genmoai / mochi-1
Mochi 1 preview is an open video generation model with high-fidelity motion and strong prompt adherence in preliminary evaluation
Prediction
genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460Input
- fps
- 24
- prompt
- Close-up of a chameleon's eye, with its scaly skin changing color. Ultra high resolution 4k.
- num_frames
- 121
- guidance_scale
- 5.5
- num_inference_steps
- 30
{ "fps": 24, "prompt": "Close-up of a chameleon's eye, with its scaly skin changing color. Ultra high resolution 4k.", "num_frames": 121, "guidance_scale": 5.5, "num_inference_steps": 30 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 genmoai/mochi-1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", { input: { fps: 24, prompt: "Close-up of a chameleon's eye, with its scaly skin changing color. Ultra high resolution 4k.", num_frames: 121, guidance_scale: 5.5, num_inference_steps: 30 } } ); // 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 replicate
Import the client:import replicate
Run genmoai/mochi-1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", input={ "fps": 24, "prompt": "Close-up of a chameleon's eye, with its scaly skin changing color. Ultra high resolution 4k.", "num_frames": 121, "guidance_scale": 5.5, "num_inference_steps": 30 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run genmoai/mochi-1 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": "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", "input": { "fps": 24, "prompt": "Close-up of a chameleon\'s eye, with its scaly skin changing color. Ultra high resolution 4k.", "num_frames": 121, "guidance_scale": 5.5, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-01T03:45:02.340601Z", "created_at": "2024-12-01T03:40:25.695000Z", "data_removed": false, "error": null, "id": "41vmsc96vxrm80ckg17awwyc6m", "input": { "fps": 24, "prompt": "Close-up of a chameleon's eye, with its scaly skin changing color. Ultra high resolution 4k.", "num_frames": 121, "guidance_scale": 5.5, "num_inference_steps": 30 }, "logs": "Using seed: 48484\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:16<07:48, 16.16s/it]\n 7%|▋ | 2/30 [00:21<04:39, 9.99s/it]\n 10%|█ | 3/30 [00:28<03:46, 8.39s/it]\n 13%|█▎ | 4/30 [00:34<03:18, 7.64s/it]\n 17%|█▋ | 5/30 [00:41<03:00, 7.23s/it]\n 20%|██ | 6/30 [00:47<02:47, 6.99s/it]\n 23%|██▎ | 7/30 [00:54<02:37, 6.84s/it]\n 27%|██▋ | 8/30 [01:00<02:28, 6.74s/it]\n 30%|███ | 9/30 [01:07<02:20, 6.67s/it]\n 33%|███▎ | 10/30 [01:13<02:12, 6.62s/it]\n 37%|███▋ | 11/30 [01:20<02:05, 6.59s/it]\n 40%|████ | 12/30 [01:26<01:58, 6.57s/it]\n 43%|████▎ | 13/30 [01:33<01:51, 6.55s/it]\n 47%|████▋ | 14/30 [01:39<01:44, 6.54s/it]\n 50%|█████ | 15/30 [01:46<01:37, 6.53s/it]\n 53%|█████▎ | 16/30 [01:52<01:31, 6.52s/it]\n 57%|█████▋ | 17/30 [01:59<01:24, 6.52s/it]\n 60%|██████ | 18/30 [02:06<01:18, 6.51s/it]\n 63%|██████▎ | 19/30 [02:12<01:11, 6.51s/it]\n 67%|██████▋ | 20/30 [02:19<01:05, 6.51s/it]\n 70%|███████ | 21/30 [02:25<00:58, 6.51s/it]\n 73%|███████▎ | 22/30 [02:31<00:52, 6.50s/it]\n 77%|███████▋ | 23/30 [02:38<00:45, 6.50s/it]\n 80%|████████ | 24/30 [02:44<00:38, 6.49s/it]\n 83%|████████▎ | 25/30 [02:51<00:32, 6.49s/it]\n 87%|████████▋ | 26/30 [02:57<00:25, 6.49s/it]\n 90%|█████████ | 27/30 [03:04<00:19, 6.49s/it]\n 93%|█████████▎| 28/30 [03:11<00:13, 6.52s/it]\n 97%|█████████▋| 29/30 [03:17<00:06, 6.51s/it]\n100%|██████████| 30/30 [03:24<00:00, 6.51s/it]\n100%|██████████| 30/30 [03:24<00:00, 6.80s/it]", "metrics": { "predict_time": 221.469770524, "total_time": 276.645601 }, "output": "https://replicate.delivery/xezq/AF0YFy7wRq4YOJC20hGKxkN7Q0y7opcXkQdfx3mG21Df2Y2TA/output.mp4", "started_at": "2024-12-01T03:41:20.870831Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-7e4enlphbmwnz6o5ulpjnkzcjd2bnobfkjzfwn4penkqivlf6s7q", "get": "https://api.replicate.com/v1/predictions/41vmsc96vxrm80ckg17awwyc6m", "cancel": "https://api.replicate.com/v1/predictions/41vmsc96vxrm80ckg17awwyc6m/cancel" }, "version": "1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460" }
Generated inUsing seed: 48484 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:16<07:48, 16.16s/it] 7%|▋ | 2/30 [00:21<04:39, 9.99s/it] 10%|█ | 3/30 [00:28<03:46, 8.39s/it] 13%|█▎ | 4/30 [00:34<03:18, 7.64s/it] 17%|█▋ | 5/30 [00:41<03:00, 7.23s/it] 20%|██ | 6/30 [00:47<02:47, 6.99s/it] 23%|██▎ | 7/30 [00:54<02:37, 6.84s/it] 27%|██▋ | 8/30 [01:00<02:28, 6.74s/it] 30%|███ | 9/30 [01:07<02:20, 6.67s/it] 33%|███▎ | 10/30 [01:13<02:12, 6.62s/it] 37%|███▋ | 11/30 [01:20<02:05, 6.59s/it] 40%|████ | 12/30 [01:26<01:58, 6.57s/it] 43%|████▎ | 13/30 [01:33<01:51, 6.55s/it] 47%|████▋ | 14/30 [01:39<01:44, 6.54s/it] 50%|█████ | 15/30 [01:46<01:37, 6.53s/it] 53%|█████▎ | 16/30 [01:52<01:31, 6.52s/it] 57%|█████▋ | 17/30 [01:59<01:24, 6.52s/it] 60%|██████ | 18/30 [02:06<01:18, 6.51s/it] 63%|██████▎ | 19/30 [02:12<01:11, 6.51s/it] 67%|██████▋ | 20/30 [02:19<01:05, 6.51s/it] 70%|███████ | 21/30 [02:25<00:58, 6.51s/it] 73%|███████▎ | 22/30 [02:31<00:52, 6.50s/it] 77%|███████▋ | 23/30 [02:38<00:45, 6.50s/it] 80%|████████ | 24/30 [02:44<00:38, 6.49s/it] 83%|████████▎ | 25/30 [02:51<00:32, 6.49s/it] 87%|████████▋ | 26/30 [02:57<00:25, 6.49s/it] 90%|█████████ | 27/30 [03:04<00:19, 6.49s/it] 93%|█████████▎| 28/30 [03:11<00:13, 6.52s/it] 97%|█████████▋| 29/30 [03:17<00:06, 6.51s/it] 100%|██████████| 30/30 [03:24<00:00, 6.51s/it] 100%|██████████| 30/30 [03:24<00:00, 6.80s/it]
Prediction
genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460IDtacc8z4y21rma0ckg1p80h5q0wStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- fps
- 24
- prompt
- A fantastic floating island with waterfalls falling into the sky, lush vegetation and the city of Granada perched on the edge, suspended above the clouds.
