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sniklaus
/
3d-ken-burns
3D Ken Burns Effect from a Single Image
Prediction
sniklaus/3d-ken-burns:9d1d7a66Input
- image
{ "image": "https://replicate.delivery/mgxm/82d4c0eb-8024-4350-a610-2cc78609ee26/mountain.jpeg" }npm install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run sniklaus/3d-ken-burns using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "sniklaus/3d-ken-burns:9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e", { input: { image: "https://replicate.delivery/mgxm/82d4c0eb-8024-4350-a610-2cc78609ee26/mountain.jpeg" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the clientimport replicateRun sniklaus/3d-ken-burns using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "sniklaus/3d-ken-burns:9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e", input={ "image": "https://replicate.delivery/mgxm/82d4c0eb-8024-4350-a610-2cc78609ee26/mountain.jpeg" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run sniklaus/3d-ken-burns 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": "9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e", "input": { "image": "https://replicate.delivery/mgxm/82d4c0eb-8024-4350-a610-2cc78609ee26/mountain.jpeg" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Install Cogbrew install cogIf you don’t have Homebrew, there are other installation options available.
Pull and run sniklaus/3d-ken-burns using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/sniklaus/3d-ken-burns@sha256:9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e \ -i 'image="https://replicate.delivery/mgxm/82d4c0eb-8024-4350-a610-2cc78609ee26/mountain.jpeg"'To learn more, take a look at the Cog documentation.
Pull and run sniklaus/3d-ken-burns using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/sniklaus/3d-ken-burns@sha256:9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6ecurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/mgxm/82d4c0eb-8024-4350-a610-2cc78609ee26/mountain.jpeg" } }' \ http://localhost:5000/predictionsOutput
{ "completed_at": "2022-06-15T21:27:14.344132Z", "created_at": "2022-06-15T21:24:11.048810Z", "data_removed": false, "error": null, "id": "h74vnnagpfabjn6porabw3s7re", "input": { "image": "https://replicate.delivery/mgxm/82d4c0eb-8024-4350-a610-2cc78609ee26/mountain.jpeg" }, "logs": "Moviepy - Building video /tmp/tmpmpe0f1dh/output.mp4.\nMoviepy - Writing video /tmp/tmpmpe0f1dh/output.mp4\n\n\nt: 0%| | 0/150 [00:00<?, ?it/s, now=None]\nt: 6%|▌ | 9/150 [00:00<00:01, 80.60it/s, now=None]\nt: 12%|█▏ | 18/150 [00:00<00:01, 76.72it/s, now=None]\nt: 17%|█▋ | 26/150 [00:00<00:01, 74.17it/s, now=None]\nt: 23%|██▎ | 34/150 [00:00<00:01, 71.81it/s, now=None]\nt: 28%|██▊ | 42/150 [00:00<00:01, 71.72it/s, now=None]\nt: 33%|███▎ | 50/150 [00:00<00:01, 57.50it/s, now=None]\nt: 38%|███▊ | 57/150 [00:00<00:01, 50.76it/s, now=None]\nt: 42%|████▏ | 63/150 [00:01<00:01, 44.97it/s, now=None]\nt: 45%|████▌ | 68/150 [00:01<00:01, 41.96it/s, now=None]\nt: 49%|████▊ | 73/150 [00:01<00:02, 38.34it/s, now=None]\nt: 51%|█████▏ | 77/150 [00:01<00:01, 38.18it/s, now=None]\nt: 54%|█████▍ | 81/150 [00:01<00:01, 38.36it/s, now=None]\nt: 57%|█████▋ | 85/150 [00:01<00:01, 35.63it/s, now=None]\nt: 60%|██████ | 90/150 [00:01<00:01, 35.13it/s, now=None]\nt: 63%|██████▎ | 94/150 [00:02<00:01, 36.23it/s, now=None]\nt: 65%|██████▌ | 98/150 [00:02<00:01, 37.13it/s, now=None]\nt: 68%|██████▊ | 102/150 [00:02<00:01, 37.08it/s, now=None]\nt: 71%|███████ | 106/150 [00:02<00:01, 36.83it/s, now=None]\nt: 73%|███████▎ | 110/150 [00:02<00:01, 37.40it/s, now=None]\nt: 76%|███████▌ | 114/150 [00:02<00:00, 36.57it/s, now=None]\nt: 79%|███████▊ | 118/150 [00:02<00:00, 37.36it/s, now=None]\nt: 81%|████████▏ | 122/150 [00:02<00:00, 35.79it/s, now=None]\nt: 84%|████████▍ | 126/150 [00:02<00:00, 36.83it/s, now=None]\nt: 87%|████████▋ | 130/150 [00:03<00:00, 34.05it/s, now=None]\nt: 90%|█████████ | 135/150 [00:03<00:00, 37.55it/s, now=None]\nt: 93%|█████████▎| 139/150 [00:03<00:00, 36.19it/s, now=None]\nt: 95%|█████████▌| 143/150 [00:03<00:00, 37.12it/s, now=None]\nt: 98%|█████████▊| 147/150 [00:03<00:00, 36.20it/s, now=None]\nMoviepy - Done !