ji4chenli / t2v-turbo
Fast and High-Quality Text-to-video Generation
Prediction
ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0dddIDatba23kf0srgg0cft3grcmht9cStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- fps
- 8
- prompt
- With the style of low-poly game art, A majestic, white horse gallops gracefully across a moonlit beach
- num_frames
- 16
- guidance_scale
- 7.5
- num_inference_steps
- 4
{ "fps": 8, "prompt": "With the style of low-poly game art, A majestic, white horse gallops gracefully across a moonlit beach", "num_frames": 16, "guidance_scale": 7.5, "num_inference_steps": 4 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run ji4chenli/t2v-turbo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd", { input: { fps: 8, prompt: "With the style of low-poly game art, A majestic, white horse gallops gracefully across a moonlit beach", num_frames: 16, guidance_scale: 7.5, num_inference_steps: 4 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun ji4chenli/t2v-turbo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd", input={ "fps": 8, "prompt": "With the style of low-poly game art, A majestic, white horse gallops gracefully across a moonlit beach", "num_frames": 16, "guidance_scale": 7.5, "num_inference_steps": 4 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run ji4chenli/t2v-turbo 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": "ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd", "input": { "fps": 8, "prompt": "With the style of low-poly game art, A majestic, white horse gallops gracefully across a moonlit beach", "num_frames": 16, "guidance_scale": 7.5, "num_inference_steps": 4 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-31T22:59:51.708557Z", "created_at": "2024-05-31T22:59:46.054000Z", "data_removed": false, "error": null, "id": "atba23kf0srgg0cft3grcmht9c", "input": { "fps": 8, "prompt": "With the style of low-poly game art, A majestic, white horse gallops gracefully across a moonlit beach", "num_frames": 16, "guidance_scale": 7.5, "num_inference_steps": 4 }, "logs": "Using seed: 53951\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:02, 1.30it/s]\n 50%|█████ | 2/4 [00:01<00:01, 1.30it/s]\n 75%|███████▌ | 3/4 [00:02<00:00, 1.30it/s]\n100%|██████████| 4/4 [00:03<00:00, 1.30it/s]\n100%|██████████| 4/4 [00:03<00:00, 1.30it/s]", "metrics": { "predict_time": 5.614316, "total_time": 5.654557 }, "output": "https://replicate.delivery/pbxt/1MOy1Q8MeY3jNSot5tDTE22ByCyhm8tOrbIGP0XfkoHmhA6SA/out.mp4", "started_at": "2024-05-31T22:59:46.094241Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/atba23kf0srgg0cft3grcmht9c", "cancel": "https://api.replicate.com/v1/predictions/atba23kf0srgg0cft3grcmht9c/cancel" }, "version": "027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd" }Generated inUsing seed: 53951 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:02, 1.30it/s] 50%|█████ | 2/4 [00:01<00:01, 1.30it/s] 75%|███████▌ | 3/4 [00:02<00:00, 1.30it/s] 100%|██████████| 4/4 [00:03<00:00, 1.30it/s] 100%|██████████| 4/4 [00:03<00:00, 1.30it/s]Prediction
ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0dddIDj8mp440959rgp0cft3ms1rj4swStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- fps
- 8
- prompt
- medium shot of Christine, a beautiful 25-year-old brunette resembling Selena Gomez, anxiously looking up as she walks down a New York street, cinematic style
- num_frames
- 24
- guidance_scale
- 7.5
- num_inference_steps
- 8
