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cjwbw /style-your-hair:c4c7e5a6
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=cjwbw/style-your-hair
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 cjwbw/style-your-hair using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"cjwbw/style-your-hair:c4c7e5a657e2e1abccd57625093522a9928edeccee77e3f55d57c664bcd96fa2",
{
input: {
source_image: "https://replicate.delivery/mgxm/5fc43629-17f6-4269-abd8-284a488f925a/06041.png",
target_image: "https://replicate.delivery/mgxm/f6a1e6a6-b9c2-4eee-9f3a-5b9bc9246b69/29995.png"
}
}
);
// To access the file URL:
console.log(output.url()); //=> "http://example.com"
// To write the file to disk:
fs.writeFile("my-image.png", output);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 cjwbw/style-your-hair using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"cjwbw/style-your-hair:c4c7e5a657e2e1abccd57625093522a9928edeccee77e3f55d57c664bcd96fa2",
input={
"source_image": "https://replicate.delivery/mgxm/5fc43629-17f6-4269-abd8-284a488f925a/06041.png",
"target_image": "https://replicate.delivery/mgxm/f6a1e6a6-b9c2-4eee-9f3a-5b9bc9246b69/29995.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 cjwbw/style-your-hair 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": "c4c7e5a657e2e1abccd57625093522a9928edeccee77e3f55d57c664bcd96fa2",
"input": {
"source_image": "https://replicate.delivery/mgxm/5fc43629-17f6-4269-abd8-284a488f925a/06041.png",
"target_image": "https://replicate.delivery/mgxm/f6a1e6a6-b9c2-4eee-9f3a-5b9bc9246b69/29995.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.
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terms of service and privacy policy
Output
{
"completed_at": "2022-08-20T21:13:16.015283Z",
"created_at": "2022-08-20T20:50:17.524428Z",
"data_removed": false,
"error": null,
"id": "jg4kjtslvfbldou3g6pvzgur4i",
"input": {
"source_image": "https://replicate.delivery/mgxm/5fc43629-17f6-4269-abd8-284a488f925a/06041.png",
"target_image": "https://replicate.delivery/mgxm/f6a1e6a6-b9c2-4eee-9f3a-5b9bc9246b69/29995.png"
},
"logs": "Loading StyleGAN2 from checkpoint: pretrained_models/ffhq.pt\ntorch.Size([512])\nSetting up Perceptual loss...\nLoading model from: /src/losses/lpips/weights/v0.1/vgg.pth\n...[net-lin [vgg]] initialized\n...Done\nflip is better, kp_diff : 20.9365177154541 > kp_diff_flip : 19.572078704833984\nNumber of images: 2\n\ntmpsuc8gk1429995_flip\nImages: 0%| | 0/2 [00:00<?, ?it/s]\n\nEmbedding: 0%| | 0/1100 [00:00<?, ?it/s]\u001b[A\n\nEmbedding: 0%| | 1/1100 [00:04<1:21:26, 4.45s/it]\u001b[A\n\nEmbedding: 0%| | 2/1100 [00:04<35:48, 1.96s/it] \u001b[A\n\nEmbedding: 0%| | 3/1100 [00:04<22:06, 1.21s/it]\u001b[A\n\nEmbedding: 0%| | 4/1100 [00:05<15:43, 1.16it/s]\u001b[A\n\nEmbedding: 0%| | 5/1100 [00:05<12:08, 1.50it/s]\u001b[A\n\nEmbedding: 1%| | 6/1100 [00:05<10:01, 1.82it/s]\u001b[A\n\nEmbedding: 1%| | 7/1100 [00:06<08:39, 2.10it/s]\u001b[A\n\nEmbedding: 1%| | 8/1100 [00:06<07:44, 2.35it/s]\u001b[A\n\nEmbedding: 1%| | 9/1100 [00:06<07:08, 2.54it/s]\u001b[A\n\nEmbedding: 1%| | 10/1100 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81.78s/it]\nImages: 100%|██████████| 2/2 [02:43<00:00, 81.79s/it]\nLoading StyleGAN2 from checkpoint: pretrained_models/ffhq.pt\ntorch.Size([512])\nSetting up Perceptual loss...\nLoading model from: /src/losses/lpips/weights/v0.1/vgg.pth\n...