\nMoviepy - video ready /tmp/tmpmpe0f1dh/output.mp4\n", "metrics": { "predict_time": 5.068942, "total_time": 183.295322 }, "output": "https://replicate.delivery/mgxm/884b2212-e18a-46c7-adff-87967929ca1d/output.mp4", "started_at": "2022-06-15T21:27:09.275190Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/h74vnnagpfabjn6porabw3s7re", "cancel": "https://api.replicate.com/v1/predictions/h74vnnagpfabjn6porabw3s7re/cancel" }, "version": "9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e" }Generated inMoviepy - Building video /tmp/tmpmpe0f1dh/output.mp4. Moviepy - Writing video /tmp/tmpmpe0f1dh/output.mp4 t: 0%| | 0/150 [00:00<?, ?it/s, now=None] t: 6%|▌ | 9/150 [00:00<00:01, 80.60it/s, now=None] t: 12%|█▏ | 18/150 [00:00<00:01, 76.72it/s, now=None] t: 17%|█▋ | 26/150 [00:00<00:01, 74.17it/s, now=None] t: 23%|██▎ | 34/150 [00:00<00:01, 71.81it/s, now=None] t: 28%|██▊ | 42/150 [00:00<00:01, 71.72it/s, now=None] t: 33%|███▎ | 50/150 [00:00<00:01, 57.50it/s, now=None] t: 38%|███▊ | 57/150 [00:00<00:01, 50.76it/s, now=None] t: 42%|████▏ | 63/150 [00:01<00:01, 44.97it/s, now=None] t: 45%|████▌ | 68/150 [00:01<00:01, 41.96it/s, now=None] t: 49%|████▊ | 73/150 [00:01<00:02, 38.34it/s, now=None] t: 51%|█████▏ | 77/150 [00:01<00:01, 38.18it/s, now=None] t: 54%|█████▍ | 81/150 [00:01<00:01, 38.36it/s, now=None] t: 57%|█████▋ | 85/150 [00:01<00:01, 35.63it/s, now=None] t: 60%|██████ | 90/150 [00:01<00:01, 35.13it/s, now=None] t: 63%|██████▎ | 94/150 [00:02<00:01, 36.23it/s, now=None] t: 65%|██████▌ | 98/150 [00:02<00:01, 37.13it/s, now=None] t: 68%|██████▊ | 102/150 [00:02<00:01, 37.08it/s, now=None] t: 71%|███████ | 106/150 [00:02<00:01, 36.83it/s, now=None] t: 73%|███████▎ | 110/150 [00:02<00:01, 37.40it/s, now=None] t: 76%|███████▌ | 114/150 [00:02<00:00, 36.57it/s, now=None] t: 79%|███████▊ | 118/150 [00:02<00:00, 37.36it/s, now=None] t: 81%|████████▏ | 122/150 [00:02<00:00, 35.79it/s, now=None] t: 84%|████████▍ | 126/150 [00:02<00:00, 36.83it/s, now=None] t: 87%|████████▋ | 130/150 [00:03<00:00, 34.05it/s, now=None] t: 90%|█████████ | 135/150 [00:03<00:00, 37.55it/s, now=None] t: 93%|█████████▎| 139/150 [00:03<00:00, 36.19it/s, now=None] t: 95%|█████████▌| 143/150 [00:03<00:00, 37.12it/s, now=None] t: 98%|█████████▊| 147/150 [00:03<00:00, 36.20it/s, now=None] Moviepy - Done ! Moviepy - video ready /tmp/tmpmpe0f1dh/output.mp4Prediction
sniklaus/3d-ken-burns:9d1d7a66IDfenktneogvakvplqvdgoob2hgiStatusSucceededSourceWebHardware–Total durationCreatedInput
- image
{ "image": "https://replicate.delivery/mgxm/676d965c-ca87-41c3-8a27-b24a8fa3cb5a/grandcanyon.png" }npm install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run sniklaus/3d-ken-burns using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "sniklaus/3d-ken-burns:9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e", { input: { image: "https://replicate.delivery/mgxm/676d965c-ca87-41c3-8a27-b24a8fa3cb5a/grandcanyon.png" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the clientimport replicateRun sniklaus/3d-ken-burns using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "sniklaus/3d-ken-burns:9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e", input={ "image": "https://replicate.delivery/mgxm/676d965c-ca87-41c3-8a27-b24a8fa3cb5a/grandcanyon.png" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run sniklaus/3d-ken-burns 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": "9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e", "input": { "image": "https://replicate.delivery/mgxm/676d965c-ca87-41c3-8a27-b24a8fa3cb5a/grandcanyon.png" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Install Cogbrew install cogIf you don’t have Homebrew, there are other installation options available.
Pull and run sniklaus/3d-ken-burns using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/sniklaus/3d-ken-burns@sha256:9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e \ -i 'image="https://replicate.delivery/mgxm/676d965c-ca87-41c3-8a27-b24a8fa3cb5a/grandcanyon.png"'To learn more, take a look at the Cog documentation.
