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Depth-Any-Video estimates depth from an image or video you submit on Replicate. To try it, choose a file, confirm whether it is a video, keep the listed settings as a starting point, and submit a prediction. Replicate’s hosted run is the simplest documented path; the project also describes local Docker use, but the available documentation does not establish a minimum local GPU specification.
What Depth-Any-Video does
Replicate lists chenxwh/depth-any-video as a public model for video depth estimation. Depth estimation aims to infer how near or far parts of a scene are from visual input. The model page accepts an image or video file, so you can select the input mode that matches what you are submitting.
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The project readme describes a synthetic-data pipeline involving 40,000 video clips captured from diverse games, generative video diffusion priors, rotary position encoding, flow matching, mixed-duration training, and depth interpolation. These are the project’s descriptions of its approach, not independent validation of output accuracy or quality. The clip count is reported by the readme, not presented here as an audited dataset figure. The readme cites the 2024 preprint Depth Any Video with Scalable Synthetic Data (arXiv:2410.10815). Read the project readme.
What to prepare and which settings to use
The main upload field is input_image_or_video. Replicate’s current page lists the following settings and defaults; they describe that interface and are not universal recommendations or a tested tuning guide.
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| Control | Listed default or guidance | What to consider |
|---|---|---|
input_is_video |
True | Confirm that the mode matches your file; switch it for an image input. |
| Denoising steps | 3; page says 1–3 work fine | Start with the default unless you have a reason to change it. |
| Frames per forward pass | 32; frame count should be even | Keep the even-number requirement in mind if changing the count. |
| Decode chunk size | 16 | Leave at the listed default for an initial run. |
| Interpolation frames | 16 | Leave at the listed default for an initial run. |
| Overlap frames | 6 | Leave at the listed default for an initial run. |
| Maximum resolution | 1024 | The page lists this value; it does not establish that it is best for every input. |
| Seed | Control is available; default not stated | Set it only if you have a reason to control the seed. |
Replicate may update its model page, settings, or defaults. Check the live model interface before submitting if a specific control matters to your workflow.
How to submit a run on Replicate
- Open the model page. Go to chenxwh/depth-any-video on Replicate.
- Select your input. Upload a supported image or video in
input_image_or_videoand setinput_is_videoto match it. - Review the parameters. For a first attempt, use the interface’s listed defaults. If you change the frame count, follow the page’s guidance that it should be even.
- Submit the prediction. Use Replicate’s interface, or send an authenticated API request with the model’s version-pinned identifier and an input object. The API reference documents the request format and file inputs.
- Wait for the result state. An API prediction is asynchronous: it starts, processes, and then reaches a completed, failed, or canceled state. Check the prediction status rather than assuming the request returns a finished result immediately. Replicate’s learn-more guide explains the API lifecycle.
For API file inputs, Replicate accepts HTTP URLs or data URLs. Its API reference recommends HTTP URLs for files over 256 KB or for repeated use, and data URLs for small files when temporary use is preferable. API use requires a Replicate API token.
Rank #2
- Maximum video resolution: 1920 x 1080
- Maximum frame rate: 30 FPS
- Host interface: USB 3.0
- Height: 1"
- Width: 3.9"
Hosted Replicate use versus running it locally
The project readme says the model is open source and can be run locally with Docker; Replicate also shows Cog and Docker instructions. The two routes differ in setup and who supplies the compute, but the available documentation does not support a speed, quality, or total-cost ranking.
| Consideration | Hosted on Replicate | Local deployment |
|---|---|---|
| Setup | Use the model page or authenticated API. | Follow the project’s Docker or Replicate’s Cog and Docker instructions; local setup requires managing the deployment environment. |
| Compute | Replicate’s listing identifies A100 (80GB) hardware for its hosted service. | The reviewed documentation does not state a minimum local GPU specification or confirm compatibility with a particular consumer GPU. |
| Input and output handling | Supply a file through the interface or API and follow the prediction lifecycle. | Handle files and results within the local deployment; consult the project instructions for its setup details. |
| Cost | Replicate lists an estimated per-run charge; the amount varies with inputs and may change. | The available sources do not establish the total cost of local compute or operation. |
For most beginners, the hosted interface is the more direct way to try the model. Choose local deployment if you are prepared to work through Docker-based setup and arrange compatible compute yourself; the hosted A100 label alone does not tell you what local hardware will work. Project setup information.
Rank #3
- UPC: 735858352291
- Weight: 0.550 lbs
What to expect for cost and completion time
Replicate’s model page lists an estimated cost of about $0.093 per run, or roughly 10 runs per dollar, and says predictions typically complete within 67 seconds. These are service estimates, not guarantees: the page says cost depends on inputs and prediction time can vary significantly. Both figures and the listed hardware may change, so check the current model page before budgeting or planning a deadline.
The reviewed documentation does not establish a particular depth accuracy, temporal consistency, or output quality for your footage. Treat a completed run as a model output to evaluate for your own use, not as a promise that every scene will be interpreted correctly.
Quick Recap
Best Value
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Rank #4
- Design: Compact camera peripheral with dimensions of 90 x 25.8 x 25 mm, perfect for indoor security use
- Resolution: 1080p video capture resolution and 2 megapixel effective still resolution for clear and detailed images
- Connectivity: USB-C 3.1 Gen 1 connectors for easy integration with compatible devices
- Features: Stereoscopic depth technology, IR pass filter, and rolling shutter RGB sensor for enhanced depth quality and performance range
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