ji4chenli
/
t2v-turbo-v2
Enhancing Video Model Post-Training through Data, Reward, and Conditional Guidance Design
Prediction
ji4chenli/t2v-turbo-v2:cdd7ad23ID00acswwqxsrgm0cjhb487eg5g0StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- fps
- 8
- prompt
- With the style of low-poly game art, A majestic, white horse gallops gracefully across a moonlit beach
- motion_gs
- 0.05
- num_frames
- 16
- percentage
- 0.5
- guidance_scale
- 7.5
- num_inference_steps
- 16
{ "fps": 8, "prompt": "With the style of low-poly game art, A majestic, white horse gallops gracefully across a moonlit beach", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run ji4chenli/t2v-turbo-v2 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-v2:cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5", { input: { fps: 8, prompt: "With the style of low-poly game art, A majestic, white horse gallops gracefully across a moonlit beach", motion_gs: 0.05, num_frames: 16, percentage: 0.5, guidance_scale: 7.5, num_inference_steps: 16 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run ji4chenli/t2v-turbo-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ji4chenli/t2v-turbo-v2:cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5", input={ "fps": 8, "prompt": "With the style of low-poly game art, A majestic, white horse gallops gracefully across a moonlit beach", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run ji4chenli/t2v-turbo-v2 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": "cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5", "input": { "fps": 8, "prompt": "With the style of low-poly game art, A majestic, white horse gallops gracefully across a moonlit beach", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-10-14T11:29:36.010195Z", "created_at": "2024-10-14T11:27:34.894000Z", "data_removed": false, "error": null, "id": "00acswwqxsrgm0cjhb487eg5g0", "input": { "fps": 8, "prompt": "With the style of low-poly game art, A majestic, white horse gallops gracefully across a moonlit beach", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 }, "logs": "Using seed: 23534\n 0%| | 0/16 [00:00<?, ?it/s]\n 6%|▋ | 1/16 [00:00<00:06, 2.31it/s]\n 12%|█▎ | 2/16 [00:00<00:03, 3.55it/s]\n 19%|█▉ | 3/16 [00:00<00:03, 3.27it/s]\n 25%|██▌ | 4/16 [00:01<00:03, 3.15it/s]\n 31%|███▏ | 5/16 [00:01<00:03, 3.09it/s]\n 38%|███▊ | 6/16 [00:01<00:03, 3.05it/s]\n 44%|████▍ | 7/16 [00:02<00:02, 3.03it/s]\n 50%|█████ | 8/16 [00:02<00:02, 3.02it/s]\n 56%|█████▋ | 9/16 [00:02<00:02, 3.01it/s]\n 62%|██████▎ | 10/16 [00:03<00:01, 3.01it/s]\n 69%|██████▉ | 11/16 [00:03<00:01, 3.00it/s]\n 75%|███████▌ | 12/16 [00:03<00:01, 3.00it/s]\n 81%|████████▏ | 13/16 [00:04<00:01, 3.00it/s]\n 88%|████████▊ | 14/16 [00:04<00:00, 3.00it/s]\n 94%|█████████▍| 15/16 [00:04<00:00, 3.00it/s]\n100%|██████████| 16/16 [00:05<00:00, 2.99it/s]\n100%|██████████| 16/16 [00:05<00:00, 3.02it/s]", "metrics": { "predict_time": 8.009425347, "total_time": 121.116195 }, "output": "https://replicate.delivery/pbxt/K0zaYeQK51W6BK8EtxDC5tes1uSslcChobSYX3e8MSr9UWNnA/out.mp4", "started_at": "2024-10-14T11:29:28.000770Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/00acswwqxsrgm0cjhb487eg5g0", "cancel": "https://api.replicate.com/v1/predictions/00acswwqxsrgm0cjhb487eg5g0/cancel" }, "version": "cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5" }
