A one-click AI video, image, and audio studio for creators. Maestro pairs a modern React UI with a powerful generation backend and adds a Director mode that uses an LLM to plan music videos and short films from a single prompt. Optimized for the latest LTX-2.3 models & LoRAs, with support for virtually all open weight models.
The flagship feature. Drop in an audio track or write a story; a local LLM plans every shot, writes screenplays/lyrics, generates start frames & keyframes with character consistency, polishes prompts per model & LoRA-specific prompting guides, and runs the full multi-clip generation. Two skills:
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Music Video — beat-aware shot planning aligned to your audio. The LLM analyzes BPM, sections (verse/chorus/bridge), and energy, then writes shots that hit the downbeats. Speaker transcription & diarization lets you name and target different voices or singers.
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Short Film — screenplay-driven scenes with named characters, dialogue, and continuity across cuts. Pacing-bias slider controls cut frequency.
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Auto Mode runs the entire pipeline end-to-end (analyze → plan → generate images → generate clips → combine). Manual mode lets you review and edit at every step.
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Director v2 architecture with structured shot planning, mode-specific prompt renderers, and a 3-pass refinement (screenplay → shot breakdown → per-model polish). Director v2 optimizes what the LLM is being asked to do across several passes, with each pass optimizing the LLM request for creativity (when writing the screenplay), structured outputs (when outputting JSON), and prompt refinement, which injects LoRA prompting guides into the context.
Detects your GPU, VRAM, and RAM on first launch and picks the right profile, quantization, VAE tiling, and VRAM safety coefficient. No more "Profile 1 vs 2 vs 4.5" guesswork. Power users still have full manual control under "Show advanced settings."
- OOM recovery banner auto-suggests lowering the VRAM headroom when a generation runs out, with one-click apply.
- Live download status during model setup ("Downloading transcription model (first use downloads ~300MB)..." instead of a vague spinner).
Direct access to every model and every knob:
- Video — LTX-2.3, Wan1/2, Hunyuan, and many more.
- Image — Flux 2 Klein 9B (default), Qwen Image Edit, and many more
- Audio — TTS: Kugelaudio, Qwen3 TTS. Music: ACE-Step. SFX: MMAudio
- Multi-clip generation with per-clip prompts, seamless overlapping (sliding window) transitions, and shared LoRAs
- Blend video Mode Remember Sora 1 blend mode, where you could overlap two videos, and use AI to blend them together?
- Frames Injection (KFI) for character continuity in long videos
- Sliding window for arbitrarily long generations
- Spatial upsampling, film grain, codec selection as post-processing options
Maestro auto-downloads llama-server (~600 MB one-time) and your chosen GGUF model on first use. Defaults to Gemma 4 4B (Recommended) — fast, capable, and runs comfortably on smaller GPUs. Auto-detects CUDA and binds the LLM to GPU when available.
- Pre-curated registry: Gemma 4 (2B / 4B / 26B MoE / 31B) and Qwen3.6 27B — uncensored/abliterated instruct variants tuned for creative prompting
- External providers also supported: OpenAI, Anthropic, custom OpenAI-compatible endpoints (currently experimental)
- Vision support so LLMs can enhance prompting based on reference images
- Auto-unloads after 60s idle to free VRAM for video gen
- Search, filter, and one-click install any LoRA from CivitAI without leaving Maestro
- LoRA update detection — Check button refreshes from CivitAI, shows update badges on outdated LoRAs
- My LoRAs view with filters for Updates and direct uninstall
- AI-generated LoRA prompting guides Helps remove the guesswork from LoRAs. AI generates LoRA guides when LoRA is downloaded based on CIVITAI and HuggingFace repos. The guides explain what each LoRA does and how to use it, provide prompt examples, and recommend weight settings that are automatically applied when LoRA is selected.
- Recommended weight ranges (sourced from CivitAI sidecars, HuggingFace, or fallback heuristics) shown directly on the weight sliders
- Multi-LoRA pack auto-extraction for archives that bundle several LoRAs
Three themes, switchable in Settings → System:
- Golden Hour (default) — warm cinematic palette with sunset-gradient CTAs and spotlight bezels
- Classic — the original cool charcoal palette with blue accents
- Onyx — minimalist monochrome, pure black with neutral grey surfaces
- Retake — re-roll a section of an existing video with a new prompt
- Outpaint — extend a video's frame in any direction
- Edit Anything — allows users to modify, add, or remove elements from existing videos using text prompts and In-Context LoRA (IC-LoRA) models
Multiple isolated output directories with a quick switcher in the sidebar. Useful for separating client projects, NSFW vs SFW, or experiments. Pinned and favorited outputs are tracked per workspace.
- NSFW mode is opt-in with a disclaimer step. Disabled by default. Gates uncensored model variants, NSFW LoRAs in the CivitAI browser, and the Settings → Services NSFW toggle.
- Experimental features gate hides power-user toggles (external API keys, Voice Reference, Inpaint, Restyle, Wan2GP Enhancer) by default for a focused first-launch experience.