Pull and run sniklaus/3d-ken-burns using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/sniklaus/3d-ken-burns@sha256:9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6ecurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/mgxm/676d965c-ca87-41c3-8a27-b24a8fa3cb5a/grandcanyon.png" } }' \ http://localhost:5000/predictionsOutput
{ "completed_at": "2022-06-15T21:27:24.952893Z", "created_at": "2022-06-15T21:24:19.990130Z", "data_removed": false, "error": null, "id": "fenktneogvakvplqvdgoob2hgi", "input": { "image": "https://replicate.delivery/mgxm/676d965c-ca87-41c3-8a27-b24a8fa3cb5a/grandcanyon.png" }, "logs": "Moviepy - Building video /tmp/tmpl0ntjbgn/output.mp4.\nMoviepy - Writing video /tmp/tmpl0ntjbgn/output.mp4\n\n\nt: 0%| | 0/150 [00:00<?, ?it/s, now=None]\nt: 5%|▍ | 7/150 [00:00<00:02, 69.66it/s, now=None]\nt: 10%|█ | 15/150 [00:00<00:01, 70.14it/s, now=None]\nt: 15%|█▌ | 23/150 [00:00<00:01, 70.27it/s, now=None]\nt: 21%|██ | 31/150 [00:00<00:01, 71.24it/s, now=None]\nt: 26%|██▌ | 39/150 [00:00<00:01, 70.90it/s, now=None]\nt: 31%|███▏ | 47/150 [00:00<00:01, 67.25it/s, now=None]\nt: 36%|███▌ | 54/150 [00:00<00:01, 50.30it/s, now=None]\nt: 40%|████ | 60/150 [00:01<00:02, 43.75it/s, now=None]\nt: 43%|████▎ | 65/150 [00:01<00:02, 36.58it/s, now=None]\nt: 47%|████▋ | 70/150 [00:01<00:02, 36.90it/s, now=None]\nt: 49%|████▉ | 74/150 [00:01<00:02, 33.24it/s, now=None]\nt: 52%|█████▏ | 78/150 [00:01<00:02, 34.40it/s, now=None]\nt: 55%|█████▍ | 82/150 [00:01<00:01, 34.03it/s, now=None]\nt: 57%|█████▋ | 86/150 [00:01<00:01, 34.67it/s, now=None]\nt: 60%|██████ | 90/150 [00:02<00:01, 35.12it/s, now=None]\nt: 63%|██████▎ | 94/150 [00:02<00:01, 32.51it/s, now=None]\nt: 65%|██████▌ | 98/150 [00:02<00:01, 33.31it/s, now=None]\nt: 68%|██████▊ | 102/150 [00:02<00:01, 31.80it/s, now=None]\nt: 71%|███████ | 106/150 [00:02<00:01, 31.19it/s, now=None]\nt: 73%|███████▎ | 110/150 [00:02<00:01, 32.49it/s, now=None]\nt: 76%|███████▌ | 114/150 [00:02<00:01, 33.55it/s, now=None]\nt: 79%|███████▊ | 118/150 [00:02<00:00, 33.29it/s, now=None]\nt: 81%|████████▏ | 122/150 [00:03<00:00, 30.07it/s, now=None]\nt: 84%|████████▍ | 126/150 [00:03<00:00, 31.85it/s, now=None]\nt: 87%|████████▋ | 131/150 [00:03<00:00, 35.34it/s, now=None]\nt: 90%|█████████ | 135/150 [00:03<00:00, 34.44it/s, now=None]\nt: 93%|█████████▎| 139/150 [00:03<00:00, 35.26it/s, now=None]\nt: 95%|█████████▌| 143/150 [00:03<00:00, 32.48it/s, now=None]\nt: 98%|█████████▊| 147/150 [00:03<00:00, 32.21it/s, now=None]\nMoviepy - Done !\nMoviepy - video ready /tmp/tmpl0ntjbgn/output.mp4\n", "metrics": { "predict_time": 5.688445, "total_time": 184.962763 }, "output": "https://replicate.delivery/mgxm/4731d049-2e41-455f-93e6-b47b89dfb968/output.mp4", "started_at": "2022-06-15T21:27:19.264448Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fenktneogvakvplqvdgoob2hgi", "cancel": "https://api.replicate.com/v1/predictions/fenktneogvakvplqvdgoob2hgi/cancel" }, "version": "9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e" }Generated inMoviepy - Building video /tmp/tmpl0ntjbgn/output.mp4. Moviepy - Writing video /tmp/tmpl0ntjbgn/output.mp4 t: 0%| | 0/150 [00:00<?, ?it/s, now=None] t: 5%|▍ | 7/150 [00:00<00:02, 69.66it/s, now=None] t: 10%|█ | 15/150 [00:00<00:01, 70.14it/s, now=None] t: 15%|█▌ | 23/150 [00:00<00:01, 70.27it/s, now=None] t: 21%|██ | 31/150 [00:00<00:01, 71.24it/s, now=None] t: 26%|██▌ | 39/150 [00:00<00:01, 70.90it/s, now=None] t: 31%|███▏ | 47/150 [00:00<00:01, 67.25it/s, now=None] t: 36%|███▌ | 54/150 [00:00<00:01, 50.30it/s, now=None] t: 40%|████ | 60/150 [00:01<00:02, 43.75it/s, now=None] t: 43%|████▎ | 65/150 [00:01<00:02, 36.58it/s, now=None] t: 47%|████▋ | 70/150 [00:01<00:02, 36.90it/s, now=None] t: 49%|████▉ | 74/150 [00:01<00:02, 33.24it/s, now=None] t: 52%|█████▏ | 78/150 [00:01<00:02, 34.40it/s, now=None] t: 55%|█████▍ | 82/150 [00:01<00:01, 34.03it/s, now=None] t: 57%|█████▋ | 86/150 [00:01<00:01, 34.67it/s, now=None] t: 60%|██████ | 90/150 [00:02<00:01, 35.12it/s, now=None] t: 63%|██████▎ | 94/150 [00:02<00:01, 32.51it/s, now=None] t: 65%|██████▌ | 98/150 [00:02<00:01, 33.31it/s, now=None] t: 68%|██████▊ | 102/150 [00:02<00:01, 31.80it/s, now=None] t: 71%|███████ | 106/150 [00:02<00:01, 31.19it/s, now=None] t: 73%|███████▎ | 110/150 [00:02<00:01, 32.49it/s, now=None] t: 76%|███████▌ | 114/150 [00:02<00:01, 33.55it/s, now=None] t: 79%|███████▊ | 118/150 [00:02<00:00, 33.29it/s, now=None] t: 81%|████████▏ | 122/150 [00:03<00:00, 30.07it/s, now=None] t: 84%|████████▍ | 126/150 [00:03<00:00, 31.85it/s, now=None] t: 87%|████████▋ | 131/150 [00:03<00:00, 35.34it/s, now=None] t: 90%|█████████ | 135/150 [00:03<00:00, 34.44it/s, now=None] t: 93%|█████████▎| 139/150 [00:03<00:00, 35.26it/s, now=None] t: 95%|█████████▌| 143/150 [00:03<00:00, 32.48it/s, now=None] t: 98%|█████████▊| 147/150 [00:03<00:00, 32.21it/s, now=None] Moviepy - Done ! Moviepy - video ready /tmp/tmpl0ntjbgn/output.mp4Prediction
sniklaus/3d-ken-burns:9d1d7a66IDsokij7ddwzdupbbldawohaspquStatusSucceededSourceWebHardware–Total durationCreatedInput
- image
{ "image": "https://replicate.delivery/mgxm/36da82b5-dd3f-4577-bde7-885e15d780f4/doublestrike.jpeg" }npm install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run sniklaus/3d-ken-burns using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "sniklaus/3d-ken-burns:9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e", { input: { image: "https://replicate.delivery/mgxm/36da82b5-dd3f-4577-bde7-885e15d780f4/doublestrike.jpeg" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the clientimport replicateRun sniklaus/3d-ken-burns using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "sniklaus/3d-ken-burns:9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e", input={ "image": "https://replicate.delivery/mgxm/36da82b5-dd3f-4577-bde7-885e15d780f4/doublestrike.jpeg" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run sniklaus/3d-ken-burns 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": "9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e", "input": { "image": "https://replicate.delivery/mgxm/36da82b5-dd3f-4577-bde7-885e15d780f4/doublestrike.jpeg" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Install Cogbrew install cogIf you don’t have Homebrew, there are other installation options available.
Pull and run sniklaus/3d-ken-burns using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/sniklaus/3d-ken-burns@sha256:9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e \ -i 'image="https://replicate.delivery/mgxm/36da82b5-dd3f-4577-bde7-885e15d780f4/doublestrike.jpeg"'To learn more, take a look at the Cog documentation.
Pull and run sniklaus/3d-ken-burns using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/sniklaus/3d-ken-burns@sha256:9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6ecurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/mgxm/36da82b5-dd3f-4577-bde7-885e15d780f4/doublestrike.jpeg" } }' \ http://localhost:5000/predictionsOutput
{ "completed_at": "2022-06-15T21:27:39.593336Z", "created_at": "2022-06-15T21:24:37.959479Z", "data_removed": false, "error": null, "id": "sokij7ddwzdupbbldawohaspqu", "input": { "image": "https://replicate.delivery/mgxm/36da82b5-dd3f-4577-bde7-885e15d780f4/doublestrike.jpeg" }, "logs": "Moviepy - Building video /tmp/tmp2vtj7t_6/output.mp4.\nMoviepy - Writing video /tmp/tmp2vtj7t_6/output.mp4\n\n\nt: 0%| | 0/150 [00:00<?, ?it/s, now=None]\nt: 3%|▎ | 4/150 [00:00<00:03, 36.77it/s, now=None]\nt: 6%|▌ | 9/150 [00:00<00:03, 42.46it/s, now=None]\nt: 11%|█ | 16/150 [00:00<00:02, 51.23it/s, now=None]\nt: 15%|█▍ | 22/150 [00:00<00:02, 53.54it/s, now=None]\nt: 19%|█▉ | 29/150 [00:00<00:02, 56.20it/s, now=None]\nt: 24%|██▍ | 36/150 [00:00<00:01, 58.09it/s, now=None]\nt: 28%|██▊ | 42/150 [00:00<00:01, 58.12it/s, now=None]\nt: 32%|███▏ | 48/150 [00:00<00:02, 49.57it/s, now=None]\nt: 36%|███▌ | 54/150 [00:01<00:02, 46.45it/s, now=None]\nt: 39%|███▉ | 59/150 [00:01<00:02, 42.95it/s, now=None]\nt: 43%|████▎ | 64/150 [00:01<00:02, 42.26it/s, now=None]\nt: 46%|████▌ | 69/150 [00:01<00:01, 42.01it/s, now=None]\nt: 49%|████▉ | 74/150 [00:01<00:01, 40.63it/s, now=None]\nt: 53%|█████▎ | 79/150 [00:01<00:01, 41.73it/s, now=None]\nt: 56%|█████▌ | 84/150 [00:01<00:01, 41.51it/s, now=None]\nt: 59%|█████▉ | 89/150 [00:01<00:01, 41.94it/s, now=None]\nt: 63%|██████▎ | 94/150 [00:02<00:01, 38.62it/s, now=None]\nt: 65%|██████▌ | 98/150 [00:02<00:01, 37.19it/s, now=None]\nt: 69%|██████▊ | 103/150 [00:02<00:01, 40.08it/s, now=None]\nt: 72%|███████▏ | 108/150 [00:02<00:01, 38.50it/s, now=None]\nt: 75%|███████▌ | 113/150 [00:02<00:00, 37.71it/s, now=None]\nt: 79%|███████▊ | 118/150 [00:02<00:00, 39.42it/s, now=None]\nt: 81%|████████▏ | 122/150 [00:02<00:00, 39.15it/s, now=None]\nt: 85%|████████▍ | 127/150 [00:02<00:00, 42.00it/s, now=None]\nt: 88%|████████▊ | 132/150 [00:03<00:00, 41.89it/s, now=None]\nt: 91%|█████████▏| 137/150 [00:03<00:00, 41.85it/s, now=None]\nt: 95%|█████████▍| 142/150 [00:03<00:00, 38.72it/s, now=None]\nt: 98%|█████████▊| 147/150 [00:03<00:00, 40.97it/s, now=None]\nMoviepy - Done !