Speed Test: GGUF Q8 vs NVFP4 (87% Faster)
00:03:09 Z Image Turbo Speed Test: BF16 vs FP8 Scaled vs GGUF Improvements
00:03:32 Installing & Using Image Comparison Slider Tool for Quality Check
00:03:55 Z Image Turbo Quality: BF16 vs GGUF Q8 vs FP8 Scaled
00:04:13 Z Image Turbo Quality: NVFP4 Degradation Analysis
00:04:27 FLUX 2 Dev Speed Test: GGUF Q8 vs NVFP4 (100% Faster)
00:04:43 FLUX 2 Dev Speed Test: FP8 Scaled vs BF16 Performance
00:05:12 FLUX 2 Dev Quality: BF16 vs GGUF Q8 vs Mixed FP8 Scaled
00:05:38 FLUX 2 Dev Quality: NVFP4 Mixed Precision Analysis
00:05:54 Benchmark Settings: 2048px Resolution & Quality 1 Preset Details
00:06:25 FLUX 1 Dev Speed Test: GGUF Q8 vs NVFP4 (118% Faster)
00:07:21 FLUX 1 Dev Speed Test: BF16 & FP8 Scaled Performance Stats
00:07:42 FLUX 1 Dev Quality: BF16 vs GGUF Q8 vs FP8 Scaled
00:07:55 FLUX 1 Dev Quality: NVFP4 Visual Degradation Review
00:08:06 FLUX 1 Kontext Dev: Model Intro & Outpainting Tutorial Reference
00:08:40 FLUX 1 Kontext Dev Speed: GGUF Q8 vs NVFP4 (93% Faster)
00:08:59 FLUX 1 Kontext Dev Speed: BF16 & FP8 Scaled Comparisons
00:09:12 FLUX 1 Kontext Dev Quality: Original vs Edited Image (Hair Change)
00:09:36 FLUX 1 Kontext Dev Quality: BF16 vs GGUF Q8 vs FP8 Scaled
00:09:51 How to Use SwarmUI Unified Model Downloader & Bundles
00:10:36 Downloading Models via URL from CivitAI & Hugging Face to Cloud
00:11:45 SECourses Musubi Trainer: Creating Custom FP8 Quantized Models
00:12:44 The New FLUX SRPO NVFP4 Mixed Precision Model Overview
00:13:15 Live Demo: FLUX SRPO NVFP4 Speed Test on RTX 5090 (5.7s)
00:13:52 VRAM Usage Analysis: NVFP4 on RTX 5090 (14GB Usage)
00:14:16 Live Comparison: BF16 Speed & VRAM Test on RTX 5090
00:15:15 Troubleshooting: Fixing Low RAM/VRAM Issues with Arguments
00:16:25 Why You Should Upgrade to ComfyUI CUDA 13 Version
00:16:51 SimplePod AI: Updated Instructions & Template Setup
00:17:29 RTX 6000 Blackwell Fix & nvitop Utilization Verification
00:18:18 Conclusion, Contact Info & Support Channels
In this video, you will learn:
Speed differences between GGUF Q8, NVFP4, BF16, and FP8.
Visual quality analysis using the Image Comparison Slider.
How to use the new NVFP4 and FP8 Quantizer tools.
How to fix Low VRAM/RAM issues with specific arguments.
Performance benchmarks on RTX 5090 and RTX 6000.

DEV Community: Furkan Gözükara

Ultimate Image Processing Toolkit by SECourses

Ultimate Image Processing APP Premium by SECourses : Batch Cropping, Zooming In, Resizing, Duplicate Image Removing, Face Extraction, SAM 2, SAM 3.1 and Yolo Segmentation, Masking for Windows, RunPod, Massed Compute

Installers and APP is shared here with instructions : https://www.patreon.com/posts/120352012

APP : https://www.patreon.com/posts/120352012

28 July 2026 V1.0

Windows Requirements

Contents

Requirements

Installation

Windows — automatic

Manual

Cloud (RunPod / SimplePod / Massed Compute)

Launching the app

Presets

Tab 1 — Image Cropper

The three detection engines

How the cropping logic works

The run panel

Real results

Tab 2 — Segmentation

Negative prompts

Settings and the YOLO backend

Batch processing

Tab 3 — Extract Faces & Objects

Tab 4 — Image Resizer

It works out what you pointed it at

Framing choices

Tab 5 — Generate Tiled Images

Tab 6 — Find Duplicates

Choosing an algorithm

Tab 7 — Move Low-Res Files

Tab 8 — Rename Files

Tab 9 — Performance & VRAM

Runtime probe

Recommended workflow

Output folder layout

Quick reference

Measured benchmarks

Ideogram 4 Is Here: The Ultimate JSON Prompting Masterclass

Full Tutorial

Tutorial Resources

Supported Models

Qwen Vision Models

Joy Caption Models

Torch 2.13 Runtime

Application Preview

Torch Compile Performance

Installers

Video Chapters

Covered in the Tutorial

ACE-Step XL 1.5 Premium + Facebook / META SAM Audio + Auto-Editor Trim + Audio Tools Enhancement Full Tutorial

ACE-Step XL 1.5 Premium + Facebook / META SAM Audio + Auto-Editor Trim + Audio Tools Enhancement Full Tutorial

Video Tutorial

Source Video, Links, And Chapters

Video Chapters

Few Updates Made Since Video Tutorial So They Are Listed Here

18 June 2026 V5.5 Update

18 June 2026 V5.4 Update

18 June 2026 V5.3 Update

1. What ACE-Step XL 1.5 Premium Is

2. Install And Start On Windows

3. Quick Song Generation

Wildcards For Prompt Variation

4. Results, Seeds, And Reuse

5. Advanced Generation Modes

6. Remix, Repaint, Extract, LEGO, And Auto-Editor Features

7. Audio Processing

8. SAM Audio Segment

9. Library, Metadata, Presets, Dataset, And Training Pages

10. RunPod Deployment

11. Massed Compute Deployment

12. SimplePod Deployment

13. Troubleshooting And Best Practices

ACESTEP XL 1.5 Remix Mode Full Tutorial

ACESTEP XL 1.5 Remix Mode Full Tutorial

The ULTIMATE Local AI Update Just Dropped! ACE-Step 1.5, Paints-Undo & Whisper Premium Will WOW You

Full tutorial link > https://www.youtube.com/watch?v=hzKSt5WUAm0

Info

Important links: