CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model
Run this model in Node.js with one line of code:
npm install replicateREPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
import Replicate from "replicate";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});Run camenduru/crm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"camenduru/crm:9a17c48c7e8a6c1b75788b454a234c13833277afafda546e014e59c3837f8932",
{
input: {
seed: 1234,
scale: 5.5,
steps: 30,
image_path: "https://replicate.delivery/pbxt/KYNDxJM3CvnQzh7eKtaiTfhKXkQBdWJQAhj4jYhJNlGG0SlA/3D%E5%8D%A1%E9%80%9A%E7%8B%97.webp",
back_groud_color: "#7F7F7F",
foreground_ratio: 1,
background_choice: "Auto Remove background"
}
}
);
console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateREPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
import replicateRun camenduru/crm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"camenduru/crm:9a17c48c7e8a6c1b75788b454a234c13833277afafda546e014e59c3837f8932",
input={
"seed": 1234,
"scale": 5.5,
"steps": 30,
"image_path": "https://replicate.delivery/pbxt/KYNDxJM3CvnQzh7eKtaiTfhKXkQBdWJQAhj4jYhJNlGG0SlA/3D%E5%8D%A1%E9%80%9A%E7%8B%97.webp",
"back_groud_color": "#7F7F7F",
"foreground_ratio": 1,
"background_choice": "Auto Remove background"
}
)
print(output)To learn more, take a look at the guide on getting started with Python.
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run camenduru/crm 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": "9a17c48c7e8a6c1b75788b454a234c13833277afafda546e014e59c3837f8932",
"input": {
"seed": 1234,
"scale": 5.5,
"steps": 30,
"image_path": "https://replicate.delivery/pbxt/KYNDxJM3CvnQzh7eKtaiTfhKXkQBdWJQAhj4jYhJNlGG0SlA/3D%E5%8D%A1%E9%80%9A%E7%8B%97.webp",
"back_groud_color": "#7F7F7F",
"foreground_ratio": 1,
"background_choice": "Auto Remove background"
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/camenduru/crm@sha256:9a17c48c7e8a6c1b75788b454a234c13833277afafda546e014e59c3837f8932 \
-i 'seed=1234' \
-i 'scale=5.5' \
-i 'steps=30' \
-i 'image_path="https://replicate.delivery/pbxt/KYNDxJM3CvnQzh7eKtaiTfhKXkQBdWJQAhj4jYhJNlGG0SlA/3D%E5%8D%A1%E9%80%9A%E7%8B%97.webp"' \
-i 'back_groud_color="#7F7F7F"' \
-i 'foreground_ratio=1' \
-i 'background_choice="Auto Remove background"'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 r8.im/camenduru/crm@sha256:9a17c48c7e8a6c1b75788b454a234c13833277afafda546e014e59c3837f8932curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 1234, "scale": 5.5, "steps": 30, "image_path": "https://replicate.delivery/pbxt/KYNDxJM3CvnQzh7eKtaiTfhKXkQBdWJQAhj4jYhJNlGG0SlA/3D%E5%8D%A1%E9%80%9A%E7%8B%97.webp", "back_groud_color": "#7F7F7F", "foreground_ratio": 1, "background_choice": "Auto Remove background" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Add a payment method to run this model.
Each run costs approximately $0.37. Alternatively, try out our featured models for free.