Generated inUsing seed: 23534 0%| | 0/16 [00:00<?, ?it/s] 6%|▋ | 1/16 [00:00<00:06, 2.31it/s] 12%|█▎ | 2/16 [00:00<00:03, 3.55it/s] 19%|█▉ | 3/16 [00:00<00:03, 3.27it/s] 25%|██▌ | 4/16 [00:01<00:03, 3.15it/s] 31%|███▏ | 5/16 [00:01<00:03, 3.09it/s] 38%|███▊ | 6/16 [00:01<00:03, 3.05it/s] 44%|████▍ | 7/16 [00:02<00:02, 3.03it/s] 50%|█████ | 8/16 [00:02<00:02, 3.02it/s] 56%|█████▋ | 9/16 [00:02<00:02, 3.01it/s] 62%|██████▎ | 10/16 [00:03<00:01, 3.01it/s] 69%|██████▉ | 11/16 [00:03<00:01, 3.00it/s] 75%|███████▌ | 12/16 [00:03<00:01, 3.00it/s] 81%|████████▏ | 13/16 [00:04<00:01, 3.00it/s] 88%|████████▊ | 14/16 [00:04<00:00, 3.00it/s] 94%|█████████▍| 15/16 [00:04<00:00, 3.00it/s] 100%|██████████| 16/16 [00:05<00:00, 2.99it/s] 100%|██████████| 16/16 [00:05<00:00, 3.02it/s]
Prediction
ji4chenli/t2v-turbo-v2:cdd7ad23IDgd3ytfthp5rgm0cjhdrb0ce8nmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- fps
- 8
- prompt
- Self-portrait oil painting, a beautiful cyborg with golden hair, 8k
- motion_gs
- 0.05
- num_frames
- 16
- percentage
- 0.5
- guidance_scale
- 7.5
- num_inference_steps
- 16
{ "fps": 8, "prompt": "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run ji4chenli/t2v-turbo-v2 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-v2:cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5", { input: { fps: 8, prompt: "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k", motion_gs: 0.05, num_frames: 16, percentage: 0.5, guidance_scale: 7.5, num_inference_steps: 16 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run ji4chenli/t2v-turbo-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ji4chenli/t2v-turbo-v2:cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5", input={ "fps": 8, "prompt": "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run ji4chenli/t2v-turbo-v2 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": "cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5", "input": { "fps": 8, "prompt": "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-10-14T14:32:59.151535Z", "created_at": "2024-10-14T14:30:46.961000Z", "data_removed": false, "error": null, "id": "gd3ytfthp5rgm0cjhdrb0ce8nm", "input": { "fps": 8, "prompt": "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 }, "logs": "Using seed: 58721\n 0%| | 0/16 [00:00<?, ?it/s]\n 6%|▋ | 1/16 [00:00<00:02, 5.78it/s]\n 12%|█▎ | 2/16 [00:00<00:03, 3.75it/s]\n 19%|█▉ | 3/16 [00:00<00:03, 3.37it/s]\n 25%|██▌ | 4/16 [00:01<00:03, 3.22it/s]\n 31%|███▏ | 5/16 [00:01<00:03, 3.14it/s]\n 38%|███▊ | 6/16 [00:01<00:03, 3.09it/s]\n 44%|████▍ | 7/16 [00:02<00:02, 3.06it/s]\n 50%|█████ | 8/16 [00:02<00:02, 3.04it/s]\n 56%|█████▋ | 9/16 [00:02<00:02, 3.03it/s]\n 62%|██████▎ | 10/16 [00:03<00:01, 3.02it/s]\n 69%|██████▉ | 11/16 [00:03<00:01, 3.02it/s]\n 75%|███████▌ | 12/16 [00:03<00:01, 3.01it/s]\n 81%|████████▏ | 13/16 [00:04<00:00, 3.01it/s]\n 88%|████████▊ | 14/16 [00:04<00:00, 3.01it/s]\n 94%|█████████▍| 15/16 [00:04<00:00, 