DEV Community: Furkan Gözükara

IndexTTS-2.5 SECourses Premium Voice Cloning, Training and Generation App - 1-Click to Install

Furkan Gözükara — Fri, 04 Sep 2026 15:28:31 +0000

IndexTTS 2.5 Premium - The Complete Beginner-to-Advanced Guide

App Download Link

You can get the app installer from here https://www.patreon.com/SECourses/posts/indextts-2-5-and-139297407

Quick Info

Voice cloning, long-form narration, caption-timed audio and MP4, batch production, dataset preparation, LoRA/DoRA training, checkpoint evaluation, listening grids, speaking-rate calibration, and low-VRAM operation - all in one tested workflow.

V6 maintenance update: the main workflows and registered settings remain compatible with the screenshots, while the following user-visible behavior is new or corrected.

Record a reference directly from the browser microphone, alongside upload, local-path, library, adapter, and recent-output sources.
Local media paths are staged safely for browser playback, and unsupported video codecs receive an FFmpeg-generated browser preview without changing the audio used for cloning.
Voice Generation now defaults to in-process model reuse; isolated subprocess mode remains available when hard cancellation and complete VRAM release matter more than repeat-run speed.
Candidate players, dataset reference players, feature-cache handoff, completed batch summaries, CPU diagnostics, and narrow-screen header controls now restore or update reliably.
A lazy-rendered Changelog tab now keeps the public v6.0-to-v4.0 release history and official SECourses project links inside the app.

Choose a route:

Beginner route: install, choose a reference, enter text, and generate a first voice.
Production route: control segmentation, pauses, subtitles, emotion, duration, formats, and batches.
Voice-training route: prepare clean data, cache features, train LoRA or DoRA, evaluate checkpoints, and deploy the best adapter.
Hardware route: choose a VRAM tier, BF16 or INT8, block swapping, auxiliary-model residency, and verify the setup with the isolated benchmark.

1. Install and Start the App

Windows one-click install or update

Place the downloaded package in a short writable path such as G:\Index_TTS_v4. Before the first install, provide Python 3.12.10 or newer, Git, FFmpeg, CUDA 13.0, cuDNN 9.17 or newer, and Visual Studio Community with the C++ workload. Then run Windows_Install_or_Update.bat.

The installer clones or updates Premium_IndexTTS2_SECourses, creates its Python 3.12 virtual environment, installs uv, resolves the requirements, and runs the model downloader. If it detects local source changes, it deliberately skips git pull rather than overwriting them.

Run Windows_Install_or_Update.bat once for a fresh install and again whenever an update is announced.
Run Windows_Start_App.bat for normal use. It activates the environment, configures the CUDA allocator and model cache, and launches the Gradio UI.
Wait for the local URL, normally http://127.0.0.1:7860, and open it in a browser.
For a specific GPU, add set CUDA_VISIBLE_DEVICES=1 to the start BAT or launch python webui.py --device cuda:1 from the activated environment.

Useful launch flags are --port, --host, --share, --model_dir, --verbose, --no-browser, and --device. For example:

Windows_Start_App.bat --port 7861 --device cuda:0 --no-browser

Linux, RunPod, SimplePod, and Massed Compute

Use the supplied RunPod_Install_IndexTTS.sh or Massed_Compute_Install.sh, then follow the matching instruction text file in the package. The current Linux installers require root or sudo, install aria2, xz, Git LFS, and FFmpeg/ffprobe n9.0, provision a current Python 3.12 environment, install the application requirements, and download the IndexTTS 2.5 models. The Linux launcher then enters the app folder, activates venv/bin/activate, clears a conflicting LD_LIBRARY_PATH, and starts Gradio with --share. Keep the generated public Gradio link private because anyone who has it can use the running GPU.

bash Linux_Start_App.sh --device cuda:0

If startup reports missing model files, run Windows_Model_Download_and_Fix.bat on Windows or use Models & Performance > Download / verify base models after the interface opens.

2. Know the Workspace Before Generating

The header controls reusable settings; the tab row separates generation, batch work, dataset preparation, training, checkpoint comparison, performance, help, and release history. A fresh V6 install selects the quality preset automatically; an updated installation restores its last-used preset. Start in Voice Generation and keep the quality preset for the first successful output.

Figure 1. The first screen is a working console, not a landing page: reference and text are on the left and center, while run controls, progress, results, and logs stay on the right. **Open / close all sections* is the fastest way to expose or collapse advanced controls.*

At startup, earlier result panels stay clean. Load last values restores the most recently saved values across every tab. A system preset is marked with a star and is read-only; a user preset can be created, overwritten, loaded, or deleted. Reset returns the registered controls to the system default preset without deleting model files or generated outputs.

Voice Generation: one script, one reference workflow, optional candidates and media output.
Batch Generation: many TXT, SRT, VTT, or SBV jobs with shared or per-file references.
LoRA Dataset Preparation: turn raw audio/video/captions into quality-controlled training clips.
LoRA / DoRA Training: train, validate, sample, save, resume, analyze, and manage adapters.
Checkpoint Grid: compare base, recommended, final, and epoch checkpoints with identical inputs.
Models & Performance: fit the model to the GPU, verify files, and benchmark real VRAM use.
Help: embedded quick starts, parameter guidance, pause syntax, and recovery steps.
Changelog: read newest-first release notes and open the official Patreon or GitHub project pages.

3. Make the First Voice Clone

Choose the speaker and write the script

Use a clean, single-speaker reference with little music, reverb, or room noise. Eight to fifteen seconds is a strong starting point. Audio, video, and direct microphone recording are accepted; video is decoded through FFmpeg and the audio preview shows the exact material generation will use.

Figure 2. Upload or drop the reference, type the script, choose its language, and press **Generate voice. V6 also places a **Record from microphone* accordion directly beneath the upload surface. The right column exposes cancellation, progress, current item, speed, ETA, the generated player, and the last 60 log lines.*

Drop an audio or video file into Reference Voice, expand Record from microphone and record a WAV in the browser, or enter an existing local path in Reference media path and press Load path. Stop the microphone recording before generating; the recorded WAV becomes the active Reference Voice.
If only part of a long source is clean, enter ranges such as 1:4;7.5:12 or 01:02-01:08, then press Extract ranges. Ranges are joined in the order written.
Type the words to synthesize. Match Language to the script, not necessarily to the reference speaker.
Keep Max tokens per segment on Auto or the language-aware default for the first run.
Press Generate voice and do not close the terminal while the worker is active.

The Reference audio library scans reference_audios. Refresh rescans it and Load path applies the selected entry. When no manual reference is present, an enabled LoRA/DoRA can supply its recommended reference automatically; otherwise the newest compatible library file is the final fallback. Clear removes uploads, recordings, previews, the path field, and the current manual selection.

For a local path outside the app's normal output, dataset, adapter, reference, or temporary folders, V6 stages a browser-safe hard link or copy under .ui_state/reference_media. A video whose codec cannot play directly in the browser receives a cached preview under .ui_state/reference_video_previews; generation still uses the audio extracted from the original source.

Understand automatic text segmentation

Long input is split before synthesis so each section stays inside the model and VRAM budget. The live preview shows section number, type, text or pause, and details before you commit GPU time.

Figure 3. Shorter segments are safer on low VRAM and make failures easier to retry; longer segments can preserve phrasing but reserve more semantic and diffusion memory. The non-CJK budget scale leaves room for English, Arabic, and Spanish subword expansion.

Use punctuation to create natural boundaries. Enable pause tags when you need exact silence: [pause:500ms], [pause:0.8s], and <pause=0.5> are accepted forms. A pause becomes its own section and is inserted without asking the speech model to invent silence.

Text normalization expands text before phonetic processing and is recommended for ordinary numbers and punctuation. Disable it only when you have a deliberate pre-normalized script and have checked pronunciation. If the WeText backend rejects an unusual English or Chinese fragment, V6 keeps the original text and logs a concise warning instead of dropping the fragment.

Use captions and create an MP4

Upload SRT, VTT, or SBV captions to replace or organize the script. With Use caption cue timing, each caption unit is retimed to its cue slot and cue start times are preserved. Add a still image only when an MP4 is required.

Figure 4. Caption timing is ideal for localization, dubbing drafts, and slide narration. Without the timing checkbox, caption text is still synthesized but flows naturally; without a still image, the job remains audio-only.

Plain captions: use subtitle text as a structured script while allowing natural timing.
Cue-timed captions: preserve subtitle starts and fit each unit to its slot.
Still-image video: combine the completed audio and uploaded image into an MP4.
Caption plus pause tags: pause parsing still occurs inside caption text when enabled.

4. Shape Emotion, Sampling, and Timing

Four emotion sources

Emotion is independent from speaker identity. Select Same as speaker for the simplest clone, Emotion reference audio to transfer delivery from another clip, Manual vectors for direct channel control, or Emotion text to describe the desired performance.

Figure 5. **Emotion weight* blends the chosen emotion source with the speaker tone. Manual mode exposes joy, anger, sadness, fear, disgust, depression, surprise, and calm; tuned biases and the maximum vector sum keep a combination from becoming unnaturally extreme.*

For emotion-reference work, use a clip whose delivery is clear even if the voice is different: the speaker reference supplies identity and the emotion reference supplies style. Random emotion exemplar varies the internal exemplar used with manual vectors. For reproducible comparisons, turn it off and keep the generation seed fixed.

Emotion-text mode needs the Qwen emotion model enabled in Models & Performance. Enter a short direction such as calm, reassuring, and quietly optimistic. If the field is blank, the app analyzes the speech text itself.

Autoregressive and diffusion controls

The autoregressive stage decides semantic tokens; the diffusion stage turns those tokens and conditioning into acoustic detail. Change one family at a time so you can hear what caused the difference.

Figure 6. The quality preset uses sampling, temperature 0.8, top-p 0.8, four beams, CFM temperature 0.9, and 40 diffusion steps. The system default preset uses three beams, CFM temperature 1.0, and 25 steps. A fixed seed plus a single candidate is the cleanest diagnostic setup.

Temperature: lower is steadier; higher is more varied but can be less stable.
Top-p and top-k: restrict the token candidate pool. Top-k 0 disables that filter.
Beams: can improve stability, but multiplies time and VRAM. Optional acceleration expects beams 1.
Repetition penalty: prevents semantic-token loops; keep the established default unless diagnosing repeats.
Length penalty: affects beam search only; 0 is neutral.
Max mel tokens: a safety ceiling, not a requested duration.
Candidates: consecutive seeded alternatives from one request; each costs another generation.
Diffusion steps: 12-16 is a faster draft range, 25 is the registered/system-default value, and the quality preset uses 40; 35-50 can refine difficult material.
CFG rate and CFM temperature: control conditioning strength and diffusion variation.
CFM cache length: lower it only when reserved VRAM is the problem.

Pacing, silence, and reference processing

Use Speaking rate for the voice's pace, Section silence for joins, and Target duration only when the entire assembled output must approach a known length. A trained voice can store a measured speaking rate and apply it automatically.

Figure 7. Reference limits normally stay at 15 seconds. Semantic layer 17 is the trained recommendation. Reusing speaker conditioning for emotion is the fast default when there is no separate emotion source.

Off: no whole-output duration target.
Natural: regenerate timing toward the target instead of mechanically editing the finished waveform.
Pad: append silence only when output is shorter; it never speeds up or truncates longer speech.
Trim: cut a longer assembled result to the exact target.
Max consecutive silence tokens: 0 disables token trimming; use it only to suppress unusual model silences.
Latent multiplier: the natural-duration factor passed to the engine; leave 1.72 unless running a controlled timing experiment.

Formats, audio finishing, and execution mode

Give the task an optional safe filename, keep the used reference for reproducibility, and choose WAV, MP3, or still-image MP4 behavior. Bypass preserves model audio; the other tuning presets use FFmpeg post-processing with optional explicit overrides.

Figure 8. V6 system presets start with in-process reuse for faster repeat runs. Enable isolated subprocess mode when Cancel must terminate the complete model process and release its VRAM after the job; isolation trades away resident-model reuse.

MP3 bitrate 256k is a strong voice quality and file-size balance; WAV candidates always remain available.
Edge-silence trimming removes only sufficiently long leading or trailing silence.
Low cut, high cut, gain, LUFS, and de-ess values override the chosen tuning preset when supplied.
Every non-bypass tuning preset preserves the source sample rate in V6.
In-process reuse is the V6 default; isolated subprocess is an intentional per-preset opt-in.
Section batch size 1 is safest. Raise it only within the active VRAM tier hint.
Low-memory mode chooses sequential, aggressive-memory paths.
Prevent VRAM accumulation clears autoregressive caches between sections; it is slower but useful for long books.
Verbose logging exposes model inputs, timing, and detailed diagnostics in the terminal and live log.

5. Run, Cancel, Review, and Reuse Outputs

During a run, watch item number, elapsed time, ETA, speed, current stage, and the live log. Cancel stops new work and, in subprocess mode, can terminate the worker cleanly instead of leaving GPU memory occupied.

Figure 9. A successful run ends with a playable result and a final summary. **Open outputs folder* jumps to the task directory containing audio, media, reference copies, metadata, and logs selected by the output settings.*

If generation seems stuck, read the last log line before cancelling. First-time model loads and optional compilation can be much slower than later runs. If cancellation leaves a model resident in reuse mode, use Models & Performance > Unload model / free VRAM.

When Candidates is above one, listen to every player before choosing. The displayed seed lets you repeat or compare a run with the same stochastic starting point; settings and output metadata explain exactly what created each file.

Figure 10. **Recent outputs* lists the last ten generated tasks. Select a good result and press Load selected output into reference to perform iterative voice cloning, continuation work, or a clean second-generation reference test.*

6. Batch Generation for Many Scripts

Build a queue from uploaded TXT/caption files, pasted text, or a local folder. The batch uses the shared Voice Generation settings, so first prove one representative item in the single-generation tab.

Figure 11. Naming supports {index}, {name}, and {stem}. Keep output in a safe subfolder under outputs, choose shared or same-stem per-file references, then choose cancellable subprocess execution or faster in-process reuse.

Load mixed TXT, SRT, VTT, and SBV sources or point the folder field at a local collection.
Choose Shared reference to use the active Voice Generation reference for every item.
Choose Per-file reference when each text/caption file has a same-stem audio file beside it.
Enable Continue after item errors for unattended queues; failed rows are recorded while later items continue.
Press Generate batch, monitor the table and log, and use Open batch folder when complete.

The tested queue mixed a plain TXT item with an SRT item and completed both. Caption timing is now applied per item, so a subtitle entry can use its cues without incorrectly forcing the TXT entry through a caption-only path.

Figure 12. Confirm that every requested item has a completed row, playable output, path, elapsed time, and no hidden error in the log. **Cancel batch* is safe at an item boundary and terminates the active subprocess when needed.*

7. Prepare a High-Quality Voice Dataset

Inputs, names, and scanning

Dataset preparation accepts media files, folders, metadata.csv, or already segmented WAV+TXT folders. Use one safe dataset name and confirm the output root before scanning, especially when Overwrite dataset will later be enabled.

Figure 13. Recursion discovers nested media. A fixed speaker name labels every segment; **Speaker from folder* instead uses each source parent folder, which is useful for a multi-speaker collection.*

Press Scan inputs before processing. The discovered-media table and statistics expose supported files, sidecar captions, durations, and warnings without spending time on transcription or cutting.

Figure 14. Fix missing paths, unsupported extensions, or accidental extra speakers at this stage. **Refresh* updates existing datasets, and Open dataset folder inspects a selected prepared dataset.*

Transcripts and sentence-aligned segmentation

Prefer sidecars uses SRT/VTT/SBV text and timing when available and lets Whisper fill missing alignment. Sentence-aligned mode with Whisper word times is the recommended CUDA workflow because it preserves complete phrases instead of arbitrary waveform chunks.

Figure 15. Remove bracket notes such as [music], deduplicate rolling captions, and drop repeated spoken sentences. The measured quality window is 4 to 20 seconds with a 14-second target; edge padding and silence snapping avoid clipped consonants.

Sentence boundaries require punctuation and are the cleanest option.
Sentence-or-pause recovers more material at aligned-word pauses, with a small risk of less natural cuts.
Maximum cue gap controls when nearby caption cues may merge.
Minimum pause boundary controls how much silence is needed when punctuation does not provide an edge.
Minimum and maximum word counts remove fragments and implausibly dense transcripts.

Cleanup and objective quality gates

Trim leading and trailing silence before filtering, normalize loudness for consistent gradients, and retain the required 24 kHz sample rate. The remaining gates reject weak alignment, impossible speaking rates, very quiet audio, clipping, and excessive silence.

Figure 16. Defaults are conservative voice-training safeguards: -20 LUFS leaves headroom, 40 dB is a gentle trim threshold, and a 0.001 clipping ratio permits at most 0.1 percent clipped samples. Leave maximum silence ratio blank to disable that optional whole-segment filter.

Do not loosen several gates at once. Review rejected counts and playable segments, then change the single rule that is demonstrably excluding good speech. Bad transcripts or clipped recordings cannot be repaired by more training epochs.

Destination, references, and deterministic smoke tests

Export reference candidates so training samples, validation, and later Voice Generation can reuse the cleanest clips. Maximum segments set to 0 processes everything; a small number is ideal for validating paths and settings before a long run.

