camenduru
/
open-sora-plan-512x512
Open Sora Plan Text To Video
Prediction
camenduru/open-sora-plan-512x512:6dca0dc6IDfm0ye72ccdrgj0ceq3eawfaf3gStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 0
- prompt
- A quiet beach at dawn, the waves gently lapping at the shore and the sky painted in pastel hues.
- force_images
- sample_steps
- 50
- guidance_scale
- 10
- randomize_seed
{ "seed": 0, "prompt": "A quiet beach at dawn, the waves gently lapping at the shore and the sky painted in pastel hues.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment 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 camenduru/open-sora-plan-512x512 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "camenduru/open-sora-plan-512x512:6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", { input: { seed: 0, prompt: "A quiet beach at dawn, the waves gently lapping at the shore and the sky painted in pastel hues.", force_images: false, sample_steps: 50, guidance_scale: 10, randomize_seed: true } } ); 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 theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun camenduru/open-sora-plan-512x512 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "camenduru/open-sora-plan-512x512:6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", input={ "seed": 0, "prompt": "A quiet beach at dawn, the waves gently lapping at the shore and the sky painted in pastel hues.", "force_images": False, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": True } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run camenduru/open-sora-plan-512x512 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": "6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", "input": { "seed": 0, "prompt": "A quiet beach at dawn, the waves gently lapping at the shore and the sky painted in pastel hues.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-04-07T14:07:26.169016Z", "created_at": "2024-04-07T14:00:29.027000Z", "data_removed": false, "error": null, "id": "fm0ye72ccdrgj0ceq3eawfaf3g", "input": { "seed": 0, "prompt": "A quiet beach at dawn, the waves gently lapping at the shore and the sky painted in pastel hues.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true }, "logs": "Setting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the\ninstallation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones\nthat match your environment. Please note that you may need to restart your runtime after installation.\nSetting `clean_caption` to False...\nSetting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the\ninstallation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones\nthat match your environment. Please note that you may need to restart your runtime after installation.\nSetting `clean_caption` to False...\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:11<09:03, 11.09s/it]\n 4%|▍ | 2/50 [00:12<04:10, 5.22s/it]\n 6%|▌ | 3/50 [00:13<02:36, 3.34s/it]\n 8%|▊ | 4/50 [00:14<01:52, 2.45s/it]\n 10%|█ | 5/50 [00:15<01:28, 1.97s/it]\n 12%|█▏ | 6/50 [00:16<01:13, 1.67s/it]\n 14%|█▍ | 7/50 [00:17<01:03, 1.49s/it]\n 16%|█▌ | 8/50 [00:18<00:57, 1.36s/it]\n 18%|█▊ | 9/50 [00:19<00:52, 1.28s/it]\n 20%|██ | 10/50 [00:21<00:49, 1.23s/it]\n 22%|██▏ | 11/50 [00:22<00:46, 1.19s/it]\n 24%|██▍ | 12/50 [00:23<00:44, 1.16s/it]\n 26%|██▌ | 13/50 [00:24<00:42, 1.14s/it]\n 28%|██▊ | 14/50 [00:25<00:40, 1.13s/it]\n 30%|███ | 15/50 [00:26<00:39, 1.12s/it]\n 32%|███▏ | 16/50 [00:27<00:38, 1.12s/it]\n 34%|███▍ | 17/50 [00:28<00:36, 1.12s/it]\n 36%|███▌ | 18/50 [00:29<00:35, 1.11s/it]\n 38%|███▊ | 19/50 [00:30<00:34, 1.11s/it]\n 40%|████ | 20/50 [00:32<00:33, 1.11s/it]\n 42%|████▏ | 21/50 [00:33<00:32, 1.11s/it]\n 44%|████▍ | 22/50 [00:34<00:31, 1.11s/it]\n 46%|████▌ | 23/50 [00:35<00:29, 1.11s/it]\n 48%|████▊ | 24/50 [00:36<00:28, 1.11s/it]\n 50%|█████ | 25/50 [00:37<00:27, 1.11s/it]\n 52%|█████▏ | 26/50 [00:38<00:26, 1.11s/it]\n 54%|█████▍ | 27/50 [00:39<00:25, 1.11s/it]\n 56%|█████▌ | 28/50 [00:40<00:24, 1.11s/it]\n 58%|█████▊ | 29/50 [00:42<00:23, 1.11s/it]\n 60%|██████ | 30/50 [00:43<00:22, 1.11s/it]\n 62%|██████▏ | 31/50 [00:44<00:21, 1.11s/it]\n 64%|██████▍ | 32/50 [00:45<00:20, 1.11s/it]\n 66%|██████▌ | 33/50 [00:46<00:18, 1.11s/it]\n 68%|██████▊ | 34/50 [00:47<00:17, 1.11s/it]\n 70%|███████ | 35/50 [00:48<00:16, 1.11s/it]\n 72%|███████▏ | 36/50 [00:49<00:15, 1.11s/it]\n 74%|███████▍ | 37/50 [00:50<00:14, 1.11s/it]\n 76%|███████▌ | 38/50 [00:52<00:13, 1.11s/it]\n 78%|███████▊ | 39/50 [00:53<00:12, 1.11s/it]\n 80%|████████ | 40/50 [00:54<00:11, 1.11s/it]\n 82%|████████▏ | 41/50 [00:55<00:10, 1.11s/it]\n 84%|████████▍ | 42/50 [00:56<00:08, 1.12s/it]\n 86%|████████▌ | 43/50 [00:57<00:07, 1.12s/it]\n 88%|████████▊ | 44/50 [00:58<00:06, 1.11s/it]\n 90%|█████████ | 45/50 [00:59<00:05, 1.11s/it]\n 92%|█████████▏| 46/50 [01:00<00:04, 1.11s/it]\n 94%|█████████▍| 47/50 [01:02<00:03, 1.11s/it]\n 96%|█████████▌| 48/50 [01:03<00:02, 1.11s/it]\n 98%|█████████▊| 49/50 [01:04<00:01, 1.11s/it]\n100%|██████████| 50/50 [01:05<00:00, 1.11s/it]\n100%|██████████| 50/50 [01:05<00:00, 1.31s/it]", "metrics": { "predict_time": 76.30664, "total_time": 417.142016 }, "output": "https://replicate.delivery/pbxt/t2EtLj0MWhp4OJoJ0ymohD9NDpnKzt0eb9hDyxQsqrZO1CUJA/tmp.mp4", "started_at": "2024-04-07T14:06:09.862376Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fm0ye72ccdrgj0ceq3eawfaf3g", "cancel": "https://api.replicate.com/v1/predictions/fm0ye72ccdrgj0ceq3eawfaf3g/cancel" }, "version": "83b03885f983bd923e70afa5a51e2369d55aa34d2439014a484df3107a4322ca" }Generated inSetting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the installation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. Setting `clean_caption` to False... Setting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the installation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. Setting `clean_caption` to False... 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:11<09:03, 11.09s/it] 4%|▍ | 2/50 [00:12<04:10, 5.22s/it] 6%|▌ | 3/50 [00:13<02:36, 3.34s/it] 8%|▊ | 4/50 [00:14<01:52, 2.45s/it] 10%|█ | 5/50 [00:15<01:28, 1.97s/it] 12%|█▏ | 6/50 [00:16<01:13, 1.67s/it] 14%|█▍ | 7/50 [00:17<01:03, 1.49s/it] 16%|█▌ | 8/50 [00:18<00:57, 1.36s/it] 18%|█▊ | 9/50 [00:19<00:52, 1.28s/it] 20%|██ | 10/50 [00:21<00:49, 1.23s/it] 22%|██▏ | 11/50 [00:22<00:46, 1.19s/it] 24%|██▍ | 12/50 [00:23<00:44, 1.16s/it] 26%|██▌ | 13/50 [00:24<00:42, 1.14s/it] 28%|██▊ | 14/50 [00:25<00:40, 1.13s/it] 30%|███ | 15/50 [00:26<00:39, 1.12s/it] 32%|███▏ | 16/50 [00:27<00:38, 1.12s/it] 34%|███▍ | 17/50 [00:28<00:36, 1.12s/it] 36%|███▌ | 18/50 [00:29<00:35, 1.11s/it] 38%|███▊ | 19/50 [00:30<00:34, 1.11s/it] 40%|████ | 20/50 [00:32<00:33, 1.11s/it] 42%|████▏ | 21/50 [00:33<00:32, 1.11s/it] 44%|████▍ | 22/50 [00:34<00:31, 1.11s/it] 46%|████▌ | 23/50 [00:35<00:29, 1.11s/it] 48%|████▊ | 24/50 [00:36<00:28, 1.11s/it] 50%|█████ | 25/50 [00:37<00:27, 1.11s/it] 52%|█████▏ | 26/50 [00:38<00:26, 1.11s/it] 54%|█████▍ | 27/50 [00:39<00:25, 1.11s/it] 56%|█████▌ | 28/50 [00:40<00:24, 1.11s/it] 58%|█████▊ | 29/50 [00:42<00:23, 1.11s/it] 60%|██████ | 30/50 [00:43<00:22, 1.11s/it] 62%|██████▏ | 31/50 [00:44<00:21, 1.11s/it] 64%|██████▍ | 32/50 [00:45<00:20, 1.11s/it] 66%|██████▌ | 33/50 [00:46<00:18, 1.11s/it] 68%|██████▊ | 34/50 [00:47<00:17, 1.11s/it] 70%|███████ | 35/50 [00:48<00:16, 1.11s/it] 72%|███████▏ | 36/50 [00:49<00:15, 1.11s/it] 74%|███████▍ | 37/50 [00:50<00:14, 1.11s/it] 76%|███████▌ | 38/50 [00:52<00:13, 1.11s/it] 78%|███████▊ | 39/50 [00:53<00:12, 1.11s/it] 80%|████████ | 40/50 [00:54<00:11, 1.11s/it] 82%|████████▏ | 41/50 [00:55<00:10, 1.11s/it] 84%|████████▍ | 42/50 [00:56<00:08, 1.12s/it] 86%|████████▌ | 43/50 [00:57<00:07, 1.12s/it] 88%|████████▊ | 44/50 [00:58<00:06, 1.11s/it] 90%|█████████ | 45/50 [00:59<00:05, 1.11s/it] 92%|█████████▏| 46/50 [01:00<00:04, 1.11s/it] 94%|█████████▍| 47/50 [01:02<00:03, 1.11s/it] 96%|█████████▌| 48/50 [01:03<00:02, 1.11s/it] 98%|█████████▊| 49/50 [01:04<00:01, 1.11s/it] 100%|██████████| 50/50 [01:05<00:00, 1.11s/it] 100%|██████████| 50/50 [01:05<00:00, 1.31s/it]Prediction
camenduru/open-sora-plan-512x512:6dca0dc6IDy7f8kh5mtsrgm0ceq3mr04zzj0StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 0
- prompt
- A quiet beach at dawn, the waves gently lapping at the shore and the sky painted in pastel hues....
