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codehappynice / codemusic
The current model is used for graphics replacement processing
- Public
- 660.6K runs
-
T4
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=codehappynice/codemusic
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 codehappynice/codemusic using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"codehappynice/codemusic:66c4e814fc3f45fec3755bb89498fc1ba3fcb9f23f9bd139a3491c918c254562",
{
input: {
video_path: "https://nicefish.s3.us-east-2.amazonaws.com/nicefish/testfile/multi_people_1080p.mp4",
multisepcific_dir: "https://nicefish.s3.us-east-2.amazonaws.com/nicefish/testfile/multispecific.zip"
}
}
);
console.log(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 codehappynice/codemusic using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"codehappynice/codemusic:66c4e814fc3f45fec3755bb89498fc1ba3fcb9f23f9bd139a3491c918c254562",
input={
"video_path": "https://nicefish.s3.us-east-2.amazonaws.com/nicefish/testfile/multi_people_1080p.mp4",
"multisepcific_dir": "https://nicefish.s3.us-east-2.amazonaws.com/nicefish/testfile/multispecific.zip"
}
)
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 codehappynice/codemusic 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": "codehappynice/codemusic:66c4e814fc3f45fec3755bb89498fc1ba3fcb9f23f9bd139a3491c918c254562",
"input": {
"video_path": "https://nicefish.s3.us-east-2.amazonaws.com/nicefish/testfile/multi_people_1080p.mp4",
"multisepcific_dir": "https://nicefish.s3.us-east-2.amazonaws.com/nicefish/testfile/multispecific.zip"
}
}' \
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
https://replicate.delivery/czjl/aZPNbGZ2jYo7KFS59CW8ri8Ubr9CGyNza9pT9jg2NHwvB06E/output.mp4
{
"completed_at": "2024-10-28T08:48:00.041620Z",
"created_at": "2024-10-28T08:35:31.484000Z",
"data_removed": false,
"error": null,
"id": "wwsmn0gt3hrgc0cjt92bjgr6d8",
"input": {
"video_path": "https://nicefish.s3.us-east-2.amazonaws.com/nicefish/testfile/multi_people_1080p.mp4",
"multisepcific_dir": "https://nicefish.s3.us-east-2.amazonaws.com/nicefish/testfile/multispecific.zip"
},
"logs": "ZIP文件已保存到: ./demo_file/multispecific.zip\n文件已解压到: ./demo_file/multispecific\nZIP文件已保存到: ./demo_file/multi_people_1080p.mp4\nApplied providers: ['CUDAExecutionProvider', 'CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}, 'CUDAExecutionProvider': {'sdpa_kernel': '0', 'use_tf32': '1', 'prefer_nhwc': '0', 'tunable_op_max_tuning_duration_ms': '0', 'enable_skip_layer_norm_strict_mode': '0', 'tunable_op_tuning_enable': '0', 'tunable_op_enable': '0', 'use_ep_level_unified_stream': '0', 'device_id': '0', 'has_user_compute_stream': '0', 'gpu_external_empty_cache': '0', 'cudnn_conv_algo_search': 'EXHAUSTIVE', 'cudnn_conv1d_pad_to_nc1d': '0', 'gpu_mem_limit': '18446744073709551615', 'gpu_external_alloc': '0', 'gpu_external_free': '0', 'arena_extend_strategy': 'kNextPowerOfTwo', 'do_copy_in_default_stream': '1', 'enable_cuda_graph': '0', 'user_compute_stream': '0', 'cudnn_conv_use_max_workspace': '1'}}\nfind model: ./insightface_func/models/antelope/glintr100.onnx recognition\nApplied providers: ['CUDAExecutionProvider', 'CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}, 'CUDAExecutionProvider': {'sdpa_kernel': '0', 'use_tf32': '1', 'prefer_nhwc': '0', 'tunable_op_max_tuning_duration_ms': '0', 'enable_skip_layer_norm_strict_mode': '0', 'tunable_op_tuning_enable': '0', 'tunable_op_enable': '0', 'use_ep_level_unified_stream': '0', 'device_id': '0', 'has_user_compute_stream': '0', 'gpu_external_empty_cache': '0', 'cudnn_conv_algo_search': 'EXHAUSTIVE', 'cudnn_conv1d_pad_to_nc1d': '0', 'gpu_mem_limit': '18446744073709551615', 'gpu_external_alloc': '0', 'gpu_external_free': '0', 'arena_extend_strategy': 'kNextPowerOfTwo', 'do_copy_in_default_stream': '1', 'enable_cuda_graph': '0', 'user_compute_stream': '0', 'cudnn_conv_use_max_workspace': '1'}}\nfind model: ./insightface_func/models/antelope/scrfd_10g_bnkps.onnx detection\nset det-size: (640, 640)\n['./demo_file/multispecific/SRC_01.png', './demo_file/multispecific/SRC_02.png', './demo_file/multispecific/SRC_03.png']\n['./demo_file/multispecific/DST_01.jpg', './demo_file/multispecific/DST_02.jpg', './demo_file/multispecific/DST_03.jpg']\n(142, 366, 4)\nDownloading: \"https://download.pytorch.org/models/resnet18-5c106cde.