arielreplicate/robust_video_matting
robust_video_matting
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arielreplicate/robust_video_matting
extract foreground of a video
arielreplicate/dichotomous_image_segmentation
Highly Accurate Dichotomous Image Segmentation （ECCV 2022）
arielreplicate/stable_diffusion_infinite_zoom
Use Runway's Stable-diffusion inpainting model to create an infinite loop video
arielreplicate/infinite_nature
Create a fly by scenery video from a single image
arielreplicate/multi-stage-blended-diffusion
Multi stage image editing of images using stable-diffusion with a mask and a prompt
arielreplicate/paella_fast_text2image
Fast text2image model
arielreplicate/stable_diffusion2_upscaling
Image super-resolution with stable-diffusion V2
arielreplicate/paella_fast_image_interpolation
Fast image interpolation model
arielreplicate/paella_fast_image_variation
Fast image variation model
arielreplicate/paella_fast_outpainting
Fast image outpainting model
arielreplicate/scalable_diffusion_with_transformers
Latent diffusion models, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches
arielreplicate/deoldify_image
Add colours to old images
arielreplicate/tres_iqa
Assess the quality of an image
arielreplicate/deoldify_video
Add colours to old video footage.
arielreplicate/gscorecam-clip-analyzer
Shows what CLIP looks at in an image given text
arielreplicate/instruct-pix2pix
Edit images with human instructions
arielreplicate/crestereo
High accuracy depth maps from pairs of stereo images
Prediction
arielreplicate/robust_video_matting:2d2de06a76a837a4ba92b6164bf8bfd3ddb524a1fb64b0d8ae055af17fa22503IDiqaowdlwcnbxresiand63tzn5aStatusSucceededSourceWebHardware–Total durationCreatedInput
- input_video
- output_type
- green-screen
{ "input_video": "https://replicate.delivery/pbxt/HqiGGuuwynO7sCHpcQdYQsIf04NotwOrDdbhBf4M6Pou6MGg/butter.mp4", "output_type": "green-screen" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run arielreplicate/robust_video_matting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "arielreplicate/robust_video_matting:2d2de06a76a837a4ba92b6164bf8bfd3ddb524a1fb64b0d8ae055af17fa22503", { input: { input_video: "https://replicate.delivery/pbxt/HqiGGuuwynO7sCHpcQdYQsIf04NotwOrDdbhBf4M6Pou6MGg/butter.mp4", output_type: "green-screen" } } ); // 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 replicateImport the client:import replicateRun arielreplicate/robust_video_matting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "arielreplicate/robust_video_matting:2d2de06a76a837a4ba92b6164bf8bfd3ddb524a1fb64b0d8ae055af17fa22503", input={ "input_video": "https://replicate.delivery/pbxt/HqiGGuuwynO7sCHpcQdYQsIf04NotwOrDdbhBf4M6Pou6MGg/butter.mp4", "output_type": "green-screen" } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run arielreplicate/robust_video_matting 