- force_images
- sample_steps
- 50
- guidance_scale
- 10
- randomize_seed
{ "seed": 0, "prompt": "A quiet beach at dawn, the waves gently lapping at the shore and the sky painted in pastel hues....", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment 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 camenduru/open-sora-plan-512x512 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "camenduru/open-sora-plan-512x512:6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", { input: { seed: 0, prompt: "A quiet beach at dawn, the waves gently lapping at the shore and the sky painted in pastel hues....", force_images: false, sample_steps: 50, guidance_scale: 10, randomize_seed: true } } ); 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 theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun camenduru/open-sora-plan-512x512 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "camenduru/open-sora-plan-512x512:6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", input={ "seed": 0, "prompt": "A quiet beach at dawn, the waves gently lapping at the shore and the sky painted in pastel hues....", "force_images": False, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": True } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run camenduru/open-sora-plan-512x512 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": "6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", "input": { "seed": 0, "prompt": "A quiet beach at dawn, the waves gently lapping at the shore and the sky painted in pastel hues....", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-04-07T14:16:24.126122Z", "created_at": "2024-04-07T14:15:07.734000Z", "data_removed": false, "error": null, "id": "y7f8kh5mtsrgm0ceq3mr04zzj0", "input": { "seed": 0, "prompt": "A quiet beach at dawn, the waves gently lapping at the shore and the sky painted in pastel hues....", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true }, "logs": "Setting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the\ninstallation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones\nthat match your environment. Please note that you may need to restart your runtime after installation.\nSetting `clean_caption` to False...\nSetting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the\ninstallation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones\nthat match your environment. Please note that you may need to restart your runtime after installation.\nSetting `clean_caption` to False...\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:11<09:06, 11.15s/it]\n 4%|▍ | 2/50 [00:12<04:12, 5.25s/it]\n 6%|▌ | 3/50 [00:13<02:38, 3.36s/it]\n 8%|▊ | 4/50 [00:14<01:54, 2.48s/it]\n 10%|█ | 5/50 [00:15<01:29, 1.99s/it]\n 12%|█▏ | 6/50 [00:16<01:14, 1.69s/it]\n 14%|█▍ | 7/50 [00:17<01:04, 1.51s/it]\n 16%|█▌ | 8/50 [00:18<00:58, 1.38s/it]\n 18%|█▊ | 9/50 [00:20<00:53, 1.30s/it]\n 20%|██ | 10/50 [00:21<00:49, 1.24s/it]\n 22%|██▏ | 11/50 [00:22<00:47, 1.21s/it]\n 24%|██▍ | 12/50 [00:23<00:44, 1.18s/it]\n 26%|██▌ | 13/50 [00:24<00:42, 1.16s/it]\n 28%|██▊ | 14/50 [00:25<00:41, 1.15s/it]\n 30%|███ | 15/50 [00:26<00:39, 1.14s/it]\n 32%|███▏ | 16/50 [00:27<00:38, 1.13s/it]\n 34%|███▍ | 17/50 [00:29<00:37, 1.13s/it]\n 36%|███▌ | 18/50 [00:30<00:36, 1.13s/it]\n 38%|███▊ | 19/50 [00:31<00:34, 1.12s/it]\n 40%|████ | 20/50 [00:32<00:33, 1.12s/it]\n 42%|████▏ | 21/50 [00:33<00:32, 1.12s/it]\n 44%|████▍ | 22/50 [00:34<00:31, 1.12s/it]\n 46%|████▌ | 23/50 [00:35<00:30, 1.12s/it]\n 48%|████▊ | 24/50 [00:36<00:29, 1.12s/it]\n 50%|█████ | 25/50 [00:38<00:28, 1.12s/it]\n 52%|█████▏ | 26/50 [00:39<00:26, 1.12s/it]\n 54%|█████▍ | 27/50 [00:40<00:25, 1.12s/it]\n 56%|█████▌ | 28/50 [00:41<00:24, 1.12s/it]\n 58%|█████▊ | 29/50 [00:42<00:23, 1.12s/it]\n 60%|██████ | 30/50 [00:43<00:22, 1.12s/it]\n 62%|██████▏ | 31/50 [00:44<00:21, 1.12s/it]\n 64%|██████▍ | 32/50 [00:45<00:20, 1.12s/it]\n 66%|██████▌ | 33/50 [00:46<00:19, 1.12s/it]\n 68%|██████▊ | 34/50 [00:48<00:17, 1.12s/it]\n 70%|███████ | 35/50 [00:49<00:16, 1.12s/it]\n 72%|███████▏ | 36/50 [00:50<00:15, 1.12s/it]\n 74%|███████▍ | 37/50 [00:51<00:14, 1.12s/it]\n 76%|███████▌ | 38/50 [00:52<00:13, 1.12s/it]\n 78%|███████▊ | 39/50 [00:53<00:12, 1.12s/it]\n 80%|████████ | 40/50 [00:54<00:11, 1.12s/it]\n 82%|████████▏ | 41/50 [00:55<00:10, 1.12s/it]\n 84%|████████▍ | 42/50 [00:57<00:08, 1.12s/it]\n 86%|████████▌ | 43/50 [00:58<00:07, 1.12s/it]\n 88%|████████▊ | 44/50 [00:59<00:06, 1.12s/it]\n 90%|█████████ | 45/50 [01:00<00:05, 1.12s/it]\n 92%|█████████▏| 46/50 [01:01<00:04, 1.12s/it]\n 94%|█████████▍| 47/50 [01:02<00:03, 1.12s/it]\n 96%|█████████▌| 48/50 [01:03<00:02, 1.12s/it]\n 98%|█████████▊| 49/50 [01:04<00:01, 1.12s/it]\n100%|██████████| 50/50 [01:06<00:00, 1.12s/it]\n100%|██████████| 50/50 [01:06<00:00, 1.32s/it]", "metrics": { "predict_time": 76.355605, "total_time": 76.392122 }, "output": "https://replicate.delivery/pbxt/1iLFiufiNgxfxUYf2bkcFNAtBzbg9S6MIOWVZI5qPgfaLXgKB/tmp.mp4", "started_at": "2024-04-07T14:15:07.770517Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/y7f8kh5mtsrgm0ceq3mr04zzj0", "cancel": "https://api.replicate.com/v1/predictions/y7f8kh5mtsrgm0ceq3mr04zzj0/cancel" }, "version": "83b03885f983bd923e70afa5a51e2369d55aa34d2439014a484df3107a4322ca" }Generated inSetting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the installation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. Setting `clean_caption` to False... Setting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the installation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. Setting `clean_caption` to False... 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:11<09:06, 11.15s/it] 4%|▍ | 2/50 [00:12<04:12, 5.25s/it] 6%|▌ | 3/50 [00:13<02:38, 3.36s/it] 8%|▊ | 4/50 [00:14<01:54, 2.48s/it] 10%|█ | 5/50 [00:15<01:29, 1.99s/it] 12%|█▏ | 6/50 [00:16<01:14, 1.69s/it] 14%|█▍ | 7/50 [00:17<01:04, 1.51s/it] 16%|█▌ | 8/50 [00:18<00:58, 1.38s/it] 18%|█▊ | 9/50 [00:20<00:53, 1.30s/it] 20%|██ | 10/50 [00:21<00:49, 1.24s/it] 22%|██▏ | 11/50 [00:22<00:47, 1.21s/it] 24%|██▍ | 12/50 [00:23<00:44, 1.18s/it] 26%|██▌ | 13/50 [00:24<00:42, 1.16s/it] 28%|██▊ | 14/50 [00:25<00:41, 1.15s/it] 30%|███ | 15/50 [00:26<00:39, 1.14s/it] 32%|███▏ | 16/50 [00:27<00:38, 1.13s/it] 34%|███▍ | 17/50 [00:29<00:37, 1.13s/it] 36%|███▌ | 18/50 [00:30<00:36, 1.13s/it] 38%|███▊ | 19/50 [00:31<00:34, 1.12s/it] 40%|████ | 20/50 [00:32<00:33, 1.12s/it] 42%|████▏ | 21/50 [00:33<00:32, 1.12s/it] 44%|████▍ | 22/50 [00:34<00:31, 1.12s/it] 46%|████▌ | 23/50 [00:35<00:30, 1.12s/it] 48%|████▊ | 24/50 [00:36<00:29, 1.12s/it] 50%|█████ | 25/50 [00:38<00:28, 1.12s/it] 52%|█████▏ | 26/50 [00:39<00:26, 1.12s/it] 54%|█████▍ | 27/50 [00:40<00:25, 1.12s/it] 56%|█████▌ | 28/50 [00:41<00:24, 1.12s/it] 58%|█████▊ | 29/50 [00:42<00:23, 1.12s/it] 60%|██████ | 30/50 [00:43<00:22, 1.12s/it] 62%|██████▏ | 31/50 [00:44<00:21, 1.12s/it] 64%|██████▍ | 32/50 [00:45<00:20, 1.12s/it] 66%|██████▌ | 33/50 [00:46<00:19, 1.12s/it] 68%|██████▊ | 34/50 [00:48<00:17, 1.12s/it] 70%|███████ | 35/50 [00:49<00:16, 1.12s/it] 72%|███████▏ | 36/50 [00:50<00:15, 1.12s/it] 74%|███████▍ | 37/50 [00:51<00:14, 1.12s/it] 76%|███████▌ | 38/50 [00:52<00:13, 1.12s/it] 78%|███████▊ | 39/50 [00:53<00:12, 1.12s/it] 80%|████████ | 40/50 [00:54<00:11, 1.12s/it] 82%|████████▏ | 41/50 [00:55<00:10, 1.12s/it] 84%|████████▍ | 42/50 [00:57<00:08, 1.12s/it] 86%|████████▌ | 43/50 [00:58<00:07, 1.12s/it] 88%|████████▊ | 44/50 [00:59<00:06, 1.12s/it] 90%|█████████ | 45/50 [01:00<00:05, 1.12s/it] 92%|█████████▏| 46/50 [01:01<00:04, 1.12s/it] 94%|█████████▍| 47/50 [01:02<00:03, 1.12s/it] 96%|█████████▌| 48/50 [01:03<00:02, 1.12s/it] 98%|█████████▊| 49/50 [01:04<00:01, 1.12s/it] 100%|██████████| 50/50 [01:06<00:00, 1.12s/it] 100%|██████████| 50/50 [01:06<00:00, 1.32s/it]Prediction
camenduru/open-sora-plan-512x512:6dca0dc6IDp520wybmwxrgp0ceq3nshj0xzgStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 0
- prompt
- Sunset over the sea.
