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camenduru /instantmesh:4f151757
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=camenduru/instantmesh
or set up a project from scratch
Install Replicate’s Node.js client library:
npm install replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:
import Replicate from "replicate";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});Run camenduru/instantmesh using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"camenduru/instantmesh:4f151757fd04d508b84f2192a17f58d11673971f05d9cb1fd8bd8149c6fc7cbb",
{
input: {
seed: 42,
image_path: "https://replicate.delivery/pbxt/Kkiy632mjGZRwBhjxlFIjo2tZBnioG7gKnIqID4BaaRMLHxO/hatsune_miku.png",
export_video: true,
sample_steps: 75,
export_texmap: true,
remove_background: true
}
}
);
// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"
// To write the file to disk:
fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:
pip install replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:
import replicateRun camenduru/instantmesh using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"camenduru/instantmesh:4f151757fd04d508b84f2192a17f58d11673971f05d9cb1fd8bd8149c6fc7cbb",
input={
"seed": 42,
"image_path": "https://replicate.delivery/pbxt/Kkiy632mjGZRwBhjxlFIjo2tZBnioG7gKnIqID4BaaRMLHxO/hatsune_miku.png",
"export_video": True,
"sample_steps": 75,
"export_texmap": True,
"remove_background": True
}
)
print(output)To learn more, take a look at the guide on getting started with Python.
Set the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run camenduru/instantmesh using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \
-H "Authorization: Bearer $REPLICATE_API_TOKEN" \
-H "Content-Type: application/json" \
-H "Prefer: wait" \
-d $'{
"version": "4f151757fd04d508b84f2192a17f58d11673971f05d9cb1fd8bd8149c6fc7cbb",
"input": {
"seed": 42,
"image_path": "https://replicate.delivery/pbxt/Kkiy632mjGZRwBhjxlFIjo2tZBnioG7gKnIqID4BaaRMLHxO/hatsune_miku.png",
"export_video": true,
"sample_steps": 75,
"export_texmap": true,
"remove_background": true
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Add a payment method to run this model.
Sign in with GitHub
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terms of service and privacy policy
Output
{
"completed_at": "2024-04-16T09:29:30.098892Z",
"created_at": "2024-04-16T09:24:46.668000Z",
"data_removed": false,
"error": null,
"id": "02vh42gxshrge0cewrwtdhq6yc",
"input": {
"seed": 42,
"image_path": "https://replicate.delivery/pbxt/Kkiy632mjGZRwBhjxlFIjo2tZBnioG7gKnIqID4BaaRMLHxO/hatsune_miku.png",
"export_video": true,
"sample_steps": 75,
"export_texmap": true,
"remove_background": true
},
"logs": "Downloading data from 'https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net.onnx' to file '/root/.u2net/u2net.onnx'.\n 0%| | 0.00/176M [00:00<?, ?B/s]\n 2%|▌ | 2.76M/176M [00:00<00:06, 27.6MB/s]\n 4%|█▋ | 7.79M/176M [00:00<00:04, 40.9MB/s]\n 7%|██▊ | 12.8M/176M [00:00<00:03, 45.2MB/s]\n 10%|███▊ | 17.9M/176M [00:00<00:03, 47.5MB/s]\n 13%|████▉ | 23.0M/176M [00:00<00:03, 48.7MB/s]\n 16%|██████ | 28.1M/176M [00:00<00:02, 49.4MB/s]\n 19%|███████▏ | 33.2M/176M [00:00<00:02, 50.0MB/s]\n 22%|████████▏ | 38.2M/176M [00:00<00:02, 50.0MB/s]\n 25%|█████████▎ | 43.2M/176M [00:00<00:02, 48.4MB/s]\n 27%|██████████▍ | 48.3M/176M [00:01<00:02, 49.2MB/s]\n 30%|███████████▌ | 53.3M/176M [00:01<00:02, 49.4MB/s]\n 33%|████████████▌ | 58.4M/176M [00:01<00:02, 49.9MB/s]\n 36%|█████████████▋ | 63.5M/176M [00:01<00:02, 50.2MB/s]\n 39%|██████████████▊ | 68.7M/176M [00:01<00:02, 50.6MB/s]\n 42%|███████████████▉ | 73.8M/176M [00:01<00:02, 50.7MB/s]\n 45%|█████████████████ | 78.9M/176M [00:01<00:01, 50.9MB/s]\n 48%|██████████████████▏ | 84.0M/176M [00:01<00:01, 50.9MB/s]\n 51%|███████████████████▏ | 89.1M/176M [00:01<00:01, 50.8MB/s]\n 54%|████████████████████▎ | 94.2M/176M [00:01<00:01, 50.9MB/s]\n 56%|█████████████████████▍ | 99.2M/176M [00:02<00:01, 49.2MB/s]\n 59%|███████████████████████ | 104M/176M [00:02<00:01, 49.7MB/s]\n 62%|████████████████████████▏ | 109M/176M [00:02<00:01, 50.1MB/s]\n 65%|█████████████████████████▍ | 115M/176M [00:02<00:01, 50.4MB/s]\n 68%|██████████████████████████▌ | 120M/176M [00:02<00:01, 50.6MB/s]\n 71%|███████████████████████████▋ | 125M/176M [00:02<00:01, 50.7MB/s]\n 74%|████████████████████████████▊ | 130M/176M [00:02<00:00, 50.9MB/s]\n 77%|█████████████████████████████▉ | 135M/176M [00:02<00:00, 50.9MB/s]\n 80%|███████████████████████████████ | 140M/176M [00:02<00:00, 50.6MB/s]\n 82%|████████████████████████████████▏ | 145M/176M [00:02<00:00, 50.6MB/s]\n 85%|█████████████████████████████████▎ | 150M/176M [00:03<00:00, 50.4MB/s]\n 88%|██████████████████████████████████▍ | 155M/176M [00:03<00:00, 50.3MB/s]\n 91%|███████████████████████████████████▌ | 160M/176M [00:03<00:00, 50.5MB/s]\n 94%|████████████████████████████████████▋ | 165M/176M [00:03<00:00, 50.7MB/s]\n 97%|█████████████████████████████████████▊ | 170M/176M [00:03<00:00, 50.6MB/s]\n100%|██████████████████████████████████████▉| 176M/176M [00:03<00:00, 50.9MB/s]\n0%| | 0.00/176M [00:00<?, ?B/s]\n100%|████████████████████████████████████████| 176M/176M [00:00<00:00, 828GB/s]\nSeed set to 42\n 0%| | 0/75 [00:00<?, ?it/s]\n 1%|▏ | 1/75 [00:00<00:44, 1.66it/s]\n 3%|▎ | 2/75 [00:00<00:22, 3.22it/s]\n 4%|▍ | 3/75 [00:00<00:15, 4.65it/s]\n 7%|▋ | 5/75 [00:01<00:10, 6.67it/s]\n 8%|▊ | 6/75 [00:01<00:09, 7.22it/s]\n 11%|█ | 8/75 [00:01<00:07, 8.42it/s]\n 13%|█▎ | 10/75 [00:01<00:07, 9.14it/s]\n 16%|█▌ | 12/75 [00:01<00:06, 9.59it/s]\n 19%|█▊ | 14/75 [00:01<00:06, 9.87it/s]\n 21%|██▏ | 16/75 [00:02<00:06, 9.79it/s]\n 24%|██▍ | 18/75 [00:02<00:05, 9.98it/s]\n 27%|██▋ | 20/75 [00:02<00:05, 10.11it/s]\n 29%|██▉ | 22/75 [00:02<00:05, 10.18it/s]\n 32%|███▏ | 24/75 [00:02<00:04, 10.26it/s]\n 35%|███▍ | 26/75 [00:03<00:04, 10.29it/s]\n 37%|███▋ | 28/75 [00:03<00:04, 10.02it/s]\n 40%|████ | 30/75 [00:03<00:04, 10.15it/s]\n 43%|████▎ | 32/75 [00:03<00:04, 10.22it/s]\n 45%|████▌ | 34/75 [00:03<00:04, 10.22it/s]\n 48%|████▊ | 36/75 [00:04<00:03, 10.11it/s]\n 51%|█████ | 38/75 [00:04<00:03, 10.03it/s]\n 53%|█████▎ | 40/75 [00:04<00:03, 10.16it/s]\n 56%|█████▌ | 42/75 [00:04<00:03, 10.21it/s]\n 59%|█████▊ | 44/75 [00:04<00:03, 10.22it/s]\n 61%|██████▏ | 46/75 [00:05<00:02, 10.21it/s]\n 64%|██████▍ | 48/75 [00:05<00:02, 10.00it/s]\n 67%|██████▋ | 50/75 [00:05<00:02, 10.01it/s]\n 69%|██████▉ | 52/75 [00:05<00:02, 10.09it/s]\n 72%|███████▏ | 54/75 [00:05<00:02, 10.20it/s]\n 75%|███████▍ | 56/75 [00:06<00:01, 10.21it/s]\n 77%|███████▋ | 58/75 [00:06<00:01, 9.91it/s]\n 80%|████████ | 60/75 [00:06<00:01, 10.02it/s]\n 83%|████████▎ | 62/75 [00:06<00:01, 