\nMoviepy - video ready /tmp/tmp2vtj7t_6/output.mp4\n", "metrics": { "predict_time": 4.981875, "total_time": 181.633857 }, "output": "https://replicate.delivery/mgxm/06b23b74-8361-4b90-bc4e-1624cac97a1e/output.mp4", "started_at": "2022-06-15T21:27:34.611461Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/sokij7ddwzdupbbldawohaspqu", "cancel": "https://api.replicate.com/v1/predictions/sokij7ddwzdupbbldawohaspqu/cancel" }, "version": "9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e" }Generated inMoviepy - Building video /tmp/tmp2vtj7t_6/output.mp4. Moviepy - Writing video /tmp/tmp2vtj7t_6/output.mp4 t: 0%| | 0/150 [00:00<?, ?it/s, now=None] t: 3%|▎ | 4/150 [00:00<00:03, 36.77it/s, now=None] t: 6%|▌ | 9/150 [00:00<00:03, 42.46it/s, now=None] t: 11%|█ | 16/150 [00:00<00:02, 51.23it/s, now=None] t: 15%|█▍ | 22/150 [00:00<00:02, 53.54it/s, now=None] t: 19%|█▉ | 29/150 [00:00<00:02, 56.20it/s, now=None] t: 24%|██▍ | 36/150 [00:00<00:01, 58.09it/s, now=None] t: 28%|██▊ | 42/150 [00:00<00:01, 58.12it/s, now=None] t: 32%|███▏ | 48/150 [00:00<00:02, 49.57it/s, now=None] t: 36%|███▌ | 54/150 [00:01<00:02, 46.45it/s, now=None] t: 39%|███▉ | 59/150 [00:01<00:02, 42.95it/s, now=None] t: 43%|████▎ | 64/150 [00:01<00:02, 42.26it/s, now=None] t: 46%|████▌ | 69/150 [00:01<00:01, 42.01it/s, now=None] t: 49%|████▉ | 74/150 [00:01<00:01, 40.63it/s, now=None] t: 53%|█████▎ | 79/150 [00:01<00:01, 41.73it/s, now=None] t: 56%|█████▌ | 84/150 [00:01<00:01, 41.51it/s, now=None] t: 59%|█████▉ | 89/150 [00:01<00:01, 41.94it/s, now=None] t: 63%|██████▎ | 94/150 [00:02<00:01, 38.62it/s, now=None] t: 65%|██████▌ | 98/150 [00:02<00:01, 37.19it/s, now=None] t: 69%|██████▊ | 103/150 [00:02<00:01, 40.08it/s, now=None] t: 72%|███████▏ | 108/150 [00:02<00:01, 38.50it/s, now=None] t: 75%|███████▌ | 113/150 [00:02<00:00, 37.71it/s, now=None] t: 79%|███████▊ | 118/150 [00:02<00:00, 39.42it/s, now=None] t: 81%|████████▏ | 122/150 [00:02<00:00, 39.15it/s, now=None] t: 85%|████████▍ | 127/150 [00:02<00:00, 42.00it/s, now=None] t: 88%|████████▊ | 132/150 [00:03<00:00, 41.89it/s, now=None] t: 91%|█████████▏| 137/150 [00:03<00:00, 41.85it/s, now=None] t: 95%|█████████▍| 142/150 [00:03<00:00, 38.72it/s, now=None] t: 98%|█████████▊| 147/150 [00:03<00:00, 40.97it/s, now=None] Moviepy - Done ! Moviepy - video ready /tmp/tmp2vtj7t_6/output.mp4Prediction
sniklaus/3d-ken-burns:9d1d7a66IDfsrnpr32d5dmtj2smz6srfpxuyStatusSucceededSourceWebHardware–Total durationCreatedInput
- image
{ "image": "https://replicate.delivery/mgxm/e9495af8-a521-4a8d-84d5-765de9dabdec/canada.jpeg" }npm install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run sniklaus/3d-ken-burns using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "sniklaus/3d-ken-burns:9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e", { input: { image: "https://replicate.delivery/mgxm/e9495af8-a521-4a8d-84d5-765de9dabdec/canada.jpeg" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the clientimport replicateRun sniklaus/3d-ken-burns using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "sniklaus/3d-ken-burns:9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e", input={ "image": "https://replicate.delivery/mgxm/e9495af8-a521-4a8d-84d5-765de9dabdec/canada.jpeg" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run sniklaus/3d-ken-burns 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": "9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e", "input": { "image": "https://replicate.delivery/mgxm/e9495af8-a521-4a8d-84d5-765de9dabdec/canada.jpeg" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Install Cogbrew install cogIf you don’t have Homebrew, there are other installation options available.