- force_images
- sample_steps
- 50
- guidance_scale
- 10
- randomize_seed
{ "seed": 0, "prompt": "Sunset over the sea.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment 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 camenduru/open-sora-plan-512x512 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "camenduru/open-sora-plan-512x512:6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", { input: { seed: 0, prompt: "Sunset over the sea.", force_images: false, sample_steps: 50, guidance_scale: 10, randomize_seed: true } } ); 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 theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun camenduru/open-sora-plan-512x512 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "camenduru/open-sora-plan-512x512:6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", input={ "seed": 0, "prompt": "Sunset over the sea.", "force_images": False, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": True } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run camenduru/open-sora-plan-512x512 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": "6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", "input": { "seed": 0, "prompt": "Sunset over the sea.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-04-07T14:18:57.526375Z", "created_at": "2024-04-07T14:17:02.439000Z", "data_removed": false, "error": null, "id": "p520wybmwxrgp0ceq3nshj0xzg", "input": { "seed": 0, "prompt": "Sunset over the sea.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true }, "logs": "Setting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the\ninstallation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones\nthat match your environment. Please note that you may need to restart your runtime after installation.\nSetting `clean_caption` to False...\nSetting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the\ninstallation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones\nthat match your environment. Please note that you may need to restart your runtime after installation.\nSetting `clean_caption` to False...\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:11<09:08, 11.19s/it]\n 4%|▍ | 2/50 [00:12<04:12, 5.27s/it]\n 6%|▌ | 3/50 [00:13<02:38, 3.37s/it]\n 8%|▊ | 4/50 [00:14<01:54, 2.48s/it]\n 10%|█ | 5/50 [00:15<01:29, 1.99s/it]\n 12%|█▏ | 6/50 [00:16<01:14, 1.70s/it]\n 14%|█▍ | 7/50 [00:17<01:04, 1.51s/it]\n 16%|█▌ | 8/50 [00:19<00:58, 1.38s/it]\n 18%|█▊ | 9/50 [00:20<00:53, 1.30s/it]\n 20%|██ | 10/50 [00:21<00:49, 1.25s/it]\n 22%|██▏ | 11/50 [00:22<00:47, 1.21s/it]\n 24%|██▍ | 12/50 [00:23<00:44, 1.18s/it]\n 26%|██▌ | 13/50 [00:24<00:42, 1.16s/it]\n 28%|██▊ | 14/50 [00:25<00:41, 1.15s/it]\n 30%|███ | 15/50 [00:26<00:39, 1.14s/it]\n 32%|███▏ | 16/50 [00:27<00:38, 1.13s/it]\n 34%|███▍ | 17/50 [00:29<00:37, 1.13s/it]\n 36%|███▌ | 18/50 [00:30<00:36, 1.13s/it]\n 38%|███▊ | 19/50 [00:31<00:34, 1.12s/it]\n 40%|████ | 20/50 [00:32<00:33, 1.12s/it]\n 42%|████▏ | 21/50 [00:33<00:32, 1.12s/it]\n 44%|████▍ | 22/50 [00:34<00:31, 1.12s/it]\n 46%|████▌ | 23/50 [00:35<00:30, 1.12s/it]\n 48%|████▊ | 24/50 [00:36<00:29, 1.12s/it]\n 50%|█████ | 25/50 [00:38<00:28, 1.12s/it]\n 52%|█████▏ | 26/50 [00:39<00:26, 1.12s/it]\n 54%|█████▍ | 27/50 [00:40<00:25, 1.12s/it]\n 56%|█████▌ | 28/50 [00:41<00:24, 1.12s/it]\n 58%|█████▊ | 29/50 [00:42<00:23, 1.12s/it]\n 60%|██████ | 30/50 [00:43<00:22, 1.12s/it]\n 62%|██████▏ | 31/50 [00:44<00:21, 1.12s/it]\n 64%|██████▍ | 32/50 [00:45<00:20, 1.12s/it]\n 66%|██████▌ | 33/50 [00:47<00:19, 1.12s/it]\n 68%|██████▊ | 34/50 [00:48<00:17, 1.12s/it]\n 70%|███████ | 35/50 [00:49<00:16, 1.12s/it]\n 72%|███████▏ | 36/50 [00:50<00:15, 1.12s/it]\n 74%|███████▍ | 37/50 [00:51<00:14, 1.12s/it]\n 76%|███████▌ | 38/50 [00:52<00:13, 1.12s/it]\n 78%|███████▊ | 39/50 [00:53<00:12, 1.12s/it]\n 80%|████████ | 40/50 [00:54<00:11, 1.12s/it]\n 82%|████████▏ | 41/50 [00:55<00:10, 1.12s/it]\n 84%|████████▍ | 42/50 [00:57<00:08, 1.12s/it]\n 86%|████████▌ | 43/50 [00:58<00:07, 1.12s/it]\n 88%|████████▊ | 44/50 [00:59<00:06, 1.12s/it]\n 90%|█████████ | 45/50 [01:00<00:05, 1.12s/it]\n 92%|█████████▏| 46/50 [01:01<00:04, 1.13s/it]\n 94%|█████████▍| 47/50 [01:02<00:03, 1.12s/it]\n 96%|█████████▌| 48/50 [01:03<00:02, 1.12s/it]\n 98%|█████████▊| 49/50 [01:04<00:01, 1.12s/it]\n100%|██████████| 50/50 [01:06<00:00, 1.12s/it]\n100%|██████████| 50/50 [01:06<00:00, 1.32s/it]", "metrics": { "predict_time": 76.419248, "total_time": 115.087375 }, "output": "https://replicate.delivery/pbxt/cWIEfqYJhTx7Maf8OgGjcaWlnuuGvrjDA7UFGUGlx5NP1FoSA/tmp.mp4", "started_at": "2024-04-07T14:17:41.107127Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/p520wybmwxrgp0ceq3nshj0xzg", "cancel": "https://api.replicate.com/v1/predictions/p520wybmwxrgp0ceq3nshj0xzg/cancel" }, "version": "83b03885f983bd923e70afa5a51e2369d55aa34d2439014a484df3107a4322ca" }Generated inSetting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the installation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. Setting `clean_caption` to False... Setting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the installation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. Setting `clean_caption` to False... 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:11<09:08, 11.19s/it] 4%|▍ | 2/50 [00:12<04:12, 5.27s/it] 6%|▌ | 3/50 [00:13<02:38, 3.37s/it] 8%|▊ | 4/50 [00:14<01:54, 2.48s/it] 10%|█ | 5/50 [00:15<01:29, 1.99s/it] 12%|█▏ | 6/50 [00:16<01:14, 1.70s/it] 14%|█▍ | 7/50 [00:17<01:04, 1.51s/it] 16%|█▌ | 8/50 [00:19<00:58, 1.38s/it] 18%|█▊ | 9/50 [00:20<00:53, 1.30s/it] 20%|██ | 10/50 [00:21<00:49, 1.25s/it] 22%|██▏ | 11/50 [00:22<00:47, 1.21s/it] 24%|██▍ | 12/50 [00:23<00:44, 1.18s/it] 26%|██▌ | 13/50 [00:24<00:42, 1.16s/it] 28%|██▊ | 14/50 [00:25<00:41, 1.15s/it] 30%|███ | 15/50 [00:26<00:39, 1.14s/it] 32%|███▏ | 16/50 [00:27<00:38, 1.13s/it] 34%|███▍ | 17/50 [00:29<00:37, 1.13s/it] 36%|███▌ | 18/50 [00:30<00:36, 1.13s/it] 38%|███▊ | 19/50 [00:31<00:34, 1.12s/it] 40%|████ | 20/50 [00:32<00:33, 1.12s/it] 42%|████▏ | 21/50 [00:33<00:32, 1.12s/it] 44%|████▍ | 22/50 [00:34<00:31, 1.12s/it] 46%|████▌ | 23/50 [00:35<00:30, 1.12s/it] 48%|████▊ | 24/50 [00:36<00:29, 1.12s/it] 50%|█████ | 25/50 [00:38<00:28, 1.12s/it] 52%|█████▏ | 26/50 [00:39<00:26, 1.12s/it] 54%|█████▍ | 27/50 [00:40<00:25, 1.12s/it] 56%|█████▌ | 28/50 [00:41<00:24, 1.12s/it] 58%|█████▊ | 29/50 [00:42<00:23, 1.12s/it] 60%|██████ | 30/50 [00:43<00:22, 1.12s/it] 62%|██████▏ | 31/50 [00:44<00:21, 1.12s/it] 64%|██████▍ | 32/50 [00:45<00:20, 1.12s/it] 66%|██████▌ | 33/50 [00:47<00:19, 1.12s/it] 68%|██████▊ | 34/50 [00:48<00:17, 1.12s/it] 70%|███████ | 35/50 [00:49<00:16, 1.12s/it] 72%|███████▏ | 36/50 [00:50<00:15, 1.12s/it] 74%|███████▍ | 37/50 [00:51<00:14, 1.12s/it] 76%|███████▌ | 38/50 [00:52<00:13, 1.12s/it] 78%|███████▊ | 39/50 [00:53<00:12, 1.12s/it] 80%|████████ | 40/50 [00:54<00:11, 1.12s/it] 82%|████████▏ | 41/50 [00:55<00:10, 1.12s/it] 84%|████████▍ | 42/50 [00:57<00:08, 1.12s/it] 86%|████████▌ | 43/50 [00:58<00:07, 1.12s/it] 88%|████████▊ | 44/50 [00:59<00:06, 1.12s/it] 90%|█████████ | 45/50 [01:00<00:05, 1.12s/it] 92%|█████████▏| 46/50 [01:01<00:04, 1.13s/it] 94%|█████████▍| 47/50 [01:02<00:03, 1.12s/it] 96%|█████████▌| 48/50 [01:03<00:02, 1.12s/it] 98%|█████████▊| 49/50 [01:04<00:01, 1.12s/it] 100%|██████████| 50/50 [01:06<00:00, 1.12s/it] 100%|██████████| 50/50 [01:06<00:00, 1.32s/it]Prediction
camenduru/open-sora-plan-512x512:6dca0dc6ID7c4y8yjx81rgm0ceq3pr4nzj08StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 0
- prompt
- a cat wearing sunglasses and working as a lifeguard at pool.