3.00it/s]\n100%|██████████| 16/16 [00:05<00:00, 3.00it/s]\n100%|██████████| 16/16 [00:05<00:00, 3.10it/s]", "metrics": { "predict_time": 6.638807099, "total_time": 132.190535 }, "output": "https://replicate.delivery/pbxt/tDfLpUzYoD0DWK9gegZ0RK8XeWt9jXPOxeaZ8WoxvhLrZ3aOB/out.mp4", "started_at": "2024-10-14T14:32:52.512728Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/gd3ytfthp5rgm0cjhdrb0ce8nm", "cancel": "https://api.replicate.com/v1/predictions/gd3ytfthp5rgm0cjhdrb0ce8nm/cancel" }, "version": "cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5" }
Generated inUsing seed: 58721 0%| | 0/16 [00:00<?, ?it/s] 6%|▋ | 1/16 [00:00<00:02, 5.78it/s] 12%|█▎ | 2/16 [00:00<00:03, 3.75it/s] 19%|█▉ | 3/16 [00:00<00:03, 3.37it/s] 25%|██▌ | 4/16 [00:01<00:03, 3.22it/s] 31%|███▏ | 5/16 [00:01<00:03, 3.14it/s] 38%|███▊ | 6/16 [00:01<00:03, 3.09it/s] 44%|████▍ | 7/16 [00:02<00:02, 3.06it/s] 50%|█████ | 8/16 [00:02<00:02, 3.04it/s] 56%|█████▋ | 9/16 [00:02<00:02, 3.03it/s] 62%|██████▎ | 10/16 [00:03<00:01, 3.02it/s] 69%|██████▉ | 11/16 [00:03<00:01, 3.02it/s] 75%|███████▌ | 12/16 [00:03<00:01, 3.01it/s] 81%|████████▏ | 13/16 [00:04<00:00, 3.01it/s] 88%|████████▊ | 14/16 [00:04<00:00, 3.01it/s] 94%|█████████▍| 15/16 [00:04<00:00, 3.00it/s] 100%|██████████| 16/16 [00:05<00:00, 3.00it/s] 100%|██████████| 16/16 [00:05<00:00, 3.10it/s]
Prediction
ji4chenli/t2v-turbo-v2:cdd7ad23IDs3x5qpyeddrgj0cjhdrbdrcskcStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- fps
- 8
- prompt
- Darth vader surfing in waves.
- motion_gs
- 0.05
- num_frames
- 16
- percentage
- 0.5
- guidance_scale
- 7.5
- num_inference_steps
- 16
{ "fps": 8, "prompt": "Darth vader surfing in waves.", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run ji4chenli/t2v-turbo-v2 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-v2:cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5", { input: { fps: 8, prompt: "Darth vader surfing in waves.", motion_gs: 0.05, num_frames: 16, percentage: 0.5, guidance_scale: 7.5, num_inference_steps: 16 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run ji4chenli/t2v-turbo-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ji4chenli/t2v-turbo-v2:cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5", input={ "fps": 8, "prompt": "Darth vader surfing in waves.", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run ji4chenli/t2v-turbo-v2 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": "cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5", "input": { "fps": 8, "prompt": "Darth vader surfing in waves.", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-10-14T14:33:12.546420Z", "created_at": "2024-10-14T14:31:18.891000Z", "data_removed": false, "error": null, "id": "s3x5qpyeddrgj0cjhdrbdrcskc", "input": { "fps": 8, "prompt": "Darth vader surfing in waves.", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 }, "logs": "Using seed: 35348\n 0%| | 0/16 [00:00<?, ?it/s]\n 6%|▋ | 1/16 [00:00<00:02, 5.77it/s]\n 12%|█▎ | 2/16 [00:00<00:03, 3.74it/s]\n 19%|█▉ | 3/16 [00:00<00:03, 3.36it/s]\n 25%|██▌ | 4/16 [00:01<00:03, 3.20it/s]\n 31%|███▏ | 5/16 [00:01<00:03, 3.12it/s]\n 38%|███▊ | 6/16 [00:01<00:03, 3.08it/s]\n 44%|████▍ | 7/16 [00:02<00:02, 3.05it/s]\n 50%|█████ | 8/16 [00:02<00:02, 3.03it/s]\n 56%|█████▋ | 9/16 [00:02<00:02, 3.02it/s]\n 62%|██████▎ | 10/16 [00:03<00:01, 3.01it/s]\n 69%|██████▉ | 11/16 [00:03<00:01, 3.00it/s]\n 75%|███████▌ | 12/16 [00:03<00:01, 3.00it/s]\n 81%|████████▏ | 13/16 [00:04<00:01, 3.00it/s]\n 88%|████████▊ | 14/16 [00:04<00:00, 2.99it/s]\n 94%|█████████▍| 15/16 [00:04<00:00, 2.99it/s]\n100%|██████████| 16/16 [00:05<00:00, 2.99it/s]\n100%|██████████| 16/16 [00:05<00:00, 3.08it/s]", "metrics": { "predict_time": 6.727615608, "total_time": 113.65542 }, "output": "https://replicate.delivery/pbxt/NHCeISSJ3ZUvCCtOprIfsDeyMs17YxgKVCZVrkfDRDOja3aOB/out.mp4", "started_at": "2024-10-14T14:33:05.818804Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/s3x5qpyeddrgj0cjhdrbdrcskc", "cancel": "https://api.replicate.com/v1/predictions/s3x5qpyeddrgj0cjhdrbdrcskc/cancel" }, "version": "cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5" }
Generated inUsing seed: 35348 0%| | 0/16 [00:00<?, ?it/s] 6%|▋ | 1/16 [00:00<00:02, 5.77it/s] 12%|█▎ | 2/16 [00:00<00:03, 3.74it/s] 19%|█▉ | 3/16 [00:00<00:03, 3.36it/s] 25%|██▌ | 4/16 [00:01<00:03, 3.20it/s] 31%|███▏ | 5/16 [00:01<00:03, 3.12it/s] 38%|███▊ | 6/16 [00:01<00:03, 3.08it/s] 44%|████▍ | 7/16 [00:02<00:02, 3.05it/s] 50%|█████ | 8/16 [00:02<00:02, 3.03it/s] 56%|█████▋ | 9/16 [00:02<00:02, 3.02it/s] 62%|██████▎ | 10/16 [00:03<00:01, 3.01it/s] 69%|██████▉ | 11/16 [00:03<00:01, 3.00it/s] 75%|███████▌ | 12/16 [00:03<00:01, 3.00it/s] 81%|████████▏ | 13/16 [00:04<00:01, 3.00it/s] 88%|████████▊ | 14/16 [00:04<00:00, 2.99it/s] 94%|█████████▍| 15/16 [00:04<00:00, 2.99it/s] 100%|██████████| 16/16 [00:05<00:00, 2.99it/s] 100%|██████████| 16/16 [00:05<00:00, 3.08it/s]
Prediction
ji4chenli/t2v-turbo-v2:cdd7ad23IDz95bteg6yhrgp0cjhdz8yntr78StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- fps
- 8
- prompt
- A musician strums his guitar, serenading the moonlit night.
- motion_gs
- 0.05
- num_frames
- 16
- percentage
- 0.5
- guidance_scale
- 7.5
- num_inference_steps
- 16
{ "fps": 8, "prompt": "A musician strums his guitar, serenading the moonlit night.", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run ji4chenli/t2v-turbo-v2 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-v2:cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5", { input: { fps: 8, prompt: "A musician strums his guitar, serenading the moonlit night.", motion_gs: 0.05, num_frames: 16, percentage: 0.5, guidance_scale: 7.5, num_inference_steps: 16 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run ji4chenli/t2v-turbo-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ji4chenli/t2v-turbo-v2:cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5", input={ "fps": 8, "prompt": "A musician strums his guitar, serenading the moonlit night.", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run ji4chenli/t2v-turbo-v2 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": "cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5", "input": { "fps": 8, "prompt": "A musician strums his guitar, serenading the moonlit