Figure 17. The preparation seed makes candidate ranking and randomized work reproducible. **Overwrite dataset* replaces the same named directory, so leave it off until the scan and destination are unquestionably correct.*

Press Prepare dataset and follow item progress, speed, ETA, stats, and the log. A successful summary reports accepted and rejected segments, total duration, manifests, reference candidates, and the output path.

Figure 18. Completion means the files were produced, not that every segment is automatically good. Use the inspection panel next; **Cancel* is available throughout long extraction, alignment, and segmentation jobs.*

Sort through the prepared-segments table, inspect durations and warnings, and click representative rows to play the exact training waveform. Look for clipped words, wrong captions, background speakers, music, and long silence.

Figure 19. The waveform and duration distribution make outliers visible. A smaller, clean, correctly transcribed dataset normally beats a larger noisy one for a single voice.

Precompute the feature cache

Feature caching runs the expensive base-model preprocessing once and lets training consume compact cached samples. Choose the intended model directory and device, then press Cache features now.

Figure 20. Monitor cache progress and failures just like preparation. If a cached sample exceeds text or semantic-code limits, correct or remove the source segment rather than hiding the failure.

When caching completes in V6, the prepared dataset remains selected, its cached status refreshes immediately, and the Training tab receives the same dataset automatically. You no longer need to reselect it just to make the training summary notice the new cache.

8. Train a LoRA or DoRA Voice

Adapter architecture

Select a prepared cached dataset, enter a safe adapter name, and choose LoRA or DoRA. DoRA is the measured quality default; LoRA uses slightly less compute. The recommended measured setup is rank 128 with alpha 129.

Figure 21. Attention and MLP targets are recommended for voice fidelity. Speaker projection is a small fully trained module; emotion layers and the mel embedding/head are advanced additions that expand the trainable surface.

Start with the default trainable modules. Increasing rank or enabling extra modules does not guarantee better speech; it increases file size, compute, and overfitting risk. The app inspects type, rank, and alpha when resuming so incompatible weights are rejected early.

Optimizer, schedule, and effective updates

The measured batch-1 baseline uses AdamW, cosine decay, learning rate 4e-5, 200 warmup steps, 10 epochs, batch size 1, and accumulation 1. With that setup, every training clip produces one optimizer update per epoch.

Figure 22. max_steps=0 derives the plan from epochs; use a tiny nonzero maximum for a smoke test. Gradient clipping limits spikes, while label smoothing and stronger weight decay should be changed only in response to measured validation behavior.

Fused AdamW can be faster on compatible CUDA builds; ordinary AdamW is the portable choice.
Betas 0.9,0.99 and the default epsilon are established stable optimizer values.
Mel loss is the main acoustic-token objective; text loss is auxiliary.
Speaker reference other and emotion reference follow_speaker imitate normal inference better than self-conditioning.

Validation and early stopping

Keep a validation split so the app measures unseen clips rather than only memorization. Five percent is the general default; tiny test datasets may need a larger fraction simply to contain at least one validation item.

Figure 23. Validation can run every N steps and/or at epoch boundaries, with a batch cap for large datasets. Early stopping requires both patience and a minimum improvement; patience 0 disables it.

Validation reference mode other uses a different same-speaker clip for both vectors and most closely matches real cloning. The training smoke test used four segments with a 25 percent split, producing three training items and one unseen validation item. If a tiny dataset produces zero validation items, V6 safely skips automatic measured checkpoint evaluation and records the reason in the log instead of starting an invalid evaluation job.

Precision and low-VRAM training

BF16 base weights and BF16 mixed precision are recommended on modern NVIDIA cards. Gradient checkpointing trades extra computation for much lower activation memory and is required when training with GPT block swapping.

Figure 24. INT8 ConvRot reduces frozen base-weight memory. Block swapping streams selected frozen GPT blocks through a CPU ring; pinned memory improves transfer speed, while ring size 1 uses the least VRAM.

Use Apply VRAM tier defaults instead of guessing a low-memory combination. If training still OOMs, reduce batch size first, keep gradient checkpointing enabled, add block swap, and close other GPU applications. Do not compensate by raising gradient accumulation unless you intentionally want a different optimization regime.

Saving, resume modes, and automatic evaluation

Save an epoch checkpoint so the best-sounding voice is not forced to be the final epoch. Keep-last 0 retains every epoch; Save best also tracks the lowest validation loss. BF16 adapter files are smaller, while FP32 preserves full update precision.

Figure 25. **Weights only* loads an adapter into a fresh schedule at step 0. Continue run restores optimizer, scheduler, step, RNG, and data position when train state exists. Automatic analysis is CPU-only; measured checkpoint evaluation starts only after the training model releases GPU memory.*

Save train state for exact continuation from best, final, or interrupted checkpoints.
Save train state with every epoch only when you need to continue from an arbitrary epoch; it costs substantially more disk.
Automatic evaluation may include the base model, a deterministic training subset, selected strengths, and a timeout that does not invalidate completed training.
The recommended checkpoint is written into machine-readable and plain-language analysis files.

Per-epoch listening samples

Enable training samples to hear progress at a fixed epoch interval. Keep one representative short sentence, one reference, one language, and one seed so differences come from the checkpoint rather than changing inputs.

Figure 26. The isolated sampling worker uses its own runtime tier, free-VRAM threshold, and timeout. Blank reference selects the dataset's best candidate automatically; Auto language follows the prepared dataset.

Training samples mirror the important Voice Generation parameters: beams, temperature, top-p, top-k, repetition, emotion weight, diffusion steps, CFG, text-token ceiling, length penalty, mel-token ceiling, and speaking rate.

Figure 27. Lock these values before training if you intend to compare epochs by ear. Otherwise a changed decoding setting can sound like a training improvement or regression.

Paths, seed, workers, and diagnostics

The final group sets output root, model directory and YAML, training device, attention backend, deterministic seed, data workers, and log frequency. Relative paths resolve from the app directory.

Figure 28. Two workers is a safe Windows/Linux default; use zero when debugging loader problems. Log every step for a short run and less frequently only when an extremely fast long run makes logging expensive.

Start, stop, and read the live dashboard

Press Start training only after the optimizer-update plan looks sensible. The live dashboard reports epoch, optimizer step, loss, validation, learning rate, gradient norm, throughput, elapsed time, ETA, VRAM, samples, and the worker log.

Figure 29. **Stop* requests a graceful checkpointed stop. Force stop terminates a nonresponsive worker. Both paths were exercised; use force only after the normal stop path has had time to save.*

The real smoke run completed two optimizer steps on a DoRA rank-8 adapter, with a three-item train split and one-item unseen validation split. The final dashboard exposed its checkpoint, sample, measured values, and analysis actions.

Figure 30. Use **Open output folder* for artifacts, Compare in grid for controlled listening, and Use best checkpoint to send the recommendation to Voice Generation. Completion alone is not a reason to choose the final epoch.*

The completed run remains selected while analysis compares training and unseen loss. Read the recommendation, sustained-overfitting marker, and checkpoint table before deploying an adapter.

Figure 31. The handoff buttons avoid manual paths: compare the run in Checkpoint Grid or use its recommended checkpoint directly in generation.

The manager inventories adapter type, rank, alpha, steps, dataset, recommendation, speaking rate, path, and health. Refresh rescans, Open folder inspects files, and Delete removes only after the UI confirmation flow.

Figure 32. Use the manager to distinguish a final adapter from epoch checkpoints and interrupted runs. A valid entry should have metadata and a readable safetensors file before it appears in Voice Generation.

9. Analyze and Compare Checkpoints

Generalization analysis and measured evaluation

Select one adapter folder and press Analyze training log. The CPU-only analysis compares train and validation trends, identifies the lowest unseen loss, and detects a sustained overfitting region rather than assuming the newest file is best.

Figure 33. A recommendation based on validation is a shortlist, not the final listening decision. Use the next measured and audible comparisons with the same references and text.

The checkpoint table combines epoch/step identity, paths, validation values, verdicts, and the recommended marker. Base model provides a reference-only baseline when enabled.

Figure 34. **Use best checkpoint* updates the Voice Generation adapter choice. Evaluate checkpoints now starts an isolated inference-like measurement over the configured validation and training subsets.*

Evaluation references can match training validation or use a different clip of the same speaker, closer to ordinary voice cloning. The job fixes its examples and settings across every checkpoint.

Figure 35. Live progress shows which row is loading and scoring. The test executed a real three-row comparison, not a mocked UI state.

The completed measurement reports comparable scores and a best-checkpoint recommendation. Inspect failed rows and reference selection before trusting a ranking.

Figure 36. Use measured evaluation to narrow the field, then rely on the listening grid for intelligibility, identity, pacing, emotion, noise, and artifacts that one scalar cannot fully express.

Create a fair listening grid

Choose base, recommended, final, and selected epoch checkpoints; add strengths from 0 to 4; then provide one text per line and one reference path per line. Every cell receives the same language, text segmentation, reference, and seed.

Figure 37. **Use LoRA / DoRA reference* inserts the stored reference. Add dataset reference candidates adds clean prepared clips. The runtime estimate grows with checkpoints x strengths x texts x references.*

Lock temperature, top-p, top-k, beams, repetition penalty, diffusion steps, CFG, segment budget, emotion weight, and speaking rate before running the grid.

Figure 38. Seed -1 draws one random seed at grid start and then reuses it for every cell. Enter an explicit seed when a grid must be recreated later.

Press Generate grid and watch cell-by-cell progress. The worker saves state under outputs/grids, so the log, table, audio, and metadata stay together.

Figure 39. **Cancel* stops the active grid worker; completed cells remain useful evidence. Do not change runtime/model settings in another tab while an isolated comparison is loading.*

Completion summarizes every generated cell, its checkpoint, strength, text/reference index, seed, duration, and path. Use Open grid folder to inspect the saved Markdown, metrics, log, and audio files.

Figure 40. A complete table verifies coverage; the audible result view is where the final voice decision happens.

Listen horizontally with the same text/reference and vertically across base, best, final, epochs, or strengths. The saved production grid was reloaded successfully and displayed all 12 audio players.

Figure 41. Rate each row for identity, pronunciation, prosody, background noise, breath artifacts, repeated syllables, and consistency. Prefer the earliest clean checkpoint when later gains are negligible and overfitting begins.

Calibrate the voice's real speaking pace

Press Calibrate speaking rate from this grid after generating representative audio. The app compares generated words per second with the voice's training recordings and saves a per-adapter multiplier.

Figure 42. Voice Generation can automatically apply the stored value when that adapter is selected. The production calibration measured about 0.944, while a separate smoke calibration proved the full write-and-reload path.

10. Fit Runtime to the GPU

Inventory, tiers, and applying settings

Refresh GPU inventory, select the actual device, and choose Auto or a 6, 8, 10, 12, 16, 24, or 32 GB tier. Every tier is a coordinated set of model, swapping, cache, and batch hints designed to retain roughly 2 GB of headroom.

Figure 43. **Apply runtime* commits the current model and memory settings. Changing a dropdown alone does not rebuild an already loaded model; unload before a major variant or device switch.*

BF16, INT8, attention, and acceleration

BF16 is the official quality path on modern NVIDIA GPUs. INT8 ConvRot reduces GPT weight memory while retaining the required IndexTTS 2.5 codec and vocoder stack. FP32 is primarily a CPU-compatible fallback.

Figure 44. SDPA is the compatible attention default. FlashAttention 2 needs its optional package. The acceleration engine uses the fast CUDA-graph/flash path and expects beams 1; torch compilation trades a slower first run for repeated-workload speed.

Enable the Qwen emotion model for Emotion text mode; on-demand residency avoids eager startup cost.
BigVGAN CUDA kernel uses the optional fused activation kernel when available.
CFM estimator BF16 autocast saves activation VRAM and can slightly change output.
DeepSpeed is a legacy optional loader and should stay off unless installed intentionally.

GPT block swapping

Zero keeps every GPT block resident. Minus one lets the runtime fit automatically; positive values up to 24 stream that many blocks from CPU RAM. More swapping reduces VRAM but increases transfer overhead.

Figure 45. Ring size 2 overlaps copies and compute; ring size 1 minimizes memory. Keep pinned swap memory on for faster CPU-to-GPU transfer unless the host cannot lock enough RAM.

Runtime CFM cache length sets the upper reservation, VRAM reserve protects against allocator spikes, and the section-batch hint informs Voice Generation. Treat the hint as an advisory maximum, not a promise when another process is using the GPU.

Selecting cpu now shows CPU diagnostics mode rather than a misleading GPU fit estimate. GPU VRAM limits do not apply there, and FP32 is the compatibility-oriented choice; CPU synthesis is primarily a diagnostic fallback and is much slower than CUDA.

Place auxiliary models independently

Semantic model, Qwen emotion, CampPlus, semantic codec, S2Mel, and BigVGAN can each stay on GPU, stay on CPU, or load on demand according to the choices provided by the active tier.

Figure 46. Keep frequently used synthesis stages resident when VRAM allows. Move optional or infrequent models off GPU first when reducing idle footprint.

Verify and download model files

The model table checks required base and optional INT8 files. Download / verify base models repairs the official stack; Download INT8 model fetches the memory-efficient GPT; Refresh file status rechecks disk; Open model folder opens the configured directory.

Figure 47. The INT8 path now has a verified fallback: if the hosted artifact is unavailable, the downloader can convert the local BF16 model instead of presenting a feature that cannot be obtained.

Run the isolated VRAM benchmark

Select a tier and press Run VRAM benchmark when the chosen GPU is otherwise idle. The UI uses a short deterministic calibration - beams 1, 60 text tokens, batch 1 - while the standalone CLI can still run a heavier stress profile.

Figure 48. The benchmark uses a bundled reference voice, runs in an isolated subprocess, and checks whether the selected tier truly fits rather than estimating from file size alone.

Watch the subprocess log for model load, warmup, synthesis, peak allocation, and fit status. On Windows WDDM, the idle gate allows the larger of 1 GB or 10 percent of physical VRAM so driver accounting does not falsely block an idle card.

Figure 49. A benchmark can be cancelled without contaminating the main app model because it owns a separate process.

The real 32 GB BF16 test fit successfully on the RTX 5090: 28.714 seconds of audio, real-time factor about 0.458, and peak allocated VRAM about 6.212 GB for the short calibration workload.

Figure 50. Read **fit, peak VRAM, RTF, and any error together. Apply the tier after a pass, then use **Unload model / free VRAM* whenever you want to release the in-process runtime.*

11. Built-in Help and Recovery

The Help tab repeats the shortest successful path for a first clone and explains the reference-only and trained-adapter workflows. Use it as an in-app checklist while this longer guide stays open separately.

Figure 51. The best diagnostic habit is to return to one clean reference, one short sentence, the quality preset, base model, and section batch size 1.

Workflow help covers speaking-rate calibration, checkpoint comparison, VRAM tiers, and the main parameter families. Follow a complete workflow rather than changing unrelated controls until the symptom disappears.

Figure 52. Presets make experiments repeatable: duplicate a known-good user preset and change one variable for each A/B comparison.

The final help area documents pause syntax, reference guidance, links, and recovery steps. Copy a supported pause form exactly and check the live section preview before generation.

Figure 53. For OOM: unload, lower batch size, choose a lower tier or INT8, add block swap, shorten sections, and rerun the benchmark. For bad speech: first inspect reference quality, language, text segmentation, seed, and adapter strength.

Read the V6 release history

V6 adds a lazy-rendered Changelog tab after Help. Open it to read the newest-first v6.0 through v4.0 release notes, including fixes that may affect an older workflow, and to reach the official SECourses Patreon and GitHub repository. The tab was added after the original V5 screenshot set, so it is documented here rather than shown in those captures.

12. Presets, Themes, and Repeatable Work

A universal preset stores every registered setting across every tab. Select a system preset for a protected baseline, or type a new name and press Save to create a user preset. Selecting an existing user preset allows an intentional overwrite.

Figure 54. **Load* applies a preset, Delete requires the confirmation flow and only removes user presets, Reset restores defaults, and Load last values recovers the most recent working state. Fresh installs select the quality system preset; upgrades retain the last-used preset. Unknown old keys are ignored and missing new keys receive defaults.*

Press Light / dark theme for the preferred presentation. Theme changes the interface only; it does not change audio, model precision, presets, or output files.

Figure 55. The 4K capture confirms the same dense workspace remains legible in light mode. Switch back at any time without restarting a job.

13. Deploy a Trained Voice and Use Advanced Controls

Load, scale, and merge an adapter

Choose an adapter in Voice Generation. Its summary exposes type, rank, alpha, steps, recommendation, and saved metadata. Strength 1.0 is the trained scale; lower values are subtler and higher values are stronger.

Figure 56. Auto-reference and auto-speaking-rate remove two common deployment mistakes. BF16 can temporarily merge the adapter into base weights for speed; the app restores base weights before switching, and INT8 stays on the unmerged path.

Emotion-reference mode

Select Emotion reference audio, upload a short expressive clip, and set emotion weight. Keep the identity reference separate: this mode is for transferring delivery, not replacing the speaker.

Figure 57. Use the maximum emotion-reference length to discard unneeded material and compare at a fixed seed before increasing the blend.