- force_images
- sample_steps
- 50
- guidance_scale
- 10
- randomize_seed
{ "seed": 0, "prompt": "a cat wearing sunglasses and working as a lifeguard at pool.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment 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 camenduru/open-sora-plan-512x512 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "camenduru/open-sora-plan-512x512:6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", { input: { seed: 0, prompt: "a cat wearing sunglasses and working as a lifeguard at pool.", force_images: false, sample_steps: 50, guidance_scale: 10, randomize_seed: true } } ); 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 theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun camenduru/open-sora-plan-512x512 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "camenduru/open-sora-plan-512x512:6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", input={ "seed": 0, "prompt": "a cat wearing sunglasses and working as a lifeguard at pool.", "force_images": False, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": True } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run camenduru/open-sora-plan-512x512 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": "6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", "input": { "seed": 0, "prompt": "a cat wearing sunglasses and working as a lifeguard at pool.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-04-07T14:20:24.011260Z", "created_at": "2024-04-07T14:19:07.456000Z", "data_removed": false, "error": null, "id": "7c4y8yjx81rgm0ceq3pr4nzj08", "input": { "seed": 0, "prompt": "a cat wearing sunglasses and working as a lifeguard at pool.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true }, "logs": "Setting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the\ninstallation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones\nthat match your environment. Please note that you may need to restart your runtime after installation.\nSetting `clean_caption` to False...\nSetting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the\ninstallation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones\nthat match your environment. Please note that you may need to restart your runtime after installation.\nSetting `clean_caption` to False...\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:11<09:08, 11.20s/it]\n 4%|▍ | 2/50 [00:12<04:12, 5.27s/it]\n 6%|▌ | 3/50 [00:13<02:38, 3.38s/it]\n 8%|▊ | 4/50 [00:14<01:54, 2.48s/it]\n 10%|█ | 5/50 [00:15<01:29, 1.99s/it]\n 12%|█▏ | 6/50 [00:16<01:14, 1.70s/it]\n 14%|█▍ | 7/50 [00:17<01:04, 1.51s/it]\n 16%|█▌ | 8/50 [00:19<00:58, 1.38s/it]\n 18%|█▊ | 9/50 [00:20<00:53, 1.30s/it]\n 20%|██ | 10/50 [00:21<00:49, 1.25s/it]\n 22%|██▏ | 11/50 [00:22<00:47, 1.21s/it]\n 24%|██▍ | 12/50 [00:23<00:44, 1.18s/it]\n 26%|██▌ | 13/50 [00:24<00:42, 1.16s/it]\n 28%|██▊ | 14/50 [00:25<00:41, 1.15s/it]\n 30%|███ | 15/50 [00:26<00:39, 1.14s/it]\n 32%|███▏ | 16/50 [00:27<00:38, 1.13s/it]\n 34%|███▍ | 17/50 [00:29<00:37, 1.13s/it]\n 36%|███▌ | 18/50 [00:30<00:36, 1.13s/it]\n 38%|███▊ | 19/50 [00:31<00:34, 1.12s/it]\n 40%|████ | 20/50 [00:32<00:33, 1.13s/it]\n 42%|████▏ | 21/50 [00:33<00:32, 1.13s/it]\n 44%|████▍ | 22/50 [00:34<00:31, 1.13s/it]\n 46%|████▌ | 23/50 [00:35<00:30, 1.13s/it]\n 48%|████▊ | 24/50 [00:36<00:29, 1.12s/it]\n 50%|█████ | 25/50 [00:38<00:28, 1.12s/it]\n 52%|█████▏ | 26/50 [00:39<00:27, 1.13s/it]\n 54%|█████▍ | 27/50 [00:40<00:26, 1.13s/it]\n 56%|█████▌ | 28/50 [00:41<00:24, 1.13s/it]\n 58%|█████▊ | 29/50 [00:42<00:23, 1.13s/it]\n 60%|██████ | 30/50 [00:43<00:22, 1.12s/it]\n 62%|██████▏ | 31/50 [00:44<00:21, 1.13s/it]\n 64%|██████▍ | 32/50 [00:46<00:20, 1.13s/it]\n 66%|██████▌ | 33/50 [00:47<00:19, 1.12s/it]\n 68%|██████▊ | 34/50 [00:48<00:17, 1.12s/it]\n 70%|███████ | 35/50 [00:49<00:16, 1.13s/it]\n 72%|███████▏ | 36/50 [00:50<00:15, 1.13s/it]\n 74%|███████▍ | 37/50 [00:51<00:14, 1.12s/it]\n 76%|███████▌ | 38/50 [00:52<00:13, 1.12s/it]\n 78%|███████▊ | 39/50 [00:53<00:12, 1.12s/it]\n 80%|████████ | 40/50 [00:54<00:11, 1.12s/it]\n 82%|████████▏ | 41/50 [00:56<00:10, 1.12s/it]\n 84%|████████▍ | 42/50 [00:57<00:08, 1.12s/it]\n 86%|████████▌ | 43/50 [00:58<00:07, 1.12s/it]\n 88%|████████▊ | 44/50 [00:59<00:06, 1.12s/it]\n 90%|█████████ | 45/50 [01:00<00:05, 1.12s/it]\n 92%|█████████▏| 46/50 [01:01<00:04, 1.12s/it]\n 94%|█████████▍| 47/50 [01:02<00:03, 1.12s/it]\n 96%|█████████▌| 48/50 [01:03<00:02, 1.12s/it]\n 98%|█████████▊| 49/50 [01:05<00:01, 1.12s/it]\n100%|██████████| 50/50 [01:06<00:00, 1.12s/it]\n100%|██████████| 50/50 [01:06<00:00, 1.32s/it]", "metrics": { "predict_time": 76.518646, "total_time": 76.55526 }, "output": "https://replicate.delivery/pbxt/JnutS3Xfw0zOXCrCPP8GyRy5YmOVZ2fG7wYIn036fmlMtLQlA/tmp.mp4", "started_at": "2024-04-07T14:19:07.492614Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/7c4y8yjx81rgm0ceq3pr4nzj08", "cancel": "https://api.replicate.com/v1/predictions/7c4y8yjx81rgm0ceq3pr4nzj08/cancel" }, "version": "83b03885f983bd923e70afa5a51e2369d55aa34d2439014a484df3107a4322ca" }Generated inSetting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the installation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. Setting `clean_caption` to False... Setting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the installation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. Setting `clean_caption` to False... 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:11<09:08, 11.20s/it] 4%|▍ | 2/50 [00:12<04:12, 5.27s/it] 6%|▌ | 3/50 [00:13<02:38, 3.38s/it] 8%|▊ | 4/50 [00:14<01:54, 2.48s/it] 10%|█ | 5/50 [00:15<01:29, 1.99s/it] 12%|█▏ | 6/50 [00:16<01:14, 1.70s/it] 14%|█▍ | 7/50 [00:17<01:04, 1.51s/it] 16%|█▌ | 8/50 [00:19<00:58, 1.38s/it] 18%|█▊ | 9/50 [00:20<00:53, 1.30s/it] 20%|██ | 10/50 [00:21<00:49, 1.25s/it] 22%|██▏ | 11/50 [00:22<00:47, 1.21s/it] 24%|██▍ | 12/50 [00:23<00:44, 1.18s/it] 26%|██▌ | 13/50 [00:24<00:42, 1.16s/it] 28%|██▊ | 14/50 [00:25<00:41, 1.15s/it] 30%|███ | 15/50 [00:26<00:39, 1.14s/it] 32%|███▏ | 16/50 [00:27<00:38, 1.13s/it] 34%|███▍ | 17/50 [00:29<00:37, 1.13s/it] 36%|███▌ | 18/50 [00:30<00:36, 1.13s/it] 38%|███▊ | 19/50 [00:31<00:34, 1.12s/it] 40%|████ | 20/50 [00:32<00:33, 1.13s/it] 42%|████▏ | 21/50 [00:33<00:32, 1.13s/it] 44%|████▍ | 22/50 [00:34<00:31, 1.13s/it] 46%|████▌ | 23/50 [00:35<00:30, 1.13s/it] 48%|████▊ | 24/50 [00:36<00:29, 1.12s/it] 50%|█████ | 25/50 [00:38<00:28, 1.12s/it] 52%|█████▏ | 26/50 [00:39<00:27, 1.13s/it] 54%|█████▍ | 27/50 [00:40<00:26, 1.13s/it] 56%|█████▌ | 28/50 [00:41<00:24, 1.13s/it] 58%|█████▊ | 29/50 [00:42<00:23, 1.13s/it] 60%|██████ | 30/50 [00:43<00:22, 1.12s/it] 62%|██████▏ | 31/50 [00:44<00:21, 1.13s/it] 64%|██████▍ | 32/50 [00:46<00:20, 1.13s/it] 66%|██████▌ | 33/50 [00:47<00:19, 1.12s/it] 68%|██████▊ | 34/50 [00:48<00:17, 1.12s/it] 70%|███████ | 35/50 [00:49<00:16, 1.13s/it] 72%|███████▏ | 36/50 [00:50<00:15, 1.13s/it] 74%|███████▍ | 37/50 [00:51<00:14, 1.12s/it] 76%|███████▌ | 38/50 [00:52<00:13, 1.12s/it] 78%|███████▊ | 39/50 [00:53<00:12, 1.12s/it] 80%|████████ | 40/50 [00:54<00:11, 1.12s/it] 82%|████████▏ | 41/50 [00:56<00:10, 1.12s/it] 84%|████████▍ | 42/50 [00:57<00:08, 1.12s/it] 86%|████████▌ | 43/50 [00:58<00:07, 1.12s/it] 88%|████████▊ | 44/50 [00:59<00:06, 1.12s/it] 90%|█████████ | 45/50 [01:00<00:05, 1.12s/it] 92%|█████████▏| 46/50 [01:01<00:04, 1.12s/it] 94%|█████████▍| 47/50 [01:02<00:03, 1.12s/it] 96%|█████████▌| 48/50 [01:03<00:02, 1.12s/it] 98%|█████████▊| 49/50 [01:05<00:01, 1.12s/it] 100%|██████████| 50/50 [01:06<00:00, 1.12s/it] 100%|██████████| 50/50 [01:06<00:00, 1.32s/it]Prediction
camenduru/open-sora-plan-512x512:6dca0dc6ID791wk7rnsxrge0ceq3vvzezzxcStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedby @camenduruInput
- seed
- 0
- prompt
- A serene waterfall cascading down moss-covered rocks, its soothing sound creating a harmonious symphony with nature.