night.", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-10-14T14:48:33.547270Z", "created_at": "2024-10-14T14:45:45.332000Z", "data_removed": false, "error": null, "id": "z95bteg6yhrgp0cjhdz8yntr78", "input": { "fps": 8, "prompt": "A musician strums his guitar, serenading the moonlit night.", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 }, "logs": "Using seed: 32871\n 0%| | 0/16 [00:00<?, ?it/s]\n 6%|▋ | 1/16 [00:00<00:02, 5.78it/s]\n 12%|█▎ | 2/16 [00:00<00:03, 3.75it/s]\n 19%|█▉ | 3/16 [00:00<00:03, 3.38it/s]\n 25%|██▌ | 4/16 [00:01<00:03, 3.23it/s]\n 31%|███▏ | 5/16 [00:01<00:03, 3.15it/s]\n 38%|███▊ | 6/16 [00:01<00:03, 3.10it/s]\n 44%|████▍ | 7/16 [00:02<00:02, 3.08it/s]\n 50%|█████ | 8/16 [00:02<00:02, 3.06it/s]\n 56%|█████▋ | 9/16 [00:02<00:02, 3.04it/s]\n 62%|██████▎ | 10/16 [00:03<00:01, 3.04it/s]\n 69%|██████▉ | 11/16 [00:03<00:01, 3.03it/s]\n 75%|███████▌ | 12/16 [00:03<00:01, 3.02it/s]\n 81%|████████▏ | 13/16 [00:04<00:00, 3.02it/s]\n 88%|████████▊ | 14/16 [00:04<00:00, 3.02it/s]\n 94%|█████████▍| 15/16 [00:04<00:00, 3.02it/s]\n100%|██████████| 16/16 [00:05<00:00, 3.02it/s]\n100%|██████████| 16/16 [00:05<00:00, 3.11it/s]", "metrics": { "predict_time": 7.678017408, "total_time": 168.21527 }, "output": "https://replicate.delivery/pbxt/WQlMFg3fp32wNqUynPQTjhHUQkEelXU7Y9tAYHz9BXYAFumTA/out.mp4", "started_at": "2024-10-14T14:48:25.869253Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/z95bteg6yhrgp0cjhdz8yntr78", "cancel": "https://api.replicate.com/v1/predictions/z95bteg6yhrgp0cjhdz8yntr78/cancel" }, "version": "cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5" }
Generated inUsing seed: 32871 0%| | 0/16 [00:00<?, ?it/s] 6%|▋ | 1/16 [00:00<00:02, 5.78it/s] 12%|█▎ | 2/16 [00:00<00:03, 3.75it/s] 19%|█▉ | 3/16 [00:00<00:03, 3.38it/s] 25%|██▌ | 4/16 [00:01<00:03, 3.23it/s] 31%|███▏ | 5/16 [00:01<00:03, 3.15it/s] 38%|███▊ | 6/16 [00:01<00:03, 3.10it/s] 44%|████▍ | 7/16 [00:02<00:02, 3.08it/s] 50%|█████ | 8/16 [00:02<00:02, 3.06it/s] 56%|█████▋ | 9/16 [00:02<00:02, 3.04it/s] 62%|██████▎ | 10/16 [00:03<00:01, 3.04it/s] 69%|██████▉ | 11/16 [00:03<00:01, 3.03it/s] 75%|███████▌ | 12/16 [00:03<00:01, 3.02it/s] 81%|████████▏ | 13/16 [00:04<00:00, 3.02it/s] 88%|████████▊ | 14/16 [00:04<00:00, 3.02it/s] 94%|█████████▍| 15/16 [00:04<00:00, 3.02it/s] 100%|██████████| 16/16 [00:05<00:00, 3.02it/s] 100%|██████████| 16/16 [00:05<00:00, 3.11it/s]
Prediction
ji4chenli/t2v-turbo-v2:cdd7ad23ID15hx6zp53srgm0cjhdyv21dhdrStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- fps
- 8
- prompt
- light wind, feathers moving, she moves her gaze, 4k
- motion_gs
- 0.05
- num_frames
- 16
- percentage
- 0.5
- guidance_scale
- 7.5
- num_inference_steps
- 16
{ "fps": 8, "prompt": "light wind, feathers moving, she moves her gaze, 4k", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run ji4chenli/t2v-turbo-v2 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-v2:cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5", { input: { fps: 8, prompt: "light wind, feathers moving, she moves her gaze, 4k", motion_gs: 0.05, num_frames: 16, percentage: 0.5, guidance_scale: 7.5, num_inference_steps: 16 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run