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-5c106cde.pth\n 0%| | 0.00/44.7M [00:00<?, ?B/s]\n 15%|█▍ | 6.50M/44.7M [00:00<00:00, 68.0MB/s]\n 68%|██████▊ | 30.5M/44.7M [00:00<00:00, 176MB/s] \n100%|██████████| 44.7M/44.7M [00:00<00:00, 180MB/s]\n 0%| | 0/594 [00:00<?, ?it/s]\n 0%| | 1/594 [00:02<21:19, 2.16s/it]\n 0%| | 2/594 [00:02<13:27, 1.36s/it]\n 1%| | 3/594 [00:03<10:38, 1.08s/it]\n 1%| | 4/594 [00:04<09:26, 1.04it/s]\n 1%| | 5/594 [00:05<08:39, 1.13it/s]\n 1%| | 6/594 [00:05<08:15, 1.19it/s]\n 1%| | 7/594 [00:06<07:59, 1.23it/s]\n 1%|▏ | 8/594 [00:07<07:58, 1.22it/s]\n 2%|▏ | 9/594 [00:08<07:44, 1.26it/s]\n 2%|▏ | 10/594 [00:09<07:48, 1.25it/s]\n 2%|▏ | 11/594 [00:10<09:28, 1.03it/s]\n 2%|▏ | 12/594 [00:11<09:50, 1.01s/it]\n 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now=None]\nt: 97%|█████████▋| 580/595 [00:18<00:00, 36.10it/s, now=None]\nt: 98%|█████████▊| 584/595 [00:18<00:00, 36.97it/s, now=None]\nt: 99%|█████████▉| 588/595 [00:18<00:00, 36.99it/s, now=None]\nt: 99%|█████████▉| 592/595 [00:18<00:00, 34.12it/s, now=None]\nMoviepy - Done !\nMoviepy - video ready /tmp/tmp0muo6_46/output.mp4\ndelete video success\ndelete multisepcific_dir success\ndelete temp_path success",
"metrics": {
"predict_time": 571.439588623,
"total_time": 748.55762
},
"output": "https://replicate.delivery/czjl/aZPNbGZ2jYo7KFS59CW8ri8Ubr9CGyNza9pT9jg2NHwvB06E/output.mp4",
"started_at": "2024-10-28T08:38:28.602031Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/wwsmn0gt3hrgc0cjt92bjgr6d8",
"cancel": "https://api.replicate.com/v1/predictions/wwsmn0gt3hrgc0cjt92bjgr6d8/cancel"
},
"version": "448fce2e877bdd3537e89535963eab5f609ceae1855e22255f3d119dd12f86ef"
}Generated in
ZIP文件已保存到: ./demo_file/multispecific.zip
文件已解压到: ./demo_file/multispecific
ZIP文件已保存到: ./demo_file/multi_people_1080p.mp4
Applied providers: ['CUDAExecutionProvider', 'CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}, 'CUDAExecutionProvider': {'sdpa_kernel': '0', 'use_tf32': '1', 'prefer_nhwc': '0', 'tunable_op_max_tuning_duration_ms': '0', 'enable_skip_layer_norm_strict_mode': '0', 'tunable_op_tuning_enable': '0', 'tunable_op_enable': '0', 'use_ep_level_unified_stream': '0', 'device_id': '0', 'has_user_compute_stream': '0', 'gpu_external_empty_cache': '0', 'cudnn_conv_algo_search': 'EXHAUSTIVE', 'cudnn_conv1d_pad_to_nc1d': '0', 'gpu_mem_limit': '18446744073709551615', 'gpu_external_alloc': '0', 'gpu_external_free': '0', 'arena_extend_strategy': 'kNextPowerOfTwo', 'do_copy_in_default_stream': '1', 'enable_cuda_graph': '0', 'user_compute_stream': '0', 'cudnn_conv_use_max_workspace': '1'}}
find model: ./insightface_func/models/antelope/glintr100.onnx recognition
Applied providers: ['CUDAExecutionProvider', 'CPUExecutionProvider'], with options: {'CPUExecutionProvider': {}, 'CUDAExecutionProvider': {'sdpa_kernel': '0', 'use_tf32': '1', 'prefer_nhwc': '0', 'tunable_op_max_tuning_duration_ms': '0', 'enable_skip_layer_norm_strict_mode': '0', 'tunable_op_tuning_enable': '0', 'tunable_op_enable': '0', 'use_ep_level_unified_stream': '0', 'device_id': '0', 'has_user_compute_stream': '0', 'gpu_external_empty_cache': '0', 'cudnn_conv_algo_search': 'EXHAUSTIVE', 'cudnn_conv1d_pad_to_nc1d': '0', 'gpu_mem_limit': '18446744073709551615', 'gpu_external_alloc': '0', 'gpu_external_free': '0', 'arena_extend_strategy': 'kNextPowerOfTwo', 'do_copy_in_default_stream': '1', 'enable_cuda_graph': '0', 'user_compute_stream': '0', 'cudnn_conv_use_max_workspace': '1'}}
find model: ./insightface_func/models/antelope/scrfd_10g_bnkps.onnx detection
set det-size: (640, 640)
['./demo_file/multispecific/SRC_01.png', './demo_file/multispecific/SRC_02.png', './demo_file/multispecific/SRC_03.png']
['./demo_file/multispecific/DST_01.jpg', './demo_file/multispecific/DST_02.jpg', './demo_file/multispecific/DST_03.jpg']
(142, 366, 4)
Downloading: "https://download.pytorch.org/models/resnet18-5c106cde.pth" to /root/.cache/torch/hub/checkpoints/resnet18-5c106cde.pth
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Moviepy - Building video /tmp/tmp0muo6_46/output.mp4.
MoviePy - Writing audio in outputTEMP_MPY_wvf_snd.mp4
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MoviePy - Done.
Moviepy - Writing video /tmp/tmp0muo6_46/output.mp4
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Moviepy - Done !
Moviepy - video ready /tmp/tmp0muo6_46/output.mp4
delete video success
delete multisepcific_dir success
delete temp_path successThis output was created using a different version of the model, codehappynice/codemusic:448fce2e.
Run time and cost
This model costs approximately $0.020 to run on Replicate, or 50 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 T4 GPU hardware. Predictions typically complete within 90 seconds. The predict time for this model varies significantly based on the inputs.