- force_images
- sample_steps
- 50
- guidance_scale
- 10
- randomize_seed
{ "seed": 0, "prompt": "A serene waterfall cascading down moss-covered rocks, its soothing sound creating a harmonious symphony with nature.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment 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 camenduru/open-sora-plan-512x512 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "camenduru/open-sora-plan-512x512:6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", { input: { seed: 0, prompt: "A serene waterfall cascading down moss-covered rocks, its soothing sound creating a harmonious symphony with nature.", force_images: false, sample_steps: 50, guidance_scale: 10, randomize_seed: true } } ); 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 theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun camenduru/open-sora-plan-512x512 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "camenduru/open-sora-plan-512x512:6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", input={ "seed": 0, "prompt": "A serene waterfall cascading down moss-covered rocks, its soothing sound creating a harmonious symphony with nature.", "force_images": False, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": True } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run camenduru/open-sora-plan-512x512 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": "6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", "input": { "seed": 0, "prompt": "A serene waterfall cascading down moss-covered rocks, its soothing sound creating a harmonious symphony with nature.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-04-07T14:34:40.042102Z", "created_at": "2024-04-07T14:29:44.527000Z", "data_removed": false, "error": null, "id": "791wk7rnsxrge0ceq3vvzezzxc", "input": { "seed": 0, "prompt": "A serene waterfall cascading down moss-covered rocks, its soothing sound creating a harmonious symphony with nature.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true }, "logs": "Setting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the\ninstallation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones\nthat match your environment. Please note that you may need to restart your runtime after installation.\nSetting `clean_caption` to False...\nSetting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the\ninstallation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones\nthat match your environment. Please note that you may need to restart your runtime after installation.\nSetting `clean_caption` to False...\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:06<05:04, 6.21s/it]\n 4%|▍ | 2/50 [00:06<02:20, 2.93s/it]\n 6%|▌ | 3/50 [00:07<01:28, 1.87s/it]\n 8%|▊ | 4/50 [00:08<01:03, 1.38s/it]\n 10%|█ | 5/50 [00:08<00:49, 1.11s/it]\n 12%|█▏ | 6/50 [00:09<00:41, 1.06it/s]\n 14%|█▍ | 7/50 [00:09<00:36, 1.19it/s]\n 16%|█▌ | 8/50 [00:10<00:32, 1.30it/s]\n 18%|█▊ | 9/50 [00:11<00:29, 1.38it/s]\n 20%|██ | 10/50 [00:11<00:27, 1.44it/s]\n 22%|██▏ | 11/50 [00:12<00:26, 1.49it/s]\n 24%|██▍ | 12/50 [00:13<00:24, 1.52it/s]\n 26%|██▌ | 13/50 [00:13<00:23, 1.55it/s]\n 28%|██▊ | 14/50 [00:14<00:22, 1.57it/s]\n 30%|███ | 15/50 [00:14<00:22, 1.58it/s]\n 32%|███▏ | 16/50 [00:15<00:21, 1.59it/s]\n 34%|███▍ | 17/50 [00:16<00:20, 1.59it/s]\n 36%|███▌ | 18/50 [00:16<00:20, 1.60it/s]\n 38%|███▊ | 19/50 [00:17<00:19, 1.60it/s]\n 40%|████ | 20/50 [00:18<00:18, 1.60it/s]\n 42%|████▏ | 21/50 [00:18<00:18, 1.60it/s]\n 44%|████▍ | 22/50 [00:19<00:17, 1.60it/s]\n 46%|████▌ | 23/50 [00:19<00:16, 1.60it/s]\n 48%|████▊ | 24/50 [00:20<00:16, 1.60it/s]\n 50%|█████ | 25/50 [00:21<00:15, 1.60it/s]\n 52%|█████▏ | 26/50 [00:21<00:14, 1.60it/s]\n 54%|█████▍ | 27/50 [00:22<00:14, 1.60it/s]\n 56%|█████▌ | 28/50 [00:23<00:13, 1.60it/s]\n 58%|█████▊ | 29/50 [00:23<00:13, 1.61it/s]\n 60%|██████ | 30/50 [00:24<00:12, 1.61it/s]\n 62%|██████▏ | 31/50 [00:24<00:11, 1.61it/s]\n 64%|██████▍ | 32/50 [00:25<00:11, 1.60it/s]\n 66%|██████▌ | 33/50 [00:26<00:10, 1.60it/s]\n 68%|██████▊ | 34/50 [00:26<00:09, 1.60it/s]\n 70%|███████ | 35/50 [00:27<00:09, 1.60it/s]\n 72%|███████▏ | 36/50 [00:28<00:08, 1.60it/s]\n 74%|███████▍ | 37/50 [00:28<00:08, 1.60it/s]\n 76%|███████▌ | 38/50 [00:29<00:07, 1.60it/s]\n 78%|███████▊ | 39/50 [00:29<00:06, 1.60it/s]\n 80%|████████ | 40/50 [00:30<00:06, 1.60it/s]\n 82%|████████▏ | 41/50 [00:31<00:05, 1.60it/s]\n 84%|████████▍ | 42/50 [00:31<00:04, 1.60it/s]\n 86%|████████▌ | 43/50 [00:32<00:04, 1.60it/s]\n 88%|████████▊ | 44/50 [00:33<00:03, 1.60it/s]\n 90%|█████████ | 45/50 [00:33<00:03, 1.60it/s]\n 92%|█████████▏| 46/50 [00:34<00:02, 1.60it/s]\n 94%|█████████▍| 47/50 [00:34<00:01, 1.60it/s]\n 96%|█████████▌| 48/50 [00:35<00:01, 1.60it/s]\n 98%|█████████▊| 49/50 [00:36<00:00, 1.60it/s]\n100%|██████████| 50/50 [00:36<00:00, 1.60it/s]\n100%|██████████| 50/50 [00:36<00:00, 1.36it/s]", "metrics": { "predict_time": 43.112944, "total_time": 295.515102 }, "output": "https://replicate.delivery/pbxt/5faN5iCjXenetIyKWvAhQESw9A3L8RJrMLXenED6w1e4fgBqE/tmp.mp4", "started_at": "2024-04-07T14:33:56.929158Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/791wk7rnsxrge0ceq3vvzezzxc", "cancel": "https://api.replicate.com/v1/predictions/791wk7rnsxrge0ceq3vvzezzxc/cancel" }, "version": "83b03885f983bd923e70afa5a51e2369d55aa34d2439014a484df3107a4322ca" }Generated inSetting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the installation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. Setting `clean_caption` to False... Setting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the installation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. Setting `clean_caption` to False... 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:06<05:04, 6.21s/it] 4%|▍ | 2/50 [00:06<02:20, 2.93s/it] 6%|▌ | 3/50 [00:07<01:28, 1.87s/it] 8%|▊ | 4/50 [00:08<01:03, 1.38s/it] 10%|█ | 5/50 [00:08<00:49, 1.11s/it] 12%|█▏ | 6/50 [00:09<00:41, 1.06it/s] 14%|█▍ | 7/50 [00:09<00:36, 1.19it/s] 16%|█▌ | 8/50 [00:10<00:32, 1.30it/s] 18%|█▊ | 9/50 [00:11<00:29, 1.38it/s] 20%|██ | 10/50 [00:11<00:27, 1.44it/s] 22%|██▏ | 11/50 [00:12<00:26, 1.49it/s] 24%|██▍ | 12/50 [00:13<00:24, 1.52it/s] 26%|██▌ | 13/50 [00:13<00:23, 1.55it/s] 28%|██▊ | 14/50 [00:14<00:22, 1.57it/s] 30%|███ | 15/50 [00:14<00:22, 1.58it/s] 32%|███▏ | 16/50 [00:15<00:21, 1.59it/s] 34%|███▍ | 17/50 [00:16<00:20, 1.59it/s] 36%|███▌ | 18/50 [00:16<00:20, 1.60it/s] 38%|███▊ | 19/50 [00:17<00:19, 1.60it/s] 40%|████ | 20/50 [00:18<00:18, 1.60it/s] 42%|████▏ | 21/50 [00:18<00:18, 1.60it/s] 44%|████▍ | 22/50 [00:19<00:17, 1.60it/s] 46%|████▌ | 23/50 [00:19<00:16, 1.60it/s] 48%|████▊ | 24/50 [00:20<00:16, 1.60it/s] 50%|█████ | 25/50 [00:21<00:15, 1.60it/s] 52%|█████▏ | 26/50 [00:21<00:14, 1.60it/s] 54%|█████▍ | 27/50 [00:22<00:14, 1.60it/s] 56%|█████▌ | 28/50 [00:23<00:13, 1.60it/s] 58%|█████▊ | 29/50 [00:23<00:13, 1.61it/s] 60%|██████ | 30/50 [00:24<00:12, 1.61it/s] 62%|██████▏ | 31/50 [00:24<00:11, 1.61it/s] 64%|██████▍ | 32/50 [00:25<00:11, 1.60it/s] 66%|██████▌ | 33/50 [00:26<00:10, 1.60it/s] 68%|██████▊ | 34/50 [00:26<00:09, 1.60it/s] 70%|███████ | 35/50 [00:27<00:09, 1.60it/s] 72%|███████▏ | 36/50 [00:28<00:08, 1.60it/s] 74%|███████▍ | 37/50 [00:28<00:08, 1.60it/s] 76%|███████▌ | 38/50 [00:29<00:07, 1.60it/s] 78%|███████▊ | 39/50 [00:29<00:06, 1.60it/s] 80%|████████ | 40/50 [00:30<00:06, 1.60it/s] 82%|████████▏ | 41/50 [00:31<00:05, 1.60it/s] 84%|████████▍ | 42/50 [00:31<00:04, 1.60it/s] 86%|████████▌ | 43/50 [00:32<00:04, 1.60it/s] 88%|████████▊ | 44/50 [00:33<00:03, 1.60it/s] 90%|█████████ | 45/50 [00:33<00:03, 1.60it/s] 92%|█████████▏| 46/50 [00:34<00:02, 1.60it/s] 94%|█████████▍| 47/50 [00:34<00:01, 1.60it/s] 96%|█████████▌| 48/50 [00:35<00:01, 1.60it/s] 98%|█████████▊| 49/50 [00:36<00:00, 1.60it/s] 100%|██████████| 50/50 [00:36<00:00, 1.60it/s] 100%|██████████| 50/50 [00:36<00:00, 1.36it/s]Prediction
camenduru/open-sora-plan-512x512:6dca0dc6ID73nyha38wsrge0ceq3vsd0a2mwStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- seed
- 0
- prompt
- a serene winter scene in a forest. The forest is blanketed in a thick layer of snow, which has settled on the branches of the trees, creating a canopy of white. The trees, a mix of evergreens and deciduous, stand tall and silent, their forms partially obscured by the snow. The ground is a uniform white, with no visible tracks or signs of human activity. The sun is low in the sky, casting a warm glow that contrasts with the cool tones of the snow. The light filters through the trees, creating a soft, diffused illumination that highlights the texture of the snow and the contours of the trees. The overall style of the scene is naturalistic, with a focus on the tranquility and beauty of the winter landscape.
