Alis Studio

A local, model-agnostic image-generation studio for Apple silicon — a clean, native-feeling web UI that runs text-to-image models entirely on your Mac with MLX. No cloud, no accounts, your images never leave your machine.

Ships with Krea 2 Turbo (pure-MLX), plus Qwen-Image and FLUX.1 (schnell / dev) via mflux. More models plug in as small backends — see Adding a model.

A real run in the app: pick a model, set resolution + aspect ratio, steps, seed, and more in the model-adaptive settings panel, then Generate. (Shown: Krea 2 Turbo 8-bit, 1024², 8-step Turbo.) Light and dark follow your system.

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Quickstart

Requires an Apple-silicon Mac (M1+) with ≥ 24 GB unified memory. On macOS use python3.

git clone https://github.com/avlp12/alis-studio.git
cd alis-studio
python3 -m pip install -r requirements.txt
python3 app.py            # opens http://localhost:7860 in your browser

Type a prompt, pick a model, click Generate. The first run downloads the model weights from Hugging Face (a few minutes); after that it's instant to start. A 1024² image takes ~50 s on an M3 Ultra (8-step Turbo; slower chips take longer).

Run as a native app

Prefer a real window to a browser tab? Run it as a native macOS app — its own title bar, dock icon, and menu, drawn with the system WKWebView (no browser or Chromium bundle):

python3 -m pip install pywebview
python3 desktop.py

Same UI and server; the window just hosts it natively. (alis-studio-desktop is also installed as a console script when you pip install.)

Standalone app — build a self-contained .dmg

Want something you can just double-click, with no Python or pip to set up? Build a self-contained app that bundles its own Python interpreter and every dependency inside the .app:

python3 -m pip install pillow   # optional — for the app icon
bash packaging/build_dmg.sh     # needs `uv`  →  https://docs.astral.sh/uv/

This produces dist/Alis Studio.app and dist/Alis-Studio-<version>.dmg (~400 MB). Open the DMG and drag Alis Studio to Applications, then launch it like any other app — nothing else to install. As always, the first image downloads the model weights from Hugging Face; those are far too large to ship inside a DMG.

The app is ad-hoc signed, which is all you need to run it on the Mac that built it. To hand the DMG to other machines you'd sign it with a Developer ID and notarize it — otherwise Gatekeeper blocks a downloaded, un-notarized app.

• Detailed settings — a model-adaptive panel on the right: resolution with aspect-ratio presets, steps, batch size, seed (with randomize), guidance, sampler, negative prompt. Each model exposes exactly the controls it supports; the panel renders itself from the backend.
• Model picker — the Model control in Settings opens a popover grouping every model and build; switch with a click, download (live progress) or delete weights inline, see disk usage.
• Live progress — a per-step bar as the model denoises. Light + dark follow your system.
• NSFW safety filter runs by default (pure-MLX, no PyTorch); toggle it with the shield icon.
• Bind to your LAN with ALIS_HOST=0.0.0.0 python3 app.py (only on networks you trust); change the port with ALIS_PORT=7861.

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Models

The Model section in Settings (and the whole settings panel) is built from whatever backends are installed — the UI discovers them at startup via /api/models and /api/catalog, so adding a model needs no UI changes. Each model's builds are grouped under its name; switch with Use, manage downloads inline, and the previous build is freed when you switch (two big models won't fit at once).

Model	Builds	Download
Krea 2 Turbo	8-bit (14.2 GB) · mixed-4/8 (9.8 GB). 8-step Turbo.	managed in-app (resumable, with progress)
Qwen-Image	8/4-bit, bf16. Apache-2.0, open.	auto on first use via mflux (~40 GB)
FLUX.1 schnell	8/4-bit, bf16. Apache-2.0 weights, gated repo.	auto on first use via mflux (~24 GB)
FLUX.1 dev	8/4-bit, bf16. Non-commercial, gated.	auto on first use via mflux (~24 GB)

Krea 2 Turbo ships explicit download management (our own resumable HTTP-bridge downloader with a live progress bar). The FLUX / Qwen-Image backends are handled by mflux, which downloads the weights on first Generate. The FLUX repos are gated — accept the license on Hugging Face and run huggingface-cli login first, or Alis Studio shows an actionable error. Qwen-Image is open and needs no access.

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Adding a model

Drop a file in studio/backends/, subclass Backend, and register it. The backend declares its settings (params) and downloadable builds (catalog); the UI renders the settings panel and the model manager from those declarations — no UI code to touch.

# studio/backends/mymodel.py
from .base import Backend

class MyBackend(Backend):
    id = "my-model"
    label = "My Model"
    variants = [{"id": "default", "label": "default"}]
    # settings the UI renders into the right-hand panel (see studio/backends/base.py for all types):
    params = [
        {"key": "resolution", "label": "Resolution", "type": "resolution", "group": "Output",
         "sizes": [512, 1024], "default_size": 1024, "aspects": ["1:1", "3:2", "16:9"],
         "default_aspect": "1:1", "min": 256, "max": 1536, "multiple": 16},
        {"key": "steps", "label": "Steps", "type": "int", "group": "Output", "min": 1, "max": 50, "default": 20},
        {"key": "guidance", "label": "Guidance", "type": "float", "group": "Sampling",
         "min": 0, "max": 12, "step": 0.5, "default": 7.5},
        {"key": "seed", "label": "Seed", "type": "seed", "group": "Sampling", "default": 0},
    ]
    catalog = [{"variant": "default", "label": "default", "size_gb": 6.0, "note": "fp16"}]

    @classmethod
    def is_available(cls):
        try:
            import my_package  # noqa
            return True
        except Exception:
            return False

    def generate(self, *, prompt, variant, params, step_callback):
        # params carries width, height, steps, seed, num_images + your custom keys.
        # call step_callback(step, total) per denoising step for the live progress bar.
        return [pil_image, ...]

    # optional — enables the model manager's download / delete / installed state:
    def is_installed(self, variant): ...
    def download(self, variant, progress): ...   # call progress(done_bytes, total_bytes)
    def delete(self, variant): ...

Then add it to studio/registry.py:

from .backends.mymodel import MyBackend
BACKENDS = [Krea2Backend, MyBackend]

Backends whose dependencies aren't installed are skipped automatically, so the app always runs with whatever you have.

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How it works

• app.py → studio/server.py — a Python standard-library HTTP server (zero web dependencies). Serves web/index.html and a small JSON/NDJSON API:
  • GET /api/models — registered models + their settings schema (drives the dropdown + settings panel)
  • POST /api/generate — streams NDJSON: one line per denoising step, then the base64 PNGs
  • GET /api/catalog · POST /api/download (streamed progress) · POST /api/delete — the model manager
• studio/registry.py — the list of available models.
• studio/backends/ — one file per model; each wraps a model behind the small Backend interface.
• studio/download.py — the resilient HTTP-bridge downloader (resumable, integrity-checked).

Generation is serialized (one GPU, one job at a time). The NSFW filter is applied at the app level to every backend's output, reusing Krea 2's pure-MLX classifier.

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Privacy & license

Everything runs locally — prompts and images never leave your Mac.

This application is MIT licensed (LICENSE). Each model carries its own license: the Krea 2 Turbo backend uses weights under the Krea 2 Community License (commercial use requires annual revenue under $1M; content filtering required for deployments — the built-in filter is on by default). You're responsible for complying with the license of any model you load.

Part of the Alis MLX line — see also krea2_alis_mlx.