- num_frames
- 121
- guidance_scale
- 5.5
- num_inference_steps
- 30
{ "fps": 24, "prompt": "A fantastic floating island with waterfalls falling into the sky, lush vegetation and the city of Granada perched on the edge, suspended above the clouds.", "num_frames": 121, "guidance_scale": 5.5, "num_inference_steps": 30 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 genmoai/mochi-1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", { input: { fps: 24, prompt: "A fantastic floating island with waterfalls falling into the sky, lush vegetation and the city of Granada perched on the edge, suspended above the clouds.", num_frames: 121, guidance_scale: 5.5, num_inference_steps: 30 } } ); // 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 replicate
Import the client:import replicate
Run genmoai/mochi-1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", input={ "fps": 24, "prompt": "A fantastic floating island with waterfalls falling into the sky, lush vegetation and the city of Granada perched on the edge, suspended above the clouds.", "num_frames": 121, "guidance_scale": 5.5, "num_inference_steps": 30 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run genmoai/mochi-1 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": "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", "input": { "fps": 24, "prompt": "A fantastic floating island with waterfalls falling into the sky, lush vegetation and the city of Granada perched on the edge, suspended above the clouds.", "num_frames": 121, "guidance_scale": 5.5, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-01T04:17:18.114108Z", "created_at": "2024-12-01T04:13:42.288000Z", "data_removed": false, "error": null, "id": "tacc8z4y21rma0ckg1p80h5q0w", "input": { "fps": 24, "prompt": "A fantastic floating island with waterfalls falling into the sky, lush vegetation and the city of Granada perched on the edge, suspended above the clouds.", "num_frames": 121, "guidance_scale": 5.5, "num_inference_steps": 30 }, "logs": "Using seed: 13082\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:13<06:27, 13.35s/it]\n 7%|▋ | 2/30 [00:19<04:07, 8.82s/it]\n 10%|█ | 3/30 [00:25<03:29, 7.75s/it]\n 13%|█▎ | 4/30 [00:31<03:08, 7.25s/it]\n 17%|█▋ | 5/30 [00:38<02:54, 6.97s/it]\n 20%|██ | 6/30 [00:44<02:43, 6.82s/it]\n 23%|██▎ | 7/30 [00:51<02:34, 6.71s/it]\n 27%|██▋ | 8/30 [00:57<02:26, 6.65s/it]\n 30%|███ | 9/30 [01:04<02:18, 6.59s/it]\n 33%|███▎ | 10/30 [01:10<02:11, 6.55s/it]\n 37%|███▋ | 11/30 [01:17<02:03, 6.52s/it]\n 40%|████ | 12/30 [01:23<01:57, 6.51s/it]\n 43%|████▎ | 13/30 [01:30<01:50, 6.49s/it]\n 47%|████▋ | 14/30 [01:36<01:43, 6.48s/it]\n 50%|█████ | 15/30 [01:43<01:37, 6.47s/it]\n 53%|█████▎ | 16/30 [01:49<01:30, 6.47s/it]\n 57%|█████▋ | 17/30 [01:56<01:24, 6.46s/it]\n 60%|██████ | 18/30 [02:02<01:17, 6.46s/it]\n 63%|██████▎ | 19/30 [02:09<01:11, 6.46s/it]\n 67%|██████▋ | 20/30 [02:15<01:04, 6.46s/it]\n 70%|███████ | 21/30 [02:21<00:58, 6.46s/it]\n 73%|███████▎ | 22/30 [02:28<00:51, 6.46s/it]\n 77%|███████▋ | 23/30 [02:34<00:45, 6.46s/it]\n 80%|████████ | 24/30 [02:41<00:38, 6.46s/it]\n 83%|████████▎ | 25/30 [02:47<00:32, 6.46s/it]\n 87%|████████▋ | 26/30 [02:54<00:25, 6.46s/it]\n 90%|█████████ | 27/30 [03:00<00:19, 6.46s/it]\n 93%|█████████▎| 28/30 [03:07<00:12, 6.46s/it]\n 97%|█████████▋| 29/30 [03:13<00:06, 6.46s/it]\n100%|██████████| 30/30 [03:20<00:00, 6.46s/it]\n100%|██████████| 30/30 [03:20<00:00, 6.67s/it]", "metrics": { "predict_time": 215.819518263, "total_time": 215.826108 }, "output": "https://replicate.delivery/xezq/wuOouGD6Zb5nNpUARxKNOB9FYwTWYU0rjtpOZuwLDnoTVm9E/output.mp4", "started_at": "2024-12-01T04:13:42.294590Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-7ogaiooyfxylq53qucaznasclfqfr7v5gfadjmijynjmlbkh4tka", "get": "https://api.replicate.com/v1/predictions/tacc8z4y21rma0ckg1p80h5q0w", "cancel": "https://api.replicate.com/v1/predictions/tacc8z4y21rma0ckg1p80h5q0w/cancel" }, "version": "1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460" }
Generated inUsing seed: 13082 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:13<06:27, 13.35s/it] 7%|▋ | 2/30 [00:19<04:07, 8.82s/it] 10%|█ | 3/30 [00:25<03:29, 7.75s/it] 13%|█▎ | 4/30 [00:31<03:08, 7.25s/it] 17%|█▋ | 5/30 [00:38<02:54, 6.97s/it] 20%|██ | 6/30 [00:44<02:43, 6.82s/it] 23%|██▎ | 7/30 [00:51<02:34, 6.71s/it] 27%|██▋ | 8/30 [00:57<02:26, 6.65s/it] 30%|███ | 9/30 [01:04<02:18, 6.59s/it] 33%|███▎ | 10/30 [01:10<02:11, 6.55s/it] 37%|███▋ | 11/30 [01:17<02:03, 6.52s/it] 40%|████ | 12/30 [01:23<01:57, 6.51s/it] 43%|████▎ | 13/30 [01:30<01:50, 6.49s/it] 47%|████▋ | 14/30 [01:36<01:43, 6.48s/it] 50%|█████ | 15/30 [01:43<01:37, 6.47s/it] 53%|█████▎ | 16/30 [01:49<01:30, 6.47s/it] 57%|█████▋ | 17/30 [01:56<01:24, 6.46s/it] 60%|██████ | 18/30 [02:02<01:17, 6.46s/it] 63%|██████▎ | 19/30 [02:09<01:11, 6.46s/it] 67%|██████▋ | 20/30 [02:15<01:04, 6.46s/it] 70%|███████ | 21/30 [02:21<00:58, 6.46s/it] 73%|███████▎ | 22/30 [02:28<00:51, 6.46s/it] 77%|███████▋ | 23/30 [02:34<00:45, 6.46s/it] 80%|████████ | 24/30 [02:41<00:38, 6.46s/it] 83%|████████▎ | 25/30 [02:47<00:32, 6.46s/it] 87%|████████▋ | 26/30 [02:54<00:25, 6.46s/it] 90%|█████████ | 27/30 [03:00<00:19, 6.46s/it] 93%|█████████▎| 28/30 [03:07<00:12, 6.46s/it] 97%|█████████▋| 29/30 [03:13<00:06, 6.46s/it] 100%|██████████| 30/30 [03:20<00:00, 6.46s/it] 100%|██████████| 30/30 [03:20<00:00, 6.67s/it]