ji4chenli/t2v-turbo-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ji4chenli/t2v-turbo-v2:cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5", input={ "fps": 8, "prompt": "light wind, feathers moving, she moves her gaze, 4k", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run ji4chenli/t2v-turbo-v2 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": "cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5", "input": { "fps": 8, "prompt": "light wind, feathers moving, she moves her gaze, 4k", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-10-14T14:48:17.611844Z", "created_at": "2024-10-14T14:45:28.478000Z", "data_removed": false, "error": null, "id": "15hx6zp53srgm0cjhdyv21dhdr", "input": { "fps": 8, "prompt": "light wind, feathers moving, she moves her gaze, 4k", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 }, "logs": "Using seed: 50027\n 0%| | 0/16 [00:00<?, ?it/s]\n 6%|▋ | 1/16 [00:00<00:06, 2.29it/s]\n 12%|█▎ | 2/16 [00:00<00:03, 3.55it/s]\n 19%|█▉ | 3/16 [00:00<00:03, 3.29it/s]\n 25%|██▌ | 4/16 [00:01<00:03, 3.17it/s]\n 31%|███▏ | 5/16 [00:01<00:03, 3.11it/s]\n 38%|███▊ | 6/16 [00:01<00:03, 3.08it/s]\n 44%|████▍ | 7/16 [00:02<00:02, 3.05it/s]\n 50%|█████ | 8/16 [00:02<00:02, 3.04it/s]\n 56%|█████▋ | 9/16 [00:02<00:02, 3.03it/s]\n 62%|██████▎ | 10/16 [00:03<00:01, 3.02it/s]\n 69%|██████▉ | 11/16 [00:03<00:01, 3.02it/s]\n 75%|███████▌ | 12/16 [00:03<00:01, 3.02it/s]\n 81%|████████▏ | 13/16 [00:04<00:00, 3.01it/s]\n 88%|████████▊ | 14/16 [00:04<00:00, 3.01it/s]\n 94%|█████████▍| 15/16 [00:04<00:00, 3.01it/s]\n100%|██████████| 16/16 [00:05<00:00, 3.01it/s]\n100%|██████████| 16/16 [00:05<00:00, 3.04it/s]", "metrics": { "predict_time": 7.934892945, "total_time": 169.133844 }, "output": "https://replicate.delivery/pbxt/UGxvt7X9N26bNBUHJt55S4dujwVIkfXMRqlYgl6BD8OYCXzJA/out.mp4", "started_at": "2024-10-14T14:48:09.676951Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/15hx6zp53srgm0cjhdyv21dhdr", "cancel": "https://api.replicate.com/v1/predictions/15hx6zp53srgm0cjhdyv21dhdr/cancel" }, "version": "cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5" }
Generated inUsing seed: 50027 0%| | 0/16 [00:00<?, ?it/s] 6%|▋ | 1/16 [00:00<00:06, 2.29it/s] 12%|█▎ | 2/16 [00:00<00:03, 3.55it/s] 19%|█▉ | 3/16 [00:00<00:03, 3.29it/s] 25%|██▌ | 4/16 [00:01<00:03, 3.17it/s] 31%|███▏ | 5/16 [00:01<00:03, 3.11it/s] 38%|███▊ | 6/16 [00:01<00:03, 3.08it/s] 44%|████▍ | 7/16 [00:02<00:02, 3.05it/s] 50%|█████ | 8/16 [00:02<00:02, 3.04it/s] 56%|█████▋ | 9/16 [00:02<00:02, 3.03it/s] 62%|██████▎ | 10/16 [00:03<00:01, 3.02it/s] 69%|██████▉ | 11/16 [00:03<00:01, 3.02it/s] 75%|███████▌ | 12/16 [00:03<00:01, 3.02it/s] 81%|████████▏ | 13/16 [00:04<00:00, 3.01it/s] 88%|████████▊ | 14/16 [00:04<00:00, 3.01it/s] 94%|█████████▍| 15/16 [00:04<00:00, 3.01it/s] 100%|██████████| 16/16 [00:05<00:00, 3.01it/s] 100%|██████████| 16/16 [00:05<00:00, 3.04it/s]
Prediction
ji4chenli/t2v-turbo-v2:cdd7ad23IDpd905myqr9rgj0cjhdyspnwv34StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- fps
- 8
- prompt
- Corgi dog riding a bike in Times Square. It is wearing sunglasses and a beach hat.
