jzhang38 / fast-mochi
Fast Mochi by Hao AI Lab
Prediction
jzhang38/fast-mochi:518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112dbID2khzfa8zcsrme0ckv2xab6tjrgStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- seed
- 1024
- prompt
- A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest. The playful yet serene atmosphere is complemented by soft natural light filtering through the petals. Mid-shot, warm and cheerful tones.
- num_frames
- 151
- guidance_scale
- 1.5
- num_inference_steps
- 8
{ "seed": 1024, "prompt": "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest. The playful yet serene atmosphere is complemented by soft natural light filtering through the petals. Mid-shot, warm and cheerful tones.", "num_frames": 151, "guidance_scale": 1.5, "num_inference_steps": 8 }
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 jzhang38/fast-mochi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jzhang38/fast-mochi:518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112db", { input: { seed: 1024, prompt: "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest. The playful yet serene atmosphere is complemented by soft natural light filtering through the petals. Mid-shot, warm and cheerful tones.", num_frames: 151, guidance_scale: 1.5, 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 replicate
Import the client:import replicate
Run jzhang38/fast-mochi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jzhang38/fast-mochi:518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112db", input={ "seed": 1024, "prompt": "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest. The playful yet serene atmosphere is complemented by soft natural light filtering through the petals. Mid-shot, warm and cheerful tones.", "num_frames": 151, "guidance_scale": 1.5, "num_inference_steps": 8 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run jzhang38/fast-mochi 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": "jzhang38/fast-mochi:518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112db", "input": { "seed": 1024, "prompt": "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest. The playful yet serene atmosphere is complemented by soft natural light filtering through the petals. Mid-shot, warm and cheerful tones.", "num_frames": 151, "guidance_scale": 1.5, "num_inference_steps": 8 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-18T07:48:01.433077Z", "created_at": "2024-12-18T07:44:56.678000Z", "data_removed": false, "error": null, "id": "2khzfa8zcsrme0ckv2xab6tjrg", "input": { "seed": 1024, "prompt": "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest. The playful yet serene atmosphere is complemented by soft natural light filtering through the petals. Mid-shot, warm and cheerful tones.", "num_frames": 151, "guidance_scale": 1.5, "num_inference_steps": 8 }, "logs": "Using seed: 1024\n 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:23<02:45, 23.59s/it]\n 25%|██▌ | 2/8 [00:32<01:29, 14.94s/it]\n 38%|███▊ | 3/8 [00:41<01:01, 12.26s/it]\n 50%|█████ | 4/8 [00:50<00:43, 10.99s/it]\n 62%|██████▎ | 5/8 [00:59<00:30, 10.31s/it]\n 75%|███████▌ | 6/8 [01:08<00:19, 9.88s/it]\n 88%|████████▊ | 7/8 [01:17<00:09, 9.62s/it]\n100%|██████████| 8/8 [01:26<00:00, 9.44s/it]\n100%|██████████| 8/8 [01:26<00:00, 10.86s/it]", "metrics": { "predict_time": 125.794456267, "total_time": 184.755077 }, "output": "https://replicate.delivery/xezq/MbBivDvuld71DZ19cX1tO1HxF3Gm84Dmmn8lqMeezZ5xAD8TA/output.mp4", "started_at": "2024-12-18T07:45:55.638621Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-ub2vozr4jts4aczhttqs2zbm7liqmozmsmqd43hjc5hevd2n2p5q", "get": "https://api.replicate.com/v1/predictions/2khzfa8zcsrme0ckv2xab6tjrg", "cancel": "https://api.replicate.com/v1/predictions/2khzfa8zcsrme0ckv2xab6tjrg/cancel" }, "version": "518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112db" }
Generated inUsing seed: 1024 0%| | 0/8 [00:00<?, ?it/s] 12%|█▎ | 1/8 [00:23<02:45, 23.59s/it] 25%|██▌ | 2/8 [00:32<01:29, 14.94s/it] 38%|███▊ | 3/8 [00:41<01:01, 12.26s/it] 50%|█████ | 4/8 [00:50<00:43, 10.99s/it] 62%|██████▎ | 5/8 [00:59<00:30, 10.31s/it] 75%|███████▌ | 6/8 [01:08<00:19, 9.88s/it] 88%|████████▊ | 7/8 [01:17<00:09, 9.62s/it] 100%|██████████| 8/8 [01:26<00:00, 9.44s/it] 100%|██████████| 8/8 [01:26<00:00, 10.86s/it]
Prediction
jzhang38/fast-mochi:518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112dbIDejvp95rpm9rm80ckv2ytfgm8yrStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- seed
- 1024
- prompt
- Will Smith casually eats noodles, his relaxed demeanor contrasting with the energetic background of a bustling street food market. The scene captures a mix of humor and authenticity. Mid-shot framing, vibrant lighting.