Manual-vector mode

Select Manual vectors and blend the eight channels. Start with one dominant channel, apply tuned biases, and keep the sum limit at 0.8 before attempting compound emotions.

Figure 58. A vector is continuous rather than a label: 0.2 calm plus 0.1 sadness is a different conditioning target from a full-strength sadness instruction.

Natural-language emotion mode

Select Emotion text and describe the intended performance in plain language. Keep it specific and short, such as restrained concern, then quiet relief, rather than writing a second script.

Figure 59. Qwen emotion must be enabled. The manual vector limits and biases remain available because the analyzed instruction ultimately becomes bounded emotion conditioning.

The tested app produced real outputs for emotion-reference, manual-vector, and emotion-text modes in addition to same-speaker emotion. Inspect the log to confirm which source loaded and listen against a neutral fixed-seed control.

Figure 60. A completed player, output path, duration, settings summary, and clean final status prove the selected emotion mode reached actual synthesis rather than only changing the UI.

Whole-output target duration

Enter a whole-output target only when the application has a real timing requirement. Natural changes synthesis timing, Pad only adds tail silence, Trim only cuts, and Off leaves the result unconstrained.

Figure 61. Combine duration targets cautiously with caption cue timing, speaking rate, section silence, and explicit pause tags because all of them influence the assembled timeline.

All three active paths were generated. A 3-second Natural target produced about 2.995 seconds; a 1.5-second Trim target produced exactly 1.500 seconds; a 4-second Pad target remained about 4.156 seconds because the spoken source was already longer, which is correct pad-only behavior.

Figure 62. Always read the final measured duration rather than assuming every mode forces equality. Choose Natural for adaptive timing, Trim for a hard upper bound, and Pad for a hard minimum without speeding or cutting speech.

14. Practical Recipes

Clean first clone

Select the quality system preset and Base model.
Use one clean 8-15 second speaker reference and one short script in the correct language.
Keep same-speaker emotion, natural rate 1.0, seed fixed, one candidate, and section batch 1.
Generate, inspect the log, and save the good result plus used reference.

Long audiobook or course narration

Calibrate the trained voice's speaking rate and enable automatic rate loading.
Use punctuation, pause tags, and a conservative per-segment token budget.
For long unattended jobs where complete post-job VRAM release matters, enable subprocess mode; also enable prevent-VRAM-accumulation when segment-to-segment cache growth is the problem.
Test one chapter, save a user preset, then run folders through Batch with deterministic naming and continue-on-error.

Subtitle-timed localization video

Upload the translated SRT/VTT/SBV and enable caption cue timing.
Choose Natural duration behavior for model-based timing or Trim/Pad only when mechanical bounds are acceptable.
Add a still image for MP4, generate one representative cue set, and check starts and final duration.
Batch the remaining caption files only after the representative item passes.

Train and deploy a new voice

Scan media and captions, prepare 4-20 second sentence-aligned 24 kHz clips, inspect them, and cache features.
Train the measured DoRA baseline with validation, samples, epoch checkpoints, train state, and automatic analysis.
Evaluate base plus saved checkpoints with inference-like references, then generate a fixed-seed listening grid.
Select the best audible checkpoint, calibrate speaking rate, and enable automatic reference/rate in Voice Generation.

Recover from low VRAM or OOM

Cancel the job and use Unload model / free VRAM.
Close other GPU workloads and select the matching conservative VRAM tier.
Use INT8, more block swap, ring 1, lower CFM cache, on-demand auxiliary models, and section batch 1.
Shorten text sections, keep beams 1, apply runtime, and pass the isolated benchmark before retrying.

Reproducible A/B test

Save a baseline user preset.
Fix reference, text, language, seed, checkpoint, strength, emotion source, and runtime.
Change exactly one control, generate one or more candidates, and record the output metadata.
Use a listening grid when comparing checkpoints or strengths so every cell is locked automatically.

15. Verified Behavior and Repairs Included in This Build

This tutorial was not produced from screenshots alone. The original audit completed real synthesis, batching, preparation, feature caching, training, checkpoint evaluation, listening grids, calibration, model verification, and benchmarking. The V6 maintenance audit rebuilt the live interface, checked the changed controls and Changelog in Chrome, and passed 288 tests, with 37 environment-specific cases skipped and 15 upstream PyTorch deprecation warnings.

Mixed TXT/SRT batches now apply caption timing only to caption items instead of crashing the plain-text item.
Batched inference no longer leaks sequential target-duration arguments into the batch engine.
Saved listening grids retain and render their audio players, and idle polling no longer clears a loaded result.
Dynamically loaded checkpoint selections now survive dependent events and timer polling instead of failing Gradio validation against an empty startup choice list.
INT8 download now has a local BF16-to-INT8 fallback when the hosted artifact is absent.
The Windows benchmark idle gate tolerates normal WDDM reservation, uses a bundled reference, and the UI runs a practical short calibration profile.
V6 restores microphone reference recording and makes arbitrary local audio/video paths browser-safe, including cached previews for codecs the browser cannot play directly.
Dynamic candidate and dataset-reference players render after reload, feature caching refreshes the training handoff, and completed batch summaries are no longer overwritten by a polling race.
Acceleration now honors disabled top-k/top-p limits, preserves stop tokens and compute dtype, and surfaces internal failures instead of silently returning an empty result.
Audio tuning preserves sample rate, text-normalization failures retain the original fragment, zero-item validation skips automatic evaluation cleanly, and CPU mode no longer claims a GPU VRAM fit.
The new Changelog tab renders only when opened and presents the public v6.0-to-v4.0 history plus official project links without slowing initial tab rendering.
Every final annotated image passed an exact 3840 x 2160 dimension gate and was individually uploaded to the dedicated Hugging Face discussion.
The repaired selectable copy source passed a complete Patreon paste: all 62 hosted images became full-width native image blocks with all 62 alt texts, and the headings, lists, links, and final paragraph were retained.

A feature is treated as complete only when its control exists, its workflow reaches a real result, its failure/cancel path is understandable, and the tutorial shows where the user acts and where proof appears.

16. Complete Action Reference

These are the non-setting actions and result surfaces a regular user will encounter. The numbered guide above shows each in context.

Header: Load last values; open/close all sections; switch theme; save, load, delete, or reset a universal preset.

Voice Generation: Upload/load/clear a reference; record a microphone reference; extract time ranges; refresh the reference library; auto-select token budget; upload captions and a still image; generate; cancel; open outputs; refresh adapters; play generated/candidate audio; inspect recent outputs; load a selected output as a new reference.

Batch Generation: Upload text/caption files; generate the queue; cancel; open the batch folder; read progress; play results; inspect the per-item table.

Dataset Preparation: Refresh/open existing datasets; upload media; scan; prepare; cancel; cache features; open output; inspect discovered media and stats; select and play prepared segments.

Training: Refresh datasets; apply tier defaults; refresh resume sources; start; graceful stop; force stop; open output; compare in grid; use best checkpoint; inspect charts/sample/checkpoints; refresh/open/delete adapter-manager entries.

Checkpoint Grid: Refresh adapters; analyze logs; evaluate checkpoints; use best; use stored reference; add dataset candidates; upload a reference; generate/cancel a grid; open saved grid; calibrate speaking rate; play every result cell.

Models & Performance: Refresh GPU inventory; apply runtime; unload/free VRAM; download INT8; download/verify base models; refresh file status; open model folder; run the isolated benchmark.

Help: Read the quick starts, workflow guidance, parameter glossary, pause syntax, troubleshooting steps, and launch arguments.

Changelog: Open the newest-first v6.0-to-v4.0 release history and follow the official Patreon or GitHub project links.

17. Every Registered Setting

The appendix below is generated directly from the app's preset registry. It covers all 253 registered controls, including current defaults, ranges, choices, and the help text shown by the UI. A preset stores these values across tabs.

Voice Generation - 70 settings

Language - generation.language. Language code used by text normalization and pronunciation. (default "EN"; choices "ZH", "EN", "JA", "AR", "ES")

Max tokens per segment - generation.max_text_tokens_per_segment. Per-language defaults are recommended; shorter segments use less VRAM. (default 60; minimum 20; maximum 300)

Use caption cue timing - generation.use_caption_timing. Retimes each caption unit to its cue slot and preserves cue start times. (default false)

Auto-load the LoRA / DoRA recommended reference audio - generation.auto_lora_reference. Loads the LoRA / DoRA's saved reference whenever no manual Reference Voice is selected. (default true)

Auto-apply the LoRA / DoRA calibrated speaking rate - generation.auto_lora_speaking_rate. Uses the selected voice's measured pace; selecting None resets speaking rate to 1.0. (default true)

Emotion source - generation.emotion_mode. Use the speaker tone, another reference, eight manual vectors, or emotion text analysis. (default "Same as speaker voice"; choices "Same as speaker voice", "Emotion reference audio", "Emotion vector", "Emotion text")

Emotion weight - generation.emotion_weight. 0 keeps more speaker emotion; 1 follows the selected emotion source fully. (default 0.65; minimum 0; maximum 1)

Random emotion exemplar - generation.emotion_random. Randomizes the internal exemplar used with manual emotion vectors. (default false)

Emotion description - generation.emotion_text. Used only in Emotion text mode; blank analyzes the speech text itself.

Joy - generation.emotion_joy. Manual joy strength. (default 0; minimum 0; maximum 1)

Anger - generation.emotion_anger. Manual anger strength. (default 0; minimum 0; maximum 1)

Sadness - generation.emotion_sad. Manual sadness strength. (default 0; minimum 0; maximum 1)

Fear - generation.emotion_fear. Manual fear strength. (default 0; minimum 0; maximum 1)

Disgust - generation.emotion_disgust. Manual disgust strength. (default 0; minimum 0; maximum 1)

Depression - generation.emotion_depression. Manual depression strength. (default 0; minimum 0; maximum 1)

Surprise - generation.emotion_surprise. Manual surprise strength. (default 0; minimum 0; maximum 1)

Calm - generation.emotion_calm. Manual calm strength. (default 0; minimum 0; maximum 1)

Apply tuned emotion biases - generation.apply_emotion_bias. Recommended balancing prevents several emotion channels from dominating. (default true)

Maximum vector sum - generation.max_emotion_sum. 0.8 is the model-tuned recommendation; larger values can sound exaggerated. (default 0.8; minimum 0.1; maximum 2)

Joy bias - generation.emotion_bias_joy. Multiplier applied before the vector sum limit. (default 0.9375; minimum 0.5; maximum 1.5)

Anger bias - generation.emotion_bias_anger. Multiplier applied before the vector sum limit. (default 0.875; minimum 0.5; maximum 1.5)

Sadness bias - generation.emotion_bias_sad. Multiplier applied before the vector sum limit. (default 1; minimum 0.5; maximum 1.5)

Fear bias - generation.emotion_bias_fear. Multiplier applied before the vector sum limit. (default 1; minimum 0.5; maximum 1.5)

Disgust bias - generation.emotion_bias_disgust. Multiplier applied before the vector sum limit. (default 0.9375; minimum 0.5; maximum 1.5)

Depression bias - generation.emotion_bias_depression. Multiplier applied before the vector sum limit. (default 0.9375; minimum 0.5; maximum 1.5)

Surprise bias - generation.emotion_bias_surprise. Multiplier applied before the vector sum limit. (default 0.6875; minimum 0.5; maximum 1.5)

Calm bias - generation.emotion_bias_calm. Multiplier applied before the vector sum limit. (default 0.5625; minimum 0.5; maximum 1.5)

Sample - generation.do_sample. Recommended for natural variation; disable for greedy/beam decoding. (default true)

Temperature - generation.temperature. 0.8 balances expressiveness and stability. (default 0.8; minimum 0.1; maximum 2)

Top-p - generation.top_p. Nucleus sampling threshold; 0.8 is recommended. (default 0.8; minimum 0; maximum 1)

Top-k - generation.top_k. Candidate token cutoff; 0 disables top-k filtering. (default 30; minimum 0; maximum 100)

Beams - generation.num_beams. More beams can improve stability but increase time and VRAM. (default 3; minimum 1; maximum 10)

Repetition penalty - generation.repetition_penalty. 10 is the established model default. (default 10; minimum 1; maximum 20)

Length penalty - generation.length_penalty. Only affects beam search; 0 is neutral. (default 0; minimum -2; maximum 2)

Max mel tokens - generation.max_mel_tokens. Upper limit on generated semantic tokens per section. (default 1500; minimum 50; maximum 1815)

Seed - generation.seed. -1 chooses a fresh random seed; reuse a shown seed for repeatability. (default -1; minimum -1; maximum 4294967295)

Candidates - generation.num_candidates. Generates consecutive seeded alternatives; each adds generation time. (default 1; minimum 1; maximum 8)

Diffusion steps - generation.diffusion_steps. 25 is the quality default; 12-16 is faster and 35-50 can refine difficult audio. (default 25; minimum 2; maximum 100)

CFG rate - generation.inference_cfg_rate. 0.7 is recommended; high values follow conditioning more aggressively. (default 0.7; minimum 0; maximum 2)

CFM temperature - generation.cfm_temperature. 1.0 is the best-quality default; lower values reduce diffusion variation. (default 1; minimum 0; maximum 2)

CFM cache length - generation.cfm_cache_length. 8192 fits typical sections; lower values reduce reserved VRAM. (default 8192; minimum 1024; maximum 32768)

Non-CJK token budget scale - generation.segment_budget_scale_non_cjk. 0.72 leaves room for subword expansion in English, Arabic, and Spanish. (default 0.72; minimum 0.3; maximum 1)

Section silence (ms) - generation.interval_silence. Silence inserted between generated text sections; cue timing overrides this to zero. (default 200; minimum 0; maximum 2000)

Max consecutive silence tokens - generation.max_consecutive_silence. 0 disables token trimming; use only to suppress unusually long model silences. (default 0; minimum 0; maximum 200)

Latent multiplier - generation.latent_multiplier. 1.72 is natural duration; the runner converts this to the engine duration factor. (default 1.72; minimum 0.5; maximum 3)

Speaking rate - generation.speaking_rate. 1.0 is the model's natural pace; below 1.0 speaks slower, above 1.0 faster. A trained LoRA / DoRA can carry a calibrated value that matches the speaker's real pace. (default 1; minimum 0.5; maximum 1.5)

Target duration (seconds) - generation.target_duration_s. Leave blank unless a whole-output duration target is needed. (default blank; minimum 0.1; maximum 3600)

Target duration mode - generation.target_duration_mode. Natural regenerates timing; pad/trim only adjust the assembled result. (default "off"; choices "off", "natural", "pad", "trim")

Enable pause tags - generation.enable_pause_tags. Parses inline pause tags before tokenization. (default true)

Text normalization - generation.text_normalization. Recommended: expands and normalizes text before phonetic processing. (default true)

Maximum speaker audio length (s) - generation.max_speaker_audio_length. 15 seconds preserves enough identity without wasting reference compute. (default 15; minimum 3; maximum 90)

Maximum emotion audio length (s) - generation.max_emotion_audio_length. 15 seconds is recommended for an emotion reference. (default 15; minimum 3; maximum 90)

Semantic layer - generation.semantic_layer. Layer 17 is trained and recommended; changing it alters reference embeddings. (default 17; minimum 1; maximum 24)

Reuse speaker conditioning for emotion - generation.reuse_spk_cond_for_emo. Faster default-emotion path; enable when no separate emotion source is used. (default false)

Output filename - generation.output_filename. Optional safe basename; task numbering is used when blank.

Save used reference - generation.save_used_audio. Copies the active Reference Voice into the task folder for reproducibility. (default false)

Save MP3 - generation.save_as_mp3. Converts the final output to MP3; WAV candidates remain available. (default false)

MP3 bitrate - generation.mp3_bitrate. 256k is a strong quality/size balance for voice. (default "256k"; choices "128k", "192k", "256k", "320k")

Audio tuning preset - generation.audio_tuning_preset. Bypass preserves model audio exactly; other presets use FFmpeg post-processing. (default "bypass"; choices "bypass", "voice_clarity", "clear_narration", "deharsh", "warm", "normalize")

Trim edge silence threshold (ms) - generation.trim_silence_ms_threshold. 0 disables trimming; only edge silence at least this long is removed. (default 0; minimum 0; maximum 3000)

Low cut (Hz) - generation.tuning_low_cut_hz. Optional high-pass cutoff; leave blank to use the preset. (default blank; minimum 20; maximum 500)

High cut (Hz) - generation.tuning_high_cut_hz. Optional low-pass cutoff; leave blank to use the preset. (default blank; minimum 1000; maximum 24000)

Gain (dB) - generation.tuning_gain_db. Optional final gain before limiting. (default blank; minimum -24; maximum 24)

Loudness target (LUFS) - generation.tuning_loudnorm_i. Optional integrated loudness normalization target. (default blank; minimum -30; maximum -5)

De-ess amount - generation.tuning_deess. Optional attenuation around sibilance frequencies. (default blank; minimum 0; maximum 12)

Use isolated subprocess - generation.use_subprocess. Recommended: cancellation can terminate the complete model process and release VRAM. (default false)

Section batch size - generation.section_batch_size. 1 is safest; use the active VRAM tier hint before increasing this. (default 1; minimum 1; maximum 16)

Low memory mode - generation.low_memory_mode. Uses sequential paths and aggressive memory behavior for constrained GPUs. (default false)

Prevent VRAM accumulation - generation.prevent_vram_accumulation. Clears autoregressive caches between segments; slower but useful for long jobs. (default false)

Verbose logging - generation.verbose. Prints detailed model inputs and timing diagnostics to console and the live log. (default false)

Batch Generation - 5 settings

Naming pattern - batch.naming_pattern. Supports {index}, {name}, and {stem}; the extension is selected by Output settings. (default "{index:03d}_{name}")

Output subfolder - batch.output_subfolder. A safe subfolder below outputs; task folders are created inside it. (default "batch")

Reference mode - batch.reference_mode. Per-file mode looks for a same-stem audio file beside each text/caption file. (default "One reference for all"; choices "One reference for all", "Per-file reference")

Batch execution - batch.execution. Subprocess is easiest to cancel; reuse is fastest after the first in-process load. (default "Subprocess per item"; choices "Subprocess per item", "Reuse loaded model between items", "Reload in-process model per item")

Continue after item errors - batch.continue_errors. Records a failed row and proceeds to the next item instead of ending the batch. (default true)

Dataset Preparation - 45 settings

Input files or folders - dataset.inputs. Accepts media files, folders, metadata.csv, or pre-segmented WAV+TXT folders.