By signing in, you agree to our
terms of service and privacy policy
{
"completed_at": "2024-03-12T05:26:48.440479Z",
"created_at": "2024-03-12T05:20:04.032651Z",
"data_removed": false,
"error": null,
"id": "qorgb5rb5rvgpxvbtmdviall3y",
"input": {
"seed": 1234,
"scale": 5.5,
"steps": 30,
"image_path": "https://replicate.delivery/pbxt/KYNDxJM3CvnQzh7eKtaiTfhKXkQBdWJQAhj4jYhJNlGG0SlA/3D%E5%8D%A1%E9%80%9A%E7%8B%97.webp",
"back_groud_color": "#7F7F7F",
"foreground_ratio": 1,
"background_choice": "Auto Remove background"
},
"logs": "Auto Remove background\nalhpa channl not enpty, skip remove background, using alpha channel as mask\nDDIM Sampler: 0%| | 0/31 [00:00<?, ?it/s]\nDDIM Sampler: 3%|▎ | 1/31 [00:00<00:07, 3.95it/s]\nDDIM Sampler: 6%|▋ | 2/31 [00:00<00:05, 5.24it/s]\nDDIM Sampler: 10%|▉ | 3/31 [00:00<00:04, 5.91it/s]\nDDIM Sampler: 13%|█▎ | 4/31 [00:00<00:04, 6.29it/s]\nDDIM Sampler: 16%|█▌ | 5/31 [00:00<00:03, 6.52it/s]\nDDIM Sampler: 19%|█▉ | 6/31 [00:00<00:03, 6.67it/s]\nDDIM Sampler: 23%|██▎ | 7/31 [00:01<00:03, 6.77it/s]\nDDIM Sampler: 26%|██▌ | 8/31 [00:01<00:03, 6.83it/s]\nDDIM Sampler: 29%|██▉ | 9/31 [00:01<00:03, 6.88it/s]\nDDIM Sampler: 32%|███▏ | 10/31 [00:01<00:03, 6.91it/s]\nDDIM Sampler: 35%|███▌ | 11/31 [00:01<00:02, 6.93it/s]\nDDIM Sampler: 39%|███▊ | 12/31 [00:01<00:02, 6.94it/s]\nDDIM Sampler: 42%|████▏ | 13/31 [00:01<00:02, 6.95it/s]\nDDIM Sampler: 45%|████▌ | 14/31 [00:02<00:02, 6.96it/s]\nDDIM Sampler: 48%|████▊ | 15/31 [00:02<00:02, 6.96it/s]\nDDIM Sampler: 52%|█████▏ | 16/31 [00:02<00:02, 6.97it/s]\nDDIM Sampler: 55%|█████▍ | 17/31 [00:02<00:02, 6.97it/s]\nDDIM Sampler: 58%|█████▊ | 18/31 [00:02<00:01, 6.97it/s]\nDDIM Sampler: 61%|██████▏ | 19/31 [00:02<00:01, 6.98it/s]\nDDIM Sampler: 65%|██████▍ | 20/31 [00:02<00:01, 6.97it/s]\nDDIM Sampler: 68%|██████▊ | 21/31 [00:03<00:01, 6.97it/s]\nDDIM Sampler: 71%|███████ | 22/31 [00:03<00:01, 6.97it/s]\nDDIM Sampler: 74%|███████▍ | 23/31 [00:03<00:01, 6.97it/s]\nDDIM Sampler: 77%|███████▋ | 24/31 [00:03<00:01, 6.98it/s]\nDDIM Sampler: 81%|████████ | 25/31 [00:03<00:00, 6.98it/s]\nDDIM Sampler: 84%|████████▍ | 26/31 [00:03<00:00, 6.97it/s]\nDDIM Sampler: 87%|████████▋ | 27/31 [00:03<00:00, 6.97it/s]\nDDIM Sampler: 90%|█████████ | 28/31 [00:04<00:00, 6.97it/s]\nDDIM Sampler: 94%|█████████▎| 29/31 [00:04<00:00, 6.97it/s]\nDDIM Sampler: 97%|█████████▋| 30/31 [00:04<00:00, 6.97it/s]\nDDIM Sampler: 100%|██████████| 31/31 [00:04<00:00, 6.97it/s]\nDDIM Sampler: 100%|██████████| 31/31 [00:04<00:00, 6.80it/s]\nDDIM Sampler: 0%| | 0/31 [00:00<?, ?it/s]\nDDIM Sampler: 3%|▎ | 1/31 [00:00<00:04, 6.06it/s]\nDDIM Sampler: 6%|▋ | 2/31 [00:00<00:04, 7.24it/s]\nDDIM Sampler: 10%|▉ | 3/31 [00:00<00:03, 7.72it/s]\nDDIM Sampler: 13%|█▎ | 4/31 [00:00<00:03, 