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": "arielreplicate/robust_video_matting:2d2de06a76a837a4ba92b6164bf8bfd3ddb524a1fb64b0d8ae055af17fa22503", "input": { "input_video": "https://replicate.delivery/pbxt/HqiGGuuwynO7sCHpcQdYQsIf04NotwOrDdbhBf4M6Pou6MGg/butter.mp4", "output_type": "green-screen" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-11-25T14:17:06.618254Z", "created_at": "2022-11-25T14:11:53.705436Z", "data_removed": false, "error": null, "id": "iqaowdlwcnbxresiand63tzn5a", "input": { "input_video": "https://replicate.delivery/pbxt/HqiGGuuwynO7sCHpcQdYQsIf04NotwOrDdbhBf4M6Pou6MGg/butter.mp4", "output_type": "green-screen" }, "logs": "0%| | 0/382 [00:00<?, ?it/s]/root/.pyenv/versions/3.8.15/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\nreturn torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n 3%|▎ | 12/382 [00:02<01:25, 4.30it/s]\n 6%|▋ | 24/382 [00:04<01:10, 5.05it/s]\n 9%|▉ | 36/382 [00:07<01:05, 5.29it/s]\n 13%|█▎ | 48/382 [00:10<01:19, 4.19it/s]\n 16%|█▌ | 60/382 [00:14<01:22, 3.92it/s]\n 19%|█▉ | 72/382 [00:17<01:22, 3.74it/s]\n 22%|██▏ | 84/382 [00:20<01:16, 3.88it/s]\n 25%|██▌ | 96/382 [00:23<01:12, 3.96it/s]\n 28%|██▊ | 108/382 [00:26<01:06, 4.10it/s]\n 31%|███▏ | 120/382 [00:28<01:03, 4.13it/s]\n 35%|███▍ | 132/382 [00:31<00:59, 4.23it/s]\n 38%|███▊ | 144/382 [00:34<00:57, 4.12it/s]\n 41%|████ | 156/382 [00:37<00:55, 4.07it/s]\n 44%|████▍ | 168/382 [00:40<00:53, 4.04it/s]\n 47%|████▋ | 180/382 [00:43<00:49, 4.07it/s]\n 50%|█████ | 192/382 [00:46<00:46, 4.11it/s]\n 53%|█████▎ | 204/382 [00:49<00:42, 4.16it/s]\n 57%|█████▋ | 216/382 [00:52<00:40, 4.05it/s]\n 60%|█████▉ | 228/382 [00:55<00:38, 4.05it/s]\n 63%|██████▎ | 240/382 [00:58<00:35, 4.02it/s]\n 66%|██████▌ | 252/382 [01:01<00:31, 4.11it/s]\n 69%|██████▉ | 264/382 [01:04<00:28, 4.17it/s]\n 72%|███████▏ | 276/382 [01:06<00:24, 4.28it/s]\n 75%|███████▌ | 288/382 [01:09<00:22, 4.22it/s]\n 79%|███████▊ | 300/382 [01:12<00:19, 4.23it/s]\n 82%|████████▏ | 312/382 [01:15<00:16, 4.26it/s]\n 85%|████████▍ | 324/382 [01:17<00:13, 4.28it/s]\n 88%|████████▊ | 336/382 [01:20<00:10, 4.24it/s]\n 91%|█████████ | 348/382 [01:23<00:07, 4.25it/s]\n 94%|█████████▍| 360/382 [01:26<00:05, 4.20it/s]\n 97%|█████████▋| 372/382 [01:29<00:02, 4.20it/s]\n100%|██████████| 382/382 [01:31<00:00, 4.20it/s]", "metrics": { "predict_time": 97.211127, "total_time": 312.912818 }, "output": "https://replicate.delivery/pbxt/V3iFlL5JotpbLtkUqGcnrpsiRfXume3Jm0020Iu0hX0hBoDQA/green-screen.mp4", "started_at": "2022-11-25T14:15:29.407127Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/iqaowdlwcnbxresiand63tzn5a", "cancel": "https://api.replicate.com/v1/predictions/iqaowdlwcnbxresiand63tzn5a/cancel" }, "version": "2d2de06a76a837a4ba92b6164bf8bfd3ddb524a1fb64b0d8ae055af17fa22503" }Generated in0%| | 0/382 [00:00<?, ?it/s]/root/.pyenv/versions/3.8.15/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.) return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) 3%|▎ | 12/382 [00:02<01:25, 4.30it/s] 6%|▋ | 24/382 [00:04<01:10, 5.05it/s] 9%|▉ | 36/382 [00:07<01:05, 5.29it/s] 13%|█▎ | 48/382 [00:10<01:19, 4.19it/s] 16%|█▌ | 60/382 [00:14<01:22, 3.92it/s] 19%|█▉ | 72/382 [00:17<01:22, 3.74it/s] 22%|██▏ | 84/382 [00:20<01:16, 3.88it/s] 25%|██▌ | 96/382 [00:23<01:12, 3.96it/s] 28%|██▊ | 108/382 [00:26<01:06, 4.10it/s] 31%|███▏ | 120/382 [00:28<01:03, 4.13it/s] 35%|███▍ | 132/382 [00:31<00:59, 4.23it/s] 38%|███▊ | 144/382 [00:34<00:57, 4.12it/s] 41%|████ | 156/382 [00:37<00:55, 4.07it/s] 44%|████▍ | 168/382 [00:40<00:53, 4.04it/s] 47%|████▋ | 180/382 [00:43<00:49, 4.07it/s] 50%|█████ | 192/382 [00:46<00:46, 4.11it/s] 53%|█████▎ | 204/382 [00:49<00:42, 4.16it/s] 57%|█████▋ | 216/382 [00:52<00:40, 4.05it/s] 60%|█████▉ | 228/382 [00:55<00:38, 4.05it/s] 63%|██████▎ | 240/382 [00:58<00:35, 4.02it/s] 66%|██████▌ | 252/382 [01:01<00:31, 4.11it/s] 69%|██████▉ | 264/382 [01:04<00:28, 4.17it/s] 72%|███████▏ | 276/382 [01:06<00:24, 4.28it/s] 75%|███████▌ | 288/382 [01:09<00:22, 4.22it/s] 79%|███████▊ | 300/382 [01:12<00:19, 4.23it/s] 82%|████████▏ | 312/382 [01:15<00:16, 4.26it/s] 85%|████████▍ | 324/382 [01:17<00:13, 4.28it/s] 88%|████████▊ | 336/382 [01:20<00:10, 4.24it/s] 91%|█████████ | 348/382 [01:23<00:07, 4.25it/s] 94%|█████████▍| 360/382 [01:26<00:05, 4.20it/s] 97%|█████████▋| 372/382 [01:29<00:02, 4.20it/s] 100%|██████████| 382/382 [01:31<00:00, 4.20it/s]Prediction
arielreplicate/robust_video_matting:2d2de06a76a837a4ba92b6164bf8bfd3ddb524a1fb64b0d8ae055af17fa22503IDc4zkevc27fhntpw3ekgb2ok2sqStatusSucceededSourceWebHardware–Total durationCreatedInput
- input_video
- output_type
- green-screen
{ "input_video": "https://replicate.delivery/pbxt/HqiUPeOL0NMmX1yzan5USbiFloRmxVcrKqBuslIlNdofpYAr/dance2.mp4", "output_type": "green-screen" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run arielreplicate/robust_video_matting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "arielreplicate/robust_video_matting:2d2de06a76a837a4ba92b6164bf8bfd3ddb524a1fb64b0d8ae055af17fa22503", { input: { input_video: "https://replicate.delivery/pbxt/HqiUPeOL0NMmX1yzan5USbiFloRmxVcrKqBuslIlNdofpYAr/dance2.mp4", output_type: "green-screen" } } ); // 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 replicateImport the client:import replicateRun arielreplicate/robust_video_matting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "arielreplicate/robust_video_matting:2d2de06a76a837a4ba92b6164bf8bfd3ddb524a1fb64b0d8ae055af17fa22503", input={ "input_video": "https://replicate.delivery/pbxt/HqiUPeOL0NMmX1yzan5USbiFloRmxVcrKqBuslIlNdofpYAr/dance2.mp4", "output_type": "green-screen" } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run arielreplicate/robust_video_matting 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": "arielreplicate/robust_video_matting:2d2de06a76a837a4ba92b6164bf8bfd3ddb524a1fb64b0d8ae055af17fa22503", "input": { "input_video": "https://replicate.delivery/pbxt/HqiUPeOL0NMmX1yzan5USbiFloRmxVcrKqBuslIlNdofpYAr/dance2.mp4", "output_type": "green-screen" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-11-25T14:29:11.572602Z", "created_at": "2022-11-25T14:26:35.172178Z", "data_removed": false, "error": null, "id": "c4zkevc27fhntpw3ekgb2ok2sq", "input": { "input_video": "https://replicate.delivery/pbxt/HqiUPeOL0NMmX1yzan5USbiFloRmxVcrKqBuslIlNdofpYAr/dance2.mp4", "output_type": "green-screen" }, "logs": "0%| | 