Pull and run sniklaus/3d-ken-burns using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/sniklaus/3d-ken-burns@sha256:9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e \ -i 'image="https://replicate.delivery/mgxm/e9495af8-a521-4a8d-84d5-765de9dabdec/canada.jpeg"'To learn more, take a look at the Cog documentation.
Pull and run sniklaus/3d-ken-burns using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/sniklaus/3d-ken-burns@sha256:9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6ecurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/mgxm/e9495af8-a521-4a8d-84d5-765de9dabdec/canada.jpeg" } }' \ http://localhost:5000/predictionsOutput
{ "completed_at": "2022-06-15T21:32:54.785602Z", "created_at": "2022-06-15T21:32:44.349729Z", "data_removed": false, "error": null, "id": "fsrnpr32d5dmtj2smz6srfpxuy", "input": { "image": "https://replicate.delivery/mgxm/e9495af8-a521-4a8d-84d5-765de9dabdec/canada.jpeg" }, "logs": "Moviepy - Building video /tmp/tmpc5_8wruj/output.mp4.\nMoviepy - Writing video /tmp/tmpc5_8wruj/output.mp4\n\n\nt: 0%| | 0/150 [00:00<?, ?it/s, now=None]\nt: 5%|▌ | 8/150 [00:00<00:01, 72.90it/s, now=None]\nt: 11%|█ | 16/150 [00:00<00:01, 68.80it/s, now=None]\nt: 15%|█▌ | 23/150 [00:00<00:01, 66.44it/s, now=None]\nt: 20%|██ | 30/150 [00:00<00:01, 65.59it/s, now=None]\nt: 25%|██▍ | 37/150 [00:00<00:01, 63.15it/s, now=None]\nt: 29%|██▉ | 44/150 [00:00<00:01, 62.85it/s, now=None]\nt: 34%|███▍ | 51/150 [00:00<00:01, 49.87it/s, now=None]\nt: 38%|███▊ | 57/150 [00:01<00:02, 44.63it/s, now=None]\nt: 41%|████▏ | 62/150 [00:01<00:02, 41.75it/s, now=None]\nt: 45%|████▍ | 67/150 [00:01<00:01, 42.04it/s, now=None]\nt: 48%|████▊ | 72/150 [00:01<00:01, 42.13it/s, now=None]\nt: 51%|█████▏ | 77/150 [00:01<00:01, 41.48it/s, now=None]\nt: 55%|█████▍ | 82/150 [00:01<00:01, 40.63it/s, now=None]\nt: 58%|█████▊ | 87/150 [00:01<00:01, 40.55it/s, now=None]\nt: 61%|██████▏ | 92/150 [00:01<00:01, 41.32it/s, now=None]\nt: 65%|██████▍ | 97/150 [00:02<00:01, 40.97it/s, now=None]\nt: 68%|██████▊ | 102/150 [00:02<00:01, 40.35it/s, now=None]\nt: 71%|███████▏ | 107/150 [00:02<00:01, 41.83it/s, now=None]\nt: 75%|███████▍ | 112/150 [00:02<00:00, 41.38it/s, now=None]\nt: 78%|███████▊ | 117/150 [00:02<00:00, 41.95it/s, now=None]\nt: 81%|████████▏ | 122/150 [00:02<00:00, 42.61it/s, now=None]\nt: 85%|████████▍ | 127/150 [00:02<00:00, 40.55it/s, now=None]\nt: 88%|████████▊ | 132/150 [00:02<00:00, 42.69it/s, now=None]\nt: 91%|█████████▏| 137/150 [00:02<00:00, 42.26it/s, now=None]\nt: 95%|█████████▍| 142/150 [00:03<00:00, 42.96it/s, now=None]\nt: 98%|█████████▊| 147/150 [00:03<00:00, 42.94it/s, now=None]\nMoviepy - Done !\nMoviepy - video ready /tmp/tmpc5_8wruj/output.mp4\n", "metrics": { "predict_time": 4.6756, "total_time": 10.435873 }, "output": "https://replicate.delivery/mgxm/27c20eee-0999-426f-9b2c-39ebe97804fd/output.mp4", "started_at": "2022-06-15T21:32:50.110002Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fsrnpr32d5dmtj2smz6srfpxuy", "cancel": "https://api.replicate.com/v1/predictions/fsrnpr32d5dmtj2smz6srfpxuy/cancel" }, "version": "9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e" }Generated inMoviepy - Building video /tmp/tmpc5_8wruj/output.mp4. Moviepy - Writing video /tmp/tmpc5_8wruj/output.mp4 t: 0%| | 0/150 [00:00<?, ?it/s, now=None] t: 