- force_images
- sample_steps
- 50
- guidance_scale
- 10
- randomize_seed
{ "seed": 0, "prompt": "a serene winter scene in a forest. The forest is blanketed in a thick layer of snow, which has settled on the branches of the trees, creating a canopy of white. The trees, a mix of evergreens and deciduous, stand tall and silent, their forms partially obscured by the snow. The ground is a uniform white, with no visible tracks or signs of human activity. The sun is low in the sky, casting a warm glow that contrasts with the cool tones of the snow. The light filters through the trees, creating a soft, diffused illumination that highlights the texture of the snow and the contours of the trees. The overall style of the scene is naturalistic, with a focus on the tranquility and beauty of the winter landscape.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment 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 camenduru/open-sora-plan-512x512 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "camenduru/open-sora-plan-512x512:6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", { input: { seed: 0, prompt: "a serene winter scene in a forest. The forest is blanketed in a thick layer of snow, which has settled on the branches of the trees, creating a canopy of white. The trees, a mix of evergreens and deciduous, stand tall and silent, their forms partially obscured by the snow. The ground is a uniform white, with no visible tracks or signs of human activity. The sun is low in the sky, casting a warm glow that contrasts with the cool tones of the snow. The light filters through the trees, creating a soft, diffused illumination that highlights the texture of the snow and the contours of the trees. The overall style of the scene is naturalistic, with a focus on the tranquility and beauty of the winter landscape.", force_images: false, sample_steps: 50, guidance_scale: 10, randomize_seed: true } } ); 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 theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun camenduru/open-sora-plan-512x512 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "camenduru/open-sora-plan-512x512:6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", input={ "seed": 0, "prompt": "a serene winter scene in a forest. The forest is blanketed in a thick layer of snow, which has settled on the branches of the trees, creating a canopy of white. The trees, a mix of evergreens and deciduous, stand tall and silent, their forms partially obscured by the snow. The ground is a uniform white, with no visible tracks or signs of human activity. The sun is low in the sky, casting a warm glow that contrasts with the cool tones of the snow. The light filters through the trees, creating a soft, diffused illumination that highlights the texture of the snow and the contours of the trees. The overall style of the scene is naturalistic, with a focus on the tranquility and beauty of the winter landscape.", "force_images": False, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": True } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run camenduru/open-sora-plan-512x512 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": "6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", "input": { "seed": 0, "prompt": "a serene winter scene in a forest. The forest is blanketed in a thick layer of snow, which has settled on the branches of the trees, creating a canopy of white. The trees, a mix of evergreens and deciduous, stand tall and silent, their forms partially obscured by the snow. The ground is a uniform white, with no visible tracks or signs of human activity. The sun is low in the sky, casting a warm glow that contrasts with the cool tones of the snow. The light filters through the trees, creating a soft, diffused illumination that highlights the texture of the snow and the contours of the trees. The overall style of the scene is naturalistic, with a focus on the tranquility and beauty of the winter landscape.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-04-07T14:35:23.864186Z", "created_at": "2024-04-07T14:30:05.798000Z", "data_removed": false, "error": null, "id": "73nyha38wsrge0ceq3vsd0a2mw", "input": { "seed": 0, "prompt": "a serene winter scene in a forest. The forest is blanketed in a thick layer of snow, which has settled on the branches of the trees, creating a canopy of white. The trees, a mix of evergreens and deciduous, stand tall and silent, their forms partially obscured by the snow. The ground is a uniform white, with no visible tracks or signs of human activity. The sun is low in the sky, casting a warm glow that contrasts with the cool tones of the snow. The light filters through the trees, creating a soft, diffused illumination that highlights the texture of the snow and the contours of the trees. The overall style of the scene is naturalistic, with a focus on the tranquility and beauty of the winter landscape.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true }, "logs": "Setting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the\ninstallation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones\nthat match your environment. Please note that you may need to restart your runtime after installation.\nSetting `clean_caption` to False...\nSetting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the\ninstallation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones\nthat match your environment. Please note that you may need to restart your runtime after installation.\nSetting `clean_caption` to False...\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:06<05:11, 6.35s/it]\n 4%|▍ | 2/50 [00:06<02:23, 2.99s/it]\n 6%|▌ | 3/50 [00:07<01:29, 1.91s/it]\n 8%|▊ | 4/50 [00:08<01:04, 1.41s/it]\n 10%|█ | 5/50 [00:08<00:50, 1.13s/it]\n 12%|█▏ | 6/50 [00:09<00:42, 1.04it/s]\n 14%|█▍ | 7/50 [00:10<00:36, 1.17it/s]\n 16%|█▌ | 8/50 [00:10<00:33, 1.27it/s]\n 18%|█▊ | 9/50 [00:11<00:30, 1.35it/s]\n 20%|██ | 10/50 [00:12<00:28, 1.41it/s]\n 22%|██▏ | 11/50 [00:12<00:26, 1.46it/s]\n 24%|██▍ | 12/50 [00:13<00:25, 1.49it/s]\n 26%|██▌ | 13/50 [00:13<00:24, 1.51it/s]\n 28%|██▊ | 14/50 [00:14<00:23, 1.53it/s]\n 30%|███ | 15/50 [00:15<00:22, 1.54it/s]\n 32%|███▏ | 16/50 [00:15<00:21, 1.55it/s]\n 34%|███▍ | 17/50 [00:16<00:21, 1.56it/s]\n 36%|███▌ | 18/50 [00:17<00:20, 1.56it/s]\n 38%|███▊ | 19/50 [00:17<00:19, 1.56it/s]\n 40%|████ | 20/50 [00:18<00:19, 1.57it/s]\n 42%|████▏ | 21/50 [00:19<00:18, 1.57it/s]\n 44%|████▍ | 22/50 [00:19<00:17, 1.57it/s]\n 46%|████▌ | 23/50 [00:20<00:17, 1.57it/s]\n 48%|████▊ | 24/50 [00:20<00:16, 1.57it/s]\n 50%|█████ | 25/50 [00:21<00:15, 1.57it/s]\n 52%|█████▏ | 26/50 [00:22<00:15, 1.57it/s]\n 54%|█████▍ | 27/50 [00:22<00:14, 1.57it/s]\n 56%|█████▌ | 28/50 [00:23<00:14, 1.57it/s]\n 58%|█████▊ | 29/50 [00:24<00:13, 1.57it/s]\n 60%|██████ | 30/50 [00:24<00:12, 1.57it/s]\n 62%|██████▏ | 31/50 [00:25<00:12, 1.57it/s]\n 64%|██████▍ | 32/50 [00:26<00:11, 1.57it/s]\n 66%|██████▌ | 33/50 [00:26<00:10, 1.57it/s]\n 68%|██████▊ | 34/50 [00:27<00:10, 1.57it/s]\n 70%|███████ | 35/50 [00:27<00:09, 1.57it/s]\n 72%|███████▏ | 36/50 [00:28<00:08, 1.57it/s]\n 74%|███████▍ | 37/50 [00:29<00:08, 1.57it/s]\n 76%|███████▌ | 38/50 [00:29<00:07, 1.57it/s]\n 78%|███████▊ | 39/50 [00:30<00:07, 1.57it/s]\n 80%|████████ | 40/50 [00:31<00:06, 1.57it/s]\n 82%|████████▏ | 41/50 [00:31<00:05, 1.57it/s]\n 84%|████████▍ | 42/50 [00:32<00:05, 1.57it/s]\n 86%|████████▌ | 43/50 [00:33<00:04, 1.57it/s]\n 88%|████████▊ | 44/50 [00:33<00:03, 1.57it/s]\n 90%|█████████ | 45/50 [00:34<00:03, 1.57it/s]\n 92%|█████████▏| 46/50 [00:34<00:02, 1.57it/s]\n 94%|█████████▍| 47/50 [00:35<00:01, 1.57it/s]\n 96%|█████████▌| 48/50 [00:36<00:01, 1.57it/s]\n 98%|█████████▊| 49/50 [00:36<00:00, 1.57it/s]\n100%|██████████| 50/50 [00:37<00:00, 1.57it/s]\n100%|██████████| 50/50 [00:37<00:00, 1.33it/s]", "metrics": { "predict_time": 43.785059, "total_time": 318.066186 }, "output": "https://replicate.delivery/pbxt/8aqJfwmMJyyHCCnPwfH24zrT06irkhaL2orBq9RN1CXrEGoSA/tmp.mp4", "started_at": "2024-04-07T14:34:40.079127Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/73nyha38wsrge0ceq3vsd0a2mw", "cancel": "https://api.replicate.com/v1/predictions/73nyha38wsrge0ceq3vsd0a2mw/cancel" }, "version": "83b03885f983bd923e70afa5a51e2369d55aa34d2439014a484df3107a4322ca" }Generated inSetting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the installation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. Setting `clean_caption` to False... Setting `clean_caption=True` requires the ftfy library but it was not found in your environment. Checkout the instructions on the installation section: https://github.com/rspeer/python-ftfy/tree/master#installing and follow the ones that match your environment. Please note that you may need to restart your runtime after installation. Setting `clean_caption` to False... 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:06<05:11, 6.35s/it] 4%|▍ | 2/50 [00:06<02:23, 2.99s/it] 6%|▌ | 3/50 [00:07<01:29, 1.91s/it] 8%|▊ | 4/50 [00:08<01:04, 1.41s/it] 10%|█ | 5/50 [00:08<00:50, 1.13s/it] 12%|█▏ | 6/50 [00:09<00:42, 1.04it/s] 14%|█▍ | 7/50 [00:10<00:36, 1.17it/s] 16%|█▌ | 8/50 [00:10<00:33, 1.27it/s] 18%|█▊ | 9/50 [00:11<00:30, 1.35it/s] 20%|██ | 10/50 [00:12<00:28, 1.41it/s] 22%|██▏ | 11/50 [00:12<00:26, 1.46it/s] 24%|██▍ | 12/50 [00:13<00:25, 1.49it/s] 26%|██▌ | 13/50 [00:13<00:24, 1.51it/s] 28%|██▊ | 14/50 [00:14<00:23, 1.53it/s] 30%|███ | 15/50 [00:15<00:22, 1.54it/s] 32%|███▏ | 16/50 [00:15<00:21, 1.55it/s] 34%|███▍ | 17/50 [00:16<00:21, 1.56it/s] 36%|███▌ | 18/50 [00:17<00:20, 1.56it/s] 38%|███▊ | 19/50 [00:17<00:19, 1.56it/s] 40%|████ | 20/50 [00:18<00:19, 1.57it/s] 42%|████▏ | 21/50 [00:19<00:18, 1.57it/s] 44%|████▍ | 22/50 [00:19<00:17, 1.57it/s] 46%|████▌ | 23/50 [00:20<00:17, 1.57it/s] 48%|████▊ | 24/50 [00:20<00:16, 1.57it/s] 50%|█████ | 25/50 [00:21<00:15, 1.57it/s] 52%|█████▏ | 26/50 [00:22<00:15, 1.57it/s] 54%|█████▍ | 27/50 [00:22<00:14, 1.57it/s] 56%|█████▌ | 28/50 [00:23<00:14, 1.57it/s] 58%|█████▊ | 29/50 [00:24<00:13, 1.57it/s] 60%|██████ | 30/50 [00:24<00:12, 1.57it/s] 62%|██████▏ | 31/50 [00:25<00:12, 1.57it/s] 64%|██████▍ | 32/50 [00:26<00:11, 1.57it/s] 66%|██████▌ | 33/50 [00:26<00:10, 1.57it/s] 68%|██████▊ | 34/50 [00:27<00:10, 1.57it/s] 70%|███████ | 35/50 [00:27<00:09, 1.57it/s] 72%|███████▏ | 36/50 [00:28<00:08, 1.57it/s] 74%|███████▍ | 37/50 [00:29<00:08, 1.57it/s] 76%|███████▌ | 38/50 [00:29<00:07, 1.57it/s] 78%|███████▊ | 39/50 [00:30<00:07, 1.57it/s] 80%|████████ | 40/50 [00:31<00:06, 1.57it/s] 82%|████████▏ | 41/50 [00:31<00:05, 1.57it/s] 84%|████████▍ | 42/50 [00:32<00:05, 1.57it/s] 86%|████████▌ | 43/50 [00:33<00:04, 1.57it/s] 88%|████████▊ | 44/50 [00:33<00:03, 1.57it/s] 90%|█████████ | 45/50 [00:34<00:03, 1.57it/s] 92%|█████████▏| 46/50 [00:34<00:02, 1.57it/s] 94%|█████████▍| 47/50 [00:35<00:01, 1.57it/s] 96%|█████████▌| 48/50 [00:36<00:01, 1.57it/s] 98%|█████████▊| 49/50 [00:36<00:00, 1.57it/s] 100%|██████████| 50/50 [00:37<00:00, 1.57it/s] 100%|██████████| 50/50 [00:37<00:00, 1.33it/s]Prediction
camenduru/open-sora-plan-512x512:6dca0dc6IDc424z8d5nxrga0ceq4a9jqa5grStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- seed
- 0
- prompt
- A quiet beach at dawn, the waves gently lapping at the shore and the sky painted in pastel hues.