- num_frames
- 151
- guidance_scale
- 1.5
- num_inference_steps
- 8
{ "seed": 1024, "prompt": "Will Smith casually eats noodles, his relaxed demeanor contrasting with the energetic background of a bustling street food market. The scene captures a mix of humor and authenticity. Mid-shot framing, vibrant lighting.", "num_frames": 151, "guidance_scale": 1.5, "num_inference_steps": 8 }
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 jzhang38/fast-mochi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jzhang38/fast-mochi:518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112db", { input: { seed: 1024, prompt: "Will Smith casually eats noodles, his relaxed demeanor contrasting with the energetic background of a bustling street food market. The scene captures a mix of humor and authenticity. Mid-shot framing, vibrant lighting.", num_frames: 151, guidance_scale: 1.5, 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 replicate
Import the client:import replicate
Run jzhang38/fast-mochi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jzhang38/fast-mochi:518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112db", input={ "seed": 1024, "prompt": "Will Smith casually eats noodles, his relaxed demeanor contrasting with the energetic background of a bustling street food market. The scene captures a mix of humor and authenticity. Mid-shot framing, vibrant lighting.", "num_frames": 151, "guidance_scale": 1.5, "num_inference_steps": 8 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run jzhang38/fast-mochi 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": "jzhang38/fast-mochi:518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112db", "input": { "seed": 1024, "prompt": "Will Smith casually eats noodles, his relaxed demeanor contrasting with the energetic background of a bustling street food market. The scene captures a mix of humor and authenticity. Mid-shot framing, vibrant lighting.", "num_frames": 151, "guidance_scale": 1.5, "num_inference_steps": 8 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-18T07:50:09.518801Z", "created_at": "2024-12-18T07:48:11.042000Z", "data_removed": false, "error": null, "id": "ejvp95rpm9rm80ckv2ytfgm8yr", "input": { "seed": 1024, "prompt": "Will Smith casually eats noodles, his relaxed demeanor contrasting with the energetic background of a bustling street food market. The scene captures a mix of humor and authenticity. Mid-shot framing, vibrant lighting.", "num_frames": 151, "guidance_scale": 1.5, "num_inference_steps": 8 }, "logs": "Using seed: 1024\n 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:22<02:40, 22.89s/it]\n 25%|██▌ | 2/8 [00:31<01:28, 14.67s/it]\n 38%|███▊ | 3/8 [00:40<01:00, 12.12s/it]\n 50%|█████ | 4/8 [00:49<00:43, 10.92s/it]\n 62%|██████▎ | 5/8 [00:59<00:30, 10.27s/it]\n 75%|███████▌ | 6/8 [01:08<00:19, 9.87s/it]\n 88%|████████▊ | 7/8 [01:17<00:09, 9.62s/it]\n100%|██████████| 8/8 [01:26<00:00, 9.45s/it]\n100%|██████████| 8/8 [01:26<00:00, 10.80s/it]", "metrics": { "predict_time": 118.469424633, "total_time": 118.476801 }, "output": "https://replicate.delivery/xezq/LWqomyx5fL1zd67M6dUXl1UyL6V5gC1Zrcw2lE0oedwxCD8TA/output.mp4", "started_at": "2024-12-18T07:48:11.049376Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-5juff7z5xivceao66dc2nd76lsjvtwlkpptyevqv3vui2mn2wflq", "get": "https://api.replicate.com/v1/predictions/ejvp95rpm9rm80ckv2ytfgm8yr", "cancel": "https://api.replicate.com/v1/predictions/ejvp95rpm9rm80ckv2ytfgm8yr/cancel" }, "version": "518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112db" }
Generated inUsing seed: 1024 0%| | 0/8 [00:00<?, ?it/s] 12%|█▎ | 1/8 [00:22<02:40, 22.89s/it] 25%|██▌ | 2/8 [00:31<01:28, 14.67s/it] 38%|███▊ | 3/8 [00:40<01:00, 12.12s/it] 50%|█████ | 4/8 [00:49<00:43, 10.92s/it] 62%|██████▎ | 5/8 [00:59<00:30, 10.27s/it] 75%|███████▌ | 6/8 [01:08<00:19, 9.87s/it] 88%|████████▊ | 7/8 [01:17<00:09, 9.62s/it] 100%|██████████| 8/8 [01:26<00:00, 9.45s/it] 100%|██████████| 8/8 [01:26<00:00, 10.80s/it]
Prediction
jzhang38/fast-mochi:518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112dbIDjm2fmzj6tnrm80ckv2zr30hnjcStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- seed
- 1024
- prompt
- A hand with delicate fingers picks up a bright yellow lemon from a wooden bowl filled with lemons and sprigs of mint against a peach-colored background. The hand gently tosses the lemon up and catches it, showcasing its smooth texture. A beige string bag sits beside the bowl, adding a rustic touch to the scene. Additional lemons, one halved, are scattered around the base of the bowl. The even lighting enhances the vibrant colors and creates a fresh, inviting atmosphere.
- num_frames
- 151
- guidance_scale
- 1.5
- num_inference_steps
- 8
{ "seed": 1024, "prompt": "A hand with delicate fingers picks up a bright yellow lemon from a wooden bowl filled with lemons and sprigs of mint against a peach-colored background. The hand gently tosses the lemon up and catches it, showcasing its smooth texture. A beige string bag sits beside the bowl, adding a rustic touch to the scene. Additional lemons, one halved, are scattered around the base of the bowl. The even lighting enhances the vibrant colors and creates a fresh, inviting atmosphere.", "num_frames": 151, "guidance_scale": 1.5, "num_inference_steps": 8 }
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 jzhang38/fast-mochi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jzhang38/fast-mochi:518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112db", { input: { seed: 1024, prompt: "A hand with delicate fingers picks up a bright yellow lemon from a wooden bowl filled with lemons and sprigs of mint against a peach-colored background. The hand gently tosses the lemon up and catches it, showcasing its smooth texture. A beige string bag sits beside the bowl, adding a rustic touch to the scene. Additional lemons, one halved, are scattered around the base of the bowl. The even lighting enhances the vibrant colors and creates a fresh, inviting atmosphere.", num_frames: 151, guidance_scale: 1.5, 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 replicate
Import the client:import replicate
Run jzhang38/fast-mochi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jzhang38/fast-mochi:518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112db", input={ "seed": 1024, "prompt": "A hand with delicate fingers picks up a bright yellow lemon from a wooden bowl filled with lemons and sprigs of mint against a peach-colored background. The hand gently tosses the lemon up and catches it, showcasing its smooth texture. A beige string bag sits beside the bowl, adding a rustic touch to the scene. Additional lemons, one halved, are scattered around the base of the bowl. The even lighting enhances the vibrant colors and creates a fresh, inviting atmosphere.", "num_frames": 151, "guidance_scale": 1.5, "num_inference_steps": 8 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run jzhang38/fast-mochi 