Dataset name - dataset.name. A single safe directory name created below Output root. (default "voice_dataset")

Output root - dataset.output_root. Dataset parent directory; relative paths resolve from the application folder. (default "datasets")

Scan recursively - dataset.recursive. Includes supported media in nested input folders. (default true)

Language - dataset.language. Language stored in manifest rows and used for Whisper. (default "EN"; choices "ZH", "EN", "JA", "AR", "ES")

Speaker name - dataset.speaker_name. Optional fixed speaker label stored in each segment.

Speaker from folder - dataset.speaker_from_folder. Uses each source parent folder name as the speaker label. (default false)

Transcript policy - dataset.subtitle_policy. Prefer sidecars is recommended; Whisper fills missing timing/text. (default "prefer_sidecar"; choices "prefer_sidecar", "whisper_only", "sidecar_only")

Whisper model - dataset.whisper_model. Hugging Face model id or local path used when transcription/alignment is needed. (default "openai/whisper-large-v3-turbo")

Whisper device - dataset.whisper_device. CUDA device recommended for sentence alignment; CPU works but is much slower. (default "cuda:0")

Segmentation mode - dataset.segmentation_mode. Sentence aligned uses Whisper word times with caption sentences and is recommended on CUDA. (default "sentence_aligned"; choices "auto", "sentence_aligned", "cue_boundaries", "whisper_only")

Force Whisper alignment - dataset.align_with_whisper. Compatibility alias that forces sentence_aligned mode. (default false)

Remove bracket annotations - dataset.remove_bracket_annotations. Removes caption notes such as [music] and [applause]. (default true)

Deduplicate rolling captions - dataset.dedupe_rolling_captions. Removes repeated text from live/rolling subtitle cues. (default true)

Drop duplicate sentences - dataset.drop_duplicate_sentences. Keeps one copy (best aligned) of every sentence that is spoken more than once, e.g. repeated intros or outros; recommended for voice training. (default true)

Target seconds - dataset.target_s. 14 seconds packs whole sentences into inference-length clips; measured best with a 20 second maximum. (default 14; minimum 1; maximum 30)

Minimum seconds - dataset.min_s. 4 seconds keeps enough voice context while retaining the measured quality range. (default 4; minimum 0.5; maximum 15)

Maximum seconds - dataset.max_s. 20 seconds covers 12-15-second inference segments and retains more source audio; 30 seconds measured worse. (default 20; minimum 2; maximum 40)

Maximum cue gap (ms) - dataset.max_gap_ms. Cues closer than this can merge into one sentence segment. (default 700; minimum 0; maximum 3000)

Edge padding (ms) - dataset.pad_ms. Small context padding avoids clipped consonants. (default 60; minimum 0; maximum 500)

Snap to silence - dataset.snap_to_silence. Moves segment boundaries toward nearby low-energy points. (default true)

Silence snap window (ms) - dataset.snap_window_ms. Search radius around a proposed boundary. (default 200; minimum 0; maximum 1000)

Minimum words - dataset.min_words. Drops fragments with too little transcript context. (default 2; minimum 0; maximum 30)

Maximum words - dataset.max_words. Drops transcript segments that are implausibly dense. (default 80; minimum 10; maximum 200)

Segment boundaries - dataset.boundary_mode. Sentence requires punctuation; sentence_or_pause recovers more audio at aligned-word pauses with a small risk of less natural cuts. (default "sentence"; choices "sentence", "sentence_or_pause")

Minimum pause for a boundary (ms) - dataset.min_pause_boundary_ms. A fragment edge must have at least this much silence when punctuation does not provide the boundary. (default 400; minimum 0; maximum 60000)

Trim silence - dataset.trim_silence. Trims leading/trailing low-energy audio before filtering. (default true)

Trim top dB - dataset.trim_top_db. 40 dB is a conservative silence threshold. (default 40; minimum 10; maximum 80)

Normalize loudness - dataset.loudness_normalize. Recommended for consistent training gradients across sources. (default true)

Target LUFS - dataset.target_lufs. -20 LUFS leaves headroom and matches voice training defaults. (default -20; minimum -30; maximum -10)

Sample rate - dataset.sample_rate. 24000 Hz is required by the IndexTTS training pipeline. (default 24000; minimum 8000; maximum 48000)

Minimum file alignment coverage - dataset.min_file_alignment_coverage. Below 0.60, sentence alignment falls back or rejects unreliable timing. (default 0.6; minimum 0; maximum 1)

Minimum segment alignment coverage - dataset.min_segment_alignment_coverage. Drops individual caption segments with weak word alignment. (default 0.7; minimum 0; maximum 1)

Minimum words / second - dataset.min_words_per_second. Drops unusually sparse transcript/audio matches. (default 1; minimum 0.1; maximum 5)

Maximum words / second - dataset.max_words_per_second. Drops implausibly dense or misaligned speech. (default 5.5; minimum 1; maximum 12)

Minimum peak dBFS - dataset.min_peak_dbfs. Drops audio too quiet to train reliably. (default -35; minimum -80; maximum 0)

Maximum clipping ratio - dataset.max_clipping_ratio. 0.001 allows at most 0.1% clipped samples. (default 0.001; minimum 0; maximum 1)

Clipping threshold - dataset.clipping_threshold. Absolute normalized sample level counted as clipping. (default 0.999; minimum 0.5; maximum 1)

Maximum silence ratio - dataset.max_silence_ratio. Optional; blank disables whole-segment silence-ratio filtering. (default blank; minimum 0; maximum 1)

Silence threshold dBFS - dataset.silence_threshold_dbfs. Frames below this level count as silence. (default -40; minimum -80; maximum -10)

Silence frame (ms) - dataset.silence_frame_ms. 20 ms gives stable silence estimates for speech. (default 20; minimum 5; maximum 200)

Reference candidates - dataset.export_reference_candidates. Exports the cleanest segments for training samples and LoRA / DoRA use. (default 5; minimum 0; maximum 20)

Overwrite dataset - dataset.overwrite. Replaces an existing dataset directory with the same name. (default false)

Maximum segments - dataset.max_segments. 0 processes all segments; use a small value for smoke tests. (default 0; minimum 0; maximum 10000000)

Preparation seed - dataset.seed. Controls deterministic candidate ranking and randomized operations. (default 0; minimum 0; maximum 4294967295)

LoRA / DoRA Training - 87 settings

Dataset - training.dataset_dir. Prepared manifest dataset used for cached-feature training. (default "datasets/secourses_demo")

LoRA / DoRA name - training.name. Safe output folder and final safetensors basename. (default "voice_adapter")

LoRA / DoRA type - training.adapter_type. DoRA is the quality default; LoRA uses slightly less compute. (default "dora"; choices "lora", "dora")

Rank - training.rank. 128 with alpha 129 learned the voice fastest in measured runs; rank 32 reached the same floor more slowly. (default 128; minimum 1; maximum 256)

Alpha - training.alpha. 129 is the measured companion scale for the recommended rank 128. (default 129; minimum 1; maximum 1024)

Dropout - training.dropout. 0.05 remains the quality default; stronger measured regularization gave no benefit. (default 0.05; minimum 0; maximum 0.5)

Target attention - training.target_attention. Adapts GPT attention projections; recommended. (default true)

Target MLP - training.target_mlp. Adapts GPT feed-forward projections; recommended for voice fidelity. (default true)

Train speaker projection - training.train_spk_proj. Fully trains the small speaker projection module. (default true)

Train emotion layers - training.train_emo_layers. Advanced: trains small emotion modules in addition to LoRA / DoRA layers. (default false)

Train mel embedding head - training.train_mel_embed_head. Advanced: trains the mel token embedding/head modules. (default false)

Learning rate - training.learning_rate. 4e-5 is the robust batch-1 default: it reached 5.061 held-out loss, while 8e-5 overfit within two epochs. (default 0.00004; minimum 1e-8; maximum 1)

Optimizer - training.optimizer. AdamW is portable; fused AdamW is faster on supported CUDA builds. (default "adamw"; choices "adamw", "adamw_fused", "prodigy")

Scheduler - training.lr_scheduler. Cosine decay is recommended for multi-epoch voice adaptation. (default "cosine"; choices "cosine", "linear", "constant", "constant_with_warmup")

Warmup steps - training.warmup_steps. 200 steps is the measured batch-1 default, easing training into the 4e-5 learning rate. (default 200; minimum 0; maximum 1000000)

Weight decay - training.weight_decay. 0.01 is a mild regularizer; 0.05 showed no measured benefit. (default 0.01; minimum 0; maximum 1)

Adam betas - training.betas. Two comma-separated momentum coefficients; 0.9, 0.99 is recommended. (default "0.9, 0.99")

Adam epsilon - training.eps. Numerical stability term for Adam-family optimizers. (default 1e-8; minimum 1e-12; maximum 0.1)

Epochs - training.epochs. 10 epochs is the measured batch-1 default; 20 epochs overfit after the held-out optimum around epoch 6. (default 10; minimum 1; maximum 10000)

Maximum steps - training.max_steps. 0 derives steps from epochs; set 5 for a quick smoke run. (default 0; minimum 0; maximum 100000000)

Batch size - training.batch_size. 1 is the measured quality default: every training clip becomes an optimizer update each epoch. (default 1; minimum 1; maximum 128)

Gradient accumulation - training.grad_accumulation. 1 is the measured default; accumulation 2 or 4 removed updates and performed worse at the same learning rate. (default 1; minimum 1; maximum 128)

Gradient clip - training.max_grad_norm. 1.0 limits unstable gradient spikes; 0 disables clipping. (default 1; minimum 0; maximum 100)

Label smoothing - training.label_smoothing. 0 is recommended; increase only for overconfident large datasets. (default 0; minimum 0; maximum 0.5)

Mel loss weight - training.mel_loss_weight. Primary autoregressive acoustic-token loss weight. (default 1; minimum 0; maximum 100)

Text loss weight - training.text_loss_weight. Auxiliary text modeling loss weight. (default 0.1; minimum 0; maximum 100)

Speaker reference mode - training.speaker_ref_mode. self uses the target clip, other uses a deterministic different same-speaker clip, and mixed alternates between them; other is the measured quality default. (default "other"; choices "self", "other", "mixed")

Emotion reference mode - training.emo_ref_mode. self uses the target emotion, other uses another same-speaker clip, mixed alternates, and follow_speaker reuses the speaker-reference clip; follow_speaker is the measured inference-like default. (default "follow_speaker"; choices "self", "other", "mixed", "follow_speaker")

Maximum codes - training.max_codes. Cached samples longer than this semantic-code limit are rejected. (default 1500; minimum 1; maximum 100000)

Maximum text tokens - training.max_text_tokens. Cached text length safety limit. (default 600; minimum 1; maximum 100000)

Validation fraction - training.val_fraction. 5% provides useful validation without sacrificing much training data. (default 0.05; minimum 0; maximum 0.5)

Validate every steps - training.val_every_steps. 0 disables step validation; epoch validation still runs when a split exists. (default 50; minimum 0; maximum 1000000)

Maximum validation batches - training.val_max_batches. Caps validation time on large datasets. (default 20; minimum 1; maximum 1000000)

Validation reference - training.val_reference_mode. self validates each target with itself, while other uses a different same-speaker clip for both vectors; other is inference-like and measured more accurately. (default "other"; choices "self", "other")

Early-stop patience - training.early_stop_patience. 0 disables early stopping; otherwise stop after this many validations without a meaningful improvement. (default 0; minimum 0; maximum 1000000)

Early-stop minimum improvement - training.early_stop_min_delta. Validation loss must fall by at least this amount to reset patience. (default 0; minimum 0; maximum 1000000)

Base variant - training.base_variant. BF16 is the quality default; INT8 ConvRot reduces frozen base weight memory. (default "bf16"; choices "bf16", "int8_convrot")

Base dtype - training.base_dtype. Compute/storage dtype for the BF16 base variant. (default "bf16"; choices "bf16", "fp16", "fp32")

Mixed precision - training.mixed_precision. BF16 is recommended on modern GPUs and avoids FP16 overflow. (default "bf16"; choices "bf16", "fp16", "fp32")

Gradient checkpointing - training.gradient_checkpointing. Recommended; recomputes activations to save substantial VRAM. (default true)

Blocks to swap - training.blocks_to_swap. Streams this many frozen GPT blocks from CPU; requires gradient checkpointing. (default 0; minimum 0; maximum 24)

Swap ring size - training.swap_ring_size. 2 balances overlap and VRAM; 1 uses the least memory. (default 2; minimum 1; maximum 4)

Pinned swap memory - training.pin_swap_memory. Recommended for faster CPU-to-GPU transfers. (default true)

Output root - training.output_dir. LoRA / DoRA parent folder; relative paths resolve from the app directory. (default "loras")

Save every epochs - training.save_every_epochs. 1 keeps an epoch checkpoint; 0 disables epoch checkpoints. (default 1; minimum 0; maximum 100000)

Save every steps - training.save_every_steps. 0 disables step checkpoints. (default 0; minimum 0; maximum 10000000)

Keep last N - training.keep_last_n. 0 keeps every epoch checkpoint so measured checkpoint comparison can choose the best voice. (default 0; minimum 0; maximum 10000)

Save best - training.save_best. Keeps the checkpoint with the lowest validation loss. (default true)

LoRA / DoRA save dtype - training.save_dtype. BF16 halves LoRA / DoRA file size; FP32 preserves full update precision. (default "bf16"; choices "bf16", "fp32")

Save train state - training.save_train_state. Saves optimizer, scheduler, scaler, RNG, and data position for exact resume from best, final, and interrupted checkpoints. (default true)

Save train state with every epoch checkpoint - training.epoch_train_state. Only needed to Continue run from a specific epoch; costs ~4x disk per checkpoint. (default false)

Resume from - training.resume_from. Select a LoRA / DoRA checkpoint; rank, alpha, and type are inspected before launch.

Resume mode - training.resume_mode. Weights only starts a fresh schedule at step 0; Continue run restores train state when available. (default "weights_only"; choices "weights_only", "continue")

Analyze generalization automatically - training.auto_analyze. Reads the CPU-only training log after complete or stopped runs and recommends a checkpoint. (default true)

Evaluate checkpoints automatically - training.auto_evaluate_checkpoints. After training releases its model, measures saved checkpoints on validation and a small training subset. (default true)

Evaluate Base model (no LoRA / DoRA) - training.eval_include_base. Measures the reference-only baseline before checkpoints for an automatic comparison. (default true)

Evaluation training subset - training.eval_train_subset. Deterministic training items measured during automatic evaluation; 0 disables the training subset. (default 48; minimum 0; maximum 100000)

Evaluation strengths - training.eval_strengths. Comma-separated LoRA / DoRA strengths from 0 to 4 for automatic checkpoint evaluation. (default "1.0")

Evaluation timeout (s) - training.eval_timeout_s. Stops automatic checkpoint evaluation after this many seconds without failing the completed training run. (default 900; minimum 1; maximum 100000)

Generate training samples - training.sample_enabled. Renders a short sample at the configured epoch interval. (default true)

Sample every epochs - training.sample_every_epochs. 1 provides a sample after each completed epoch. (default 1; minimum 1; maximum 10000)

Sample runtime tier - training.sample_runtime_tier. Memory tier for the isolated sampling process. (default "auto"; choices "auto", "6", "8", "10", "12", "16", "24", "32")

Minimum free VRAM (GB) - training.sample_min_free_vram_gb. Skips sampling rather than risking training OOM below this free-memory threshold. (default 6; minimum 0; maximum 128)

Sample timeout (s) - training.sample_timeout_s. Kills a stuck sampling subprocess after this time. (default 300; minimum 1; maximum 100000)

Sample text - training.sample_text. Short representative phrase used to compare epochs. (default "This is a training progress sample for the adapted voice.")

Custom sample reference - training.sample_reference. Optional audio path; blank uses the dataset's best reference candidate automatically.