7.97it/s]\nDDIM Sampler: 16%|█▌ | 5/31 [00:00<00:03, 8.12it/s]\nDDIM Sampler: 19%|█▉ | 6/31 [00:00<00:03, 8.20it/s]\nDDIM Sampler: 23%|██▎ | 7/31 [00:00<00:02, 8.26it/s]\nDDIM Sampler: 26%|██▌ | 8/31 [00:00<00:02, 8.30it/s]\nDDIM Sampler: 29%|██▉ | 9/31 [00:01<00:02, 8.32it/s]\nDDIM Sampler: 32%|███▏ | 10/31 [00:01<00:02, 8.34it/s]\nDDIM Sampler: 35%|███▌ | 11/31 [00:01<00:02, 8.36it/s]\nDDIM Sampler: 39%|███▊ | 12/31 [00:01<00:02, 8.36it/s]\nDDIM Sampler: 42%|████▏ | 13/31 [00:01<00:02, 8.36it/s]\nDDIM Sampler: 45%|████▌ | 14/31 [00:01<00:02, 8.37it/s]\nDDIM Sampler: 48%|████▊ | 15/31 [00:01<00:01, 8.37it/s]\nDDIM Sampler: 52%|█████▏ | 16/31 [00:01<00:01, 8.37it/s]\nDDIM Sampler: 55%|█████▍ | 17/31 [00:02<00:01, 8.37it/s]\nDDIM Sampler: 58%|█████▊ | 18/31 [00:02<00:01, 8.37it/s]\nDDIM Sampler: 61%|██████▏ | 19/31 [00:02<00:01, 8.38it/s]\nDDIM Sampler: 65%|██████▍ | 20/31 [00:02<00:01, 8.38it/s]\nDDIM Sampler: 68%|██████▊ | 21/31 [00:02<00:01, 8.38it/s]\nDDIM Sampler: 71%|███████ | 22/31 [00:02<00:01, 8.38it/s]\nDDIM Sampler: 74%|███████▍ | 23/31 [00:02<00:00, 8.27it/s]\nDDIM Sampler: 77%|███████▋ | 24/31 [00:02<00:00, 8.31it/s]\nDDIM Sampler: 81%|████████ | 25/31 [00:03<00:00, 8.33it/s]\nDDIM Sampler: 84%|████████▍ | 26/31 [00:03<00:00, 8.34it/s]\nDDIM Sampler: 87%|████████▋ | 27/31 [00:03<00:00, 8.36it/s]\nDDIM Sampler: 90%|█████████ | 28/31 [00:03<00:00, 8.37it/s]\nDDIM Sampler: 94%|█████████▎| 29/31 [00:03<00:00, 8.38it/s]\nDDIM Sampler: 97%|█████████▋| 30/31 [00:03<00:00, 8.38it/s]\nDDIM Sampler: 100%|██████████| 31/31 [00:03<00:00, 8.38it/s]\nDDIM Sampler: 100%|██████████| 31/31 [00:03<00:00, 8.27it/s]\nunet takes 0.2004241943359375s\n[INFO] mesh cleaning: (10510, 3) --> (39825, 3), (20996, 3) --> (79650, 3)\nuv takes 0.31412601470947266s",
"metrics": {
"predict_time": 11.400409,
"total_time": 404.407828
},
"output": "https://replicate.delivery/pbxt/4T3FFdvkzzbxHtzq7wXaFuDrq6rSHtkqzeV4nzex9TJYmZfkA/tmpdva7st55.obj",
"started_at": "2024-03-12T05:26:37.040070Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/qorgb5rb5rvgpxvbtmdviall3y",
"cancel": "https://api.replicate.com/v1/predictions/qorgb5rb5rvgpxvbtmdviall3y/cancel"
},
"version": "9a17c48c7e8a6c1b75788b454a234c13833277afafda546e014e59c3837f8932"
}Auto Remove background
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unet takes 0.2004241943359375s
[INFO] mesh cleaning: (10510, 3) --> (39825, 3), (20996, 3) --> (79650, 3)
uv takes 0.31412601470947266sThis model costs approximately $0.37 to run on Replicate, or 2 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.
This model runs on Nvidia A100 (80GB) GPU hardware. Predictions typically complete within 5 minutes. The predict time for this model varies significantly based on the inputs.