{ "fps": 8, "prompt": "medium shot of Christine, a beautiful 25-year-old brunette resembling Selena Gomez, anxiously looking up as she walks down a New York street, cinematic style", "num_frames": 24, "guidance_scale": 7.5, "num_inference_steps": 8 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run ji4chenli/t2v-turbo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd", { input: { fps: 8, prompt: "medium shot of Christine, a beautiful 25-year-old brunette resembling Selena Gomez, anxiously looking up as she walks down a New York street, cinematic style", num_frames: 24, guidance_scale: 7.5, num_inference_steps: 8 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun ji4chenli/t2v-turbo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd", input={ "fps": 8, "prompt": "medium shot of Christine, a beautiful 25-year-old brunette resembling Selena Gomez, anxiously looking up as she walks down a New York street, cinematic style", "num_frames": 24, "guidance_scale": 7.5, "num_inference_steps": 8 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run ji4chenli/t2v-turbo 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": "ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd", "input": { "fps": 8, "prompt": "medium shot of Christine, a beautiful 25-year-old brunette resembling Selena Gomez, anxiously looking up as she walks down a New York street, cinematic style", "num_frames": 24, "guidance_scale": 7.5, "num_inference_steps": 8 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-31T23:08:16.707343Z", "created_at": "2024-05-31T23:08:04.266000Z", "data_removed": false, "error": null, "id": "j8mp440959rgp0cft3ms1rj4sw", "input": { "fps": 8, "prompt": "medium shot of Christine, a beautiful 25-year-old brunette resembling Selena Gomez, anxiously looking up as she walks down a New York street, cinematic style", "num_frames": 24, "guidance_scale": 7.5, "num_inference_steps": 8 }, "logs": "Using seed: 48466\n 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:01<00:07, 1.14s/it]\n 25%|██▌ | 2/8 [00:02<00:06, 1.14s/it]\n 38%|███▊ | 3/8 [00:03<00:05, 1.14s/it]\n 50%|█████ | 4/8 [00:04<00:04, 1.14s/it]\n 62%|██████▎ | 5/8 [00:05<00:03, 1.14s/it]\n 75%|███████▌ | 6/8 [00:06<00:02, 1.14s/it]\n 88%|████████▊ | 7/8 [00:07<00:01, 1.14s/it]\n100%|██████████| 8/8 [00:09<00:00, 1.14s/it]\n100%|██████████| 8/8 [00:09<00:00, 1.14s/it]", "metrics": { "predict_time": 12.396232, "total_time": 12.441343 }, "output": "https://replicate.delivery/pbxt/lblqcCJ3zcLKFhm7utv9kLZ41vf1xlC9i1eQLwIC0kkfSB0lA/out.mp4", "started_at": "2024-05-31T23:08:04.311111Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/j8mp440959rgp0cft3ms1rj4sw", "cancel": "https://api.replicate.com/v1/predictions/j8mp440959rgp0cft3ms1rj4sw/cancel" }, "version": "027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd" }Generated inUsing seed: 48466 0%| | 0/8 [00:00<?, ?it/s] 12%|█▎ | 1/8 [00:01<00:07, 1.14s/it] 25%|██▌ | 2/8 [00:02<00:06, 1.14s/it] 38%|███▊ | 3/8 [00:03<00:05, 1.14s/it] 50%|█████ | 4/8 [00:04<00:04, 1.14s/it] 62%|██████▎ | 5/8 [00:05<00:03, 1.14s/it] 75%|███████▌ | 6/8 [00:06<00:02, 1.14s/it] 88%|████████▊ | 7/8 [00:07<00:01, 1.14s/it] 100%|██████████| 8/8 [00:09<00:00, 1.14s/it] 100%|██████████| 8/8 [00:09<00:00, 1.14s/it]Prediction
ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0dddIDz7k45pwamxrgg0cft3ns1tr264StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- fps
- 8
- prompt
- a dog wearing vr goggles on a boat
- num_frames
- 24
- guidance_scale
- 7.5
- num_inference_steps
- 4