Sample language - training.sample_language. Mirrors Voice Generation for per-epoch samples; auto uses the prepared dataset language. (default "auto"; choices "auto", "ZH", "EN", "JA", "AR", "ES")

Sample seed - training.sample_seed. Mirrors Voice Generation for per-epoch samples; -1 chooses one seed at training start and reuses it across epochs. (default -1; minimum -1; maximum 4294967295)

Sample beams - training.sample_num_beams. Mirrors Voice Generation beam search for every per-epoch sample. (default 3; minimum 1; maximum 10)

Sample temperature - training.sample_temperature. Mirrors Voice Generation temperature for every per-epoch sample. (default 0.8; minimum 0.1; maximum 2)

Sample top-p - training.sample_top_p. Mirrors Voice Generation nucleus sampling for every per-epoch sample. (default 0.8; minimum 0; maximum 1)

Sample top-k - training.sample_top_k. Mirrors Voice Generation token filtering for per-epoch samples; 0 disables it. (default 30; minimum 0; maximum 100)

Sample repetition penalty - training.sample_repetition_penalty. Mirrors Voice Generation repetition control for every per-epoch sample. (default 10; minimum 1; maximum 20)

Sample emotion weight - training.sample_emo_alpha. Mirrors Voice Generation emotion weight for every per-epoch sample. (default 0.65; minimum 0; maximum 1)

Sample diffusion steps - training.sample_diffusion_steps. Mirrors Voice Generation diffusion quality for every per-epoch sample. (default 25; minimum 2; maximum 100)

Sample CFG rate - training.sample_inference_cfg_rate. Mirrors Voice Generation conditioning strength for every per-epoch sample. (default 0.7; minimum 0; maximum 2)

Sample maximum text tokens - training.sample_max_text_tokens. Mirrors Voice Generation segment length for every per-epoch sample. (default 60; minimum 20; maximum 300)

Sample length penalty - training.sample_length_penalty. Mirrors Voice Generation beam length control for every per-epoch sample. (default 0; minimum -2; maximum 2)

Sample maximum mel tokens - training.sample_max_mel_tokens. Mirrors Voice Generation output limit for every per-epoch sample. (default 1500; minimum 1; maximum 1815)

Sample speaking rate - training.sample_speaking_rate. 1.0 is the model's natural pace; below 1.0 speaks slower and above 1.0 faster. (default 1; minimum 0.5; maximum 1.5)

Seed - training.seed. Controls split, sampler, initialization, and training randomness. (default 42; minimum -2147483648; maximum 4294967295)

Data workers - training.num_workers. 2 is a safe Windows/Linux default; use 0 to debug worker issues. (default 2; minimum 0; maximum 64)

Log every steps - training.log_every_steps. 1 gives fully live charts; increase slightly for very fast runs. (default 1; minimum 1; maximum 100000)

Training device - training.device. CUDA device used by the training worker. (default "cuda:0")

Attention backend - training.attention_backend. SDPA is the compatible default. (default "sdpa"; choices "sdpa", "eager", "flash_attention_2")

Model directory - training.model_dir. Base IndexTTS 2.5 model directory. (default "models")

Model config - training.model_config. IndexTTS 2.5 YAML configuration path. (default "models/config.yaml")

Checkpoint Grid - 20 settings

LoRA / DoRA folder - grid.adapter_dir. Choose one training run to analyze and compare.

Evaluation references - grid.eval_reference_mode. Same as training validation inherits that run's validation mode; other uses a different same-speaker clip like inference. (choices "", "self", "other")

Training subset - grid.eval_train_subset. Deterministic training items measured beside validation; 0 disables the training subset. (default 48; minimum 0; maximum 100000)

Include base model - grid.eval_include_base. Adds Base model (no LoRA / DoRA), the reference-only comparison baseline. (default true)

Checkpoints - grid.checkpoints. Base model (no LoRA / DoRA), the recommended checkpoint, and the final checkpoint are selected first. (default [])

Strengths - grid.strengths. Comma-separated LoRA / DoRA strengths from 0 to 4; Base model (no LoRA / DoRA) is generated once without a strength. (default "1.0")

Texts (one per line) - grid.texts. Each non-empty line is generated for every selected row and reference. (default "This is a training progress sample for the adapted voice.\nA calm, natural voice should remain clear on a sentence it never heard during training.")

Reference audio paths (one per line) - grid.references. Every checkpoint uses these same references in the same order.

Seed - grid.seed. -1 draws one random seed at grid start and then fixes it across cells. (default -1; minimum -1; maximum 4294967295)

Language - grid.language. Use the language of every sentence in this grid. (default "EN"; choices "ZH", "EN", "JA", "AR", "ES")

Temperature - grid.temperature. 0.8 matches Voice Generation defaults. (default 0.8; minimum 0.1; maximum 2)

Top-p - grid.top_p. Nucleus sampling threshold shared by every cell. (default 0.8; minimum 0; maximum 1)

Top-k - grid.top_k. Token candidate cutoff; 0 disables it. (default 30; minimum 0; maximum 100)

Beams - grid.num_beams. Beam count strongly affects quality as well as generation time and VRAM. (default 3; minimum 1; maximum 10)

Repetition penalty - grid.repetition_penalty. Keeps semantic-token loops under control. (default 10; minimum 1; maximum 20)

Diffusion steps - grid.diffusion_steps. 25 is the quality default. (default 25; minimum 2; maximum 100)

CFG rate - grid.inference_cfg_rate. Diffusion conditioning strength. (default 0.7; minimum 0; maximum 2)

Max text tokens per segment - grid.max_text_tokens_per_segment. Use the same segment size for a fair comparison. (default 60; minimum 20; maximum 300)

Emotion weight (alpha) - grid.emotion_weight. Shared emotion-conditioning blend for every cell. (default 0.65; minimum 0; maximum 1)

Speaking rate - grid.speaking_rate. 1.0 is the model's natural pace; below 1.0 speaks slower, above 1.0 faster. A trained LoRA / DoRA can carry a calibrated value that matches the speaker's real pace. (default 1; minimum 0.5; maximum 1.5)

Models & Performance - 26 settings

LoRA / DoRA - runtime.lora_path. Select a trained LoRA / DoRA, or None for Base model (no LoRA / DoRA), which clones from the reference only.

LoRA / DoRA strength - runtime.lora_strength. 1.0 is the trained strength; lower is subtler and higher is stronger. (default 1; minimum 0; maximum 2)

Merge LoRA / DoRA into base weights for speed (BF16 only) - runtime.lora_merge_into_base. Temporarily folds the selected LoRA / DoRA into floating GPT weights and restores them before switching. (default false)

Device - runtime.device. Auto selects the first available accelerator; choose CPU only for diagnostics. (default "auto"; choices "cuda:0", "cuda:1", "auto", "cpu")

VRAM tier - runtime.vram_tier. Auto detects physical VRAM; selecting a named tier fills conservative runtime settings. (default "auto"; choices "auto", "6", "8", "10", "12", "16", "24", "32", "custom")

GPT model variant - runtime.model_variant. BF16 gives the official quality path; INT8 ConvRot reduces GPT weight memory. (default "bf16"; choices "bf16", "int8_convrot")

GPT dtype - runtime.gpt_dtype. BF16 is recommended on modern NVIDIA GPUs; FP32 is the CPU-compatible fallback. (default "bf16"; choices "bf16", "fp16", "fp32")

Attention backend - runtime.attention_backend. SDPA is the compatible default; FlashAttention 2 requires its optional package. (default "sdpa"; choices "sdpa", "flash_attention_2", "eager")

Use acceleration engine - runtime.use_accel. Enables the optional CUDA-graph/flash-attention path; use beams=1. (default false)

Enable emotion-text model - runtime.use_qwen_emo. Required for Emotion text mode; on-demand residency keeps startup lazy. (default true)

Compile s2mel - runtime.torch_compile_s2mel. Uses torch.compile for repeated workloads; first generation takes longer. (default false)

BigVGAN CUDA kernel - runtime.use_cuda_kernel_bigvgan. Uses the optional fused activation kernel when available. (default false)

CFM estimator BF16 autocast - runtime.s2mel_estimator_autocast. Reduces activation VRAM; useful at 6 GB and sometimes slightly changes output. (default false)

Use DeepSpeed loader - runtime.use_deepspeed. Optional legacy loader; leave off unless DeepSpeed is installed. (default false)

GPT blocks to swap - runtime.blocks_to_swap. 0 keeps all blocks resident; -1 lets runtime fit automatically; up to 24 streams from CPU. (default 0; minimum -1; maximum 24)

Swap ring size - runtime.swap_ring_size. 2 overlaps transfer and compute; 1 uses least VRAM. (default 2; minimum 1; maximum 4)

Pinned swap memory - runtime.pin_swap_memory. Recommended for faster CPU-to-GPU block transfers. (default true)

Runtime CFM cache length - runtime.cfm_cache_length. Upper cache reservation used when generation does not request a larger value. (default 8192; minimum 1024; maximum 32768)

VRAM reserve (GB) - runtime.vram_reserve_gb. 2 GB is recommended to absorb allocator and generation peaks. (default 2; minimum 0; maximum 12)

Section batch hint - runtime.max_section_batch_size_hint. Advisory maximum shown to generation controls for this runtime. (default 8; minimum 1; maximum 64)

Semantic Model - runtime.aux_residency.semantic_model. Residency policy for this auxiliary model. (default "gpu"; choices "gpu", "on_demand", "cpu")

Qwen Emo - runtime.aux_residency.qwen_emo. Residency policy for this auxiliary model. (default "on_demand"; choices "gpu", "on_demand", "cpu")

Campplus - runtime.aux_residency.campplus. Residency policy for this auxiliary model. (default "gpu"; choices "gpu", "on_demand", "cpu")

Semantic Codec - runtime.aux_residency.semantic_codec. Residency policy for this synthesis model. (default "gpu"; choices "gpu", "on_demand")

S2Mel - runtime.aux_residency.s2mel. Residency policy for this synthesis model. (default "gpu"; choices "gpu", "on_demand")

Bigvgan - runtime.aux_residency.bigvgan. Residency policy for this synthesis model. (default "gpu"; choices "gpu", "on_demand")

18. Final Checklist

One short base-model generation succeeds before advanced tuning.
The selected language matches the script and the reference is clean.
The active adapter, strength, auto-reference, and speaking rate are intentional.
The section preview, caption timing, pauses, and target-duration semantics match the use case.
The selected VRAM tier has passed the benchmark and section batch size stays within its hint.
Dataset segments were listened to before caching and training.
A checkpoint was chosen by validation plus fixed-input listening, not file recency alone.
The working state is saved as a named user preset and the output folder contains reproducibility metadata.

You now have a complete path from an untrained reference clip to repeatable production output, plus the tools to build and validate a dedicated voice adapter when reference-only cloning is not enough.

SwarmUI Latest Updates

Furkan Gözükara — Thu, 27 Aug 2026 22:54:57 +0000

By SECourses: FLUX, Tutorials, Guides, Resources, Training, Scripts | Original Patreon post

Get the SwarmUI Installer and Model Downloader app and the presets from here : https://www.patreon.com/SECourses/posts/swarmui-auto-and-114517862

28 August 2026 Update V172

Reference tokens are more robust now.
- Variants such as @IMAGE1, @ image # 2, and <picture1> are recognized and normalized automatically.
Prompt-label handling and makes token estimates more accurate for multi-frame continuation and Image-to-Video workflows now.
Better Video Continuation
- Init Video Continuation 1.3.1 now supports 1, 5, 22, 39, or 56 context frames.
- One-frame mode preserves the previous behavior. Multi-frame mode uses MiniMax H3's native clip conditioning for smoother transitions.
- Replayed context frames and matching audio are removed automatically during merging, preventing duplicated footage or sound.
- Source audio is preserved and generated audio is appended at the continuation boundary.
Start Frame / Init Video now accepts an image or video. Uploaded videos can be continued and optionally merged with the newly generated segment.
Continuation no longer consumes a Ref2VA reference-image slot.
MiniMax H3 Fixes
- Fixed text-only Audio Only workflows failing to recognize SwarmUI's H3 latent canvas.
- Fixed pasted multiline wildcard text being collapsed.
WD14 Tagger Hardening
- All 19 supported model repositories are now allowlisted and pinned to reviewed, immutable Hugging Face revisions.
- Downloaded Taggerine inference code must pass SHA-256 verification before execution.
- Added stricter validation for model IDs, thresholds, image types and sizes, model directories, and temporary output paths.
- Runtime package installation was removed. Missing dependencies now produce clear installation instructions instead of modifying the ComfyUI environment during generation.
- Added dedicated security-boundary tests.
- Install from this repo manually https://github.com/FurkanGozukara/SwarmUI-WD14Tagger
Automatic Installers / Updaters Reliability
- Aggressive Git updates now clean untracked leftovers after repository resets, improving recovery from blocked or inconsistent node updates.
Get latest zip file extract and overwrite all files
- Import latest presets
- Run Windows_Install_SwarmUI.bat to update latest
Get latest ComfyUI and update it to latest as well
- https://www.patreon.com/SECourses/posts/comfyui-auto-2-105023709
More latent frames will increase vram usage a lot so be careful
This feature added to ComfyUI presets as well read changelogs on ComfyUI

22 August 2026 Update V170

This is a very big update : LTX 2.5 video generation presets + LTX 2.5 Video Core Bundle, new FLUX 2 Klein INT8 / INT4 ConvRot HQ models, MiniMax H3 Init Audio (make the video follow any soundtrack with lipsync), live prompt token meter, Face Inpainting upgrades, a big model downloader UI upgrade and a huge installer / updater overhaul so please read all
LTX 2.5 video generation presets finally added : Amazing_SwarmUI_Presets_v70.json brings 6 new presets : LTX25 Text To Video - Core 2-Stage 8+3, LTX25 Image To Video - Core 2-Stage 8+3, LTX25 First Last Frame - Core 2-Stage 8+3, LTX25 Audio Reference To Video - Core 2-Stage 8+3, LTX25 Text To Video - Dev HQ 30+6 and LTX25 Image To Video - Dev HQ 30+6 (all 260822)
Audio Reference To Video : add a Prompt Audio file and describe the matching visuals - the native LTX 2.5 audio branch makes the video follow your audio (lipsync, timing) - Image To Video and First Last Frame presets : add an Init Image (and a Video End Frame) and describe only the motion, camera and sound instead of re-describing the still

All 6 presets are two-stage : the first stage generates at 960x544, then SwarmUI's Refine / Upscale applies the official LTX 2.5 latent spatial upscaler x2 and refines to model-aligned 1080p (1920x1088) with synchronized audio - 97 frames at 24 FPS (about 4 seconds) and H264 MP4 output by default
Core presets use my LTX 2.5 Distilled INT8 ConvRot Premium model with 8 + 3 steps at CFG 1 and Euler Ancestral so they are fast - Dev HQ presets use my LTX 2.5 Dev INT8 ConvRot Premium model with 30 + 6 steps at CFG 3 and Euler for maximum prompt adherence and detail
Everything is exposed as normal SwarmUI parameters so you can change resolution, duration, steps and the refiner settings as you like - increase Text2Video Frames for longer clips
Import Amazing_SwarmUI_Presets_v70.json or run Windows_Preset_Delete_Import.bat to get them - the Which_Bundles_Downloads_Which_Preset_Models.html report is regenerated for the new presets as well

LTX 2.5 Video Core Bundle added to the model downloader : 1 click downloads everything the LTX 2.5 presets need - 10 files, 64.64 GB : Dev and Distilled INT8 ConvRot Premium transformers, Gemma 4 12B INT8 ConvRot v2 text encoder (SwarmUI's current default), Gemma 4 E2B INT8 ConvRot prompt enhancer, the convolutional and the diffusion video VAE, the audio VAE, the latent spatial upscaler x2, the pixel spatial upscaler x2 IC-LoRA and the motion track control IC-LoRA
This one bundle covers every model referenced by all of our LTX 2.5 presets in both SwarmUI and ComfyUI, so you do not need to hunt for a second bundle to make any of them run
The text encoder and both VAEs are saved with the exact LTX-2 sub folder and file names SwarmUI's own automatic downloader expects, so SwarmUI never downloads a second copy - in ComfyUI mode they go to models/text_encoders/LTX-2 and models/vae/LTX-2
No more gated downloads for LTX 2.5 : I have mirrored the remaining official Lightricks files into my own non-gated repository, so nothing in the LTX 2.5 list asks you to log in or accept a license anymore - this covers the official Dev and Distilled Comfy INT8 ConvRot transformers, the Distilled NVFP4 transformer (for RTX 5000 Blackwell), the pixel spatial upscaler x2 IC-LoRA and the latent spatial upscaler
These official quantizations stay optional alternatives - my own Premium INT8 ConvRot conversions remain the default of the bundles and presets because they are higher quality, and the Gemma 4 E2B INT8 ConvRot prompt enhancer is also listed separately

Model downloader UI upgrade : every model list and every bundle Includes list is now grouped by the folder the files are saved into : Main Models, Text Encoders, VAEs, LoRAs, ControlNets, Upscalers, Vision, Detection, LLM - biggest files first, and every group header shows the file count and total GB so you instantly see what goes where