View all past Director runs with their full state — clip plans, generated images, generated clips, polish diffs. Re-run any clip without re-running the whole pipeline.
| Minimum | Recommended | |
|---|---|---|
| OS | Windows 10/11 or Linux | Windows 11 |
| GPU | NVIDIA, 6 GB VRAM | NVIDIA RTX 3090 / 4090 / 5090, 24 GB+ VRAM |
| System RAM | 16 GB | 32 GB+ |
| Disk space | 150 GB free | 500 GB free (for full model collection) |
| Python | Auto-installed by Pinokio | — |
What to expect by GPU (rough ballpark — varies with model, resolution, and length):
| Your card | First run | A short clip after models are cached |
|---|---|---|
| 24 GB (3090 / 4090 / 5090) | smooth — everything runs | ~1–3 min |
| 12–16 GB (3060 12GB / 4070 / 4080) | good — auto-tune picks an offload profile | ~4–10 min |
| 6–8 GB | works, but expect heavy offloading | slow; stick to short/low-res clips |
The first video is always the slow one: install is ~10–20 min, then the first generation on each model downloads its weights (the default video model is ~18 GB). After that, weights are cached and only generation time applies. Maestro's auto-tune sizes the settings to your card on first launch so you don't have to.
⚠ AMD GPUs and macOS are not currently supported. The pipeline depends on CUDA and several NVIDIA-only kernels. MacOS support is in development.
⚠ Model downloads are large. A typical install pulls 50–100 GB of model weights on first launch. The full collection can exceed 300 GB. Make sure you have headroom on the drive where Pinokio is installed. However, only models requested during generation will be downloaded.
- Install Pinokio.
- In Pinokio, open the Discover tab and search for Maestro — or click the Download button on the Maestro repo page and paste the URL.
- Click Install. The launcher will:
- Create a Python virtual environment in
app/env/ - Install all Python dependencies (torch, xformers, transformers, fastapi, …)
- Build the React UI in
ui/
- Create a Python virtual environment in
- When install finishes, click Start. The first generation in each model triggers a one-time weight download.
The install (without model downloads) typically takes 10–20 minutes depending on internet speed. SAM 3.1 (used only for the experimental Inpaint feature) is not installed by default — install it on demand via Pinokio menu → "Install Inpaint Support (SAM 3.1)" if you want to use Inpaint.
Click Update in the launcher menu. This pulls the latest launcher scripts and app code, reinstalls any new Python dependencies, and rebuilds the React UI.
Click Reset to wipe the install and start over. Removes app/env/, ui/node_modules/, ui/dist/, and the SAM venv if installed. Model checkpoints in app/ckpts/ are NOT removed by default — delete them manually if you want a true fresh start.
After clicking Start, the launcher shows an Open Web UI button once the server is up.
- Sidebar — mode toggle (Studio / Director), model picker, prompt, LoRAs, advanced settings
- Main feed — generated outputs, dashboard, Director pipeline status
- Settings drawer (gear icon) — model visibility, performance auto-tune, services (LLM, API keys, NSFW, theme)
- Pinokio menu — Update, Reset, Install Inpaint Support, LoRA folder shortcuts
Maestro respects Pinokio's PINOKIO_SHARE_LOCAL environment variable. Set it to false (in the per-app or global ENVIRONMENT file) to bind the server to loopback only; set to true for LAN access. Pinokio's own daemon proxy is a separate concern that may also need to honor the variable depending on your setup.
Maestro is built on top of, and indebted to, the following projects:
- Wan2GP / WanGP by @deepbeepmeep — the entire generation pipeline. Maestro inherits WanGP's non-commercial license.
- LTX-Video by Lightricks — LTX-2 and LTX-2.3 distilled models.
- Wan 2.1 / 2.2 by Alibaba — text-to-video and image-to-video.
- Flux by Black Forest Labs — image generation.
- Qwen by Alibaba — image generation and LLMs.
- Gemma by Google — Gemma 4 LLM (default for Director mode).
- SAM by Meta — segmentation backbone for Inpaint.
- MMAudio — automatic ambient audio generation.
- CivitAI — LoRA browser and weight recommendations.
- llama.cpp — local LLM inference engine.
- Pinokio by @cocktailpeanut — the launcher framework.
- The original Pinokio Wan2GP launcher by @cocktailpeanut, which Maestro forks and extends.
Maestro is released under the WanGP Non-Commercial Evaluation License 1.1, inherited from the upstream Wan2GP project. See LICENSE for the summary and app/LICENSE.txt for the full text.
TL;DR: free to use and modify for non-commercial purposes; the outputs you generate are yours to use commercially (with attribution); commercial use of the software itself (including hosted services and APIs) requires a separate commercial license from the WanGP licensor.
Third-party models, weights, and components keep their own licenses — review them before redistributing. Notably, the seed-vc voice-conversion component is GPL-3.0, so it is distributed from its own repository (Blizaine/maestro-seedvc) and cloned into app/postprocessing/seedvc/ at install time rather than shipped in this tree. Other vendored components include BigVGAN (MIT), FlashVSR sparse-sage (Apache-2.0), and IndexTTS2 (bilibili model license).
Bug reports and feature requests: github.com/Blizaine/Maestro/issues.