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": "jzhang38/fast-mochi:518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112db", "input": { "seed": 1024, "prompt": "A hand with delicate fingers picks up a bright yellow lemon from a wooden bowl filled with lemons and sprigs of mint against a peach-colored background. The hand gently tosses the lemon up and catches it, showcasing its smooth texture. A beige string bag sits beside the bowl, adding a rustic touch to the scene. Additional lemons, one halved, are scattered around the base of the bowl. The even lighting enhances the vibrant colors and creates a fresh, inviting atmosphere.", "num_frames": 151, "guidance_scale": 1.5, "num_inference_steps": 8 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-18T07:52:31.703725Z", "created_at": "2024-12-18T07:50:34.453000Z", "data_removed": false, "error": null, "id": "jm2fmzj6tnrm80ckv2zr30hnjc", "input": { "seed": 1024, "prompt": "A hand with delicate fingers picks up a bright yellow lemon from a wooden bowl filled with lemons and sprigs of mint against a peach-colored background. The hand gently tosses the lemon up and catches it, showcasing its smooth texture. A beige string bag sits beside the bowl, adding a rustic touch to the scene. Additional lemons, one halved, are scattered around the base of the bowl. The even lighting enhances the vibrant colors and creates a fresh, inviting atmosphere.", "num_frames": 151, "guidance_scale": 1.5, "num_inference_steps": 8 }, "logs": "Using seed: 1024\n 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:22<02:39, 22.77s/it]\n 25%|██▌ | 2/8 [00:31<01:27, 14.62s/it]\n 38%|███▊ | 3/8 [00:40<01:00, 12.09s/it]\n 50%|█████ | 4/8 [00:49<00:43, 10.91s/it]\n 62%|██████▎ | 5/8 [00:58<00:30, 10.25s/it]\n 75%|███████▌ | 6/8 [01:08<00:19, 9.85s/it]\n 88%|████████▊ | 7/8 [01:17<00:09, 9.59s/it]\n100%|██████████| 8/8 [01:26<00:00, 9.42s/it]\n100%|██████████| 8/8 [01:26<00:00, 10.77s/it]", "metrics": { "predict_time": 117.243072704, "total_time": 117.250725 }, "output": "https://replicate.delivery/xezq/0WyfvLVmTyS7P6HMddhFu3M9aUpVgIQemrKf9KZf73y9TMwPB/output.mp4", "started_at": "2024-12-18T07:50:34.460652Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-johhbjsrtfzinevdiy4csrqwduyv5xwtdugw5xbmzaky7knj3snq", "get": "https://api.replicate.com/v1/predictions/jm2fmzj6tnrm80ckv2zr30hnjc", "cancel": "https://api.replicate.com/v1/predictions/jm2fmzj6tnrm80ckv2zr30hnjc/cancel" }, "version": "518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112db" }
Generated inUsing seed: 1024 0%| | 0/8 [00:00<?, ?it/s] 12%|█▎ | 1/8 [00:22<02:39, 22.77s/it] 25%|██▌ | 2/8 [00:31<01:27, 14.62s/it] 38%|███▊ | 3/8 [00:40<01:00, 12.09s/it] 50%|█████ | 4/8 [00:49<00:43, 10.91s/it] 62%|██████▎ | 5/8 [00:58<00:30, 10.25s/it] 75%|███████▌ | 6/8 [01:08<00:19, 9.85s/it] 88%|████████▊ | 7/8 [01:17<00:09, 9.59s/it] 100%|██████████| 8/8 [01:26<00:00, 9.42s/it] 100%|██████████| 8/8 [01:26<00:00, 10.77s/it]
Prediction
jzhang38/fast-mochi:518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112dbIDa47ngtvx7xrm80ckv30skptv54StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- seed
- 1024
- prompt
- fox in the forest close-up quickly turned its head to the left
- num_frames
- 151
- guidance_scale
- 1.5
- num_inference_steps
- 8
{ "seed": 1024, "prompt": "fox in the forest close-up quickly turned its head to the left", "num_frames": 151, "guidance_scale": 1.5, "num_inference_steps": 8 }