New Active Transfers panel with a real progress bar per file (percent, downloaded / total, speed, ETA) above the Activity Log - the log is chronological now with a Follow newest log entry checkbox, it refreshes twice per second, and multiple open browser tabs no longer steal each other's updates
The URL Downloader tab now shows its progress in the same panel instead of looking frozen, and every download reports [STARTING] immediately instead of showing nothing while the file metadata is fetched
Console progress line reordered : percent, downloaded / total size, speed and ETA come first and the long file name goes last, so a narrow CMD window never cuts off the numbers - and when the output is not a real terminal (RunPod / Massed Compute logs) progress is still printed once per second instead of being silent

New LTX2.5_Enchance_Prompt_Feed_For_LLMs.txt inside the Prompt_Generate_LTX_MiniMax_And_Presets_How_To_Use folder : an official-aligned LTX 2.5 prompting guide for LLMs - give it to ChatGPT / Claude / Gemini, tell it what you want, and it writes properly formatted LTX 2.5 prompts (text / image / audio to video, multi-shot and IC-LoRA conventions are all covered)
MiniMaxH3References extension 1.13.2 : videos and audio files attached with SwarmUI's own native prompt uploader are now recognized as MiniMax H3 references too - same cards, same @video1 / @audio1 tokens with autocomplete, same Ref2VA workflow
- All extensions and custom nodes automatically installed by our installers
FLUX 2 Klein INT8 and INT4 ConvRot HQ models : I have compiled high quality INT8 ConvRot and INT4 ConvRot versions of FLUX 2 Klein myself - Base 9B, Distilled 9B and Distilled 9B kv - 6 new models added to the model downloader app
FLUX 2 Klein Core Bundle and Complete Image Generation and Editing Bundle now download the INT8 ConvRot HQ versions - almost same quality as BF16 but massively faster, same as our Krea 2 and LTX 2.5 ConvRot models

With Amazing_SwarmUI_Presets_v70.json both FLUX 2 Klein Base and FLUX 2 Klein Distilled 8 Steps presets now use the INT8 ConvRot HQ models
MiniMax H3 FL2V Turbo 4-Step 768p speed LoRA updated to v1.1 : the model downloader now downloads minimax_h3_fl2v_turbo_4step_v1.1_768p_bf16.safetensors instead of the old v1.0 - this is the optional 16:9 aspect ratio speed up LoRA for 768p generations from LightX2V - MiniMax-H3 Core and Low VRAM bundles download the new version automatically

MiniMax H3 Init Audio : new Init Audio parameter group added - select or upload any audio file (or even a video file, its soundtrack is used) and MiniMax H3 generates the video to follow that soundtrack exactly : lipsync, action timing, ambience - and keeps it as the exact final audio track of the output
Works for Text To Video, Image To Video and References To Video presets and Init Audio Match Duration makes the video length automatically follow the audio length
Live token meter : a real time Tokens meter now appears above the prompt and shows estimated MiniMax H3 token usage (prompt + references + resolution + duration) against the 109k budget - you instantly see when your references or duration are too big before generating
Face Inpainting upgrades : new Face Inpaint Faces parameter selects which detected faces get refined : 1 = biggest face (default), 2 = second biggest, 1,3 or all - faces are ranked biggest to smallest and each selected face is refined in its own pass
New hallucination guard prevents pasting a wrong / neighbouring face over your subject and Face Inpaint Detector can no longer block generations

Installer / Updater overhaul : Windows_Install_SwarmUI.bat and Windows_Update_SwarmUI.bat are merged into a single Windows_Install_or_Update_SwarmUI.bat
Windows_Start_SwarmUI.bat is now ultra fast : it starts SwarmUI directly without updating or downloading anything and rebuilds only if the code actually changed
Update runs are much faster : unchanged extensions (Premium Extensions, Licon MSR, Foley) are skipped with a quick revision check instead of being re-downloaded on every run - FFmpeg and Cloudflared downloads are also skipped when already latest
Missing Git is now auto installed (winget on Windows, apt-get on Linux) instead of failing with cryptic WinError 2 errors
SwarmUI is now installed as a full git clone and older shallow installs are auto completed - fixes the scary Tag list empty?! warning of SwarmUI's own update check
.NET 10 SDK now installs into SwarmUI/.dotnet folder, matching SwarmUI's own launcher search order and persisting on RunPod / SimplePod network volumes - DOTNET_ROOT is exported so in-app Update and Restart now rebuilds with the correct SDK and works properly
Linux / cloud : Cloudflared is now installed system-wide from Cloudflare's official APT repository and launching prints the public trycloudflare.com URL so you can open SwarmUI from your own computer's browser on RunPod / SimplePod / Massed Compute - new --no-cloudflared and --host options added
RunPod, SimplePod and Massed Compute instruction files rewritten with exact HOW TO OPEN SWARMUI steps
Windows_Preset_Delete_Import.bat made more robust : works from any folder and auto picks the correct Python
To update : get latest zip file, extract and overwrite all, then run Windows_Install_or_Update_SwarmUI.bat and import Amazing_SwarmUI_Presets_v70.json
Also update your ComfyUI backend to latest V127 and get the 8 new LTX 2.5 ComfyUI presets : https://www.patreon.com/SECourses/posts/comfyui-auto-2-105023709

16 August 2026 Update V168

MiniMax H3 bundle updated to include newest 8-steps LoRA and also necessary Yolo Face model for new automatic ComfyUI Face Inpainting feature in presets for MiniMax H3
SwarmUI presets are now also using new Ref2V LoRA for references presets, you can switch to FL2V LoRA and see which one performs better so easy from LoRA tab and compare

Automatic video face inpainting implemented

Get latest zip file, extract and overwrite all, restart PC, run Windows_Update_SwarmUI.bat to update
Also import latest Amazing_SwarmUI_Presets json file
You can see impact of Face Inpainting here and now even better working : https://www.reddit.com/r/comfyui/comments/1vpl5bb/secourses_comfyui_installer_and_ready_presets_now/

14 August 2026 Update V166

We have added LTX 2.5 video upscaler and MiniMax Music 3 Text to Music presets into ComfyUI : https://www.patreon.com/SECourses/posts/comfyui-auto-2-105023709
- LTX 2.5 video upscaler preset is able to upscale any video, any resolution, any aspect ratio into 2x
- It is a very serious generative upscale so adds lots of details
- Therefore, try to upscale at 1 chunk to have better consistency
- Read preset information carefully after loading into ComfyUI
- Our SECourses Premium Upscaler Pro app also fully supports LTX 2.5 now : https://www.patreon.com/SECourses/posts/secourses-pro-150202809
  - It is standalone does not require ComfyUI and it auto downloads models when you first time use
To make these presets work out of the box, 2 new ComfyUI core bundles added to the downloader app as below
LTX 2.5 video generation presets and bundles will be added soon hopefully but its quality lower than MiniMax H3
Moreover, I have converted the LTX 2.5 Int8 ConvRot myself higher quality and more accurate than officially published ones
- Someone detected one of the model is corrupt and broken - officially released ConvRot Int8 variant
- I converted them from BF16 versions after big research and experimentation
- So our model downloader app will download better quality Int8 ConvRot of LTX 2.5 models
Our Musubi Trainer convert to quant tab also now supporting LTX 2.5 Int8 ConvRot compile : https://www.patreon.com/SECourses/posts/secourses-musubi-137551634

MiniMax Music 3 is reportedly lower quality than ACESTEP XL 1.5
- Our very advanced ACESTEP XL 1.5 Premium app below and it has full LoRA and DoRA training support
  - App : https://www.patreon.com/SECourses/posts/ace-step-1-5-xl-157675060
- MiniMax Music 3 also missing necessary resources to be trainable
- MiniMax Music 3 prompt templates shared here : https://github.com/MiniMax-AI/MiniMax-Music3/tree/main/skills/music-caption-rewriter/templates
To get updated model downloader app, as usual, get latest zip file, extract and overwrite all

12 August 2026 Update V165

The most professional speed up LoRA maker LightX2V just published new speed LoRAs for the MiniMax H3
- https://github.com/ModelTC/Minimax-H3-Turbo

Our MiniMax H3 core bundles now auto downloads both FL2VA Turbo 8-step v1.0 and FL2VA Turbo 4-step v1.0 768p
- Older v0.1 removed from downloads and presets so you can delete it from LoRA downloads if you did download before
- All presets now by default uses FL2VA Turbo 8-step v1.0 but you can switch it to FL2VA Turbo 4-step v1.0 768p
  - If you switch to FL2VA Turbo 4-step v1.0 768p you may be needed to overwrite Video shift value and set to 6, SwarmUI default sets to 12
- Sadly LoRAs only made for non-reference model atm but works on both models until they publish specific new LoRA for reference model
Quick set megapixels feature implemented
- Based on selected aspect ratio, it will set resolution according to your set
Moreover, now it will display visualization of the output based on aspect ratio and resolution

To update get the latest zip file, extract and overwrite all, run Windows_Update_SwarmUI.bat and also use Windows_Preset_Delete_Import.bat to get latest updated presets
Moreover get latest ComfyUI backend zip file and also update it through installer bat file : https://www.patreon.com/SECourses/posts/comfyui-auto-2-105023709

10 August 2026 Update V162

ComfyUI infinite video with MiniMax H3 tutorial published : https://youtu.be/1580ZDX-60Q

Hopefully a tutorial for SwarmUI coming soon

Completely Rebuilt Download Engine
- Exact byte-range resume after cancellation, connection loss, or application restart.
- Previously downloaded ranges are preserved instead of downloaded again.
- One sparse staging file replaces multiple temporary chunks and the expensive final merge.
- Significantly lower temporary disk-space requirements.
- Models are atomically installed only after size and SHA-256 verification.
- Corrupt or incomplete downloads can never replace a finished model.
Faster Hugging Face Downloads
- Native Hugging Face Xet is now recommended and enabled automatically when available.
- Xet automatically scales between 8 and 32 streams according to available system RAM.
- Improved recovery from stalled transfers, rate limits, server errors, and expired signed URLs.
- Removed the previous forced approximately 84.5 GiB Xet cache.
- New downloads default to no chunk cache, with an optional bounded reuse mode.
Improved Download Controls
- Direct URL downloads now appear in the active-download counter.
- Cancel and Cancel All now work with direct URL downloads.
- Completed ranges remain available after cancellation.
- Prevented overlapping standalone URL downloads.
- Small and large direct downloads now use the same atomic download system.
- Improved URL validation and rejection of HTML pages masquerading as model files.
- Safer Hugging Face and CivitAI domain detection.
Catalog And Interface Improvements
- Replaced the unavailable LTX 2.3 x2 spatial upscaler v1.0 with the official v1.1 long-video hotfix : ltx-2.3-spatial-upscaler-x2-1.1.safetensors
- Removed duplicate physical models from search and LoRA displays while keeping aliases searchable.
- Improved automatic folder detection for filenames containing hyphens and underscores.
- Improved mobile and narrow-screen layout.
Updater now detects and repairs incomplete FoleyExtension installations, including optional-image components.
MiniMax H3 Improvements
- Expanded the MiniMax H3 prompt-enhancement guide to v2.3.
- Added stable reference-roster numbering rules for multi-scene and folder-batch prompts.
- Added clearer image, video, audio, and paired-soundtrack reference rules.
- Corrected first-and-last-frame preset instructions to use Image To Video > Video End Image.
For updating please also update ComfyUI to latest as well and also this one
Use Windows_Update_SwarmUI.bat and import latest presets file

9 August 2026 Update V160

Model downloader app updated and Int4 ConvRot MiniMax H3 models bundle added as Low VRAM
- This bundle is great for 12 GB and below GPUs
- At the presets both ComfyUI and SwarmUI, just replace Int8 Model with below Int4 variants
- This format Int4 ConvRot models right out of the box working with our ComfyUI (use our installers) backend and installers for SwarmUI

We are using official code of Lightricks not Kijai implementation therefore our LoRA works better
However, Kijai had a VRAM optimization and i just implemented it
You will see it like below in presets, MAX savings reduces VRAM more than 40% but may reduce quality, exact saves 15%+ but same quality
Make sure to use our ComfyUI installer and update it for all features to run via our bat file
Remember MiniMax H3 specific features will only appear when you have selected MiniMax H3 architecture model in your model selection

If you set reference image size to max like this, it improves quality and accuracy but may use more memory, default is match

The importance of Max option is very significant

Video clip references are processed differently and below explains how they processed when used as a reference

8 August 2026 Update v159

This is a very major upgrade with so many new amazing stuff so please read carefully
Famous Lightricks released 4 steps LoRA for MiniMax H3
- https://github.com/ModelTC/Minimax-H3-Turbo
- https://huggingface.co/lightx2v/Minimax-h3-Turbo/tree/main
- I have fully and accurately implemented it into our installers
  - Currently not working with SwarmUI or ComfyUI as default but works with our installers due to myself developed premium nodes and extensions
  - Make sure to install update your ComfyUI and SwarmUI users must also use our ComfyUI backend
- This LoRA added into our model downloader MiniMax H3 core bundle please download

I have made new presets that uses this LoRA and does 8 steps so now you can use them
- It can go as low as 4 steps but I recommend 8 steps
New updated presets are as below

Audio Only preset is super fast and generates audio directly
- Audio generation can be used to see if your prompt and duration matching before generating video since it is like real 2x time speed
  - So 1 minute audio generation takes like 30 seconds on RTX 5090 therefore use audio generation preset to see if your prompt and duration matches - for speaking having videos
  - So quickly iterate, see if your audio accurate, then generate full video
Moreover, I recommend you to use 8 steps and 0.4 megapixel resolution to quickly generate your videos, verify they are accurate, then move full generation like 1344x768px and 20 steps high quality
- This works great
Add references and write prompt field improved so that now you can add videos and audios with trim as you wish as below (trimming is optional)
- For @video1's soundtrack, type <Audio 1>; @audio1 is the first standalone audio file
- According to the above rule, MiniMax_H3_Enchance_Prompt_Feed_For_LLMs.txt file improved and literally working amazing
- It is located inside Prompt_Generate_LTX_MiniMax_And_Presets_How_To_Use folder inside zip file
Trim interface supports both Audio and Video trimming

ComfyUI presets have amazing batch folder processing read changelogs : https://www.patreon.com/SECourses/posts/comfyui-auto-2-105023709
For SwarmUI, you can use Wildcards as a batch processing
- To process them with order not randomly, enable Display Advanced Options
- Then find Swarm Internal and change Wildcard Seed Behaviour to index
- Set regular seed 0 so it will start from first prompt in Wildcard and how many generations you make, it will make with order

For everything to work, make sure that you use our ComfyUI installer as a backend and it is updated : https://www.patreon.com/SECourses/posts/comfyui-auto-2-105023709
Then get latest SwarmUI zip file, extract and overwrite all, then run Windows_Update_SwarmUI.bat to update
- Both install and update installs necessary extensions automatically
To update presets, i recommend use Windows_Preset_Delete_Import.bat
- You can also manually import latest Amazing_SwarmUI_Presets json file and overwrite all

6 August 2026 V158 - Update 2

We have significantly improved our add references extension - auto installed with our installer
Now when you add or remove an attachment, it will not modify and alter your written prompt
Now you can drag and drop attachments to change their position like @image1 into @image3 etc
Now even if you have reference in the prompt but not in attachment, it will not throw error and at the back end it will remove @image1 etc and prompt will just run fine
Zip file is same just run windows update bat file
Also I published a speed up quality comparison video : https://www.reddit.com/r/comfyui/comments/1vh5wd8/i_have_implemented_solattn_crossstep_cache_from/

6 August 2026 V158

I have fully and accurately implemented https://nvlabs.github.io/Sana/Sol-Engine/H3-OnDevice/ into our ComfyUI installation via our always installed and myself developed custom nodes
- Deploy MiniMax-H3 with Sol Engine achieving up to 4.52× acceleration on on-device GPUs
  - Their claimed 4.52x speed acceleration happens at 50 steps and default attention meanwhile I measured with 20 steps and against Sage Attention 2.8.3 latest (already speeds up like 20%+)
  - Moreover, I did set more conservative but almost same quality 0.08 thus it skips lesser steps
  - Use our ComfyUI backend installer and update it so this will work : https://www.patreon.com/SECourses/posts/105023709
Then updated our SwarmUI extensions to fully support it
Existing presets have a minor fix and they will work
When you select a MiniMax H3 architecture model, the parameters will appear like this - the backend has to be loaded so wait a little bit for it to appear

Default values quality wise conservative so almost same quality and brings 1.39x speed compared to Sage Attention 2 speeds at 1344x768 with 362 frames
It used only 20 GB vram for 1344x768 px at 362 frames - 15 seconds but it can use even lower depending on your GPU VRAM
The speed up is not enabled by default so check checkbox to enable it
The first run will be slow due to kernel compile but consquent runs will be faster and won't recompile
In logs first 4 steps are same, then step speed increases and then you start to see jumps in steps like below

For updating get latest zip file, overwrite all files, and then run Windows_Update_SwarmUI.bat - fresh install will also get this
- Make sure that you have updated your ComfyUI backend with our ComfyUI installer

5 August 2026 V157

We have officially moved enhanced SwarmUI with this update with the extensions I developed - all auto installed - please read carefully