Prediction
genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460IDrpdpe90ca5rma0ckg1sv7grd74StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- fps
- 24
- prompt
- High speed tracking shot of the front of a RED sports car speeding through a narrow city
- num_frames
- 121
- guidance_scale
- 5.5
- num_inference_steps
- 30
{ "fps": 24, "prompt": "High speed tracking shot of the front of a RED sports car speeding through a narrow city", "num_frames": 121, "guidance_scale": 5.5, "num_inference_steps": 30 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 genmoai/mochi-1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", { input: { fps: 24, prompt: "High speed tracking shot of the front of a RED sports car speeding through a narrow city", num_frames: 121, guidance_scale: 5.5, num_inference_steps: 30 } } ); // 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 replicate
Import the client:import replicate
Run genmoai/mochi-1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", input={ "fps": 24, "prompt": "High speed tracking shot of the front of a RED sports car speeding through a narrow city", "num_frames": 121, "guidance_scale": 5.5, "num_inference_steps": 30 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run genmoai/mochi-1 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": "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", "input": { "fps": 24, "prompt": "High speed tracking shot of the front of a RED sports car speeding through a narrow city", "num_frames": 121, "guidance_scale": 5.5, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-01T04:25:10.532511Z", "created_at": "2024-12-01T04:20:43.729000Z", "data_removed": false, "error": null, "id": "rpdpe90ca5rma0ckg1sv7grd74", "input": { "fps": 24, "prompt": "High speed tracking shot of the front of a RED sports car speeding through a narrow city", "num_frames": 121, "guidance_scale": 5.5, "num_inference_steps": 30 }, "logs": "Using seed: 37573\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:13<06:25, 13.31s/it]\n 7%|▋ | 2/30 [00:18<04:05, 8.77s/it]\n 10%|█ | 3/30 [00:25<03:27, 7.68s/it]\n 13%|█▎ | 4/30 [00:31<03:06, 7.17s/it]\n 17%|█▋ | 5/30 [00:38<02:52, 6.89s/it]\n 20%|██ | 6/30 [00:44<02:41, 6.73s/it]\n 23%|██▎ | 7/30 [00:50<02:32, 6.63s/it]\n 27%|██▋ | 8/30 [00:57<02:24, 6.56s/it]\n 30%|███ | 9/30 [01:03<02:16, 6.52s/it]\n 33%|███▎ | 10/30 [01:10<02:09, 6.49s/it]\n 37%|███▋ | 11/30 [01:16<02:02, 6.47s/it]\n 40%|████ | 12/30 [01:23<01:56, 6.45s/it]\n 43%|████▎ | 13/30 [01:29<01:49, 6.44s/it]\n 47%|████▋ | 14/30 [01:35<01:43, 6.46s/it]\n 50%|█████ | 15/30 [01:42<01:36, 6.45s/it]\n 53%|█████▎ | 16/30 [01:48<01:30, 6.44s/it]\n 57%|█████▋ | 17/30 [01:55<01:23, 6.44s/it]\n 60%|██████ | 18/30 [02:01<01:17, 6.43s/it]\n 63%|██████▎ | 19/30 [02:08<01:10, 6.44s/it]\n 67%|██████▋ | 20/30 [02:14<01:04, 6.44s/it]\n 70%|███████ | 21/30 [02:21<00:58, 6.46s/it]\n 73%|███████▎ | 22/30 [02:27<00:51, 6.45s/it]\n 77%|███████▋ | 23/30 [02:33<00:45, 6.44s/it]\n 80%|████████ | 24/30 [02:40<00:38, 6.44s/it]\n 83%|████████▎ | 25/30 [02:46<00:32, 6.44s/it]\n 87%|████████▋ | 26/30 [02:53<00:25, 6.43s/it]\n 90%|█████████ | 27/30 [02:59<00:19, 6.43s/it]\n 93%|█████████▎| 28/30 [03:06<00:12, 6.44s/it]\n 97%|█████████▋| 29/30 [03:12<00:06, 6.44s/it]\n100%|██████████| 30/30 [03:18<00:00, 6.43s/it]\n100%|██████████| 30/30 [03:18<00:00, 6.63s/it]", "metrics": { "predict_time": 215.644920367, "total_time": 266.803511 }, "output": "https://replicate.delivery/xezq/DqWEOdDh3iJmABefdYzUmwHaAfDsIyynEeHeKWBZ9o03kLzeE/output.mp4", "started_at": "2024-12-01T04:21:34.887590Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-5ri4pl6mefasazevh7ybmsolpkif5e465vkbsefau7qkyubqmqxa", "get": "https://api.replicate.com/v1/predictions/rpdpe90ca5rma0ckg1sv7grd74", "cancel": "https://api.replicate.com/v1/predictions/rpdpe90ca5rma0ckg1sv7grd74/cancel" }, "version": "1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460" }
Generated inUsing seed: 37573 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:13<06:25, 13.31s/it] 7%|▋ | 2/30 [00:18<04:05, 8.77s/it] 10%|█ | 3/30 [00:25<03:27, 7.68s/it] 13%|█▎ | 4/30 [00:31<03:06, 7.17s/it] 17%|█▋ | 5/30 [00:38<02:52, 6.89s/it] 20%|██ | 6/30 [00:44<02:41, 6.73s/it] 23%|██▎ | 7/30 [00:50<02:32, 6.63s/it] 27%|██▋ | 8/30 [00:57<02:24, 6.56s/it] 30%|███ | 9/30 [01:03<02:16, 6.52s/it] 33%|███▎ | 10/30 [01:10<02:09, 6.49s/it] 37%|███▋ | 11/30 [01:16<02:02, 6.47s/it] 40%|████ | 12/30 [01:23<01:56, 6.45s/it] 43%|████▎ | 13/30 [01:29<01:49, 6.44s/it] 47%|████▋ | 14/30 [01:35<01:43, 6.46s/it] 50%|█████ | 15/30 [01:42<01:36, 6.45s/it] 53%|█████▎ | 16/30 [01:48<01:30, 6.44s/it] 57%|█████▋ | 17/30 [01:55<01:23, 6.44s/it] 60%|██████ | 18/30 [02:01<01:17, 6.43s/it] 63%|██████▎ | 19/30 [02:08<01:10, 6.44s/it] 67%|██████▋ | 20/30 [02:14<01:04, 6.44s/it] 70%|███████ | 21/30 [02:21<00:58, 6.46s/it] 73%|███████▎ | 22/30 [02:27<00:51, 6.45s/it] 77%|███████▋ | 23/30 [02:33<00:45, 6.44s/it] 80%|████████ | 24/30 [02:40<00:38, 6.44s/it] 83%|████████▎ | 25/30 [02:46<00:32, 6.44s/it] 87%|████████▋ | 26/30 [02:53<00:25, 6.43s/it] 90%|█████████ | 27/30 [02:59<00:19, 6.43s/it] 93%|█████████▎| 28/30 [03:06<00:12, 6.44s/it] 97%|█████████▋| 29/30 [03:12<00:06, 6.44s/it] 100%|██████████| 30/30 [03:18<00:00, 6.43s/it] 100%|██████████| 30/30 [03:18<00:00, 6.63s/it]