[net-lin [vgg]] initialized\n...Done\nSetting up Perceptual loss...\nLoading model from: /src/losses/masked_lpips/weights/v0.1/vgg.pth\n...[net-lin [vgg]] initialized\n...Done\n\nWarp Target Step 1: 0%| | 0/100 [00:00<?, ?it/s]/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torch/nn/functional.py:3657: UserWarning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and now uses scale_factor directly, instead of relying on the computed output size. If you wish to restore the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.\n warnings.warn(\n\nWarp Target Step 1: 1%| | 1/100 [00:08<13:43, 8.32s/it]\nWarp Target Step 1: 2%|▏ | 2/100 [00:08<06:14, 3.82s/it]\nWarp Target Step 1: 3%|▎ | 3/100 [00:09<04:05, 2.53s/it]\nWarp Target Step 1: 4%|▍ | 4/100 [00:10<02:51, 1.78s/it]\nWarp Target Step 1: 5%|▌ | 5/100 [00:11<02:10, 1.38s/it]\nWarp Target Step 1: 6%|▌ | 6/100 [00:11<01:46, 1.13s/it]\nWarp Target Step 1: 7%|▋ | 7/100 [00:12<01:30, 1.03it/s]\nWarp Target Step 1: 8%|▊ | 8/100 [00:13<01:19, 1.15it/s]\nWarp Target Step 1: 9%|▉ | 9/100 [00:13<01:12, 1.25it/s]\nWarp Target Step 1: 10%|█ | 10/100 [00:14<01:07, 1.33it/s]\nWarp Target Step 1: 11%|█ | 11/100 [00:15<01:04, 1.39it/s]\nWarp Target Step 1: 12%|█▏ | 12/100 [00:15<01:01, 1.43it/s]\nWarp Target Step 1: 13%|█▎ | 13/100 [00:16<00:59, 1.46it/s]\nWarp Target Step 1: 14%|█▍ | 14/100 [00:17<00:57, 1.49it/s]\nWarp Target Step 1: 15%|█▌ | 15/100 [00:17<00:56, 1.51it/s]\nWarp Target Step 1: 16%|█▌ | 16/100 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Please specify align_corners=True if the old behavior is desired. 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"metrics": {
"predict_time": 1226.291429,
"total_time": 1378.490855
},
"output": "https://replicate.delivery/mgxm/c9f54dd4-66fe-4379-b9e5-64e29fb5827d/output.png",
"started_at": "2022-08-20T20:52:49.723854Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/jg4kjtslvfbldou3g6pvzgur4i",
"cancel": "https://api.replicate.com/v1/predictions/jg4kjtslvfbldou3g6pvzgur4i/cancel"
},
"version": "c4c7e5a657e2e1abccd57625093522a9928edeccee77e3f55d57c664bcd96fa2"
}Generated in
Loading StyleGAN2 from checkpoint: pretrained_models/ffhq.pt
torch.Size([512])
Setting up Perceptual loss...
Loading model from: /src/losses/lpips/weights/v0.1/vgg.pth
...[net-lin [vgg]] initialized
...Done
flip is better, kp_diff : 20.9365177154541 > kp_diff_flip : 19.572078704833984
Number of images: 2
tmpsuc8gk1429995_flip
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Loading StyleGAN2 from checkpoint: pretrained_models/ffhq.pt
torch.Size([512])
Setting up Perceptual loss...
Loading model from: /src/losses/lpips/weights/v0.1/vgg.pth
...[net-lin [vgg]] initialized
...Done
Setting up Perceptual loss...
Loading model from: /src/losses/masked_lpips/weights/v0.1/vgg.pth
...[net-lin [vgg]] initialized
...Done
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warnings.warn(
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Early stop, Key point loss below 0.05 : 0.000
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/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torch/nn/functional.py:3609: UserWarning: Default upsampling behavior when mode=bicubic is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
warnings.warn(
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