diffusionbee.com

Yes. DiffusionBee states that you can freely use the images you generate, provided your use complies with the CreativeML Open RAIL-M license. That condition is the part to check before publishing or selling output, because the license sets rules on how the images and the model may be used.

What the license condition means in practice

DiffusionBee's site answers the usage question directly: generated images are yours to use freely "as long as you comply with the CreativeML Open RAIL-M license." The license is attached to the Stable Diffusion model the app runs, so it travels with the outputs rather than being a separate DiffusionBee permission.

Open RAIL-M licenses generally permit broad use, including commercial use, while placing restrictions on certain applications — for example, uses that violate law or cause specified harms. The exact wording governs, so read the license text itself rather than relying on a summary.

A concrete example

If you generate an illustration and put it on a product page, that is the kind of use the license is designed to allow. If you plan to use output in a sensitive context — medical imagery, impersonation, or content that could mislead — check the license's use restrictions first, since those are where the "comply with the license" condition does real work.

Where the images are created, and why it matters

DiffusionBee runs generative AI locally on your Mac. According to the site, your prompts, models, and generated images never leave your device, and nothing is sent to the cloud. For usage questions this matters in two ways:

• There is no service-side terms-of-use layer adding restrictions beyond the model license.
• You keep the files yourself, so there is no account-based export or download limit described on the site.

System requirements and generation speed

The usage answer is independent of hardware, but the practical experience is not. DiffusionBee requires macOS 13.1 or higher and recommends Apple silicon machines; the site also says it works on Intel-based machines. On an 8GB M1 MacBook Air, the site cites roughly 30 seconds per image, with faster results on higher-spec computers.

Quick reference

Before you rely on an image

1. Confirm the output was generated by DiffusionBee on your machine, so the local-processing description applies.
2. Read the CreativeML Open RAIL-M license for the model version you used, focusing on the use restrictions.
3. Keep a record of the prompt and model if the image will be used commercially, so you can show how it was produced.
4. Re-check the license if you switch models or train a custom model, since a different base model may carry different terms.

The common sticking point is treating "free to use" as unconditional. DiffusionBee's own wording ties the permission to license compliance, so the license — not the app — is the document that decides edge cases.

DiffusionBee requires macOS 13.1 or higher, recommends Apple silicon Macs, and supports Intel-based machines as well. As a speed reference, the official site states that an 8GB M1 MacBook Air takes around 30 seconds to generate an image, with speeds much higher on more powerful computers. Those two facts are the core of the answer: check your macOS version and chip first, then treat the 30-second figure as a baseline for entry-level Apple silicon rather than a promise for every machine.

System requirements at a glance

The minimum OS version is a hard gate: on anything below macOS 13.1, the app is not listed as supported. The Apple silicon recommendation is about experience rather than compatibility, since Intel machines are explicitly supported.

How fast is image generation?

DiffusionBee's own FAQ gives one concrete data point: about 30 seconds per image on an 8GB M1 MacBook Air. The site adds that speeds are much higher on computers with higher specs, but it does not publish timings for other configurations. So the practical reading is:

• 8GB M1 MacBook Air: roughly 30 seconds per image, per the official example.
• Higher-spec machines: faster, though no specific numbers are published.
• Intel Macs: supported, but no official speed figure is provided, so expect to test your own machine.

Because generation happens entirely on your device, speed depends on your own hardware rather than network conditions. That also means the first run may feel slower while models load, and larger or more complex workflows can take longer than a single simple text-to-image pass.

What else the local setup affects

Running 100% offline has practical consequences beyond speed. Your prompts, models, and generated images never leave your device, which is useful if you care about privacy or work without reliable internet. The trade-off is that performance and storage are your machine's responsibility, not a server's.

The app bundles a broad toolset in one package: text to image, image to image, generative fill, image upscaling, image variants, video tools, control images, an illusion generator, and model training on your own images. Model training and video work are typically heavier than a single image generation, so the 30-second reference should not be applied to those tasks.

Can you use what you generate?

Yes. The site says you can freely use generated images as long as you comply with the CreativeML Open RAIL-M license. That license carries conditions, so read it before commercial use rather than assuming unrestricted rights.

A quick way to decide

• Your Mac runs macOS 13.1 or later and has Apple silicon: install it and expect performance in the range of the published example, better on higher-spec machines.
• Your Mac is Intel-based: it is supported, but no official speed figure exists, so try a few generations before committing to a heavier workflow.
• Your Mac is below macOS 13.1: the app is not supported on your system as listed.
• You need privacy or offline operation: the local, 100% offline design is the main reason to choose it over cloud tools.