0/606 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n 2%|▏ | 12/606 [00:02<01:56, 5.08it/s]\u001b[A\u001b[A\u001b[A\n 4%|▍ | 24/606 [00:04<01:48, 5.37it/s]\u001b[A\u001b[A\u001b[A\n 6%|▌ | 36/606 [00:06<01:46, 5.37it/s]\u001b[A\u001b[A\u001b[A\n 8%|▊ | 48/606 [00:10<02:06, 4.40it/s]\u001b[A\u001b[A\u001b[A\n 10%|▉ | 60/606 [00:13<02:11, 4.14it/s]\u001b[A\u001b[A\u001b[A\n 12%|█▏ | 72/606 [00:16<02:10, 4.09it/s]\u001b[A\u001b[A\u001b[A\n 14%|█▍ | 84/606 [00:19<02:07, 4.08it/s]\u001b[A\u001b[A\u001b[A\n 16%|█▌ | 96/606 [00:22<02:04, 4.09it/s]\u001b[A\u001b[A\u001b[A\n 18%|█▊ | 108/606 [00:25<02:02, 4.05it/s]\u001b[A\u001b[A\u001b[A\n 20%|█▉ | 120/606 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3.89it/s]\u001b[A\u001b[A\u001b[A", "metrics": { "predict_time": 156.3639, "total_time": 156.400424 }, "output": "https://replicate.delivery/pbxt/HuwFjWn7A9bcH5CfhQ7KO79N3z4uZf9sTtLoo4PDnXY2MoDQA/green-screen.mp4", "started_at": "2022-11-25T14:26:35.208702Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/c4zkevc27fhntpw3ekgb2ok2sq", "cancel": "https://api.replicate.com/v1/predictions/c4zkevc27fhntpw3ekgb2ok2sq/cancel" }, "version": "2d2de06a76a837a4ba92b6164bf8bfd3ddb524a1fb64b0d8ae055af17fa22503" }Generated in0%| | 0/606 [00:00<?, ?it/s] 2%|▏ | 12/606 [00:02<01:56, 5.08it/s] 4%|▍ | 24/606 [00:04<01:48, 5.37it/s] 6%|▌ | 36/606 [00:06<01:46, 5.37it/s] 8%|▊ | 48/606 [00:10<02:06, 4.40it/s] 10%|▉ | 60/606 [00:13<02:11, 4.14it/s] 12%|█▏ | 72/606 [00:16<02:10, 4.09it/s] 14%|█▍ | 84/606 [00:19<02:07, 4.08it/s] 16%|█▌ | 96/606 [00:22<02:04, 4.09it/s] 18%|█▊ | 108/606 [00:25<02:02, 4.05it/s] 20%|█▉ | 120/606 [00:28<01:59, 4.07it/s] 22%|██▏ | 132/606 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arielreplicate/robust_video_matting:2d2de06a76a837a4ba92b6164bf8bfd3ddb524a1fb64b0d8ae055af17fa22503IDin5yh3obxrdmvgr6mxgalzzeliStatusSucceededSourceWebHardware–Total durationCreatedInput
- input_video
- output_type
- green-screen
{ "input_video": "https://replicate.delivery/pbxt/HqiZLXVlQCsU4hjSm3LcccJElHRir2ubhSkaGuQAS33P4FP3/bilibili.mp4", "output_type": "green-screen" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run arielreplicate/robust_video_matting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "arielreplicate/robust_video_matting:2d2de06a76a837a4ba92b6164bf8bfd3ddb524a1fb64b0d8ae055af17fa22503", { input: { input_video: "https://replicate.delivery/pbxt/HqiZLXVlQCsU4hjSm3LcccJElHRir2ubhSkaGuQAS33P4FP3/bilibili.mp4", output_type: "green-screen" } } ); // 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 replicateImport the client:import replicateRun arielreplicate/robust_video_matting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "arielreplicate/robust_video_matting:2d2de06a76a837a4ba92b6164bf8bfd3ddb524a1fb64b0d8ae055af17fa22503", input={ "input_video": "https://replicate.delivery/pbxt/HqiZLXVlQCsU4hjSm3LcccJElHRir2ubhSkaGuQAS33P4FP3/bilibili.mp4", "output_type": "green-screen" } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run