Free Depth Map Generator

A depth map is a grayscale image where each pixel encodes how far that part of the scene is from the camera, so brightness stands in for distance. In this tool, near objects are rendered bright and far objects dark. Depth maps are widely used to create 3D and parallax effects, to drive ControlNet depth conditioning in AI image generators like Stable Diffusion, to simulate portrait-mode background blur, and to build displacement maps and masks for compositing. They give artists and developers a fast way to add a sense of geometry and distance to an otherwise flat photo.

Yes, it is completely free with no signup, no account, and no usage limits. Because the Depth Anything model runs on your own device through Transformers.js instead of a paid cloud API, there are no per-image costs to pass on. You can generate as many depth maps as you want, as often as you want, without a credit card, an API key, or a rate limit.

No. The entire process, loading the model and running depth estimation, happens locally in your browser using Transformers.js and WebAssembly. After the model is downloaded once, your image is processed on your own device with zero network requests carrying it. Nothing is uploaded, logged, or stored, which makes this safe for unpublished photos and confidential work. You can confirm this by opening the Network tab in your browser DevTools while you generate a depth map.

It uses Depth Anything, a state-of-the-art monocular depth-estimation model, specifically the small variant exported to ONNX for the web. Monocular means it estimates depth from a single ordinary photo, with no special depth camera, stereo pair, or LiDAR required. The model runs through Hugging Face Transformers.js, which executes it in your browser via ONNX Runtime compiled to WebAssembly. Both Transformers.js and the model are released under the permissive Apache 2.0 license.

ControlNet has a depth conditioning mode that guides an image generator to follow the geometry of a reference depth map. Generate a depth map here, download the PNG, then load it as the control image in a ControlNet depth workflow in tools like Stable Diffusion, ComfyUI, or Automatic1111. The generator will then respect the foreground and background layout of your original photo while it creates new content. This is a popular way to keep composition and perspective consistent across AI-generated variations.

This tool follows the common convention where higher brightness means closer to the camera and lower brightness means farther away, so the foreground pops as white and the background fades to black. Some pipelines expect the opposite convention (near dark, far bright), so if a downstream tool looks inverted you can simply invert the PNG in any image editor. The important thing is that the relative ordering of depth is preserved, which is what 3D, parallax, and ControlNet workflows rely on.

You can upload common raster image formats including JPG, PNG, WebP, BMP, and GIF. The tool reads the image directly in your browser, so there is no format conversion step on a server. For the cleanest results, use a sharp, well-lit photo with a clear separation between the foreground subject and the background. Screenshots, product photos, portraits, and landscape shots all work well.

No installation is needed. It is a pure web tool that runs in any modern browser. The only thing that downloads is the Depth Anything model itself, which Transformers.js fetches on first use and caches in your browser. There is no extension, no desktop app, and no Python or Node environment to set up.

The Depth Anything small model is roughly 50MB and downloads once from the Hugging Face Hub on your first use. After that it is cached by your browser, so subsequent runs skip the download and start almost instantly. The first depth map on a page takes a little longer while the model initializes, and each run after that is faster because the model already lives in memory.

Once the model has been downloaded and cached, depth generation runs without an internet connection because everything happens on-device. It also works on mobile browsers that support WebAssembly, though phones are slower than a laptop or desktop, and very large images may be heavy on memory. For the smoothest experience, load the model once over Wi-Fi and keep input images to a reasonable size on mobile.

The depth is relative, not metric, so it tells you which parts of a scene are nearer or farther but does not give a true distance in meters. Monocular models like Depth Anything are excellent for visual and creative uses such as parallax, background blur, and ControlNet conditioning, where relative ordering is what matters. If you need precise physical measurements you would use a calibrated stereo camera, structured light, or a LiDAR sensor instead.

Depth estimation runs on your CPU through WebAssembly, so the time and memory needed grow with the number of pixels. Images larger than about 1536 pixels on the long edge can take noticeably longer and use more memory, especially on phones or older machines. If a large image is slow, resize it down before uploading; the depth map at a smaller resolution is usually more than enough for parallax, masking, and ControlNet, and you can upscale the result if needed.

Yes. The depth maps you generate are derived from your own images, and the tooling is open source under the Apache 2.0 license, which permits commercial use. You are responsible for having the rights to the input photos you process, but the model and library themselves place no restriction on commercial output. This makes the tool suitable for client work, product imagery, and creative projects.

A monocular depth map is inferred by a neural network from a single photo, learning cues like size, occlusion, texture, and perspective to guess relative distance. Stereo depth compares two cameras to triangulate distance, and LiDAR measures distance directly with laser time-of-flight, so both of those can produce metric depth in real units. Monocular depth from Depth Anything trades that physical precision for the convenience of working from any ordinary image, which is ideal for creative and web workflows.