The fastest way to settle the speed question for your own machine is to download the macOS build and generate a single test image, then compare that time against the roughly 30-second M1 MacBook Air reference.

DiffusionBee is a macOS application that runs Stable Diffusion and a broader set of AI art tools entirely on your own computer. You can use it to generate images from text, edit existing images with prompts, fill or remove objects, upscale resolution, create short videos, and even train custom models on your own pictures — all without sending prompts, models, or outputs to a cloud service. It is positioned as a free, local alternative for people who want AI image generation on a Mac without setting up a Python environment or renting GPU time. The main conditions: it requires macOS 13.1 or later, Apple silicon is recommended (though Intel Macs are supported), and the machine's specs determine how fast results appear.

What DiffusionBee actually is

DiffusionBee describes itself as "the fastest and easiest toolbox to run AI apps locally with Stable Diffusion." The key word is locally: the app runs generative AI on your hardware. According to the site's FAQ, images are generated on your computer and nothing is sent to the cloud. That has two practical consequences — your prompts and outputs stay private, and generation speed depends on your machine rather than a server queue.

The site gives a concrete benchmark: on an 8GB M1 MacBook Air, generating an image takes around 30 seconds, with faster speeds on higher-spec computers. Treat that as a reference point for older or lower-memory hardware rather than a guarantee.

What you can do with it

The app bundles a suite of tools rather than a single text-to-image box. Based on the product page, the main capabilities are:

The AI canvas is worth calling out separately: the site describes it as a "co-pilot" that blends prompting with human drawing, so you can sketch and refine rather than only typing descriptions. Model training is also fully local — the site says you can build custom models "with just a few clicks" to generate images featuring specific objects, people, or styles.

Who it fits and who should look elsewhere

DiffusionBee makes sense if you are on a Mac, want to try Stable Diffusion without configuring dependencies or paying for cloud generation, and care about keeping your images and prompts on your own device. The offline design and one-package toolset lower the setup barrier considerably.

It is a weaker fit if you are on Windows or Linux (the download offered is for macOS), if you need the fastest possible generation and don't have Apple silicon, or if you require cloud collaboration features — the local-first design is the point, not an add-on.

Practical notes before you install

• System requirement: macOS 13.1 or higher. Apple silicon is recommended; Intel machines are supported but will generally be slower.
• Licensing of outputs: the site states you can freely use generated images as long as you comply with the CreativeML Open RAIL-M license. Read that license if you plan commercial use, since it carries use restrictions.
• Performance expectations: the ~30-second figure comes from an 8GB M1 MacBook Air. Plan for longer waits on older hardware, and expect model training to be heavier than single-image generation.
• Storage and memory: local models and training data live on your disk, so budget space accordingly — the site does not publish specific size figures, so check within the app after install.

To get started, download the macOS build from the site, install it, and begin with Text to Image to confirm generation works on your machine before moving to heavier tools like model training or video.

DiffusionBee can train a custom image-generation model on your own images, entirely on your Mac. According to the site, you can "build custom models with just a few clicks, all 100% locally," and the result is a model tuned to generate images featuring specific objects, people, or styles. The conditions that matter: you need macOS 13.1 or later, Apple silicon is recommended, and training runs offline — your images and the resulting model never leave your device.

What training in DiffusionBee actually does

DiffusionBee is a local Stable Diffusion toolbox. Its training feature takes a set of your images and produces a model that biases generation toward what those images contain — a particular object, a person's likeness, or a visual style. You then use that model in the app's generation tools (text to image, image to image, and the AI canvas) instead of, or alongside, a base model.

The site frames this as one of several bundled tools: text to image, generative fill, video tools, image to image, upscaling, image variants, control images, illusion generation, and model training. Training is the one that changes the model itself rather than just the output.

Why train instead of just prompting

Prompting a general model can approximate a style or subject, but results drift between runs and fine details are hard to pin down. A trained model encodes the look of your dataset, so you spend less effort re-describing it in every prompt. The trade-off is preparation: you need a coherent image set and time for the training run, and a narrowly trained model is less flexible for unrelated subjects.

Step by step

The site describes the workflow in broad terms ("a few clicks," "100% locally") rather than documenting exact menu paths, so treat the steps below as the shape of the process and expect the labels in your installed version to differ slightly.