arielreplicate/robust_video_matting 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": "arielreplicate/robust_video_matting:2d2de06a76a837a4ba92b6164bf8bfd3ddb524a1fb64b0d8ae055af17fa22503", "input": { "input_video": "https://replicate.delivery/pbxt/HqiZLXVlQCsU4hjSm3LcccJElHRir2ubhSkaGuQAS33P4FP3/bilibili.mp4", "output_type": "green-screen" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-11-25T14:38:16.808827Z", "created_at": "2022-11-25T14:32:15.592259Z", "data_removed": false, "error": null, "id": "in5yh3obxrdmvgr6mxgalzzeli", "input": { "input_video": "https://replicate.delivery/pbxt/HqiZLXVlQCsU4hjSm3LcccJElHRir2ubhSkaGuQAS33P4FP3/bilibili.mp4", "output_type": "green-screen" }, "logs": "0%| | 0/1371 [00:00<?, ?it/s]/root/.pyenv/versions/3.8.15/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\nreturn torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n 1%| | 12/1371 [00:03<06:04, 3.73it/s]\n 2%|▏ | 24/1371 [00:05<05:13, 4.30it/s]\n 3%|▎ | 36/1371 [00:08<05:11, 4.28it/s]\n 4%|▎ | 48/1371 [00:12<05:39, 3.89it/s]\n 4%|▍ | 60/1371 [00:15<05:41, 3.84it/s]\n 5%|▌ | 72/1371 [00:18<05:36, 3.86it/s]\n 6%|▌ | 84/1371 [00:21<05:28, 3.91it/s]\n 7%|▋ | 96/1371 [00:24<05:22, 3.95it/s]\n 8%|▊ | 108/1371 [00:27<05:17, 3.98it/s]\n 9%|▉ | 120/1371 [00:30<05:10, 4.03it/s]\n 10%|▉ | 132/1371 [00:33<05:07, 4.03it/s]\n 11%|█ | 144/1371 [00:36<05:05, 4.02it/s]\n 11%|█▏ | 156/1371 [00:39<05:03, 4.01it/s]\n 12%|█▏ | 168/1371 [00:42<05:01, 4.00it/s]\n 13%|█▎ | 180/1371 [00:45<04:57, 4.00it/s]\n 14%|█▍ | 192/1371 [00:48<04:55, 3.98it/s]\n 15%|█▍ | 204/1371 [00:51<05:06, 3.81it/s]\n 16%|█▌ | 216/1371 [00:54<05:03, 3.80it/s]\n 17%|█▋ | 228/1371 [00:57<04:58, 3.82it/s]\n 18%|█▊ | 240/1371 [01:00<04:53, 3.85it/s]\n 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3.74it/s]\n 91%|█████████ | 1248/1371 [05:25<00:32, 3.80it/s]\n 92%|█████████▏| 1260/1371 [05:28<00:28, 3.84it/s]\n 93%|█████████▎| 1272/1371 [05:31<00:25, 3.88it/s]\n 94%|█████████▎| 1284/1371 [05:34<00:22, 3.89it/s]\n 95%|█████████▍| 1296/1371 [05:37<00:19, 3.89it/s]\n 95%|█████████▌| 1308/1371 [05:40<00:16, 3.87it/s]\n 96%|█████████▋| 1320/1371 [05:43<00:13, 3.88it/s]\n 97%|█████████▋| 1332/1371 [05:47<00:10, 3.86it/s]\n 98%|█████████▊| 1344/1371 [05:50<00:07, 3.85it/s]\n 99%|█████████▉| 1356/1371 [05:53<00:03, 3.86it/s]\n100%|█████████▉| 1368/1371 [05:56<00:00, 3.89it/s]\n100%|██████████| 1371/1371 [05:57<00:00, 3.88it/s]", "metrics": { "predict_time": 361.181149, "total_time": 361.216568 }, "output": "https://replicate.delivery/pbxt/rERlJsBX0oJ4BZwnJdaj2RfqBYDuzjObx3jRDg3GffHvqQHgA/green-screen.mp4", "started_at": "2022-11-25T14:32:15.627678Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/in5yh3obxrdmvgr6mxgalzzeli", "cancel": "https://api.replicate.com/v1/predictions/in5yh3obxrdmvgr6mxgalzzeli/cancel" }, "version": "2d2de06a76a837a4ba92b6164bf8bfd3ddb524a1fb64b0d8ae055af17fa22503" }Generated in0%| | 0/1371 [00:00<?, ?it/s]/root/.pyenv/versions/3.8.15/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.) return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) 1%| | 12/1371 [00:03<06:04, 3.73it/s] 2%|▏ | 24/1371 [00:05<05:13, 4.30it/s] 3%|▎ | 36/1371 [00:08<05:11, 4.28it/s] 4%|▎ | 48/1371 [00:12<05:39, 3.89it/s] 4%|▍ | 60/1371 [00:15<05:41, 3.84it/s] 5%|▌ | 72/1371 [00:18<05:36, 3.86it/s] 6%|▌ | 84/1371 [00:21<05:28, 3.91it/s] 7%|▋ | 96/1371 [00:24<05:22, 3.95it/s] 8%|▊ | 108/1371 [00:27<05:17, 3.98it/s] 9%|▉ | 120/1371 [00:30<05:10, 4.03it/s] 10%|▉ | 132/1371 [00:33<05:07, 4.03it/s] 11%|█ | 144/1371 [00:36<05:05, 4.02it/s] 11%|█▏ | 156/1371 [00:39<05:03, 4.01it/s] 12%|█▏ | 168/1371 [00:42<05:01, 4.00it/s] 13%|█▎ | 180/1371 [00:45<04:57, 4.00it/s] 14%|█▍ | 192/1371 [00:48<04:55, 3.98it/s] 15%|█▍ | 204/1371 [00:51<05:06, 3.81it/s] 16%|█▌ | 216/1371 [00:54<05:03, 3.80it/s] 17%|█▋ | 228/1371 [00:57<04:58, 3.82it/s] 18%|█▊ | 240/1371 [01:00<04:53, 3.85it/s] 18%|█▊ | 252/1371 [01:04<04:51, 3.84it/s] 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95%|█████████▍| 1296/1371 [05:37<00:19, 3.89it/s] 95%|█████████▌| 1308/1371 [05:40<00:16, 3.87it/s] 96%|█████████▋| 1320/1371 [05:43<00:13, 3.88it/s] 97%|█████████▋| 1332/1371 [05:47<00:10, 3.86it/s] 98%|█████████▊| 1344/1371 [05:50<00:07, 3.85it/s] 99%|█████████▉| 1356/1371 [05:53<00:03, 3.86it/s] 100%|█████████▉| 1368/1371 [05:56<00:00, 3.89it/s] 100%|██████████| 1371/1371 [05:57<00:00, 3.88it/s]Prediction
arielreplicate/robust_video_matting:73d2128a371922d5d1abf0712a1d974be0e4e2358cc1218e4e34714767232bacID43ujwtzsojd2phcc6hw2sbbbsuStatusSucceededSourceWebHardware–Total durationCreatedInput
- input_video
- output_type
- alpha-mask
{ "input_video": "https://replicate.delivery/pbxt/Hqikd7afw2c9koZXS8c3v08x0MFXZFhxFPBkd0iHxrEmRVrk/eddie.mp4", "output_type": "alpha-mask" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run arielreplicate/robust_video_matting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "arielreplicate/robust_video_matting:73d2128a371922d5d1abf0712a1d974be0e4e2358cc1218e4e34714767232bac", { input: { input_video: "https://replicate.delivery/pbxt/Hqikd7afw2c9koZXS8c3v08x0MFXZFhxFPBkd0iHxrEmRVrk/eddie.mp4", output_type: "alpha-mask" } } ); // 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 replicateImport the client:import replicateRun arielreplicate/robust_video_matting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "arielreplicate/robust_video_matting:73d2128a371922d5d1abf0712a1d974be0e4e2358cc1218e4e34714767232bac", input={ "input_video": "https://replicate.delivery/pbxt/Hqikd7afw2c9koZXS8c3v08x0MFXZFhxFPBkd0iHxrEmRVrk/eddie.mp4", "output_type": "alpha-mask" } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run arielreplicate/robust_video_matting 