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 jzhang38/fast-mochi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jzhang38/fast-mochi:518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112db", { input: { seed: 1024, prompt: "fox in the forest close-up quickly turned its head to the left", num_frames: 151, guidance_scale: 1.5, 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 replicate
Import the client:import replicate
Run jzhang38/fast-mochi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jzhang38/fast-mochi:518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112db", input={ "seed": 1024, "prompt": "fox in the forest close-up quickly turned its head to the left", "num_frames": 151, "guidance_scale": 1.5, "num_inference_steps": 8 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run jzhang38/fast-mochi 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": "jzhang38/fast-mochi:518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112db", "input": { "seed": 1024, "prompt": "fox in the forest close-up quickly turned its head to the left", "num_frames": 151, "guidance_scale": 1.5, "num_inference_steps": 8 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-18T07:54:58.269138Z", "created_at": "2024-12-18T07:52:59.455000Z", "data_removed": false, "error": null, "id": "a47ngtvx7xrm80ckv30skptv54", "input": { "seed": 1024, "prompt": "fox in the forest close-up quickly turned its head to the left", "num_frames": 151, "guidance_scale": 1.5, "num_inference_steps": 8 }, "logs": "Using seed: 1024\n 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:23<02:46, 23.72s/it]\n 25%|██▌ | 2/8 [00:32<01:29, 14.94s/it]\n 38%|███▊ | 3/8 [00:41<01:01, 12.21s/it]\n 50%|█████ | 4/8 [00:50<00:43, 10.93s/it]\n 62%|██████▎ | 5/8 [00:59<00:30, 10.23s/it]\n 75%|███████▌ | 6/8 [01:08<00:19, 9.81s/it]\n 88%|████████▊ | 7/8 [01:17<00:09, 9.54s/it]\n100%|██████████| 8/8 [01:26<00:00, 9.37s/it]\n100%|██████████| 8/8 [01:26<00:00, 10.80s/it]", "metrics": { "predict_time": 118.802992683, "total_time": 118.814138 }, "output": "https://replicate.delivery/xezq/ZwoNioF0gt5jGxf5BZ9U6PT8VGwDyRgGaZYEMsugqhQpjBeTA/output.mp4", "started_at": "2024-12-18T07:52:59.466145Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-ze2566ehsw6jhnfb5pj4rw2bq6xgeh7z7qfbwaxalxz2d6hdmtsq", "get": "https://api.replicate.com/v1/predictions/a47ngtvx7xrm80ckv30skptv54", "cancel": "https://api.replicate.com/v1/predictions/a47ngtvx7xrm80ckv30skptv54/cancel" }, "version": "518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112db" }
Generated inUsing seed: 1024 0%| | 0/8 [00:00<?, ?it/s] 12%|█▎ | 1/8 [00:23<02:46, 23.72s/it] 25%|██▌ | 2/8 [00:32<01:29, 14.94s/it] 38%|███▊ | 3/8 [00:41<01:01, 12.21s/it] 50%|█████ | 4/8 [00:50<00:43, 10.93s/it] 62%|██████▎ | 5/8 [00:59<00:30, 10.23s/it] 75%|███████▌ | 6/8 [01:08<00:19, 9.81s/it] 88%|████████▊ | 7/8 [01:17<00:09, 9.54s/it] 100%|██████████| 8/8 [01:26<00:00, 9.37s/it] 100%|██████████| 8/8 [01:26<00:00, 10.80s/it]
Prediction
jzhang38/fast-mochi:518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112dbIDsyjavq7menrma0ckv32sh2by10StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- seed
- 1024
- prompt
- A lone hiker stands atop a towering cliff, silhouetted against the vast horizon. The rugged landscape stretches endlessly beneath, its earthy tones blending into the soft blues of the sky. The scene captures the spirit of exploration and human resilience. High angle, dynamic framing, with soft natural lighting emphasizing the grandeur of nature.