1. Confirm your machine qualifies. macOS 13.1 or higher; Apple silicon recommended. Training is heavier than generation, so a machine that generates an image in ~30 seconds (the site's 8GB M1 MacBook Air figure) will take considerably longer to train.
2. Install and open DiffusionBee, and download a base model through the app if you haven't already — training starts from an existing model.
3. Assemble your dataset. Collect images that consistently show the object, person, or style you want. Consistency matters more than volume: mixed subjects produce a muddled model.
4. Start a training job from the app's training section, pointing it at your image folder and naming the output model.
5. Wait for the run to finish. It executes locally, so the app must stay open and the Mac awake; closing the lid or letting it sleep can interrupt training.
6. Verify the result. Generate a few test images with the new model using prompts that describe your subject. If outputs look generic, the dataset was probably too varied or too small; if they look overfit and repetitive, it was too narrow.

A concrete example

Say you want to generate illustrations in a specific hand-drawn style. Gather 20–40 images in that style with similar line weight and coloring, train a model on them, then prompt "a lighthouse at dusk" with the new model selected. If the output carries the line weight and palette of your set, training worked; if it looks like the base model, add more consistent examples and retrain.

What you get, and the limits

Two practical caveats. First, the license applies to outputs, so check its terms before publishing anything derived from a trained model. Second, the site gives a generation-speed benchmark but no training-time estimate — plan for training to take substantially longer than a single image, and test with a small dataset before committing to a large one.

When training is the right choice

Train a model if you repeatedly need the same subject or style and want consistent results without long prompts. Skip it if you only need occasional one-off images — prompting a base model is faster to start and more flexible. And if your Mac is Intel-based or below macOS 13.1, check compatibility before investing time in dataset preparation.

No. DiffusionBee runs generative AI locally on your Mac, and according to the product site, your prompts, models, and generated images never leave your device. Images are generated on your computer, with nothing sent to the cloud. This applies to the app's core workflow, including text-to-image, image-to-image, generative fill, and custom model training, all of which the site describes as running locally.

What “local” means in practice

DiffusionBee is a desktop app for macOS that packages Stable Diffusion and related creative tools into one application. Instead of sending a request to a remote server and waiting for a result, the model runs on your own hardware. The site states this directly in two places: the homepage describes the app as running “100% offline” and letting you “own your AI,” and the FAQ answers “Where are the images generated?” with “Images are generated locally on your computer. Nothing is sent to the cloud.”

That design has a few practical consequences:

• Privacy by default. Your prompts and output files stay on your machine. There is no cloud account step in the generation flow described on the site.
• Offline capability. Because processing is local, the generation itself does not depend on a network connection.
• You manage the files. Since nothing is uploaded, your generated images and any custom models you train live in your local storage, and backing them up is your responsibility.

Conditions and limits to know before relying on this

The privacy benefit is real, but it comes with hardware and platform conditions that affect whether the app is a good fit.

The speed figure is a useful benchmark: if 30 seconds per image on an entry-level Apple silicon laptop is acceptable for your workflow, local generation is practical. If you need rapid batch output, the same local-only design that protects your data also means your hardware sets the ceiling.

Where the “no cloud” claim does and doesn’t cover you

The site’s statements cover generation and model training: prompts, models, and generated images stay on the device. A few boundaries are worth keeping in mind, based on what the page does and does not say:

• Image usage rights are separate from privacy. The FAQ says you can freely use generated images as long as you comply with the CreativeML Open RAIL-M license. Local processing does not change the license terms attached to the model.
• Downloading the app and models is a separate step. The privacy claim concerns generation, not the initial acquisition of the software or model files.
• The site does not describe any cloud sync, account, or upload feature for prompts or images, so there is no documented cloud path for your creative content in the workflow described.

A concrete example

Suppose you are drafting concept art for an unreleased product and cannot risk prompt text or early visuals appearing on a third-party server. With DiffusionBee on a supported Mac, you type the prompt, the model runs on your machine, and the resulting image is written locally. On an 8GB M1 MacBook Air, the site’s stated benchmark is roughly 30 seconds per image, so a session of a few dozen iterations is a matter of minutes rather than seconds. Nothing in that loop requires the prompt or the output to leave the computer.

Bottom line

If your priority is keeping prompts and generated images on your own device, DiffusionBee’s local-only design matches that goal, provided you are on macOS 13.1 or later and accept hardware-bound generation speeds. If you are on Windows, need the fastest possible throughput, or want cloud-based collaboration features, this app’s local model is a constraint rather than an advantage.