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": "arielreplicate/robust_video_matting:73d2128a371922d5d1abf0712a1d974be0e4e2358cc1218e4e34714767232bac", "input": { "input_video": "https://replicate.delivery/pbxt/Hqikd7afw2c9koZXS8c3v08x0MFXZFhxFPBkd0iHxrEmRVrk/eddie.mp4", "output_type": "alpha-mask" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-12-11T17:58:59.726262Z", "created_at": "2022-12-11T17:56:29.408559Z", "data_removed": false, "error": null, "id": "43ujwtzsojd2phcc6hw2sbbbsu", "input": { "input_video": "https://replicate.delivery/pbxt/Hqikd7afw2c9koZXS8c3v08x0MFXZFhxFPBkd0iHxrEmRVrk/eddie.mp4", "output_type": "alpha-mask" }, "logs": "0%| | 0/143 [00:00<?, ?it/s]/root/.pyenv/versions/3.8.15/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\nreturn torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n 8%|▊ | 12/143 [00:03<00:38, 3.44it/s]\n 17%|█▋ | 24/143 [00:05<00:27, 4.35it/s]\n 25%|██▌ | 36/143 [00:07<00:22, 4.84it/s]\n 34%|███▎ | 48/143 [00:11<00:23, 3.97it/s]\n 42%|████▏ | 60/143 [00:14<00:21, 3.89it/s]\n 50%|█████ | 72/143 [00:17<00:18, 3.93it/s]\n 59%|█████▊ | 84/143 [00:21<00:15, 3.84it/s]\n 67%|██████▋ | 96/143 [00:24<00:12, 3.83it/s]\n 76%|███████▌ | 108/143 [00:27<00:09, 3.88it/s]\n 84%|████████▍ | 120/143 [00:30<00:05, 3.88it/s]\n 92%|█████████▏| 132/143 [00:33<00:02, 3.88it/s]\n100%|██████████| 143/143 [00:36<00:00, 3.85it/s]", "metrics": { "predict_time": 41.630151, "total_time": 150.317703 }, "output": "https://replicate.delivery/pbxt/5wY879GIAWqAJFxBU6UtcHGWNwmnrnLzUgf2G0Jx0XcxYeIQA/alpha-mask.mp4", "started_at": "2022-12-11T17:58:18.096111Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/43ujwtzsojd2phcc6hw2sbbbsu", "cancel": "https://api.replicate.com/v1/predictions/43ujwtzsojd2phcc6hw2sbbbsu/cancel" }, "version": "73d2128a371922d5d1abf0712a1d974be0e4e2358cc1218e4e34714767232bac" }Generated in0%| | 0/143 [00:00<?, ?it/s]/root/.pyenv/versions/3.8.15/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.) return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) 8%|▊ | 12/143 [00:03<00:38, 3.44it/s] 17%|█▋ | 24/143 [00:05<00:27, 4.35it/s] 25%|██▌ | 36/143 [00:07<00:22, 4.84it/s] 34%|███▎ | 48/143 [00:11<00:23, 3.97it/s] 42%|████▏ | 60/143 [00:14<00:21, 3.89it/s] 50%|█████ | 72/143 [00:17<00:18, 3.93it/s] 59%|█████▊ | 84/143 [00:21<00:15, 3.84it/s] 67%|██████▋ | 96/143 [00:24<00:12, 3.83it/s] 76%|███████▌ | 108/143 [00:27<00:09, 3.88it/s] 84%|████████▍ | 120/143 [00:30<00:05, 3.88it/s] 92%|█████████▏| 132/143 [00:33<00:02, 3.88it/s] 100%|██████████| 143/143 [00:36<00:00, 3.85it/s]Prediction
arielreplicate/robust_video_matting:73d2128a371922d5d1abf0712a1d974be0e4e2358cc1218e4e34714767232bacIDoriptsmtjvbmdmcizvc2d3cbgaStatusSucceededSourceWebHardware–Total durationCreatedInput
- input_video
- output_type
- foreground-mask
{ "input_video": "https://replicate.delivery/pbxt/Hqikd7afw2c9koZXS8c3v08x0MFXZFhxFPBkd0iHxrEmRVrk/eddie.mp4", "output_type": "foreground-mask" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run arielreplicate/robust_video_matting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "arielreplicate/robust_video_matting:73d2128a371922d5d1abf0712a1d974be0e4e2358cc1218e4e34714767232bac", { input: { input_video: "https://replicate.delivery/pbxt/Hqikd7afw2c9koZXS8c3v08x0MFXZFhxFPBkd0iHxrEmRVrk/eddie.mp4", output_type: "foreground-mask" } } ); // 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 replicateImport the client:import replicateRun arielreplicate/robust_video_matting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "arielreplicate/robust_video_matting:73d2128a371922d5d1abf0712a1d974be0e4e2358cc1218e4e34714767232bac", input={ "input_video": "https://replicate.delivery/pbxt/Hqikd7afw2c9koZXS8c3v08x0MFXZFhxFPBkd0iHxrEmRVrk/eddie.mp4", "output_type": "foreground-mask" } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run arielreplicate/robust_video_matting 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": "arielreplicate/robust_video_matting:73d2128a371922d5d1abf0712a1d974be0e4e2358cc1218e4e34714767232bac", "input": { "input_video": "https://replicate.delivery/pbxt/Hqikd7afw2c9koZXS8c3v08x0MFXZFhxFPBkd0iHxrEmRVrk/eddie.mp4", "output_type": "foreground-mask" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-12-11T18:00:17.356261Z", "created_at": "2022-12-11T17:59:46.010875Z", "data_removed": false, "error": null, "id": "oriptsmtjvbmdmcizvc2d3cbga", "input": { "input_video": "https://replicate.delivery/pbxt/Hqikd7afw2c9koZXS8c3v08x0MFXZFhxFPBkd0iHxrEmRVrk/eddie.mp4", "output_type": "foreground-mask" }, "logs": "0%| | 0/143 [00:00<?, ?it/s]\u001b[A\n 8%|▊ | 12/143 [00:01<00:17, 7.64it/s]\u001b[A\n 17%|█▋ | 24/143 [00:03<00:15, 7.82it/s]\u001b[A\n 25%|██▌ | 36/143 [00:04<00:13, 7.74it/s]\u001b[A\n 34%|███▎ | 48/143 [00:07<00:15, 6.05it/s]\u001b[A\n 42%|████▏ | 60/143 [00:09<00:14, 5.70it/s]\u001b[A\n 50%|█████ | 72/143 [00:12<00:13, 5.30it/s]\u001b[A\n 59%|█████▊ | 84/143 [00:14<00:11, 5.09it/s]\u001b[A\n 67%|██████▋ | 96/143 [00:17<00:09, 5.01it/s]\u001b[A\n 76%|███████▌ | 108/143 [00:19<00:07, 5.00it/s]\u001b[A\n 84%|████████▍ | 120/143 [00:22<00:04, 4.84it/s]\u001b[A\n 92%|█████████▏| 132/143 [00:24<00:02, 4.79it/s]\u001b[A\n100%|██████████| 143/143 [00:27<00:00, 4.71it/s]\u001b[A", "metrics": { "predict_time": 31.311155, "total_time": 31.345386 }, "output": "https://replicate.delivery/pbxt/dpw8YGYG4TZwGNYfKAe79EkI4yzj2m6ZzFqheKMp4KPhl5RgA/foreground-mask.mp4", "started_at": "2022-12-11T17:59:46.045106Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/oriptsmtjvbmdmcizvc2d3cbga", "cancel": "https://api.replicate.com/v1/predictions/oriptsmtjvbmdmcizvc2d3cbga/cancel" }, "version": "73d2128a371922d5d1abf0712a1d974be0e4e2358cc1218e4e34714767232bac" }Generated in0%| | 0/143 [00:00<?, ?it/s] 8%|▊ | 12/143 [00:01<00:17, 7.64it/s] 17%|█▋ | 24/143 [00:03<00:15, 7.82it/s] 25%|██▌ | 36/143 [00:04<00:13, 7.74it/s] 34%|███▎ | 48/143 [00:07<00:15, 6.05it/s] 42%|████▏ | 60/143 [00:09<00:14, 5.70it/s] 50%|█████ | 72/143 [00:12<00:13, 5.30it/s] 59%|█████▊ | 84/143 [00:14<00:11, 5.09it/s] 67%|██████▋ | 96/143 [00:17<00:09, 5.01it/s] 76%|███████▌ | 108/143 [00:19<00:07, 5.00it/s] 84%|████████▍ | 120/143 [00:22<00:04, 4.84it/s] 92%|█████████▏| 132/143 [00:24<00:02, 4.79it/s] 100%|██████████| 143/143 [00:27<00:00, 4.71it/s]
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