- force_images
- sample_steps
- 50
- guidance_scale
- 10
- randomize_seed
{ "seed": 0, "prompt": "A quiet beach at dawn, the waves gently lapping at the shore and the sky painted in pastel hues.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment 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 camenduru/open-sora-plan-512x512 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "camenduru/open-sora-plan-512x512:6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", { input: { seed: 0, prompt: "A quiet beach at dawn, the waves gently lapping at the shore and the sky painted in pastel hues.", force_images: false, sample_steps: 50, guidance_scale: 10, randomize_seed: true } } ); 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 theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun camenduru/open-sora-plan-512x512 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "camenduru/open-sora-plan-512x512:6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", input={ "seed": 0, "prompt": "A quiet beach at dawn, the waves gently lapping at the shore and the sky painted in pastel hues.", "force_images": False, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": True } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run camenduru/open-sora-plan-512x512 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": "6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", "input": { "seed": 0, "prompt": "A quiet beach at dawn, the waves gently lapping at the shore and the sky painted in pastel hues.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-04-07T15:05:51.253052Z", "created_at": "2024-04-07T15:02:01.903000Z", "data_removed": false, "error": null, "id": "c424z8d5nxrga0ceq4a9jqa5gr", "input": { "seed": 0, "prompt": "A quiet beach at dawn, the waves gently lapping at the shore and the sky painted in pastel hues.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": true }, "logs": "0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:06<05:18, 6.49s/it]\n 4%|▍ | 2/50 [00:07<02:26, 3.06s/it]\n 6%|▌ | 3/50 [00:07<01:31, 1.96s/it]\n 8%|▊ | 4/50 [00:08<01:06, 1.44s/it]\n 10%|█ | 5/50 [00:09<00:52, 1.16s/it]\n 12%|█▏ | 6/50 [00:09<00:43, 1.02it/s]\n 14%|█▍ | 7/50 [00:10<00:37, 1.14it/s]\n 16%|█▌ | 8/50 [00:11<00:33, 1.24it/s]\n 18%|█▊ | 9/50 [00:11<00:31, 1.32it/s]\n 20%|██ | 10/50 [00:12<00:28, 1.38it/s]\n 22%|██▏ | 11/50 [00:13<00:27, 1.42it/s]\n 24%|██▍ | 12/50 [00:13<00:26, 1.46it/s]\n 26%|██▌ | 13/50 [00:14<00:24, 1.48it/s]\n 28%|██▊ | 14/50 [00:14<00:24, 1.50it/s]\n 30%|███ | 15/50 [00:15<00:23, 1.51it/s]\n 32%|███▏ | 16/50 [00:16<00:22, 1.52it/s]\n 34%|███▍ | 17/50 [00:16<00:21, 1.52it/s]\n 36%|███▌ | 18/50 [00:17<00:20, 1.53it/s]\n 38%|███▊ | 19/50 [00:18<00:20, 1.53it/s]\n 40%|████ | 20/50 [00:18<00:19, 1.53it/s]\n 42%|████▏ | 21/50 [00:19<00:18, 1.53it/s]\n 44%|████▍ | 22/50 [00:20<00:18, 1.53it/s]\n 46%|████▌ | 23/50 [00:20<00:17, 1.53it/s]\n 48%|████▊ | 24/50 [00:21<00:16, 1.54it/s]\n 50%|█████ | 25/50 [00:22<00:16, 1.54it/s]\n 52%|█████▏ | 26/50 [00:22<00:15, 1.54it/s]\n 54%|█████▍ | 27/50 [00:23<00:14, 1.54it/s]\n 56%|█████▌ | 28/50 [00:24<00:14, 1.54it/s]\n 58%|█████▊ | 29/50 [00:24<00:13, 1.54it/s]\n 60%|██████ | 30/50 [00:25<00:13, 1.54it/s]\n 62%|██████▏ | 31/50 [00:26<00:12, 1.54it/s]\n 64%|██████▍ | 32/50 [00:26<00:11, 1.54it/s]\n 66%|██████▌ | 33/50 [00:27<00:11, 1.54it/s]\n 68%|██████▊ | 34/50 [00:27<00:10, 1.54it/s]\n 70%|███████ | 35/50 [00:28<00:09, 1.54it/s]\n 72%|███████▏ | 36/50 [00:29<00:09, 1.54it/s]\n 74%|███████▍ | 37/50 [00:29<00:08, 1.54it/s]\n 76%|███████▌ | 38/50 [00:30<00:07, 1.53it/s]\n 78%|███████▊ | 39/50 [00:31<00:07, 1.53it/s]\n 80%|████████ | 40/50 [00:31<00:06, 1.53it/s]\n 82%|████████▏ | 41/50 [00:32<00:05, 1.54it/s]\n 84%|████████▍ | 42/50 [00:33<00:05, 1.54it/s]\n 86%|████████▌ | 43/50 [00:33<00:04, 1.54it/s]\n 88%|████████▊ | 44/50 [00:34<00:03, 1.54it/s]\n 90%|█████████ | 45/50 [00:35<00:03, 1.54it/s]\n 92%|█████████▏| 46/50 [00:35<00:02, 1.54it/s]\n 94%|█████████▍| 47/50 [00:36<00:01, 1.54it/s]\n 96%|█████████▌| 48/50 [00:37<00:01, 1.54it/s]\n 98%|█████████▊| 49/50 [00:37<00:00, 1.54it/s]\n100%|██████████| 50/50 [00:38<00:00, 1.54it/s]\n100%|██████████| 50/50 [00:38<00:00, 1.30it/s]", "metrics": { "predict_time": 44.83855, "total_time": 229.350052 }, "output": "https://replicate.delivery/pbxt/Yx7JAh6zqVKjFlbeweqhfFFA97iZa3glz1QBj01TVKWfEagKB/tmp.mp4", "started_at": "2024-04-07T15:05:06.414502Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/c424z8d5nxrga0ceq4a9jqa5gr", "cancel": "https://api.replicate.com/v1/predictions/c424z8d5nxrga0ceq4a9jqa5gr/cancel" }, "version": "6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7" }Generated in0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:06<05:18, 6.49s/it] 4%|▍ | 2/50 [00:07<02:26, 3.06s/it] 6%|▌ | 3/50 [00:07<01:31, 1.96s/it] 8%|▊ | 4/50 [00:08<01:06, 1.44s/it] 10%|█ | 5/50 [00:09<00:52, 1.16s/it] 12%|█▏ | 6/50 [00:09<00:43, 1.02it/s] 14%|█▍ | 7/50 [00:10<00:37, 1.14it/s] 16%|█▌ | 8/50 [00:11<00:33, 1.24it/s] 18%|█▊ | 9/50 [00:11<00:31, 1.32it/s] 20%|██ | 10/50 [00:12<00:28, 1.38it/s] 22%|██▏ | 11/50 [00:13<00:27, 1.42it/s] 24%|██▍ | 12/50 [00:13<00:26, 1.46it/s] 26%|██▌ | 13/50 [00:14<00:24, 1.48it/s] 28%|██▊ | 14/50 [00:14<00:24, 1.50it/s] 30%|███ | 15/50 [00:15<00:23, 1.51it/s] 32%|███▏ | 16/50 [00:16<00:22, 1.52it/s] 34%|███▍ | 17/50 [00:16<00:21, 1.52it/s] 36%|███▌ | 18/50 [00:17<00:20, 1.53it/s] 38%|███▊ | 19/50 [00:18<00:20, 1.53it/s] 40%|████ | 20/50 [00:18<00:19, 1.53it/s] 42%|████▏ | 21/50 [00:19<00:18, 1.53it/s] 44%|████▍ | 22/50 [00:20<00:18, 1.53it/s] 46%|████▌ | 23/50 [00:20<00:17, 1.53it/s] 48%|████▊ | 24/50 [00:21<00:16, 1.54it/s] 50%|█████ | 25/50 [00:22<00:16, 1.54it/s] 52%|█████▏ | 26/50 [00:22<00:15, 1.54it/s] 54%|█████▍ | 27/50 [00:23<00:14, 1.54it/s] 56%|█████▌ | 28/50 [00:24<00:14, 1.54it/s] 58%|█████▊ | 29/50 [00:24<00:13, 1.54it/s] 60%|██████ | 30/50 [00:25<00:13, 1.54it/s] 62%|██████▏ | 31/50 [00:26<00:12, 1.54it/s] 64%|██████▍ | 32/50 [00:26<00:11, 1.54it/s] 66%|██████▌ | 33/50 [00:27<00:11, 1.54it/s] 68%|██████▊ | 34/50 [00:27<00:10, 1.54it/s] 70%|███████ | 35/50 [00:28<00:09, 1.54it/s] 72%|███████▏ | 36/50 [00:29<00:09, 1.54it/s] 74%|███████▍ | 37/50 [00:29<00:08, 1.54it/s] 76%|███████▌ | 38/50 [00:30<00:07, 1.53it/s] 78%|███████▊ | 39/50 [00:31<00:07, 1.53it/s] 80%|████████ | 40/50 [00:31<00:06, 1.53it/s] 82%|████████▏ | 41/50 [00:32<00:05, 1.54it/s] 84%|████████▍ | 42/50 [00:33<00:05, 1.54it/s] 86%|████████▌ | 43/50 [00:33<00:04, 1.54it/s] 88%|████████▊ | 44/50 [00:34<00:03, 1.54it/s] 90%|█████████ | 45/50 [00:35<00:03, 1.54it/s] 92%|█████████▏| 46/50 [00:35<00:02, 1.54it/s] 94%|█████████▍| 47/50 [00:36<00:01, 1.54it/s] 96%|█████████▌| 48/50 [00:37<00:01, 1.54it/s] 98%|█████████▊| 49/50 [00:37<00:00, 1.54it/s] 100%|██████████| 50/50 [00:38<00:00, 1.54it/s] 100%|██████████| 50/50 [00:38<00:00, 1.30it/s]Prediction
camenduru/open-sora-plan-512x512:6dca0dc6IDnt2m3dqxn9rga0ceq4cvf5ykd0StatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- seed
- 0
- prompt
- a cat wearing sunglasses and working as a lifeguard at pool.