- num_frames
- 151
- guidance_scale
- 1.5
- num_inference_steps
- 8
{ "seed": 1024, "prompt": "A lone hiker stands atop a towering cliff, silhouetted against the vast horizon. The rugged landscape stretches endlessly beneath, its earthy tones blending into the soft blues of the sky. The scene captures the spirit of exploration and human resilience. High angle, dynamic framing, with soft natural lighting emphasizing the grandeur of nature.", "num_frames": 151, "guidance_scale": 1.5, "num_inference_steps": 8 }
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 jzhang38/fast-mochi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jzhang38/fast-mochi:518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112db", { input: { seed: 1024, prompt: "A lone hiker stands atop a towering cliff, silhouetted against the vast horizon. The rugged landscape stretches endlessly beneath, its earthy tones blending into the soft blues of the sky. The scene captures the spirit of exploration and human resilience. High angle, dynamic framing, with soft natural lighting emphasizing the grandeur of nature.", num_frames: 151, guidance_scale: 1.5, 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 replicate
Import the client:import replicate
Run jzhang38/fast-mochi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jzhang38/fast-mochi:518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112db", input={ "seed": 1024, "prompt": "A lone hiker stands atop a towering cliff, silhouetted against the vast horizon. The rugged landscape stretches endlessly beneath, its earthy tones blending into the soft blues of the sky. The scene captures the spirit of exploration and human resilience. High angle, dynamic framing, with soft natural lighting emphasizing the grandeur of nature.", "num_frames": 151, "guidance_scale": 1.5, "num_inference_steps": 8 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run jzhang38/fast-mochi 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": "jzhang38/fast-mochi:518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112db", "input": { "seed": 1024, "prompt": "A lone hiker stands atop a towering cliff, silhouetted against the vast horizon. The rugged landscape stretches endlessly beneath, its earthy tones blending into the soft blues of the sky. The scene captures the spirit of exploration and human resilience. High angle, dynamic framing, with soft natural lighting emphasizing the grandeur of nature.", "num_frames": 151, "guidance_scale": 1.5, "num_inference_steps": 8 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-18T07:59:52.853532Z", "created_at": "2024-12-18T07:57:52.117000Z", "data_removed": false, "error": null, "id": "syjavq7menrma0ckv32sh2by10", "input": { "seed": 1024, "prompt": "A lone hiker stands atop a towering cliff, silhouetted against the vast horizon. The rugged landscape stretches endlessly beneath, its earthy tones blending into the soft blues of the sky. The scene captures the spirit of exploration and human resilience. High angle, dynamic framing, with soft natural lighting emphasizing the grandeur of nature.", "num_frames": 151, "guidance_scale": 1.5, "num_inference_steps": 8 }, "logs": "Using seed: 1024\n 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:22<02:37, 22.45s/it]\n 25%|██▌ | 2/8 [00:31<01:26, 14.41s/it]\n 38%|███▊ | 3/8 [00:40<00:59, 11.92s/it]\n 50%|█████ | 4/8 [00:49<00:43, 10.76s/it]\n 62%|██████▎ | 5/8 [01:02<00:35, 11.85s/it]\n 75%|███████▌ | 6/8 [01:11<00:21, 10.88s/it]\n 88%|████████▊ | 7/8 [01:20<00:10, 10.27s/it]\n100%|██████████| 8/8 [01:29<00:00, 9.86s/it]\n100%|██████████| 8/8 [01:29<00:00, 11.25s/it]", "metrics": { "predict_time": 120.729449334, "total_time": 120.736532 }, "output": "https://replicate.delivery/xezq/MeH3ILEErJ1QdSkvGXjn2QoASAfbU09t0WIuZEeP7CUxXG4nA/output.mp4", "started_at": "2024-12-18T07:57:52.124083Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-omdwvwomuyhbnsgm4rbi4vv6iwqtcf4allbdilzjhvktm65s345a", "get": "https://api.replicate.com/v1/predictions/syjavq7menrma0ckv32sh2by10", "cancel": "https://api.replicate.com/v1/predictions/syjavq7menrma0ckv32sh2by10/cancel" }, "version": "518162c93efc747d0a57a447db3f97a49e8d93f1c484dc2b7e3dadfacb1112db" }
Generated inUsing seed: 1024 0%| | 0/8 [00:00<?, ?it/s] 12%|█▎ | 1/8 [00:22<02:37, 22.45s/it] 25%|██▌ | 2/8 [00:31<01:26, 14.41s/it] 38%|███▊ | 3/8 [00:40<00:59, 11.92s/it] 50%|█████ | 4/8 [00:49<00:43, 10.76s/it] 62%|██████▎ | 5/8 [01:02<00:35, 11.85s/it] 75%|███████▌ | 6/8 [01:11<00:21, 10.88s/it] 88%|████████▊ | 7/8 [01:20<00:10, 10.27s/it] 100%|██████████| 8/8 [01:29<00:00, 9.86s/it] 100%|██████████| 8/8 [01:29<00:00, 11.25s/it]
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