🐣 Please follow me for new updates https://twitter.com/camenduru
🔥 Please join our discord server https://discord.gg/k5BwmmvJJU
🥳 Please join my patreon community https://patreon.com/camenduru
https://replicate.com/camenduru/crm
https://arxiv.org/abs/2403.05034
https://ml.cs.tsinghua.edu.cn/~zhengyi/CRM/
This model is cold. You'll get a fast response if the model is warm and already running, and a slower response if the model is cold and starting up.
Choose a file from your machine
Hint: you can also drag files onto the input
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DDIM Sampler: 100%|██████████| 31/31 [00:04<00:00, 6.97it/s]
DDIM Sampler: 100%|██████████| 31/31 [00:04<00:00, 6.80it/s]
DDIM Sampler: 0%| | 0/31 [00:00<?, ?it/s]
DDIM Sampler: 3%|▎ | 1/31 [00:00<00:04, 6.06it/s]
DDIM Sampler: 6%|▋ | 2/31 [00:00<00:04, 7.24it/s]
DDIM Sampler: 10%|▉ | 3/31 [00:00<00:03, 7.72it/s]
DDIM Sampler: 13%|█▎ | 4/31 [00:00<00:03, 7.97it/s]
DDIM Sampler: 16%|█▌ | 5/31 [00:00<00:03, 8.12it/s]
DDIM Sampler: 19%|█▉ | 6/31 [00:00<00:03, 8.20it/s]
DDIM Sampler: 23%|██▎ | 7/31 [00:00<00:02, 8.26it/s]
DDIM Sampler: 26%|██▌ | 8/31 [00:00<00:02, 8.30it/s]
DDIM Sampler: 29%|██▉ | 9/31 [00:01<00:02, 8.32it/s]
DDIM Sampler: 32%|███▏ | 10/31 [00:01<00:02, 8.34it/s]
DDIM Sampler: 35%|███▌ | 11/31 [00:01<00:02, 8.36it/s]
DDIM Sampler: 39%|███▊ | 12/31 [00:01<00:02, 8.36it/s]
DDIM Sampler: 42%|████▏ | 13/31 [00:01<00:02, 8.36it/s]
DDIM Sampler: 45%|████▌ | 14/31 [00:01<00:02, 8.37it/s]
DDIM Sampler: 48%|████▊ | 15/31 [00:01<00:01, 8.37it/s]
DDIM Sampler: 52%|█████▏ | 16/31 [00:01<00:01, 8.37it/s]
DDIM Sampler: 55%|█████▍ | 17/31 [00:02<00:01, 8.37it/s]
DDIM Sampler: 58%|█████▊ | 18/31 [00:02<00:01, 8.37it/s]
DDIM Sampler: 61%|██████▏ | 19/31 [00:02<00:01, 8.38it/s]
DDIM Sampler: 65%|██████▍ | 20/31 [00:02<00:01, 8.38it/s]
DDIM Sampler: 68%|██████▊ | 21/31 [00:02<00:01, 8.38it/s]
DDIM Sampler: 71%|███████ | 22/31 [00:02<00:01, 8.38it/s]
DDIM Sampler: 74%|███████▍ | 23/31 [00:02<00:00, 8.27it/s]
DDIM Sampler: 77%|███████▋ | 24/31 [00:02<00:00, 8.31it/s]
DDIM Sampler: 81%|████████ | 25/31 [00:03<00:00, 8.33it/s]
DDIM Sampler: 84%|████████▍ | 26/31 [00:03<00:00, 8.34it/s]
DDIM Sampler: 87%|████████▋ | 27/31 [00:03<00:00, 8.36it/s]
DDIM Sampler: 90%|█████████ | 28/31 [00:03<00:00, 8.37it/s]
DDIM Sampler: 94%|█████████▎| 29/31 [00:03<00:00, 8.38it/s]
DDIM Sampler: 97%|█████████▋| 30/31 [00:03<00:00, 8.38it/s]
DDIM Sampler: 100%|██████████| 31/31 [00:03<00:00, 8.38it/s]
DDIM Sampler: 100%|██████████| 31/31 [00:03<00:00, 8.27it/s]
unet takes 0.2004241943359375s
[INFO] mesh cleaning: (10510, 3) --> (39825, 3), (20996, 3) --> (79650, 3)
uv takes 0.31412601470947266s