Prediction
genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460IDxkj2dcjcsxrm80ckg2f9parpd0StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- fps
- 24
- prompt
- A slow zoom on a glass of wine while pouring wine from a bottle, the background is a vineyard
- num_frames
- 121
- guidance_scale
- 6
- num_inference_steps
- 30
{ "fps": 24, "prompt": "A slow zoom on a glass of wine while pouring wine from a bottle, the background is a vineyard", "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 genmoai/mochi-1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", { input: { fps: 24, prompt: "A slow zoom on a glass of wine while pouring wine from a bottle, the background is a vineyard", num_frames: 121, guidance_scale: 6, num_inference_steps: 30 } } ); // 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 replicate
Import the client:import replicate
Run genmoai/mochi-1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", input={ "fps": 24, "prompt": "A slow zoom on a glass of wine while pouring wine from a bottle, the background is a vineyard", "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run genmoai/mochi-1 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": "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", "input": { "fps": 24, "prompt": "A slow zoom on a glass of wine while pouring wine from a bottle, the background is a vineyard", "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-01T05:11:36.588511Z", "created_at": "2024-12-01T05:07:58.287000Z", "data_removed": false, "error": null, "id": "xkj2dcjcsxrm80ckg2f9parpd0", "input": { "fps": 24, "prompt": "A slow zoom on a glass of wine while pouring wine from a bottle, the background is a vineyard", "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 }, "logs": "Using seed: 36474\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:13<06:24, 13.26s/it]\n 7%|▋ | 2/30 [00:18<04:07, 8.82s/it]\n 10%|█ | 3/30 [00:25<03:29, 7.77s/it]\n 13%|█▎ | 4/30 [00:32<03:09, 7.28s/it]\n 17%|█▋ | 5/30 [00:38<02:55, 7.01s/it]\n 20%|██ | 6/30 [00:45<02:44, 6.86s/it]\n 23%|██▎ | 7/30 [00:51<02:35, 6.76s/it]\n 27%|██▋ | 8/30 [00:58<02:27, 6.69s/it]\n 30%|███ | 9/30 [01:04<02:19, 6.65s/it]\n 33%|███▎ | 10/30 [01:11<02:12, 6.62s/it]\n 37%|███▋ | 11/30 [01:17<02:05, 6.60s/it]\n 40%|████ | 12/30 [01:24<01:58, 6.59s/it]\n 43%|████▎ | 13/30 [01:31<01:51, 6.58s/it]\n 47%|████▋ | 14/30 [01:37<01:45, 6.57s/it]\n 50%|█████ | 15/30 [01:44<01:38, 6.57s/it]\n 53%|█████▎ | 16/30 [01:50<01:31, 6.57s/it]\n 57%|█████▋ | 17/30 [01:57<01:25, 6.56s/it]\n 60%|██████ | 18/30 [02:03<01:18, 6.56s/it]\n 63%|██████▎ | 19/30 [02:10<01:12, 6.56s/it]\n 67%|██████▋ | 20/30 [02:16<01:05, 6.56s/it]\n 70%|███████ | 21/30 [02:23<00:59, 6.56s/it]\n 73%|███████▎ | 22/30 [02:30<00:52, 6.56s/it]\n 77%|███████▋ | 23/30 [02:36<00:45, 6.56s/it]\n 80%|████████ | 24/30 [02:43<00:39, 6.56s/it]\n 83%|████████▎ | 25/30 [02:49<00:32, 6.56s/it]\n 87%|████████▋ | 26/30 [02:56<00:26, 6.56s/it]\n 90%|█████████ | 27/30 [03:02<00:19, 6.56s/it]\n 93%|█████████▎| 28/30 [03:09<00:13, 6.56s/it]\n 97%|█████████▋| 29/30 [03:15<00:06, 6.56s/it]\n100%|██████████| 30/30 [03:22<00:00, 6.56s/it]\n100%|██████████| 30/30 [03:22<00:00, 6.75s/it]", "metrics": { "predict_time": 218.294750385, "total_time": 218.301511 }, "output": "https://replicate.delivery/xezq/2rbEOImswKJHGRzkDKRFsTTU7iibkUvmpeZNKSOOEqfIIa2TA/output.mp4", "started_at": "2024-12-01T05:07:58.293761Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-cnp6ghq32aqsvduoowtmgxmsru3224s3bbqigtnmsd6acxogb3da", "get": "https://api.replicate.com/v1/predictions/xkj2dcjcsxrm80ckg2f9parpd0", "cancel": "https://api.replicate.com/v1/predictions/xkj2dcjcsxrm80ckg2f9parpd0/cancel" }, "version": "1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460" }
Generated inUsing seed: 36474 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:13<06:24, 13.26s/it] 7%|▋ | 2/30 [00:18<04:07, 8.82s/it] 10%|█ | 3/30 [00:25<03:29, 7.77s/it] 13%|█▎ | 4/30 [00:32<03:09, 7.28s/it] 17%|█▋ | 5/30 [00:38<02:55, 7.01s/it] 20%|██ | 6/30 [00:45<02:44, 6.86s/it] 23%|██▎ | 7/30 [00:51<02:35, 6.76s/it] 27%|██▋ | 8/30 [00:58<02:27, 6.69s/it] 30%|███ | 9/30 [01:04<02:19, 6.65s/it] 33%|███▎ | 10/30 [01:11<02:12, 6.62s/it] 37%|███▋ | 11/30 [01:17<02:05, 6.60s/it] 40%|████ | 12/30 [01:24<01:58, 6.59s/it] 43%|████▎ | 13/30 [01:31<01:51, 6.58s/it] 47%|████▋ | 14/30 [01:37<01:45, 6.57s/it] 50%|█████ | 15/30 [01:44<01:38, 6.57s/it] 53%|█████▎ | 16/30 [01:50<01:31, 6.57s/it] 57%|█████▋ | 17/30 [01:57<01:25, 6.56s/it] 60%|██████ | 18/30 [02:03<01:18, 6.56s/it] 63%|██████▎ | 19/30 [02:10<01:12, 6.56s/it] 67%|██████▋ | 20/30 [02:16<01:05, 6.56s/it] 70%|███████ | 21/30 [02:23<00:59, 6.56s/it] 73%|███████▎ | 22/30 [02:30<00:52, 6.56s/it] 77%|███████▋ | 23/30 [02:36<00:45, 6.56s/it] 80%|████████ | 24/30 [02:43<00:39, 6.56s/it] 83%|████████▎ | 25/30 [02:49<00:32, 6.56s/it] 87%|████████▋ | 26/30 [02:56<00:26, 6.56s/it] 90%|█████████ | 27/30 [03:02<00:19, 6.56s/it] 93%|█████████▎| 28/30 [03:09<00:13, 6.56s/it] 97%|█████████▋| 29/30 [03:15<00:06, 6.56s/it] 100%|██████████| 30/30 [03:22<00:00, 6.56s/it] 100%|██████████| 30/30 [03:22<00:00, 6.75s/it]
Prediction
genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460IDc1a1ntq8edrmc0ckg2p8b70tzwStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- fps
