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Input
Run this model in Node.js with one line of code:
npx create-replicate --model=camenduru/grm
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/grm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"camenduru/grm:8c489b23ddc6a9b0484f8b6f55f9b24fd792a84a6715d02f7e3ec63325bae4f0",
{
input: {
seed: 21,
model: "Zero123++ v1.2",
fuse_mesh: true,
input_image: "https://replicate.delivery/pbxt/Kf2I8ezAPJ9a6YZJUnDkoGq7urlPtjrA5hRS02D0knxS2KrW/dragon2.png"
}
}
);
// 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/grm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"camenduru/grm:8c489b23ddc6a9b0484f8b6f55f9b24fd792a84a6715d02f7e3ec63325bae4f0",
input={
"seed": 21,
"model": "Zero123++ v1.2",
"fuse_mesh": True,
"input_image": "https://replicate.delivery/pbxt/Kf2I8ezAPJ9a6YZJUnDkoGq7urlPtjrA5hRS02D0knxS2KrW/dragon2.png"
}
)
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/grm 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": "camenduru/grm:8c489b23ddc6a9b0484f8b6f55f9b24fd792a84a6715d02f7e3ec63325bae4f0",
"input": {
"seed": 21,
"model": "Zero123++ v1.2",
"fuse_mesh": true,
"input_image": "https://replicate.delivery/pbxt/Kf2I8ezAPJ9a6YZJUnDkoGq7urlPtjrA5hRS02D0knxS2KrW/dragon2.png"
}
}' \
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": "2024-03-30T16:52:11.872009Z",
"created_at": "2024-03-30T16:51:45.995453Z",
"data_removed": false,
"error": null,
"id": "rgfiruzbto74d7mhkfq6llnfwm",
"input": {
"seed": 21,
"model": "Zero123++ v1.2",
"fuse_mesh": true,
"input_image": "https://replicate.delivery/pbxt/Kf2I8ezAPJ9a6YZJUnDkoGq7urlPtjrA5hRS02D0knxS2KrW/dragon2.png"
},
"logs": "Running image-to-3d with seed 21...\n 0%| | 0/30 [00:00<?, ?it/s]\n 7%|▋ | 2/30 [00:00<00:02, 9.99it/s]\n 13%|█▎ | 4/30 [00:00<00:02, 10.24it/s]\n 20%|██ | 6/30 [00:00<00:02, 9.76it/s]\n 27%|██▋ | 8/30 [00:00<00:02, 10.08it/s]\n 33%|███▎ | 10/30 [00:00<00:01, 10.21it/s]\n 40%|████ | 12/30 [00:01<00:01, 10.23it/s]\n 47%|████▋ | 14/30 [00:01<00:01, 10.30it/s]\n 53%|█████▎ | 16/30 [00:01<00:01, 10.34it/s]\n 60%|██████ | 18/30 [00:01<00:01, 10.03it/s]\n 67%|██████▋ | 20/30 [00:01<00:00, 10.11it/s]\n 73%|███████▎ | 22/30 [00:02<00:00, 10.19it/s]\n 80%|████████ | 24/30 [00:02<00:00, 10.20it/s]\n 87%|████████▋ | 26/30 [00:02<00:00, 10.26it/s]\n 93%|█████████▎| 28/30 [00:02<00:00, 9.94it/s]\n100%|██████████| 30/30 [00:02<00:00, 10.01it/s]\n100%|██████████| 30/30 [00:02<00:00, 10.11it/s]\nIntegrate images into the TSDF volume.\n 0%| | 0/200 [00:00<?, ?it/s]\n 0%| | 1/200 [00:00<00:26, 7.58it/s]\n 2%|▏ | 4/200 [00:00<00:10, 18.13it/s]\n 4%|▍ | 9/200 [00:00<00:06, 29.80it/s]\n 6%|▋ | 13/200 [00:00<00:07, 24.91it/s]\n 8%|▊ | 16/200 [00:00<00:07, 25.94it/s]\n 10%|▉ | 19/200 [00:00<00:08, 21.92it/s]\n 11%|█ | 22/200 [00:01<00:08, 19.84it/s]\n 12%|█▎ | 25/200 [00:01<00:08, 20.77it/s]\n 15%|█▌ | 30/200 [00:01<00:07, 23.61it/s]\n 16%|█▋ | 33/200 [00:01<00:07, 23.33it/s]\n 18%|█▊ | 36/200 [00:01<00:06, 24.62it/s]\n 20%|█▉ | 39/200 [00:01<00:06, 25.79it/s]\n 21%|██ | 42/200 [00:01<00:07, 22.56it/s]\n 22%|██▎ | 45/200 [00:01<00:06, 22.93it/s]\n 24%|██▍ | 49/200 [00:02<00:06, 23.51it/s]\n 26%|██▌ | 52/200 [00:02<00:06, 24.18it/s]\n 28%|██▊ | 55/200 [00:02<00:05, 24.87it/s]\n 29%|██▉ | 58/200 [00:02<00:05, 25.32it/s]\n 31%|███ | 62/200 [00:02<00:05, 25.29it/s]\n 32%|███▎ | 65/200 [00:02<00:05, 25.24it/s]\n 34%|███▍ | 68/200 [00:02<00:05, 25.60it/s]\n 36%|███▌ | 72/200 [00:02<00:04, 28.64it/s]\n 38%|███▊ | 76/200 [00:03<00:04, 27.01it/s]\n 40%|███▉ | 79/200 [00:03<00:04, 26.65it/s]\n 41%|████ | 82/200 [00:03<00:05, 23.33it/s]\n 42%|████▎ | 85/200 [00:03<00:04, 24.28it/s]\n 44%|████▍ | 88/200 [00:03<00:04, 24.93it/s]\n 46%|████▌ | 92/200 [00:03<00:03, 28.13it/s]\n 48%|████▊ | 96/200 [00:03<00:03, 30.36it/s]\n 50%|█████ | 100/200 [00:04<00:03, 27.14it/s]\n 52%|█████▏ | 104/200 [00:04<00:03, 29.18it/s]\n 54%|█████▍ | 108/200 [00:04<00:03, 26.40it/s]\n 56%|█████▌ | 111/200 [00:04<00:03, 26.15it/s]\n 57%|█████▋ | 114/200 [00:04<00:03, 26.57it/s]\n 59%|█████▉ | 118/200 [00:04<00:03, 26.03it/s]\n 60%|██████ | 121/200 [00:04<00:03, 26.25it/s]\n 62%|██████▎ | 125/200 [00:04<00:02, 28.84it/s]\n 64%|██████▍ | 129/200 [00:05<00:02, 31.05it/s]\n 66%|██████▋ | 133/200 [00:05<00:02, 27.33it/s]\n 68%|██████▊ | 136/200 [00:05<00:02, 27.21it/s]\n 70%|███████ | 140/200 [00:05<00:02, 29.15it/s]\n 72%|███████▏ | 143/200 [00:05<00:02, 25.60it/s]\n 73%|███████▎ | 146/200 [00:05<00:02, 25.64it/s]\n 75%|███████▌ | 150/200 [00:05<00:01, 28.53it/s]\n 76%|███████▋ | 153/200 [00:05<00:01, 28.36it/s]\n 78%|███████▊ | 156/200 [00:06<00:01, 24.70it/s]\n 80%|████████ | 160/200 [00:06<00:01, 27.08it/s]\n 82%|████████▏ | 163/200 [00:06<00:01, 26.99it/s]\n 83%|████████▎ | 166/200 [00:06<00:01, 27.05it/s]\n 84%|████████▍ | 169/200 [00:06<00:01, 24.02it/s]\n 87%|████████▋ | 174/200 [00:06<00:00, 29.33it/s]\n 89%|████████▉ | 178/200 [00:06<00:00, 26.24it/s]\n 91%|█████████ | 182/200 [00:07<00:00, 28.56it/s]\n 94%|█████████▎| 187/200 [00:07<00:00, 32.72it/s]\n 96%|█████████▌| 191/200 [00:07<00:00, 29.52it/s]\n 98%|█████████▊| 196/200 [00:07<00:00, 33.36it/s]\n100%|██████████| 200/200 [00:07<00:00, 26.59it/s]\nExtract a triangle mesh from the volume and export it.\n[Open3D DEBUG] [ClusterConnectedTriangles] Compute triangle adjacency\n[Open3D DEBUG] [ClusterConnectedTriangles] Done computing triangle adjacency\n[Open3D DEBUG] [ClusterConnectedTriangles] Done clustering, #clusters=46060\n[Mesh loading] v: torch.Size([70902, 3]), f: torch.Size([141445, 3])\n[Mesh loading] vn: torch.Size([70902, 3]), fn: torch.Size([141445, 3])\ntensor(0.2557, device='cuda:0') tensor(0.7722, device='cuda:0')",
"metrics": {
"predict_time": 25.860297,
"total_time": 25.876556
},
"output": [
"https://replicate.delivery/pbxt/cX5JxYRwl1aMLdHzrhJQXrUwOOlIQMzoU5llxBr9mI4O1XpE/gs.mp4",
"https://replicate.delivery/pbxt/JkBYe7jHeMgezI0IP4ycCOjCaSw80EeHFLJUGuwonBbsT9VKB/mesh.glb",
"https://replicate.delivery/pbxt/hXZRhRPj81YuAhRzyteGb6vkmBk8fGy2G23G3h02oTb7UfKlA/gs_vis.ply",
"https://replicate.delivery/pbxt/0dVVyRrdrfVzfUzMEIcpuqjeJjYnJPwxsmF72AzwEqg2peVKB/gs.ply"
],
"started_at": "2024-03-30T16:51:46.011712Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/rgfiruzbto74d7mhkfq6llnfwm",
"cancel": "https://api.replicate.com/v1/predictions/rgfiruzbto74d7mhkfq6llnfwm/cancel"
},
"version": "8c489b23ddc6a9b0484f8b6f55f9b24fd792a84a6715d02f7e3ec63325bae4f0"
}Generated in
Running image-to-3d with seed 21...
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Extract a triangle mesh from the volume and export it.
[Open3D DEBUG] [ClusterConnectedTriangles] Compute triangle adjacency
[Open3D DEBUG] [ClusterConnectedTriangles] Done computing triangle adjacency
[Open3D DEBUG] [ClusterConnectedTriangles] Done clustering, #clusters=46060
[Mesh loading] v: torch.Size([70902, 3]), f: torch.Size([141445, 3])
[Mesh loading] vn: torch.Size([70902, 3]), fn: torch.Size([141445, 3])
tensor(0.2557, device='cuda:0') tensor(0.7722, device='cuda:0')