- motion_gs
- 0.05
- num_frames
- 16
- percentage
- 0.5
- guidance_scale
- 7.5
- num_inference_steps
- 16
{ "fps": 8, "prompt": "Corgi dog riding a bike in Times Square. It is wearing sunglasses and a beach hat.", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run ji4chenli/t2v-turbo-v2 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-v2:cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5", { input: { fps: 8, prompt: "Corgi dog riding a bike in Times Square. It is wearing sunglasses and a beach hat.", motion_gs: 0.05, num_frames: 16, percentage: 0.5, guidance_scale: 7.5, num_inference_steps: 16 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run ji4chenli/t2v-turbo-v2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ji4chenli/t2v-turbo-v2:cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5", input={ "fps": 8, "prompt": "Corgi dog riding a bike in Times Square. It is wearing sunglasses and a beach hat.", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run ji4chenli/t2v-turbo-v2 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": "cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5", "input": { "fps": 8, "prompt": "Corgi dog riding a bike in Times Square. It is wearing sunglasses and a beach hat.", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-10-14T14:48:25.655908Z", "created_at": "2024-10-14T14:45:33.250000Z", "data_removed": false, "error": null, "id": "pd905myqr9rgj0cjhdyspnwv34", "input": { "fps": 8, "prompt": "Corgi dog riding a bike in Times Square. It is wearing sunglasses and a beach hat.", "motion_gs": 0.05, "num_frames": 16, "percentage": 0.5, "guidance_scale": 7.5, "num_inference_steps": 16 }, "logs": "Using seed: 43898\n 0%| | 0/16 [00:00<?, ?it/s]\n 6%|▋ | 1/16 [00:00<00:02, 5.79it/s]\n 12%|█▎ | 2/16 [00:00<00:03, 3.76it/s]\n 19%|█▉ | 3/16 [00:00<00:03, 3.38it/s]\n 25%|██▌ | 4/16 [00:01<00:03, 3.22it/s]\n 31%|███▏ | 5/16 [00:01<00:03, 3.14it/s]\n 38%|███▊ | 6/16 [00:01<00:03, 3.09it/s]\n 44%|████▍ | 7/16 [00:02<00:02, 3.06it/s]\n 50%|█████ | 8/16 [00:02<00:02, 3.05it/s]\n 56%|█████▋ | 9/16 [00:02<00:02, 3.03it/s]\n 62%|██████▎ | 10/16 [00:03<00:01, 3.03it/s]\n 69%|██████▉ | 11/16 [00:03<00:01, 3.03it/s]\n 75%|███████▌ | 12/16 [00:03<00:01, 3.02it/s]\n 81%|████████▏ | 13/16 [00:04<00:00, 3.02it/s]\n 88%|████████▊ | 14/16 [00:04<00:00, 3.02it/s]\n 94%|█████████▍| 15/16 [00:04<00:00, 3.02it/s]\n100%|██████████| 16/16 [00:05<00:00, 3.02it/s]\n100%|██████████| 16/16 [00:05<00:00, 3.11it/s]", "metrics": { "predict_time": 7.54910647, "total_time": 172.405908 }, "output": "https://replicate.delivery/pbxt/eAOCJ9kVRjXpQCGVG7ahyH68o5QZ6bp87rUoqHV4Vf74EumTA/out.mp4", "started_at": "2024-10-14T14:48:18.106802Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pd905myqr9rgj0cjhdyspnwv34", "cancel": "https://api.replicate.com/v1/predictions/pd905myqr9rgj0cjhdyspnwv34/cancel" }, "version": "cdd7ad235a237a2076808758531221bae1a84422f30becd4fc881c9940093ce5" }
Generated inUsing seed: 43898 0%| | 0/16 [00:00<?, ?it/s] 6%|▋ | 1/16 [00:00<00:02, 5.79it/s] 12%|█▎ | 2/16 [00:00<00:03, 3.76it/s] 19%|█▉ | 3/16 [00:00<00:03, 3.38it/s] 25%|██▌ | 4/16 [00:01<00:03, 3.22it/s] 31%|███▏ | 5/16 [00:01<00:03, 3.14it/s] 38%|███▊ | 6/16 [00:01<00:03, 3.09it/s] 44%|████▍ | 7/16 [00:02<00:02, 3.06it/s] 50%|█████ | 8/16 [00:02<00:02, 3.05it/s] 56%|█████▋ | 9/16 [00:02<00:02, 3.03it/s] 62%|██████▎ | 10/16 [00:03<00:01, 3.03it/s] 69%|██████▉ | 11/16 [00:03<00:01, 3.03it/s] 75%|███████▌ | 12/16 [00:03<00:01, 3.02it/s] 81%|████████▏ | 13/16 [00:04<00:00, 3.02it/s] 88%|████████▊ | 14/16 [00:04<00:00, 3.02it/s] 94%|█████████▍| 15/16 [00:04<00:00, 3.02it/s] 100%|██████████| 16/16 [00:05<00:00, 3.02it/s] 100%|██████████| 16/16 [00:05<00:00, 3.11it/s]
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