- 24
- prompt
- a parrot flying in the blue skies
- num_frames
- 121
- guidance_scale
- 6
- num_inference_steps
- 30
{ "fps": 24, "prompt": "a parrot flying in the blue skies", "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 genmoai/mochi-1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", { input: { fps: 24, prompt: "a parrot flying in the blue skies", num_frames: 121, guidance_scale: 6, num_inference_steps: 30 } } ); // 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 replicate
Import the client:import replicate
Run genmoai/mochi-1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", input={ "fps": 24, "prompt": "a parrot flying in the blue skies", "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run genmoai/mochi-1 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": "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", "input": { "fps": 24, "prompt": "a parrot flying in the blue skies", "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-01T05:27:30.905045Z", "created_at": "2024-12-01T05:23:55.635000Z", "data_removed": false, "error": null, "id": "c1a1ntq8edrmc0ckg2p8b70tzw", "input": { "fps": 24, "prompt": "a parrot flying in the blue skies", "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 }, "logs": "Using seed: 8520\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:13<06:23, 13.23s/it]\n 7%|▋ | 2/30 [00:18<04:06, 8.80s/it]\n 10%|█ | 3/30 [00:25<03:29, 7.77s/it]\n 13%|█▎ | 4/30 [00:32<03:09, 7.29s/it]\n 17%|█▋ | 5/30 [00:38<02:55, 7.01s/it]\n 20%|██ | 6/30 [00:45<02:44, 6.85s/it]\n 23%|██▎ | 7/30 [00:51<02:35, 6.75s/it]\n 27%|██▋ | 8/30 [00:58<02:27, 6.69s/it]\n 30%|███ | 9/30 [01:04<02:19, 6.64s/it]\n 33%|███▎ | 10/30 [01:11<02:12, 6.60s/it]\n 37%|███▋ | 11/30 [01:17<02:05, 6.58s/it]\n 40%|████ | 12/30 [01:24<01:58, 6.57s/it]\n 43%|████▎ | 13/30 [01:30<01:51, 6.56s/it]\n 47%|████▋ | 14/30 [01:37<01:44, 6.56s/it]\n 50%|█████ | 15/30 [01:43<01:38, 6.55s/it]\n 53%|█████▎ | 16/30 [01:50<01:31, 6.54s/it]\n 57%|█████▋ | 17/30 [01:56<01:25, 6.54s/it]\n 60%|██████ | 18/30 [02:03<01:18, 6.54s/it]\n 63%|██████▎ | 19/30 [02:10<01:11, 6.54s/it]\n 67%|██████▋ | 20/30 [02:16<01:05, 6.54s/it]\n 70%|███████ | 21/30 [02:23<00:58, 6.54s/it]\n 73%|███████▎ | 22/30 [02:29<00:52, 6.54s/it]\n 77%|███████▋ | 23/30 [02:36<00:45, 6.53s/it]\n 80%|████████ | 24/30 [02:42<00:39, 6.54s/it]\n 83%|████████▎ | 25/30 [02:49<00:32, 6.54s/it]\n 87%|████████▋ | 26/30 [02:55<00:26, 6.53s/it]\n 90%|█████████ | 27/30 [03:02<00:19, 6.53s/it]\n 93%|█████████▎| 28/30 [03:08<00:13, 6.53s/it]\n 97%|█████████▋| 29/30 [03:15<00:06, 6.53s/it]\n100%|██████████| 30/30 [03:21<00:00, 6.53s/it]\n100%|██████████| 30/30 [03:21<00:00, 6.73s/it]", "metrics": { "predict_time": 215.263372963, "total_time": 215.270045 }, "output": "https://replicate.delivery/xezq/m8PfqG2Cu1V1MqnXKfs0YDuuBjkyTdDP2mlXjSULuMeEu0snA/output.mp4", "started_at": "2024-12-01T05:23:55.641672Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-wgkrhfotqkrxvpeldlxsmvgtuxncgvzkebhjwglfngt33shrcunq", "get": "https://api.replicate.com/v1/predictions/c1a1ntq8edrmc0ckg2p8b70tzw", "cancel": "https://api.replicate.com/v1/predictions/c1a1ntq8edrmc0ckg2p8b70tzw/cancel" }, "version": "1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460" }
Generated inUsing seed: 8520 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:13<06:23, 13.23s/it] 7%|▋ | 2/30 [00:18<04:06, 8.80s/it] 10%|█ | 3/30 [00:25<03:29, 7.77s/it] 13%|█▎ | 4/30 [00:32<03:09, 7.29s/it] 17%|█▋ | 5/30 [00:38<02:55, 7.01s/it] 20%|██ | 6/30 [00:45<02:44, 6.85s/it] 23%|██▎ | 7/30 [00:51<02:35, 6.75s/it] 27%|██▋ | 8/30 [00:58<02:27, 6.69s/it] 30%|███ | 9/30 [01:04<02:19, 6.64s/it] 33%|███▎ | 10/30 [01:11<02:12, 6.60s/it] 37%|███▋ | 11/30 [01:17<02:05, 6.58s/it] 40%|████ | 12/30 [01:24<01:58, 6.57s/it] 43%|████▎ | 13/30 [01:30<01:51, 6.56s/it] 47%|████▋ | 14/30 [01:37<01:44, 6.56s/it] 50%|█████ | 15/30 [01:43<01:38, 6.55s/it] 53%|█████▎ | 16/30 [01:50<01:31, 6.54s/it] 57%|█████▋ | 17/30 [01:56<01:25, 6.54s/it] 60%|██████ | 18/30 [02:03<01:18, 6.54s/it] 63%|██████▎ | 19/30 [02:10<01:11, 6.54s/it] 67%|██████▋ | 20/30 [02:16<01:05, 6.54s/it] 70%|███████ | 21/30 [02:23<00:58, 6.54s/it] 73%|███████▎ | 22/30 [02:29<00:52, 6.54s/it] 77%|███████▋ | 23/30 [02:36<00:45, 6.53s/it] 80%|████████ | 24/30 [02:42<00:39, 6.54s/it] 83%|████████▎ | 25/30 [02:49<00:32, 6.54s/it] 87%|████████▋ | 26/30 [02:55<00:26, 6.53s/it] 90%|█████████ | 27/30 [03:02<00:19, 6.53s/it] 93%|█████████▎| 28/30 [03:08<00:13, 6.53s/it] 97%|█████████▋| 29/30 [03:15<00:06, 6.53s/it] 100%|██████████| 30/30 [03:21<00:00, 6.53s/it] 100%|██████████| 30/30 [03:21<00:00, 6.73s/it]
Prediction
genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460Input
- fps
- 24
- prompt
- a surreal scene where an astronaut enters a vast, glowing nebula. The nebula’s swirling clouds of gas are illuminated with electric shades of pink, teal, and violet, creating a dreamlike atmosphere. As the astronaut floats deeper into the nebula, soft trails of light follow their movements. The nebula seems alive, pulsing with energy and slowly changing shape, as if responding to the astronaut’s presence. The music shifts to a deeper, more emotional tone, with ethereal chimes and a slow, resonating bass that reflects the feeling of isolation but also connection to something vast and eternal.