{ "fps": 8, "prompt": "a dog wearing vr goggles on a boat", "num_frames": 24, "guidance_scale": 7.5, "num_inference_steps": 4 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run ji4chenli/t2v-turbo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd", { input: { fps: 8, prompt: "a dog wearing vr goggles on a boat", num_frames: 24, guidance_scale: 7.5, num_inference_steps: 4 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun ji4chenli/t2v-turbo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd", input={ "fps": 8, "prompt": "a dog wearing vr goggles on a boat", "num_frames": 24, "guidance_scale": 7.5, "num_inference_steps": 4 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run ji4chenli/t2v-turbo 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": "ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd", "input": { "fps": 8, "prompt": "a dog wearing vr goggles on a boat", "num_frames": 24, "guidance_scale": 7.5, "num_inference_steps": 4 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-31T23:10:56.430759Z", "created_at": "2024-05-31T23:10:48.487000Z", "data_removed": false, "error": null, "id": "z7k45pwamxrgg0cft3ns1tr264", "input": { "fps": 8, "prompt": "a dog wearing vr goggles on a boat", "num_frames": 24, "guidance_scale": 7.5, "num_inference_steps": 4 }, "logs": "Using seed: 16841\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:01<00:03, 1.13s/it]\n 50%|█████ | 2/4 [00:02<00:02, 1.13s/it]\n 75%|███████▌ | 3/4 [00:03<00:01, 1.13s/it]\n100%|██████████| 4/4 [00:04<00:00, 1.14s/it]\n100%|██████████| 4/4 [00:04<00:00, 1.13s/it]", "metrics": { "predict_time": 7.906032, "total_time": 7.943759 }, "output": "https://replicate.delivery/pbxt/ZXu5tLgLr0IJLVIBkIotKVfiefbYnkOig1YvTtfv7k97vCoLB/out.mp4", "started_at": "2024-05-31T23:10:48.524727Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/z7k45pwamxrgg0cft3ns1tr264", "cancel": "https://api.replicate.com/v1/predictions/z7k45pwamxrgg0cft3ns1tr264/cancel" }, "version": "027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd" }Generated inUsing seed: 16841 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:01<00:03, 1.13s/it] 50%|█████ | 2/4 [00:02<00:02, 1.13s/it] 75%|███████▌ | 3/4 [00:03<00:01, 1.13s/it] 100%|██████████| 4/4 [00:04<00:00, 1.14s/it] 100%|██████████| 4/4 [00:04<00:00, 1.13s/it]Prediction
ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0dddIDnthksq2dy9rgg0cft3pafgtpymStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- fps
- 8
- prompt
- Pikachu snowboarding
- num_frames
- 24
- guidance_scale
- 7.5
- num_inference_steps
- 4
{ "fps": 8, "prompt": "Pikachu snowboarding", "num_frames": 24, "guidance_scale": 7.5, "num_inference_steps": 4 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run ji4chenli/t2v-turbo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd", { input: { fps: 8, prompt: "Pikachu snowboarding", num_frames: 24, guidance_scale: 7.5, num_inference_steps: 4 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun ji4chenli/t2v-turbo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd", input={ "fps": 8, "prompt": "Pikachu snowboarding", "num_frames": 24, "guidance_scale": 7.5, "num_inference_steps": 4 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run ji4chenli/t2v-turbo 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": "ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd", "input": { "fps": 8, "prompt": "Pikachu snowboarding", "num_frames": 24, "guidance_scale": 7.5, "num_inference_steps": 4 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-31T23:11:46.465269Z", "created_at": "2024-05-31T23:11:38.482000Z", "data_removed": false, "error": null, "id": "nthksq2dy9rgg0cft3pafgtpym", "input": { "fps": 8, "prompt": "Pikachu snowboarding", "num_frames": 24, "guidance_scale": 7.5, "num_inference_steps": 4 }, "logs": "Using seed: 27346\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:01<00:03, 1.13s/it]\n 50%|█████ | 2/4 [00:02<00:02, 1.13s/it]\n 75%|███████▌ | 3/4 [00:03<00:01, 1.13s/it]\n100%|██████████| 4/4 [00:04<00:00, 1.14s/it]\n100%|██████████| 4/4 [00:04<00:00, 1.14s/it]", "metrics": { "predict_time": 7.935387, "total_time": 7.983269 }, "output": "https://replicate.delivery/pbxt/GUjNWKCIwNYhG5JwHf0aQDwxj9EhT0SxxtT7f4yY0yXxsA6SA/out.mp4", "started_at": "2024-05-31T23:11:38.529882Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/nthksq2dy9rgg0cft3pafgtpym", "cancel": "https://api.replicate.com/v1/predictions/nthksq2dy9rgg0cft3pafgtpym/cancel" }, "version": "027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd" }Generated inUsing seed: 27346 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:01<00:03, 1.13s/it] 50%|█████ | 2/4 [00:02<00:02, 1.13s/it] 75%|███████▌ | 3/4 [00:03<00:01, 1.13s/it] 100%|██████████| 4/4 [00:04<00:00, 1.14s/it] 100%|██████████| 4/4 [00:04<00:00, 1.14s/it]Prediction
ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0dddIDwfkwb158ahrgg0cft3qbjjzzgcStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- fps
- 8
- prompt
- light wind, feathers moving, she moves her gaze, 4k
- num_frames
- 48
- guidance_scale
- 7.5
- num_inference_steps
- 8
{ "fps": 8, "prompt": "light wind, feathers moving, she moves her gaze, 4k", "num_frames": 48, "guidance_scale": 7.5, "num_inference_steps": 8 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run ji4chenli/t2v-turbo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd", { input: { fps: 8, prompt: "light wind, feathers moving, she moves her gaze, 4k", num_frames: 48, guidance_scale: 7.5, num_inference_steps: 8 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun ji4chenli/t2v-turbo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd", input={ "fps": 8, "prompt": "light wind, feathers moving, she moves her gaze, 4k", "num_frames": 48, "guidance_scale": 7.5, "num_inference_steps": 8 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run ji4chenli/t2v-turbo 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": "ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd", "input": { "fps": 8, "prompt": "light wind, feathers moving, she moves her gaze, 4k", "num_frames": 48, "guidance_scale": 7.5, "num_inference_steps": 8 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-31T23:14:36.325653Z", "created_at": "2024-05-31T23:14:12.692000Z", "data_removed": false, "error": null, "id": "wfkwb158ahrgg0cft3qbjjzzgc", "input": { "fps": 8, "prompt": "light wind, feathers moving, she moves her gaze, 4k", "num_frames": 48, "guidance_scale": 7.5, "num_inference_steps": 8 }, "logs": "Using seed: 57465\n 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:02<00:16, 2.29s/it]\n 25%|██▌ | 2/8 [00:04<00:13, 2.29s/it]\n 38%|███▊ | 3/8 [00:06<00:11, 2.30s/it]\n 50%|█████ | 4/8 [00:09<00:09, 2.30s/it]\n 62%|██████▎ | 5/8 [00:11<00:06, 2.30s/it]\n 75%|███████▌ | 6/8 [00:13<00:04, 2.30s/it]\n 88%|████████▊ | 7/8 [00:16<00:02, 2.30s/it]\n100%|██████████| 8/8 [00:18<00:00, 2.30s/it]\n100%|██████████| 8/8 [00:18<00:00, 2.30s/it]", "metrics": { "predict_time": 23.595343, "total_time": 23.633653 }, "output": "https://replicate.delivery/pbxt/y3i2VoNt1F71FRdR0l4ZijldjVRXOoecTEXKxLU05QMtXAdJA/out.mp4", "started_at": "2024-05-31T23:14:12.730310Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wfkwb158ahrgg0cft3qbjjzzgc", "cancel": "https://api.replicate.com/v1/predictions/wfkwb158ahrgg0cft3qbjjzzgc/cancel" }, "version": "027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd" }Generated inUsing seed: 57465 0%| | 0/8 [00:00<?, ?it/s] 12%|█▎ | 1/8 [00:02<00:16, 2.29s/it] 25%|██▌ | 2/8 [00:04<00:13, 2.29s/it] 38%|███▊ | 3/8 [00:06<00:11, 2.30s/it] 50%|█████ | 4/8 [00:09<00:09, 2.30s/it] 62%|██████▎ | 5/8 [00:11<00:06, 2.30s/it] 75%|███████▌ | 6/8 [00:13<00:04, 2.30s/it] 88%|████████▊ | 7/8 [00:16<00:02, 2.30s/it] 100%|██████████| 8/8 [00:18<00:00, 2.30s/it] 100%|██████████| 8/8 [00:18<00:00, 2.30s/it]
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