10.06it/s]\n 85%|████████▌ | 64/75 [00:06<00:01, 10.13it/s]\n 88%|████████▊ | 66/75 [00:07<00:00, 10.13it/s]\n 91%|█████████ | 68/75 [00:07<00:00, 10.10it/s]\n 93%|█████████▎| 70/75 [00:07<00:00, 10.20it/s]\n 96%|█████████▌| 72/75 [00:07<00:00, 10.27it/s]\n 99%|█████████▊| 74/75 [00:07<00:00, 10.33it/s]\n100%|██████████| 75/75 [00:07<00:00, 9.51it/s]\n/src/predict.py:41: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\nshow_image = torch.from_numpy(show_image) # (960, 640, 3)\n/tmp/tmpkazgrgd3.obj\n 0%| | 0/6 [00:00<?, ?it/s]/content/InstantMesh/src/models/geometry/render/neural_render.py:51: UserWarning: Using torch.cross without specifying the dim arg is deprecated.\nPlease either pass the dim explicitly or simply use torch.linalg.cross.\nThe default value of dim will change to agree with that of linalg.cross in a future release. (Triggered internally at ../aten/src/ATen/native/Cross.cpp:63.)\nface_normals = torch.cross(v1 - v0, v2 - v0)\n 17%|█▋ | 1/6 [00:00<00:02, 2.14it/s]\n 33%|███▎ | 2/6 [00:00<00:01, 2.49it/s]\n 50%|█████ | 3/6 [00:01<00:01, 2.60it/s]\n 67%|██████▋ | 4/6 [00:01<00:00, 2.67it/s]\n 83%|████████▎ | 5/6 [00:01<00:00, 2.71it/s]\n100%|██████████| 6/6 [00:02<00:00, 2.73it/s]\n100%|██████████| 6/6 [00:02<00:00, 2.65it/s]\nVideo saved to /tmp/tmpkazgrgd3.mp4\nMesh with texmap saved to /tmp/tmpkazgrgd3.obj",
"metrics": {
"predict_time": 49.499111,
"total_time": 283.430892
},
"output": [
"https://replicate.delivery/pbxt/f309uxIE1MQbcCazIYuIA66VVpdKLrCOHyfccG4fUfmmv9rKB/mv_show_images.png",
"https://replicate.delivery/pbxt/Thfx1IYFayX0Aa5FdWjuIwpYxsnPr6eSvLEgfZpiRfznv9rKB/tmpkazgrgd3.mp4",
"https://replicate.delivery/pbxt/WNSvPfczP5RDTKmSsPL4x2WnYABPPDnnyMolpxHydNe5bfVlA/tmpkazgrgd3.obj",
"https://replicate.delivery/pbxt/wl1tfZK2f0sUREXGfOrrlG623ekereReIHFLuebloexHy3erKB/tmpkazgrgd3.mtl",
"https://replicate.delivery/pbxt/zKBd46VI8QYIPpbIFo5aFxpLr0Kn0SME29rwvfuSufV5bfVlA/tmpkazgrgd3.png"
],
"started_at": "2024-04-16T09:28:40.599781Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/02vh42gxshrge0cewrwtdhq6yc",
"cancel": "https://api.replicate.com/v1/predictions/02vh42gxshrge0cewrwtdhq6yc/cancel"
},
"version": "4f151757fd04d508b84f2192a17f58d11673971f05d9cb1fd8bd8149c6fc7cbb"
}Generated in
Downloading data from 'https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net.onnx' to file '/root/.u2net/u2net.onnx'.
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/src/predict.py:41: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)
show_image = torch.from_numpy(show_image) # (960, 640, 3)
/tmp/tmpkazgrgd3.obj
0%| | 0/6 [00:00<?, ?it/s]/content/InstantMesh/src/models/geometry/render/neural_render.py:51: UserWarning: Using torch.cross without specifying the dim arg is deprecated.
Please either pass the dim explicitly or simply use torch.linalg.cross.
The default value of dim will change to agree with that of linalg.cross in a future release. (Triggered internally at ../aten/src/ATen/native/Cross.cpp:63.)
face_normals = torch.cross(v1 - v0, v2 - v0)
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67%|██████▋ | 4/6 [00:01<00:00, 2.67it/s]
83%|████████▎ | 5/6 [00:01<00:00, 2.71it/s]
100%|██████████| 6/6 [00:02<00:00, 2.73it/s]
100%|██████████| 6/6 [00:02<00:00, 2.65it/s]
Video saved to /tmp/tmpkazgrgd3.mp4
Mesh with texmap saved to /tmp/tmpkazgrgd3.obj