- num_frames
- 121
- guidance_scale
- 6
- num_inference_steps
- 30
{ "fps": 24, "prompt": "a surreal scene where an astronaut enters a vast, glowing nebula. The nebula’s swirling clouds of gas are illuminated with electric shades of pink, teal, and violet, creating a dreamlike atmosphere. As the astronaut floats deeper into the nebula, soft trails of light follow their movements. The nebula seems alive, pulsing with energy and slowly changing shape, as if responding to the astronaut’s presence. The music shifts to a deeper, more emotional tone, with ethereal chimes and a slow, resonating bass that reflects the feeling of isolation but also connection to something vast and eternal.", "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 genmoai/mochi-1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", { input: { fps: 24, prompt: "a surreal scene where an astronaut enters a vast, glowing nebula. The nebula’s swirling clouds of gas are illuminated with electric shades of pink, teal, and violet, creating a dreamlike atmosphere. As the astronaut floats deeper into the nebula, soft trails of light follow their movements. The nebula seems alive, pulsing with energy and slowly changing shape, as if responding to the astronaut’s presence. The music shifts to a deeper, more emotional tone, with ethereal chimes and a slow, resonating bass that reflects the feeling of isolation but also connection to something vast and eternal.", num_frames: 121, guidance_scale: 6, num_inference_steps: 30 } } ); // 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 replicate
Import the client:import replicate
Run genmoai/mochi-1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", input={ "fps": 24, "prompt": "a surreal scene where an astronaut enters a vast, glowing nebula. The nebula’s swirling clouds of gas are illuminated with electric shades of pink, teal, and violet, creating a dreamlike atmosphere. As the astronaut floats deeper into the nebula, soft trails of light follow their movements. The nebula seems alive, pulsing with energy and slowly changing shape, as if responding to the astronaut’s presence. The music shifts to a deeper, more emotional tone, with ethereal chimes and a slow, resonating bass that reflects the feeling of isolation but also connection to something vast and eternal.", "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run genmoai/mochi-1 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": "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", "input": { "fps": 24, "prompt": "a surreal scene where an astronaut enters a vast, glowing nebula. The nebula’s swirling clouds of gas are illuminated with electric shades of pink, teal, and violet, creating a dreamlike atmosphere. As the astronaut floats deeper into the nebula, soft trails of light follow their movements. The nebula seems alive, pulsing with energy and slowly changing shape, as if responding to the astronaut’s presence. The music shifts to a deeper, more emotional tone, with ethereal chimes and a slow, resonating bass that reflects the feeling of isolation but also connection to something vast and eternal.", "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-01T05:31:51.215112Z", "created_at": "2024-12-01T05:28:15.774000Z", "data_removed": false, "error": null, "id": "jxt1wjz0ksrm80ckg2r8y8z1f0", "input": { "fps": 24, "prompt": "a surreal scene where an astronaut enters a vast, glowing nebula. The nebula’s swirling clouds of gas are illuminated with electric shades of pink, teal, and violet, creating a dreamlike atmosphere. As the astronaut floats deeper into the nebula, soft trails of light follow their movements. The nebula seems alive, pulsing with energy and slowly changing shape, as if responding to the astronaut’s presence. The music shifts to a deeper, more emotional tone, with ethereal chimes and a slow, resonating bass that reflects the feeling of isolation but also connection to something vast and eternal.", "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 }, "logs": "Using seed: 17268\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:13<06:22, 13.18s/it]\n 7%|▋ | 2/30 [00:18<04:05, 8.78s/it]\n 10%|█ | 3/30 [00:25<03:29, 7.75s/it]\n 13%|█▎ | 4/30 [00:31<03:08, 7.27s/it]\n 17%|█▋ | 5/30 [00:38<02:55, 7.00s/it]\n 20%|██ | 6/30 [00:45<02:44, 6.85s/it]\n 23%|██▎ | 7/30 [00:51<02:35, 6.75s/it]\n 27%|██▋ | 8/30 [00:58<02:27, 6.69s/it]\n 30%|███ | 9/30 [01:04<02:19, 6.65s/it]\n 33%|███▎ | 10/30 [01:11<02:12, 6.62s/it]\n 37%|███▋ | 11/30 [01:17<02:05, 6.62s/it]\n 40%|████ | 12/30 [01:24<01:58, 6.60s/it]\n 43%|████▎ | 13/30 [01:30<01:51, 6.59s/it]\n 47%|████▋ | 14/30 [01:37<01:45, 6.58s/it]\n 50%|█████ | 15/30 [01:44<01:38, 6.57s/it]\n 53%|█████▎ | 16/30 [01:50<01:31, 6.57s/it]\n 57%|█████▋ | 17/30 [01:57<01:25, 6.57s/it]\n 60%|██████ | 18/30 [02:03<01:18, 6.57s/it]\n 63%|██████▎ | 19/30 [02:10<01:12, 6.56s/it]\n 67%|██████▋ | 20/30 [02:16<01:05, 6.56s/it]\n 70%|███████ | 21/30 [02:23<00:59, 6.56s/it]\n 73%|███████▎ | 22/30 [02:29<00:52, 6.56s/it]\n 77%|███████▋ | 23/30 [02:36<00:45, 6.56s/it]\n 80%|████████ | 24/30 [02:43<00:39, 6.56s/it]\n 83%|████████▎ | 25/30 [02:49<00:32, 6.56s/it]\n 87%|████████▋ | 26/30 [02:56<00:26, 6.56s/it]\n 90%|█████████ | 27/30 [03:02<00:19, 6.56s/it]\n 93%|█████████▎| 28/30 [03:09<00:13, 6.56s/it]\n 97%|█████████▋| 29/30 [03:15<00:06, 6.56s/it]\n100%|██████████| 30/30 [03:22<00:00, 6.55s/it]\n100%|██████████| 30/30 [03:22<00:00, 6.75s/it]", "metrics": { "predict_time": 215.433839463, "total_time": 215.441112 }, "output": "https://replicate.delivery/xezq/HCgAIg0vze0kPqihi8NtfBVG05PCbOnLVTCdWJUyiLNHba2TA/output.mp4", "started_at": "2024-12-01T05:28:15.781272Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-7zraw2zxyvritv7cnz2pasqkvv5l53kalu5e5g3sqay2pa2yhyla", "get": "https://api.replicate.com/v1/predictions/jxt1wjz0ksrm80ckg2r8y8z1f0", "cancel": "https://api.replicate.com/v1/predictions/jxt1wjz0ksrm80ckg2r8y8z1f0/cancel" }, "version": "1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460" }
Generated inUsing seed: 17268 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:13<06:22, 13.18s/it] 7%|▋ | 2/30 [00:18<04:05, 8.78s/it] 10%|█ | 3/30 [00:25<03:29, 7.75s/it] 13%|█▎ | 4/30 [00:31<03:08, 7.27s/it] 17%|█▋ | 5/30 [00:38<02:55, 7.00s/it] 20%|██ | 6/30 [00:45<02:44, 6.85s/it] 23%|██▎ | 7/30 [00:51<02:35, 6.75s/it] 27%|██▋ | 8/30 [00:58<02:27, 6.69s/it] 30%|███ | 9/30 [01:04<02:19, 6.65s/it] 33%|███▎ | 10/30 [01:11<02:12, 6.62s/it] 37%|███▋ | 11/30 [01:17<02:05, 6.62s/it] 40%|████ | 12/30 [01:24<01:58, 6.60s/it] 43%|████▎ | 13/30 [01:30<01:51, 6.59s/it] 47%|████▋ | 14/30 [01:37<01:45, 6.58s/it] 50%|█████ | 15/30 [01:44<01:38, 6.57s/it] 53%|█████▎ | 16/30 [01:50<01:31, 6.57s/it] 57%|█████▋ | 17/30 [01:57<01:25, 6.57s/it] 60%|██████ | 18/30 [02:03<01:18, 6.57s/it] 63%|██████▎ | 19/30 [02:10<01:12, 6.56s/it] 67%|██████▋ | 20/30 [02:16<01:05, 6.56s/it] 70%|███████ | 21/30 [02:23<00:59, 6.56s/it] 73%|███████▎ | 22/30 [02:29<00:52, 6.56s/it] 77%|███████▋ | 23/30 [02:36<00:45, 6.56s/it] 80%|████████ | 24/30 [02:43<00:39, 6.56s/it] 83%|████████▎ | 25/30 [02:49<00:32, 6.56s/it] 87%|████████▋ | 26/30 [02:56<00:26, 6.56s/it] 90%|█████████ | 27/30 [03:02<00:19, 6.56s/it] 93%|█████████▎| 28/30 [03:09<00:13, 6.56s/it] 97%|█████████▋| 29/30 [03:15<00:06, 6.56s/it] 100%|██████████| 30/30 [03:22<00:00, 6.55s/it] 100%|██████████| 30/30 [03:22<00:00, 6.75s/it]