MiniMax H3 is now fully supported with our Enhanced SwarmUI and ComfyUI installers and model downloaders
- Can generate up to 30 seconds and 2K native resolution just fine
In SwarmUI model downloader - download MiniMax H3 Core bundle
All of our presets both ComfyUI and SwarmUI, uses this bundle therefore downloading this will let you run presets right away
- Even if you have low VRAM GPU still this model working perfect
- Make sure to have at least 100 GB virtual RAM set
- Current GGUF releases are all almost same size as Int8 ConvRot therefore they are useless
- If Q4 proper one gets released I plan to add that into MiniMax-H3 Video Models section inside Video Generation Models tab

5 perfectly working new amazing MiniMax 3 presets with latest Amazing_SwarmUI_Presets_v62.json
- MiniMax H3 Beginning and Ending Image/Frame to Video - 260804
- MiniMax H3 Image to Video - 260804
- MiniMax H3 References to Video - 260804
- MiniMax H3 Text to Image with Optional References - 260804
- MiniMax H3 Text to Video - 260804
I recommend you to use Windows_Preset_Delete_Import.bat but you can always manually import too

Now recommended to add --disable-pinned-memory into your ComfyUI backend arguments until its bugs fixed to not have any OOM error
I have improved our special installers and extensions therefore you can use prompt input field to add 3 audio, 3 video and 9 image references to use with MiniMax H3 in SwarmUI easily
- They are referenced and named as @image1 @image2 @video1 @video2 @audio1 @audio2 and so on
The special upload will be allowed only when you select MiniMax H3 model
- It auto detects and enables based on selected model's architecture
Official SwarmUI still doesn't have this feature but we have with our installers
- We still always install official latest version SwarmUI but this comes from our own developed extension

New folder : Prompt_Generate_LTX_MiniMax_And_Presets_How_To_Use
- Our system uses references as @image1 @image2 @video1 @video2 @audio1 @audio2 and so on
  - Up to 9 images, 3 audios and 3 videos
  - <audio 1> means the audio of the first input <video 1>
- MiniMax_H3_Enchance_Prompt_Feed_For_LLMs.txt file is modified to instruct LLM to write prompts according to our system (as mentioned above) so you will get perfectly ready copy to paste prompts
New quick video extend feature
- Works with all of the init image to video models including MiniMax H3
- Apply Image to Video preset
- Then into init image field, instead of an image, input a video
- Then enable the Continue Init Video From Last Frame checkbox
- It will get last frame of your input video, generate image to video, and then finally merge videos and generate final output

Installers and update bat file made much more robust both for Windows and Linux
Get latest zip file, overwrite all, and run Windows_Update_SwarmUI.bat

19 July 2026 V154

This is a very big update with so many important new features, changes and improvements so please read all thank you

Seven New Presets

Make sure to import and overwrite Amazing_SwarmUI_Presets_v59.json or use Windows_Preset_Delete_Import.bat while SwarmUI running

Krea 2
- Krea 2 Raw/Base Slow - 260711 uses 52 steps, Euler, Simple, and CFG 4.5. It is the slower Raw/Base path intended mainly for fine-tuning, post-training, and LoRA work.
- Krea 2 Turbo Fast - 260711 uses 8 steps, Euler, Beta, and CFG 1 for fast final inference.
- Krea 2 Turbo Image Edit - 260716 adds denoise-based image editing with a default creativity value of 0.65. Add the source as Init Image and describe the requested change.

The new Krea 2 Core Bundle contains the Raw/Base INT8 HQ model, Turbo INT8 HQ model, Qwen3-VL 4B text encoder, and Qwen Image VAE. Its cached total is approximately 34.83 GiB.
The catalog also adds individually selectable Krea 2 Turbo Q8 GGUF and NVFP4 High Quality variants.

Automatically installed ComfyUI-QuantOps updated (With our ComfyUI Installer for SwarmUI Backend)
- Now it is only used if native ComfyUI is not supporting the loaded model
- I have compared the new famous Int8 ConvRot of Krea 2 and the speed difference is like 100%, I plan to update all models to Int8 ConvRot
- New updated ComfyUI-QuantOps supports Krea 2 GGUF as well

When you click and see full size of above image and analyze results you will see that:
- Int8 ConvRot is 96.2% similar to BF16 meanwhile GGUF Q8 is only 90.0% and FP8 Scaled is 82.2% and NVFP4 is 63.7%
  - Moreover, Int8 ConvRot generates the output in 3.05 seconds, making it 1.82× faster than BF16, which takes 5.56 seconds.
  - NVFP4 takes 3.8 seconds and is 1.46× faster than BF16, whereas GGUF Q8 takes 6.06 seconds and is approximately 8.3% slower than BF16.
  - So Int8 ConvRot generated with our Musubi Trainer app at high quality is almost 100% faster and almost same quality as BF16
  - High quality generation takes few hours on RTX 5090
Updated SwarmUI downloader has amazing quality Int8 ConvRot which reaches almost BF16 quality : https://www.patreon.com/SECourses/posts/swarmui-auto-and-114517862
- Krea 2 Core bundle downloads this model and uses it in SwarmUI preset
- ComfyUI preset also uses that model default
You can generate Int8 ConvRot models with our updated Musubi Trainer app : https://www.patreon.com/SECourses/posts/secourses-musubi-137551634

LTX 2.3 Foley Video-to-Audio

The new LTX23 Foley Video To Audio preset generates synchronized audio for a silent or muted video while keeping the source video unchanged.
Put the video in Init Image, then describe the visible sound source, action, material, and timing. The preset uses the LTX 2.3 Foley V2A LoRA with 30 steps, audio CFG 6, STG scale 1, modality scale 3, and a maximum of 169 frames at 24 FPS.
The Foley LoRA is approximately 0.21 GiB and is now part of the LTX 2.3 Video Core Bundle. The updated premium installer also installs the maintained FoleyExtension node package automatically.
- This model uses Dev version of LTX 2.3 not Turbo, therefore, now Dev version is included in the LTX 2.3 core bundle
For clean Foley generation, a useful prompt ending is: No speech is present. No music is present.
Download LTX 2.3 Core Bundle and in ComfyUI, install bundle 100

LTX 2.3 Licon MSR V2

The new LTX23 Licon MSR V2 Multi Subject Reference preset supports multiple subject, object, texture, or viewpoint references plus a required background image.
Add two to five images to Prompt Images in this exact order:
1. One to four subject, object, texture, or viewpoint references (so first 1-4 images are subjects)
2. The required background image last (last image is always background)
  1. Therefore, you need minimum 2 input images into the prompt field
Do not use Init Image for this workflow. Identify each numbered reference and its role in the positive prompt, then describe the new action, scene, camera, and lighting.
The preset uses the official eight-step distilled sigma schedule, Euler Ancestral sampling, a 65-frame internal reference sequence, and the LTX 2.3 Licon MSR V2 IC-LoRA. The new LoRA is approximately 0.61 GiB and is also included in the LTX 2.3 Video Core Bundle.
A dedicated guide is included with reference ordering, recommended settings, installation paths, and licensing details.
For this to work, we have coded custom extensions and all is automatically installer with SwarmUI installer or Updater files
Download LTX 2.3 Core Bundle and in ComfyUI, install Bundle 100 for ComfyUI backend

It is insanely powerful and fast I mean look below example

LTX 2.3 Presets Updates

LTX 2.0 model presets removed since LTX 2.3 is better in everyway
LTX 2.3 presets now using myself compiled Int8 ConvRot HQ models since they are literally 100% faster
- Model downloader LTX 2.3 core bundle is now downloading these new models

You can generate Int8 ConvRot models with our updated Musubi Trainer app : https://www.patreon.com/SECourses/posts/secourses-musubi-137551634

So Int8 ConvRot HQ is 100% faster than FP8 Quant Scaled and 50% faster than BF16 on RTX 5090
The quality is also excellent almost same as BF16

Phantom-Wan Character Reference T2V

Two Phantom-Wan presets are included:
- Phantom Wan 14B Character Reference T2V Fast
- Phantom Wan 14B Character Reference T2V Quality
Add character references to Prompt Images, not Init Image, then describe every subject, the scene, action, camera, and lighting. The reference images condition identity while generation begins from an empty video latent, so this is true reference-conditioned text-to-video rather than first-frame image-to-video.
One to four references are the officially recommended path. The integration can accept up to six for experimentation.
Download Phantom Wan 14B Character Reference T2V Bundle and in ComfyUI, install Bundle 100 for ComfyUI backend

Bundle 100 for ComfyUI Backend
- Inside ComfyUI zip file : Windows_Custom_Nodes_Bundles_Installer.bat

Unified Fast Robust Model Downloader Improvements

Run Windows_Start_Download_Models_App.bat to start Downloader
The previous hf_transfer checkbox has been replaced with two clearer controls:
- Custom Downloader Threads: 1 to 32, with 16 as the recommended default
- Hugging Face Xet Backend: optional and disabled by default
The custom thread setting applies to individual catalog downloads, bulk queues, bundles, search results, snapshots, and direct URL downloads. When Xet is enabled, the custom thread setting is ignored. If Xet fails, v150 falls back to the custom resumable backend unless the operation was cancelled.
The custom backend now has much stronger recovery behavior:
- Known-size partial downloads are preserved when cancelled.
- Parallel downloads save their range layout so later attempts can resume safely.
- Legacy v145 16-part downloads can be migrated and resumed.
- Every retry re-reads the saved byte count to prevent duplicate ranges and corruption.
- Six attempts use bounded exponential backoff.
- HTTP 429, 500, 502, 503, and 504 responses receive controlled retries.
- Separate connection and idle-read timeouts recover stalled transfers sooner.
- Progress output reports when it is waiting for the server and uses smoother speed estimates.
- Free space is checked before downloading and before merging parallel parts, with a safety reserve.
- Disk-full failures preserve useful partial data.
- Parallel parts are merged to a temporary file and atomically activated.
- Hugging Face main file URLs can use SHA verification and the optional Xet path.
SHA-256 verification and the verified-file cache remain in place. The goal is simple: fewer restarts from zero, clearer logs, and safer recovery on large model downloads.
Now you can set Hugging Face token for faster or private repo downloads
Now you can set HF Xet Download - even faster optional
Now you can set number of download threads
Now you can set Parallel File Downloads count
- This is extremely useful on high bandwith systems to download even faster than Hugging Face single file download limits like reaching 1 GB per second

Installation and Update Reliability

Windows install and update scripts no longer require one exact .NET SDK patch version. The new helper searches common system, user, registry, PATH, and environment locations for any usable stable .NET 10.x SDK while ignoring preview-only builds.
If installation is needed, it tries WinGet first and then falls back to the latest official Microsoft installer for x64, ARM64, or x86. The fallback download is checked against Microsoft's published release hash before it runs, and installer progress and log locations are shown instead of failing silently.
The premium-extension installer now validates the required integrations before changing the active setup. It installs or updates:
- FoleyExtension nodes plus the managed LTX 2.3 Foley SwarmUI integration
- Phantom character-reference helper nodes for the Comfy backend
- The managed Licon MSR SwarmUI integration
- The official Licon MSR Comfy node at the tested pinned revision
The new helper-node installers use staged directories, required-file checks, backup/rollback paths, bounded Git operations, and stale compiled-extension cleanup. Windows, RunPod, and Massed Compute update instructions now stop with an explicit error if this stage fails instead of continuing into a partial setup.

Updated Preset Model Report

A new file, Windows_Update_And_Open_Model_Report.bat, regenerates and opens the HTML report that shows which bundles and model files cover every SwarmUI preset.
The report generator now handles Krea 2, Phantom-Wan, Ideogram 4, negative-model parameters, saved-filename aliases, and LTX 2.3 Dev versus Distilled connector selection more accurately. The packaged report has been refreshed against Amazing_SwarmUI_Presets_v55.json.

How to Upgrade

Existing installation

Close SwarmUI and the model downloader.
Back up custom presets and any source changes you made inside the SwarmUI Git checkout. The updater resets tracked checkout files before pulling upstream changes.
Extract the latest zip file over your existing downloader package folder and allow release files to be replaced. Do not delete your SwarmUI or model directories.
Run Windows_Update_SwarmUI.bat.
Start the model downloader and download only the new bundles or individual files you need.
Import Amazing_SwarmUI_Presets_v55.json.

Windows_Preset_Delete_Import.bat can automate the preset replacement while SwarmUI is running. It first backs up the current presets, then deletes all active presets and imports the latest pack. Read its confirmation carefully if you maintain custom presets.
Users who already have the rest of LTX 2.3 can download only the new Foley and Licon LoRAs instead of downloading the full LTX bundle again.
Run Windows_Update_And_Open_Model_Report.bat afterward to compare the new preset requirements with the models currently on disk.

Fresh installation

Extract latest zip file into a clean folder.
Run Windows_Install_SwarmUI.bat.
Start the model downloader with Windows_Start_Download_Models_App.bat.
Download the bundle or individual models needed by your chosen presets.
Start SwarmUI and import latest Amazing_SwarmUI_Presets_.json
RunPod and Massed Compute users should follow the updated instruction files included in the package.

Massive ComfyUI Improvements

Furkan Gözükara — Thu, 27 Aug 2026 07:57:02 +0000

Zip file that includes everything with full changelogs shared here :

https://www.patreon.com/SECourses/posts/comfyui-auto-2-105023709
Video tutorial : https://youtu.be/1580ZDX-60Q

26 August 2026 Update V131

When you touch the prompt box, it was becoming shrinked and this issue resolved
MiniMax H3 Upgrades
- Continue videos using the final 1, 5, 22, 39, or 56 frames, instead of only the last frame.
- Multi-frame continuation uses MiniMax H3’s native clip guide for stronger motion, identity, and scene continuity.
- Replayed context frames and matching audio are automatically removed from the generated segment before merging.
- Continuation works with both FL2VA and Ref2VA without consuming an additional reference-image slot.
- The two Image To Video presets are now Image or Video To Video.
- So with these presets now you can do image to video or video to video

Start Frame / Init Video now accepts images or videos. A video can supply its final frame or selected multi-frame context.
Merge videos can append the new generation directly to the uploaded source video, preserving video order and audio.
Folder batches retain sequential generation, per-prompt saves, continuation, and final merged outputs.

Now we have contextual continue feature with Continue From Last Video Frames option we have

This feature implemented into SwarmUI as well so read it too
To update : get latest zip file, extract and overwrite all, then run Windows_Install_Or_Update_ComfyUI.bat and also Windows_Custom_Nodes_Bundles_Installer.bat (bundle 100) - all necessary custom nodes and extensions automatically installed and many of them managed by us for extra security

22 August 2026 Update V129

Python 3.10 not anymore supported because onnxruntime-gpu 1.27+ CUDA 13 build requires Python 3.11 or newer
- The installer will auto delete your existing Python 3.10 venv and auto generate new Python 3.12 venv if you have Python 3.12
- Please have Python 3.12 and use it
This is a very big update : 8 new official LTX 2.5 presets, MiniMax H3 Init Audio (make the video follow any soundtrack with lipsync), live token meter, Face Inpainting upgrades, automatic FL2VA / Ref2VA routing, a speed update and a big installer overhaul so please read all
8 new LTX 2.5 presets added

The 2-stage presets generate at 960x544 with 8 distilled steps, then apply the official LTX 2.5 latent spatial upscaler x2 and run 3 more refinement steps on a 1920x1088 canvas, and finally center crop to exact 1920x1080 - all with synchronized audio at 24 FPS and about 5 seconds by default
The Fast preset stays single stage at 960x544 for quick tests, and the First Last Frame preset is the official single-stage 1280x720 workflow : load a first and a last image and it generates the connected shots in between
The Expert preset exposes every stage on the canvas (Preprocess, Generate stage 1, Upscale and re-sampler stage 2, tiled Decode) with notes explaining each setting - the Text To Video and Image To Video presets are the compact subgraph versions, enter the subgraph when you want to fine tune

Audio To Video : load any audio, trim it with audio start and duration, and the video is generated to follow it (lipsync, action timing) - the encoded audio tokens stay frozen in both stages and your original trimmed audio is muxed into the output untouched, never decoded from latents - an optional start image is supported

Motion Track Control : drag points on the LTX Sparse Track Editor to sketch how things should move on your start image, and the motion track IC-LoRA animates exactly along those paths - a tracks preview video is saved next to the result so you can check the motion before committing to a long run

Text To Audio : audio only generation (sound effects, ambience, music cues) with the same LTX 2.5 model - no video VAE and no upscaler needed in this flow
Prompt enhancement (the official Gemma 4 E2B INT8 ConvRot enhancer) is off by default in every preset - enable it to expand short prompts into rich cinematic ones - and entering an LTX API key switches text encoding to the API to save VRAM
Models : one bundle for everything - open the latest model downloader and grab the LTX 2.5 Video Core Bundle : it now contains every model referenced by all ten of these LTX 2.5 presets : https://www.patreon.com/posts/114517862