5%|▌ | 8/150 [00:00<00:01, 72.90it/s, now=None] t: 11%|█ | 16/150 [00:00<00:01, 68.80it/s, now=None] t: 15%|█▌ | 23/150 [00:00<00:01, 66.44it/s, now=None] t: 20%|██ | 30/150 [00:00<00:01, 65.59it/s, now=None] t: 25%|██▍ | 37/150 [00:00<00:01, 63.15it/s, now=None] t: 29%|██▉ | 44/150 [00:00<00:01, 62.85it/s, now=None] t: 34%|███▍ | 51/150 [00:00<00:01, 49.87it/s, now=None] t: 38%|███▊ | 57/150 [00:01<00:02, 44.63it/s, now=None] t: 41%|████▏ | 62/150 [00:01<00:02, 41.75it/s, now=None] t: 45%|████▍ | 67/150 [00:01<00:01, 42.04it/s, now=None] t: 48%|████▊ | 72/150 [00:01<00:01, 42.13it/s, now=None] t: 51%|█████▏ | 77/150 [00:01<00:01, 41.48it/s, now=None] t: 55%|█████▍ | 82/150 [00:01<00:01, 40.63it/s, now=None] t: 58%|█████▊ | 87/150 [00:01<00:01, 40.55it/s, now=None] t: 61%|██████▏ | 92/150 [00:01<00:01, 41.32it/s, now=None] t: 65%|██████▍ | 97/150 [00:02<00:01, 40.97it/s, now=None] t: 68%|██████▊ | 102/150 [00:02<00:01, 40.35it/s, now=None] t: 71%|███████▏ | 107/150 [00:02<00:01, 41.83it/s, now=None] t: 75%|███████▍ | 112/150 [00:02<00:00, 41.38it/s, now=None] t: 78%|███████▊ | 117/150 [00:02<00:00, 41.95it/s, now=None] t: 81%|████████▏ | 122/150 [00:02<00:00, 42.61it/s, now=None] t: 85%|████████▍ | 127/150 [00:02<00:00, 40.55it/s, now=None] t: 88%|████████▊ | 132/150 [00:02<00:00, 42.69it/s, now=None] t: 91%|█████████▏| 137/150 [00:02<00:00, 42.26it/s, now=None] t: 95%|█████████▍| 142/150 [00:03<00:00, 42.96it/s, now=None] t: 98%|█████████▊| 147/150 [00:03<00:00, 42.94it/s, now=None] Moviepy - Done ! Moviepy - video ready /tmp/tmpc5_8wruj/output.mp4Prediction
sniklaus/3d-ken-burns:9d1d7a66IDccpxzjwnqrffvhs72l33vlxcdiStatusSucceededSourceWebHardware–Total durationCreatedInput
- image
{ "image": "https://replicate.delivery/mgxm/d6d3e677-23dd-48a6-963b-d288bea27f05/switz.jpg" }npm install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run sniklaus/3d-ken-burns using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "sniklaus/3d-ken-burns:9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e", { input: { image: "https://replicate.delivery/mgxm/d6d3e677-23dd-48a6-963b-d288bea27f05/switz.jpg" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the clientimport replicateRun sniklaus/3d-ken-burns using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "sniklaus/3d-ken-burns:9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e", input={ "image": "https://replicate.delivery/mgxm/d6d3e677-23dd-48a6-963b-d288bea27f05/switz.jpg" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run sniklaus/3d-ken-burns 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": "9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e", "input": { "image": "https://replicate.delivery/mgxm/d6d3e677-23dd-48a6-963b-d288bea27f05/switz.jpg" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Install Cogbrew install cogIf you don’t have Homebrew, there are other installation options available.
Pull and run sniklaus/3d-ken-burns using Cog (this will download the full model and run it in your local environment):
cog predict r8.im/sniklaus/3d-ken-burns@sha256:9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e \ -i 'image="https://replicate.delivery/mgxm/d6d3e677-23dd-48a6-963b-d288bea27f05/switz.jpg"'To learn more, take a look at the Cog documentation.