Prediction
genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460Input
- fps
- 24
- prompt
- The video opens with a close-up of a woman in a white and purple outfit, holding a glowing purple butterfly. She has dark hair and walks gracefully through a traditional Japanese-style village at night
- num_frames
- 121
- guidance_scale
- 6
- num_inference_steps
- 30
{ "fps": 24, "prompt": "The video opens with a close-up of a woman in a white and purple outfit, holding a glowing purple butterfly. She has dark hair and walks gracefully through a traditional Japanese-style village at night", "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 genmoai/mochi-1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", { input: { fps: 24, prompt: "The video opens with a close-up of a woman in a white and purple outfit, holding a glowing purple butterfly. She has dark hair and walks gracefully through a traditional Japanese-style village at night", num_frames: 121, guidance_scale: 6, num_inference_steps: 30 } } ); // 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 replicate
Import the client:import replicate
Run genmoai/mochi-1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", input={ "fps": 24, "prompt": "The video opens with a close-up of a woman in a white and purple outfit, holding a glowing purple butterfly. She has dark hair and walks gracefully through a traditional Japanese-style village at night", "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run genmoai/mochi-1 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": "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", "input": { "fps": 24, "prompt": "The video opens with a close-up of a woman in a white and purple outfit, holding a glowing purple butterfly. She has dark hair and walks gracefully through a traditional Japanese-style village at night", "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-02T21:53:14.309612Z", "created_at": "2024-12-02T21:49:40.763000Z", "data_removed": false, "error": null, "id": "7ge51m603drm80ckh5csvqc6s8", "input": { "fps": 24, "prompt": "The video opens with a close-up of a woman in a white and purple outfit, holding a glowing purple butterfly. She has dark hair and walks gracefully through a traditional Japanese-style village at night", "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 }, "logs": "Using seed: 29655\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:13<06:19, 13.09s/it]\n 7%|▋ | 2/30 [00:18<04:03, 8.69s/it]\n 10%|█ | 3/30 [00:25<03:26, 7.66s/it]\n 13%|█▎ | 4/30 [00:31<03:06, 7.17s/it]\n 17%|█▋ | 5/30 [00:37<02:52, 6.90s/it]\n 20%|██ | 6/30 [00:44<02:41, 6.74s/it]\n 23%|██▎ | 7/30 [00:50<02:32, 6.63s/it]\n 27%|██▋ | 8/30 [00:57<02:24, 6.56s/it]\n 30%|███ | 9/30 [01:03<02:16, 6.51s/it]\n 33%|███▎ | 10/30 [01:10<02:09, 6.48s/it]\n 37%|███▋ | 11/30 [01:16<02:02, 6.46s/it]\n 40%|████ | 12/30 [01:22<01:56, 6.45s/it]\n 43%|████▎ | 13/30 [01:29<01:49, 6.44s/it]\n 47%|████▋ | 14/30 [01:35<01:42, 6.44s/it]\n 50%|█████ | 15/30 [01:42<01:36, 6.43s/it]\n 53%|█████▎ | 16/30 [01:48<01:30, 6.43s/it]\n 57%|█████▋ | 17/30 [01:55<01:23, 6.44s/it]\n 60%|██████ | 18/30 [02:01<01:17, 6.44s/it]\n 63%|██████▎ | 19/30 [02:07<01:10, 6.43s/it]\n 67%|██████▋ | 20/30 [02:14<01:04, 6.43s/it]\n 70%|███████ | 21/30 [02:20<00:57, 6.43s/it]\n 73%|███████▎ | 22/30 [02:27<00:51, 6.43s/it]\n 77%|███████▋ | 23/30 [02:33<00:44, 6.43s/it]\n 80%|████████ | 24/30 [02:40<00:38, 6.46s/it]\n 83%|████████▎ | 25/30 [02:46<00:32, 6.45s/it]\n 87%|████████▋ | 26/30 [02:52<00:25, 6.44s/it]\n 90%|█████████ | 27/30 [02:59<00:19, 6.44s/it]\n 93%|█████████▎| 28/30 [03:05<00:12, 6.43s/it]\n 97%|█████████▋| 29/30 [03:12<00:06, 6.43s/it]\n100%|██████████| 30/30 [03:18<00:00, 6.43s/it]\n100%|██████████| 30/30 [03:18<00:00, 6.62s/it]", "metrics": { "predict_time": 213.540453931, "total_time": 213.546612 }, "output": "https://replicate.delivery/xezq/ZjfSNx8MiQxvZi26mtztzne27JaT7VjIIq5fd4v7MoVUy7tnA/output.mp4", "started_at": "2024-12-02T21:49:40.769158Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-knkwzhmvkgtmfxjpf5t2ak3s4uyd4lfc2a34is3kxpnrrg7jyoga", "get": "https://api.replicate.com/v1/predictions/7ge51m603drm80ckh5csvqc6s8", "cancel": "https://api.replicate.com/v1/predictions/7ge51m603drm80ckh5csvqc6s8/cancel" }, "version": "1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460" }
Generated inUsing seed: 29655 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:13<06:19, 13.09s/it] 7%|▋ | 2/30 [00:18<04:03, 8.69s/it] 10%|█ | 3/30 [00:25<03:26, 7.66s/it] 13%|█▎ | 4/30 [00:31<03:06, 7.17s/it] 17%|█▋ | 5/30 [00:37<02:52, 6.90s/it] 20%|██ | 6/30 [00:44<02:41, 6.74s/it] 23%|██▎ | 7/30 [00:50<02:32, 6.63s/it] 27%|██▋ | 8/30 [00:57<02:24, 6.56s/it] 30%|███ | 9/30 [01:03<02:16, 6.51s/it] 33%|███▎ | 10/30 [01:10<02:09, 6.48s/it] 37%|███▋ | 11/30 [01:16<02:02, 6.46s/it] 40%|████ | 12/30 [01:22<01:56, 6.45s/it] 43%|████▎ | 13/30 [01:29<01:49, 6.44s/it] 47%|████▋ | 14/30 [01:35<01:42, 6.44s/it] 50%|█████ | 15/30 [01:42<01:36, 6.43s/it] 53%|█████▎ | 16/30 [01:48<01:30, 6.43s/it] 57%|█████▋ | 17/30 [01:55<01:23, 6.44s/it] 60%|██████ | 18/30 [02:01<01:17, 6.44s/it] 63%|██████▎ | 19/30 [02:07<01:10, 6.43s/it] 67%|██████▋ | 20/30 [02:14<01:04, 6.43s/it] 70%|███████ | 21/30 [02:20<00:57, 6.43s/it] 73%|███████▎ | 22/30 [02:27<00:51, 6.43s/it] 77%|███████▋ | 23/30 [02:33<00:44, 6.43s/it] 80%|████████ | 24/30 [02:40<00:38, 6.46s/it] 83%|████████▎ | 25/30 [02:46<00:32, 6.45s/it] 87%|████████▋ | 26/30 [02:52<00:25, 6.44s/it] 90%|█████████ | 27/30 [02:59<00:19, 6.44s/it] 93%|█████████▎| 28/30 [03:05<00:12, 6.43s/it] 97%|█████████▋| 29/30 [03:12<00:06, 6.43s/it] 100%|██████████| 30/30 [03:18<00:00, 6.43s/it] 100%|██████████| 30/30 [03:18<00:00, 6.62s/it]
Prediction
genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460Input
- fps
- 24
- prompt
- A pristine snowglobe featuring a winter scene sits peacefully. The globe violently explodes, sending glass, water, and glittering fake snow in all directions. The scene is captured with high-speed photography.