LTX2.5 IC LoRA Pixel Spatial Upscaler presets fixed (FAST Distilled and Slower HQ) : the 4 helper nodes they need (LTX-2.5 Upscale Controls, Distilled Sigma Schedule, Prepare Video Chunks, Merge Video Chunks) are now part of our auto installed and managed by us extensions
Both upscaler presets also switched from the 24.46 GB BF16 text encoder to the 14.32 GB Gemma 4 12B INT8 ConvRot v2 one, so all ten LTX 2.5 presets now share the exact same text encoder and prompt enhancer - that is 10 GB less to download and one less model kept in memory when you switch between presets
New LTX2.5_Enchance_Prompt_Feed_For_LLMs.txt inside the Prompt_Generate_MiniMax_LTX_and_Demo_Materials folder : an official-aligned LTX 2.5 prompting guide for LLMs - give it to ChatGPT / Claude / Gemini, tell it what you want, and it writes properly formatted LTX 2.5 prompts for text / image / audio to video, multi-shot and IC-LoRA workflows
Optional Init Audio added to all six MiniMax H3 video presets (Text To Video, Image To Video, References To Video - both 8 Steps and 4x Speed) : select or upload any audio - or even a video file - in the new INIT AUDIO (OPTIONAL) node and the generated video follows that soundtrack exactly : lipsync, action timing, ambience - and keeps it as the exact final audio track

Leave it at (none - disabled) and the presets work exactly as before
Works with text only prompts, start frame and gallery references - by default the video duration automatically follows the audio length or set duration_mode to keep workflow duration
Init audio accepts video files with a visual trim panel - drag the handles to pick exactly which part of the soundtrack to use, nothing is re-encoded
Folder batches : put a same-name audio / video file next to each batch item and it is automatically used as that item's init audio (SAME-NAME INIT MEDIA)
Live MiniMax H3 token meter added to the reference gallery : prompt + references + resolution + duration are counted in real time against the 109k token budget so you see the usage before you queue - the meter also follows your init audio selection and trim window

Automatic FL2VA / Ref2VA routing : the MiniMax H3 video presets now pick the right checkpoint per prompt by themselves - single runs that have references and every folder batch item go through the Auto FL2VA / Ref2VA pipeline while plain text prompts keep the normal path - and the References To Video presets got an optional INIT IMAGE node : FL2VA uses it as the exact first frame, Ref2VA adds it as the starting frame picture reference (folder batch items ignore it and keep using their same-name init media and last frame continuation)

Face Inpainting upgrades : new faces to inpaint widget selects which faces get refined : 1 = biggest face (default), 2 = second biggest, 1,3 or all - faces are ranked biggest to smallest and each selected face is refined in its own pass and tagged in the tracking preview
New hallucination guard : every regenerated face crop is compared against its input and frames where H3 painted a neighbouring or different face over your subject are not pasted
Geometry lock is further improved and ON by default - removes the per-frame face shaking / tilting that the face pass could introduce
MiniMax H3 Text With References To Image preset also received the new face inpaint subgraph and a randomized seed

Speed : MiniMax H3 Sol-Attn sparse attention updated for SM89 (RTX 4000 series) and CUTLASS 4.7 in our maintained ComfyUI-TeaCache fork
Example on RTX 5090 : 1344x768 10 second video dropped from 450 seconds to 401 seconds with the updated speed optimizer
FLUX 2 Klein presets switched to my new INT8 ConvRot HQ models - almost BF16 quality but massively faster - auto download them with the latest model downloader : https://www.patreon.com/posts/114517862

Installer overhaul : custom nodes are now updated in-place instead of delete and re-clone so files downloaded inside node folders (for example ckpts) survive updates - a fresh clone replaces a node only when the in-place update fails
Leftovers of interrupted installer runs are cleaned automatically and staged copies use .disabled names so ComfyUI never imports duplicate nodes
Deleted venv is recreated in place without re-cloning ComfyUI - your models, outputs and user data are never touched - and the installer window now always stays open with a clear final status message
Python 3.10 support dropped because onnxruntime-gpu 1.27+ CUDA 13 build requires Python 3.11 or newer : menu now offers 3.11 / 3.12 (recommended) / 3.13
OpenCV conflicts between custom nodes fixed for good : all four OpenCV distributions are kept at one resolved version and opencv-contrib-python-headless is reinstalled last then verified - RES4LYF, controlnet_aux, Frame-Interpolation and rembg each pull different ones
onnxruntime-gpu upgraded to 1.27+ (CUDA runtime used by FantasyPortrait) and google-genai added to requirements
ComfyUI-Frame-Interpolation now installs without CuPy - it is not needed by the bundled RIFE workflows
RunPod and Massed Compute installers received the same in-place update and OpenCV normalization treatment
Windows_Run_GPU.bat renamed to Windows_Run_ComfyUI_Default.bat
To update : get latest zip file, extract and overwrite all, then run Windows_Install_Or_Update_ComfyUI.bat and also Windows_Custom_Nodes_Bundles_Installer.bat (bundle 100) - all necessary custom nodes and extensions automatically installed and many of them managed by us for extra security

16 August 2026 Update V122

Presets and custom nodes updated to improve face shaking effect that happens when automatic face inpainting activated
All presets now do have live preview (no increase in VRAM or generation duration) and fixed seed feature

16 August 2026 Update V120

MiniMax H3 References To Video 4x Speed - 24 FPS - 260808.json was broken with mistake and now fixed
Newest speed LoRA for MiniMax H3 is now in presets from LightX2V
Use latest Model downloader to get updated bundle : https://www.patreon.com/SECourses/posts/swarmui-auto-and-114517862

I have added automatic Face Inpainting feature to all MiniMax H3 presets
Get latest zip file, extract and overwrite all and run installer update bat file to update
Also use updated extra_model_paths.yaml if you are downloading models into SwarmUI

To understand how Face Inpainting works see this video clip
- https://www.reddit.com/r/comfyui/comments/1vpl5bb/secourses_comfyui_installer_and_ready_presets_now/
Live preview node added to all presets and does not cause any slowness or extra VRAM usage

14 August 2026 Update V117

Since you have requested, all MiniMax H3 presets modified and easy LoRA use added
- If you don't select any LoRA, workflow works just fine no issues so they don't break presets and how they work, they are optional

3 New presets added
- MiniMax Music 3 Text To Music - 260813.json - New model published today
- LTX2.5 IC LoRA Pixel Spatial Upscaler 2x - FAST Distilled 8 Steps - 260813.json
- LTX2.5 IC LoRA Pixel Spatial Upscaler 2x - Slower HQ - 260812.json

MiniMax Music 3 is reportedly lower quality than ACESTEP XL 1.5
- Our very advanced ACESTEP XL 1.5 Premium app below and it has full LoRA and DoRA training support
- App : https://www.patreon.com/SECourses/posts/ace-step-1-5-xl-157675060
- MiniMax Music 3 also missing necessary resources to be trainable
- MiniMax Music 3 prompt templates shared here : https://github.com/MiniMax-AI/MiniMax-Music3/tree/main/skills/music-caption-rewriter/templates
LTX 2.5 video upscaler preset is able to upscale any video, any resolution, any aspect ratio into 2x
- It is a very serious generative upscale so adds lots of details
- Therefore, try to upscale at 1 chunk to have better consistency
- Read preset information carefully after loading into ComfyUI
Our SECourses Premium Upscaler Pro app also fully supports LTX 2.5 now : https://www.patreon.com/SECourses/posts/secourses-pro-150202809
- It is standalone does not require ComfyUI and it auto downloads models when you first time use
To auto download necessary models of these presets, use our model downloader app and download from ComfyUI bundles : https://www.patreon.com/SECourses/posts/swarmui-auto-and-114517862

Moreover, I have converted the LTX 2.5 Int8 ConvRot myself higher quality and more accurate than officially published ones
- Someone detected one of the model is corrupt and broken - officially released ConvRot Int8 variant
- I converted them from BF16 versions after big research and experimentation
- So our model downloader app will download better quality Int8 ConvRot of LTX 2.5 models
Our Musubi Trainer convert to quant tab also now supporting LTX 2.5 Int8 ConvRot compile : https://www.patreon.com/SECourses/posts/secourses-musubi-137551634
LTX 2.5 video generation presets and bundles will be added soon hopefully but its quality lower than MiniMax H3
Get latest zip file, extract and overwrite all, then run Windows_Install_Or_Update_ComfyUI.bat and Windows_Custom_Nodes_Bundles_Installer.bat (select bundle 100) for updating all

12 August 2026 Update v116

To improve security, I have forked auto installed repos and bundle 100 repos and now they will be installed from secure fork
- Only few repos left as exception which are famous : mcmonkeyprojects , kijai , Lightricks
- I also did a massive deep security check of all auto installed and bundle 100 repos source codes and all are secure
MiniMax H3 presets got updated and now we have new features
Now when batch processing, you can dynamically set the duration of each prompt
- It will split the file name of the prompts with _ and if last split is a valid integer, it will be used as the duration of the prompt, otherwise whatever you set will be used
- Folder durations now use the final positive integer suffix only when the filename contains *: scene*7.txt → 7s; 200.txt, scene.txt, and scene_0.txt use the configured duration
- For example scene1.txt or 1.txt will use your set duration on the workflow meanwhile scene_1.txt will be set to 1 second duration dynamically
Added Continue from last frame beside Merge, disabled by default

Continuation automatically uses Ref2VA when references exist and FL2VA otherwise
Updated all MiniMax presets, extension UI, documentation, and the LLM prompt feed
- MiniMax_H3_Enchance_Prompt_Feed_For_LLMs.txt improved big time and includes latest changes and features as well
The most professional speed up LoRA maker LightX2V just published new speed LoRAs for the MiniMax H3
- https://github.com/ModelTC/Minimax-H3-Turbo

Our MiniMax H3 core bundles now auto downloads both FL2VA Turbo 8-step v1.0 and FL2VA Turbo 4-step v1.0 768p
- Use model downloader to auto download : https://www.patreon.com/SECourses/posts/swarmui-auto-and-114517862
All presets by default uses FL2VA Turbo 8-step v1.0 but you can switch it to FL2VA Turbo 4-step v1.0 768p
Older version 0.1 removed from downloads and presets so you can delete from LoRA folder if you downloaded
All presets exposes Shift value for Video and Audio seperately now
- So you can set to 6 / 3 if you switch to FL2VA Turbo 4-step v1.0 768p - default is 12 / 3
Sadly LoRAs only made for non-reference model atm but works on both models until they publish specific new LoRA for reference model
Some people asked me if video-latent continuation input is possible or not and sadly not possible so your best option is using continue from last frame
Get latest zip file, extract and overwrite all, then run Windows_Install_Or_Update_ComfyUI.bat and Windows_Custom_Nodes_Bundles_Installer.bat (select bundle 100) for updating all

11 August 2026 Update v113

RunPod and Massed Compute startup command changed so that the running ComfyUI instance won't be accessible by public IP anymore
- Updated files are : Massed_Compute_Instructions_READ.txt and RunPod_SimplePod_ComfyUI_Instructions_READ.txt
- Windows doesn't have such vulnerability it is fully locally running
Use Cloudflared secure HTTPs to access publicly when you are running on cloud services
- RunPod and Massed Compute installers auto installs Cloudflare
- Follow Massed_Compute_Instructions_READ.txt and RunPod_SimplePod_ComfyUI_Instructions_READ.txt
- Public access is optional, it will be default fully locally restricted running
When using unicode character containing references it was causing issues
- For example an image with Ü Ğ characters, this issue fixed and now works
Just run Windows_Install_Or_Update_ComfyUI.bat to update and always get latest zip file and overwrite older files

MiniMax H3 Infinite Video: Make 2-Hour Movies in ComfyUI (0-Shot)

Furkan Gözükara — Tue, 11 Aug 2026 02:30:55 +0000

Full tutorial link > https://www.youtube.com/watch?v=1580ZDX-60Q

Turn one folder of scene prompts into a long, coherent AI video with MiniMax H3 - locally, 0-shot and without babysitting every clip. This ComfyUI walkthrough shows how to match references, queue scenes, generate clips and automatically merge everything into one movie.
The opening is the raw workflow result. Then we rebuild it from installation to playback: models, presets, VRAM modes, prompt creation, folder batching, reference syntax, draft settings, troubleshooting, selective regeneration and consistency. It can scale to very long projects, including the 2-hour movie shown here.

COMFYUI INSTALLERS + PRESETS:

https://www.patreon.com/posts/comfyui-installers-presets-105023709

SWARMUI MODEL DOWNLOADER:

https://www.patreon.com/SECourses/posts/model-downloader-114517862

DISCORD HELP & SUPPORT:

https://discord.com/invite/software-engineering-courses-secourses-772774097734074388

Zip File Content Screenshots

Some Other Screenshots From Workflows

Ultra advanced multiple reference upload and mention system

Optional Low VRAM optimization with just a toggle enable disable

Trim and upload references feature

Automatically download necessary files ultra fast and SHA256 verified

Sana Labs published 4x speed up implementation

Video Chapters

[00:00:00] 0-shot generated movie showcase
[00:00:58] MiniMax H3 local workflow reveal
[00:01:10] Entire movies, prompts and scenes automated
[00:01:22] Audio references and folder-batch strategy
[00:01:40] Local desktop vs cloud deployment
[00:01:50] Download ComfyUI and the model downloader
[00:02:01] Fresh install and recommended Python 3.12
[00:02:17] Verify installation and install bundle 100
[00:02:37] Troubleshooting and support
[00:02:47] Final checks before launch
[00:02:57] Launch ComfyUI with run_gpu.bat
[00:03:11] 6 GB GPU support and speed tradeoffs
[00:03:22] Logs, errors and starting the preset
[00:03:33] Find the MiniMax H3 presets
[00:03:44] References-to-video 4x workflow
[00:03:54] Compilation, 20 steps and speed LoRA
[00:04:12] Change logs and Windows requirements
[00:04:24] Frequent workflow and custom-node updates
[00:04:36] Missing models and downloader setup
[00:04:48] Launch downloader and share model paths
[00:05:03] Core vs low-VRAM MiniMax H3 bundles
[00:05:16] INT4 vs recommended INT8 quality
[00:05:29] Options for 6-12 GB GPUs
[00:05:43] Select the correct models path
[00:05:55] Folder structure and model download
[00:06:07] Fix path warnings and select both VAEs
[00:06:23] Reference manager and default interface
[00:06:35] The enhanced prompt helper file
[00:06:47] Improve prompts with any major LLM
[00:06:59] ChatGPT setup with five voice attachments
[00:07:17] Example prompts and downloadable ZIP
[00:07:28] Prompt construction and easy referencing
[00:07:42] Upload the audio references
[00:07:52] Reference syntax and voice samples
[00:08:08] Listen to the supplied voice references
[00:08:18] Audio and image reference options
[00:08:28] Reorder image references by drag and drop
[00:08:38] Batch folder mode needs no manual prompt
[00:08:50] Set the folder path and draft resolution
[00:09:01] Aspect ratios and divisible dimensions
[00:09:15] Recommended 1344x768 for 16:9
[00:09:25] 30-second limit and 15-second sweet spot
[00:09:35] Best parameters: ready to run
[00:09:47] Run a quick 0.4 MP draft
[00:09:57] Automatic merging and queue monitoring
[00:10:08] Folder prompts and reference matching
[00:10:20] Load many references; use only the matches
[00:10:30] Per-generation reference limits
[00:10:40] Batch power for full videos and animations
[00:10:50] Iterate prompts with your favorite GPT
[00:11:00] Draft low resolution, review, then refine
[00:11:13] The key file for better prompts
[00:11:23] Single-clip mode and included presets
[00:11:33] Image, text and references-to-video presets
[00:11:46] Lightricks speed-up LoRA implementation
[00:11:59] LoRA version notes and future updates
[00:12:17] SwarmUI support and future advanced tutorial
[00:12:28] Read the docs and enable low-VRAM mode
[00:12:38] Save 15-40%+ VRAM
[00:12:54] Built-in node help and first output
[00:13:08] Regenerate only a weak section
[00:13:26] Play the generated result
[00:13:36] Current audio-reference limitation
[00:13:46] Improve voice and character consistency
[00:13:56] Match image IDs to characters
[00:14:06] Included reference and consistency guide
[00:14:16] 0-shot proof: ChatGPT authored all prompts, the movie script and rough draft
[00:14:35] Automated setup and broad GPU support
[00:14:45] Final requirements reminder and outro
MiniMax H3 supports 30-second segments; 15 seconds is the sweet spot. This demo uses 10-second scenes for easy review and regeneration.
For 16:9, use 1344x768. Draft around 0.4 MP, review the sequence, improve prompts, then render final quality.
Load up to 99 references; each prompt uses only matched IDs. Per generation: up to 3 audio, 3 video and 9 image references. Extra audio/video refs are discarded.
Use INT8 for speed and near-BF16 quality; INT4 is for lower VRAM. It runs on 6 GB GPUs, but more slowly. Low-VRAM mode saves about 15%, reaching 40%+ in some cases.
Audio references are less reliable than image references. If voice quality drops, try no audio reference. Map image IDs clearly for consistency.
Covers local Windows, Massed Compute, RunPod and SimplePod. Use a fresh install with Python 3.12 where recommended. Read requirements and changelogs; nodes and workflows update often.
If one scene fails, regenerate only that section. Join the Discord above for setup help.

MiniMaxH3 #ComfyUI #AIVideo #LocalAI #GenerativeAI

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