Pull and run sniklaus/3d-ken-burns using Docker (this will download the full model and run it in your local environment):
docker run -d -p 5000:5000 --gpus=all r8.im/sniklaus/3d-ken-burns@sha256:9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6ecurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/mgxm/d6d3e677-23dd-48a6-963b-d288bea27f05/switz.jpg" } }' \ http://localhost:5000/predictionsOutput
{ "completed_at": "2022-06-15T21:33:37.631762Z", "created_at": "2022-06-15T21:33:27.464872Z", "data_removed": false, "error": null, "id": "ccpxzjwnqrffvhs72l33vlxcdi", "input": { "image": "https://replicate.delivery/mgxm/d6d3e677-23dd-48a6-963b-d288bea27f05/switz.jpg" }, "logs": "Moviepy - Building video /tmp/tmp5glol16t/output.mp4.\nMoviepy - Writing video /tmp/tmp5glol16t/output.mp4\n\n\nt: 0%| | 0/150 [00:00<?, ?it/s, now=None]\nt: 5%|▍ | 7/150 [00:00<00:02, 67.70it/s, now=None]\nt: 10%|█ | 15/150 [00:00<00:01, 72.15it/s, now=None]\nt: 15%|█▌ | 23/150 [00:00<00:01, 73.24it/s, now=None]\nt: 21%|██ | 31/150 [00:00<00:01, 71.34it/s, now=None]\nt: 26%|██▌ | 39/150 [00:00<00:01, 68.26it/s, now=None]\nt: 31%|███ | 46/150 [00:00<00:01, 65.95it/s, now=None]\nt: 35%|███▌ | 53/150 [00:00<00:01, 54.57it/s, now=None]\nt: 39%|███▉ | 59/150 [00:00<00:01, 51.43it/s, now=None]\nt: 43%|████▎ | 65/150 [00:01<00:01, 48.17it/s, now=None]\nt: 47%|████▋ | 70/150 [00:01<00:01, 45.40it/s, now=None]\nt: 51%|█████ | 76/150 [00:01<00:01, 46.82it/s, now=None]\nt: 54%|█████▍ | 81/150 [00:01<00:01, 47.36it/s, now=None]\nt: 57%|█████▋ | 86/150 [00:01<00:01, 47.93it/s, now=None]\nt: 61%|██████ | 91/150 [00:01<00:01, 46.91it/s, now=None]\nt: 64%|██████▍ | 96/150 [00:01<00:01, 47.74it/s, now=None]\nt: 67%|██████▋ | 101/150 [00:01<00:01, 47.73it/s, now=None]\nt: 71%|███████ | 106/150 [00:02<00:00, 47.11it/s, now=None]\nt: 74%|███████▍ | 111/150 [00:02<00:00, 45.37it/s, now=None]\nt: 77%|███████▋ | 116/150 [00:02<00:00, 42.43it/s, now=None]\nt: 81%|████████ | 121/150 [00:02<00:00, 41.69it/s, now=None]\nt: 85%|████████▍ | 127/150 [00:02<00:00, 45.85it/s, now=None]\nt: 88%|████████▊ | 132/150 [00:02<00:00, 45.43it/s, now=None]\nt: 91%|█████████▏| 137/150 [00:02<00:00, 41.34it/s, now=None]\nt: 95%|█████████▍| 142/150 [00:02<00:00, 41.97it/s, now=None]\nt: 98%|█████████▊| 147/150 [00:02<00:00, 40.97it/s, now=None]\nMoviepy - Done !\nMoviepy - video ready /tmp/tmp5glol16t/output.mp4\n", "metrics": { "predict_time": 4.450298, "total_time": 10.16689 }, "output": "https://replicate.delivery/mgxm/318c1d0a-ae3c-483d-857a-a6bf610d44b1/output.mp4", "started_at": "2022-06-15T21:33:33.181464Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ccpxzjwnqrffvhs72l33vlxcdi", "cancel": "https://api.replicate.com/v1/predictions/ccpxzjwnqrffvhs72l33vlxcdi/cancel" }, "version": "9d1d7a66c3a1475fc5b1e09deb4da0d9868beaa91e9f159470f54b6a7dc8ff6e" }Generated inMoviepy - Building video /tmp/tmp5glol16t/output.mp4. Moviepy - Writing video /tmp/tmp5glol16t/output.mp4 t: 0%| | 0/150 [00:00<?, ?it/s, now=None] t: 5%|▍ | 7/150 [00:00<00:02, 67.70it/s, now=None] t: 10%|█ | 15/150 [00:00<00:01, 72.15it/s, now=None] t: 15%|█▌ | 23/150 [00:00<00:01, 73.24it/s, now=None] t: 21%|██ | 31/150 [00:00<00:01, 71.34it/s, now=None] t: 26%|██▌ | 39/150 [00:00<00:01, 68.26it/s, now=None] t: 31%|███ | 46/150 [00:00<00:01, 65.95it/s, now=None] t: 35%|███▌ | 53/150 [00:00<00:01, 54.57it/s, now=None] t: 39%|███▉ | 59/150 [00:00<00:01, 51.43it/s, now=None] t: 43%|████▎ | 65/150 [00:01<00:01, 48.17it/s, now=None] t: 47%|████▋ | 70/150 [00:01<00:01, 45.40it/s, now=None] t: 51%|█████ | 76/150 [00:01<00:01, 46.82it/s, now=None] t: 54%|█████▍ | 81/150 [00:01<00:01, 47.36it/s, now=None] t: 57%|█████▋ | 86/150 [00:01<00:01, 47.93it/s, now=None] t: 61%|██████ | 91/150 [00:01<00:01, 46.91it/s, now=None] t: 64%|██████▍ | 96/150 [00:01<00:01, 47.74it/s, now=None] t: 67%|██████▋ | 101/150 [00:01<00:01, 47.73it/s, now=None] t: 71%|███████ | 106/150 [00:02<00:00, 47.11it/s, now=None] t: 74%|███████▍ | 111/150 [00:02<00:00, 45.37it/s, now=None] t: 77%|███████▋ | 116/150 [00:02<00:00, 42.43it/s, now=None] t: 81%|████████ | 121/150 [00:02<00:00, 41.69it/s, now=None] t: 85%|████████▍ | 127/150 [00:02<00:00, 45.85it/s, now=None] t: 88%|████████▊ | 132/150 [00:02<00:00, 45.43it/s, now=None] t: 91%|█████████▏| 137/150 [00:02<00:00, 41.34it/s, now=None] t: 95%|█████████▍| 142/150 [00:02<00:00, 41.97it/s, now=None] t: 98%|█████████▊| 147/150 [00:02<00:00, 40.97it/s, now=None] Moviepy - Done ! Moviepy - video ready /tmp/tmp5glol16t/output.mp4
Want to make some of these yourself?
Run this model