- num_frames
- 121
- guidance_scale
- 6
- num_inference_steps
- 30
{ "fps": 24, "prompt": "A pristine snowglobe featuring a winter scene sits peacefully. The globe violently explodes, sending glass, water, and glittering fake snow in all directions. The scene is captured with high-speed photography.", "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 genmoai/mochi-1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", { input: { fps: 24, prompt: "A pristine snowglobe featuring a winter scene sits peacefully. The globe violently explodes, sending glass, water, and glittering fake snow in all directions. The scene is captured with high-speed photography.", num_frames: 121, guidance_scale: 6, num_inference_steps: 30 } } ); // 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 replicate
Import the client:import replicate
Run genmoai/mochi-1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", input={ "fps": 24, "prompt": "A pristine snowglobe featuring a winter scene sits peacefully. The globe violently explodes, sending glass, water, and glittering fake snow in all directions. The scene is captured with high-speed photography.", "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run genmoai/mochi-1 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": "genmoai/mochi-1:1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460", "input": { "fps": 24, "prompt": "A pristine snowglobe featuring a winter scene sits peacefully. The globe violently explodes, sending glass, water, and glittering fake snow in all directions. The scene is captured with high-speed photography.", "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-03T02:04:27.961345Z", "created_at": "2024-12-03T01:59:18Z", "data_removed": false, "error": null, "id": "q05ry6t8y1rmc0ckh8za2zf4sc", "input": { "fps": 24, "prompt": "A pristine snowglobe featuring a winter scene sits peacefully. The globe violently explodes, sending glass, water, and glittering fake snow in all directions. The scene is captured with high-speed photography.", "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 }, "logs": "Using seed: 4194\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:12<05:59, 12.39s/it]\n 7%|▋ | 2/30 [00:18<03:55, 8.42s/it]\n 10%|█ | 3/30 [00:24<03:23, 7.53s/it]\n 13%|█▎ | 4/30 [00:30<03:04, 7.11s/it]\n 17%|█▋ | 5/30 [00:37<02:51, 6.88s/it]\n 20%|██ | 6/30 [00:43<02:41, 6.74s/it]\n 23%|██▎ | 7/30 [00:50<02:32, 6.65s/it]\n 27%|██▋ | 8/30 [00:56<02:25, 6.60s/it]\n 30%|███ | 9/30 [01:03<02:17, 6.55s/it]\n 33%|███▎ | 10/30 [01:09<02:10, 6.53s/it]\n 37%|███▋ | 11/30 [01:16<02:03, 6.51s/it]\n 40%|████ | 12/30 [01:22<01:57, 6.50s/it]\n 43%|████▎ | 13/30 [01:29<01:50, 6.49s/it]\n 47%|████▋ | 14/30 [01:35<01:43, 6.48s/it]\n 50%|█████ | 15/30 [01:42<01:37, 6.48s/it]\n 53%|█████▎ | 16/30 [01:48<01:30, 6.48s/it]\n 57%|█████▋ | 17/30 [01:55<01:24, 6.48s/it]\n 60%|██████ | 18/30 [02:01<01:17, 6.47s/it]\n 63%|██████▎ | 19/30 [02:08<01:11, 6.47s/it]\n 67%|██████▋ | 20/30 [02:14<01:04, 6.47s/it]\n 70%|███████ | 21/30 [02:20<00:58, 6.47s/it]\n 73%|███████▎ | 22/30 [02:27<00:51, 6.47s/it]\n 77%|███████▋ | 23/30 [02:33<00:45, 6.47s/it]\n 80%|████████ | 24/30 [02:40<00:38, 6.47s/it]\n 83%|████████▎ | 25/30 [02:46<00:32, 6.46s/it]\n 87%|████████▋ | 26/30 [02:53<00:25, 6.47s/it]\n 90%|█████████ | 27/30 [02:59<00:19, 6.47s/it]\n 93%|█████████▎| 28/30 [03:06<00:12, 6.47s/it]\n 97%|█████████▋| 29/30 [03:12<00:06, 6.47s/it]\n100%|██████████| 30/30 [03:19<00:00, 6.47s/it]\n100%|██████████| 30/30 [03:19<00:00, 6.64s/it]", "metrics": { "predict_time": 210.536424949, "total_time": 309.961345 }, "output": "https://replicate.delivery/xezq/CfKnavergFottkJmh9Qzizc82XSjm3e081aoeETAyMEsSGcPB/output.mp4", "started_at": "2024-12-03T02:00:57.424920Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-mtr3lggtg57syren7vybfwwkewfqjskxjli55qkg2svjiklrqc7q", "get": "https://api.replicate.com/v1/predictions/q05ry6t8y1rmc0ckh8za2zf4sc", "cancel": "https://api.replicate.com/v1/predictions/q05ry6t8y1rmc0ckh8za2zf4sc/cancel" }, "version": "1944af04d098ef69bed7f9d335d102e652203f268ec4aaa2d836f6217217e460" }
Generated inUsing seed: 4194 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:12<05:59, 12.39s/it] 7%|▋ | 2/30 [00:18<03:55, 8.42s/it] 10%|█ | 3/30 [00:24<03:23, 7.53s/it] 13%|█▎ | 4/30 [00:30<03:04, 7.11s/it] 17%|█▋ | 5/30 [00:37<02:51, 6.88s/it] 20%|██ | 6/30 [00:43<02:41, 6.74s/it] 23%|██▎ | 7/30 [00:50<02:32, 6.65s/it] 27%|██▋ | 8/30 [00:56<02:25, 6.60s/it] 30%|███ | 9/30 [01:03<02:17, 6.55s/it] 33%|███▎ | 10/30 [01:09<02:10, 6.53s/it] 37%|███▋ | 11/30 [01:16<02:03, 6.51s/it] 40%|████ | 12/30 [01:22<01:57, 6.50s/it] 43%|████▎ | 13/30 [01:29<01:50, 6.49s/it] 47%|████▋ | 14/30 [01:35<01:43, 6.48s/it] 50%|█████ | 15/30 [01:42<01:37, 6.48s/it] 53%|█████▎ | 16/30 [01:48<01:30, 6.48s/it] 57%|█████▋ | 17/30 [01:55<01:24, 6.48s/it] 60%|██████ | 18/30 [02:01<01:17, 6.47s/it] 63%|██████▎ | 19/30 [02:08<01:11, 6.47s/it] 67%|██████▋ | 20/30 [02:14<01:04, 6.47s/it] 70%|███████ | 21/30 [02:20<00:58, 6.47s/it] 73%|███████▎ | 22/30 [02:27<00:51, 6.47s/it] 77%|███████▋ | 23/30 [02:33<00:45, 6.47s/it] 80%|████████ | 24/30 [02:40<00:38, 6.47s/it] 83%|████████▎ | 25/30 [02:46<00:32, 6.46s/it] 87%|████████▋ | 26/30 [02:53<00:25, 6.47s/it] 90%|█████████ | 27/30 [02:59<00:19, 6.47s/it] 93%|█████████▎| 28/30 [03:06<00:12, 6.47s/it] 97%|█████████▋| 29/30 [03:12<00:06, 6.47s/it] 100%|██████████| 30/30 [03:19<00:00, 6.47s/it] 100%|██████████| 30/30 [03:19<00:00, 6.64s/it]
Want to make some of these yourself?
Run this model