- force_images
- sample_steps
- 50
- guidance_scale
- 10
- randomize_seed
{ "seed": 0, "prompt": "a cat wearing sunglasses and working as a lifeguard at pool.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": false }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment 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 camenduru/open-sora-plan-512x512 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "camenduru/open-sora-plan-512x512:6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", { input: { seed: 0, prompt: "a cat wearing sunglasses and working as a lifeguard at pool.", force_images: false, sample_steps: 50, guidance_scale: 10, randomize_seed: false } } ); 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 theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun camenduru/open-sora-plan-512x512 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "camenduru/open-sora-plan-512x512:6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", input={ "seed": 0, "prompt": "a cat wearing sunglasses and working as a lifeguard at pool.", "force_images": False, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": False } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run camenduru/open-sora-plan-512x512 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": "6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7", "input": { "seed": 0, "prompt": "a cat wearing sunglasses and working as a lifeguard at pool.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": false } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-04-07T15:08:36.674101Z", "created_at": "2024-04-07T15:07:52.106000Z", "data_removed": false, "error": null, "id": "nt2m3dqxn9rga0ceq4cvf5ykd0", "input": { "seed": 0, "prompt": "a cat wearing sunglasses and working as a lifeguard at pool.", "force_images": false, "sample_steps": 50, "guidance_scale": 10, "randomize_seed": false }, "logs": "0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:06<05:15, 6.44s/it]\n 4%|▍ | 2/50 [00:07<02:25, 3.03s/it]\n 6%|▌ | 3/50 [00:07<01:31, 1.94s/it]\n 8%|▊ | 4/50 [00:08<01:05, 1.43s/it]\n 10%|█ | 5/50 [00:09<00:51, 1.15s/it]\n 12%|█▏ | 6/50 [00:09<00:42, 1.03it/s]\n 14%|█▍ | 7/50 [00:10<00:37, 1.15it/s]\n 16%|█▌ | 8/50 [00:10<00:33, 1.26it/s]\n 18%|█▊ | 9/50 [00:11<00:30, 1.33it/s]\n 20%|██ | 10/50 [00:12<00:28, 1.39it/s]\n 22%|██▏ | 11/50 [00:12<00:27, 1.44it/s]\n 24%|██▍ | 12/50 [00:13<00:25, 1.47it/s]\n 26%|██▌ | 13/50 [00:14<00:24, 1.49it/s]\n 28%|██▊ | 14/50 [00:14<00:23, 1.51it/s]\n 30%|███ | 15/50 [00:15<00:22, 1.52it/s]\n 32%|███▏ | 16/50 [00:16<00:22, 1.53it/s]\n 34%|███▍ | 17/50 [00:16<00:21, 1.54it/s]\n 36%|███▌ | 18/50 [00:17<00:20, 1.54it/s]\n 38%|███▊ | 19/50 [00:18<00:20, 1.54it/s]\n 40%|████ | 20/50 [00:18<00:19, 1.54it/s]\n 42%|████▏ | 21/50 [00:19<00:18, 1.55it/s]\n 44%|████▍ | 22/50 [00:19<00:18, 1.55it/s]\n 46%|████▌ | 23/50 [00:20<00:17, 1.55it/s]\n 48%|████▊ | 24/50 [00:21<00:16, 1.55it/s]\n 50%|█████ | 25/50 [00:21<00:16, 1.55it/s]\n 52%|█████▏ | 26/50 [00:22<00:15, 1.55it/s]\n 54%|█████▍ | 27/50 [00:23<00:14, 1.55it/s]\n 56%|█████▌ | 28/50 [00:23<00:14, 1.55it/s]\n 58%|█████▊ | 29/50 [00:24<00:13, 1.55it/s]\n 60%|██████ | 30/50 [00:25<00:12, 1.55it/s]\n 62%|██████▏ | 31/50 [00:25<00:12, 1.55it/s]\n 64%|██████▍ | 32/50 [00:26<00:11, 1.55it/s]\n 66%|██████▌ | 33/50 [00:27<00:10, 1.55it/s]\n 68%|██████▊ | 34/50 [00:27<00:10, 1.55it/s]\n 70%|███████ | 35/50 [00:28<00:09, 1.55it/s]\n 72%|███████▏ | 36/50 [00:29<00:09, 1.55it/s]\n 74%|███████▍ | 37/50 [00:29<00:08, 1.54it/s]\n 76%|███████▌ | 38/50 [00:30<00:07, 1.54it/s]\n 78%|███████▊ | 39/50 [00:30<00:07, 1.54it/s]\n 80%|████████ | 40/50 [00:31<00:06, 1.54it/s]\n 82%|████████▏ | 41/50 [00:32<00:05, 1.54it/s]\n 84%|████████▍ | 42/50 [00:32<00:05, 1.54it/s]\n 86%|████████▌ | 43/50 [00:33<00:04, 1.54it/s]\n 88%|████████▊ | 44/50 [00:34<00:03, 1.54it/s]\n 90%|█████████ | 45/50 [00:34<00:03, 1.54it/s]\n 92%|█████████▏| 46/50 [00:35<00:02, 1.54it/s]\n 94%|█████████▍| 47/50 [00:36<00:01, 1.54it/s]\n 96%|█████████▌| 48/50 [00:36<00:01, 1.54it/s]\n 98%|█████████▊| 49/50 [00:37<00:00, 1.54it/s]\n100%|██████████| 50/50 [00:38<00:00, 1.54it/s]\n100%|██████████| 50/50 [00:38<00:00, 1.31it/s]", "metrics": { "predict_time": 44.544994, "total_time": 44.568101 }, "output": "https://replicate.delivery/pbxt/RG8pFAV1nuZyJhevZrA3R6mzGO6zXeKhuGemYJlk67eQPagKB/tmp.mp4", "started_at": "2024-04-07T15:07:52.129107Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/nt2m3dqxn9rga0ceq4cvf5ykd0", "cancel": "https://api.replicate.com/v1/predictions/nt2m3dqxn9rga0ceq4cvf5ykd0/cancel" }, "version": "6dca0dc6a44340ff290dd59a519afff876dc11d49614eb4b06427e07a7fa93c7" }Generated in0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:06<05:15, 6.44s/it] 4%|▍ | 2/50 [00:07<02:25, 3.03s/it] 6%|▌ | 3/50 [00:07<01:31, 1.94s/it] 8%|▊ | 4/50 [00:08<01:05, 1.43s/it] 10%|█ | 5/50 [00:09<00:51, 1.15s/it] 12%|█▏ | 6/50 [00:09<00:42, 1.03it/s] 14%|█▍ | 7/50 [00:10<00:37, 1.15it/s] 16%|█▌ | 8/50 [00:10<00:33, 1.26it/s] 18%|█▊ | 9/50 [00:11<00:30, 1.33it/s] 20%|██ | 10/50 [00:12<00:28, 1.39it/s] 22%|██▏ | 11/50 [00:12<00:27, 1.44it/s] 24%|██▍ | 12/50 [00:13<00:25, 1.47it/s] 26%|██▌ | 13/50 [00:14<00:24, 1.49it/s] 28%|██▊ | 14/50 [00:14<00:23, 1.51it/s] 30%|███ | 15/50 [00:15<00:22, 1.52it/s] 32%|███▏ | 16/50 [00:16<00:22, 1.53it/s] 34%|███▍ | 17/50 [00:16<00:21, 1.54it/s] 36%|███▌ | 18/50 [00:17<00:20, 1.54it/s] 38%|███▊ | 19/50 [00:18<00:20, 1.54it/s] 40%|████ | 20/50 [00:18<00:19, 1.54it/s] 42%|████▏ | 21/50 [00:19<00:18, 1.55it/s] 44%|████▍ | 22/50 [00:19<00:18, 1.55it/s] 46%|████▌ | 23/50 [00:20<00:17, 1.55it/s] 48%|████▊ | 24/50 [00:21<00:16, 1.55it/s] 50%|█████ | 25/50 [00:21<00:16, 1.55it/s] 52%|█████▏ | 26/50 [00:22<00:15, 1.55it/s] 54%|█████▍ | 27/50 [00:23<00:14, 1.55it/s] 56%|█████▌ | 28/50 [00:23<00:14, 1.55it/s] 58%|█████▊ | 29/50 [00:24<00:13, 1.55it/s] 60%|██████ | 30/50 [00:25<00:12, 1.55it/s] 62%|██████▏ | 31/50 [00:25<00:12, 1.55it/s] 64%|██████▍ | 32/50 [00:26<00:11, 1.55it/s] 66%|██████▌ | 33/50 [00:27<00:10, 1.55it/s] 68%|██████▊ | 34/50 [00:27<00:10, 1.55it/s] 70%|███████ | 35/50 [00:28<00:09, 1.55it/s] 72%|███████▏ | 36/50 [00:29<00:09, 1.55it/s] 74%|███████▍ | 37/50 [00:29<00:08, 1.54it/s] 76%|███████▌ | 38/50 [00:30<00:07, 1.54it/s] 78%|███████▊ | 39/50 [00:30<00:07, 1.54it/s] 80%|████████ | 40/50 [00:31<00:06, 1.54it/s] 82%|████████▏ | 41/50 [00:32<00:05, 1.54it/s] 84%|████████▍ | 42/50 [00:32<00:05, 1.54it/s] 86%|████████▌ | 43/50 [00:33<00:04, 1.54it/s] 88%|████████▊ | 44/50 [00:34<00:03, 1.54it/s] 90%|█████████ | 45/50 [00:34<00:03, 1.54it/s] 92%|█████████▏| 46/50 [00:35<00:02, 1.54it/s] 94%|█████████▍| 47/50 [00:36<00:01, 1.54it/s] 96%|█████████▌| 48/50 [00:36<00:01, 1.54it/s] 98%|█████████▊| 49/50 [00:37<00:00, 1.54it/s] 100%|██████████| 50/50 [00:38<00:00, 1.54it/s] 100%|██████████| 50/50 [00:38<00:00, 1.31it/s]
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