genmoai / mochi-1-lora
A version of mochi-1 (a text to video model) that supports fine-tuned lora inference
Prediction
genmoai/mochi-1-lora:bad496d88086d5f60307291a512fdcb4fab0b30027e7ccbcc2ecf5e6606d5e20Input
- fps
- 24
- seed
- 29655
- 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
- hf_lora
- svjack/mochi_game_mix_early_lora
- lora_scale
- 1
- num_frames
- 121
- guidance_scale
- 6
- num_inference_steps
- 30
{ "fps": 24, "seed": 29655, "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", "hf_lora": "svjack/mochi_game_mix_early_lora", "lora_scale": 1, "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run genmoai/mochi-1-lora 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-lora:bad496d88086d5f60307291a512fdcb4fab0b30027e7ccbcc2ecf5e6606d5e20", { input: { fps: 24, seed: 29655, 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", hf_lora: "svjack/mochi_game_mix_early_lora", lora_scale: 1, 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 replicateImport the client:import replicateRun genmoai/mochi-1-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "genmoai/mochi-1-lora:bad496d88086d5f60307291a512fdcb4fab0b30027e7ccbcc2ecf5e6606d5e20", input={ "fps": 24, "seed": 29655, "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", "hf_lora": "svjack/mochi_game_mix_early_lora", "lora_scale": 1, "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-lora 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-lora:bad496d88086d5f60307291a512fdcb4fab0b30027e7ccbcc2ecf5e6606d5e20", "input": { "fps": 24, "seed": 29655, "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", "hf_lora": "svjack/mochi_game_mix_early_lora", "lora_scale": 1, "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-03T01:52:57.624942Z", "created_at": "2024-12-03T01:47:57.219000Z", "data_removed": false, "error": null, "id": "fvd2ksf5mdrme0ckh8st63h9dr", "input": { "fps": 24, "seed": 29655, "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", "hf_lora": "svjack/mochi_game_mix_early_lora", "lora_scale": 1, "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 }, "logs": "Using seed: 29655\nLoading LoRA: svjack/mochi_game_mix_early_lora with scale of: 1.0\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:13<06:43, 13.90s/it]\n 7%|▋ | 2/30 [00:19<04:16, 9.17s/it]\n 10%|█ | 3/30 [00:26<03:36, 8.03s/it]\n 13%|█▎ | 4/30 [00:33<03:14, 7.50s/it]\n 17%|█▋ | 5/30 [00:39<03:00, 7.21s/it]\n 20%|██ | 6/30 [00:46<02:48, 7.04s/it]\n 23%|██▎ | 7/30 [00:53<02:39, 6.92s/it]\n 27%|██▋ | 8/30 [00:59<02:30, 6.85s/it]\n 30%|███ | 9/30 [01:06<02:22, 6.80s/it]\n 33%|███▎ | 10/30 [01:13<02:15, 6.76s/it]\n 37%|███▋ | 11/30 [01:19<02:08, 6.74s/it]\n 40%|████ | 12/30 [01:26<02:01, 6.73s/it]\n 43%|████▎ | 13/30 [01:33<01:54, 6.71s/it]\n 47%|████▋ | 14/30 [01:40<01:47, 6.71s/it]\n 50%|█████ | 15/30 [01:46<01:40, 6.70s/it]\n 53%|█████▎ | 16/30 [01:53<01:33, 6.71s/it]\n 57%|█████▋ | 17/30 [02:00<01:27, 6.71s/it]\n 60%|██████ | 18/30 [02:06<01:20, 6.71s/it]\n 63%|██████▎ | 19/30 [02:13<01:13, 6.71s/it]\n 67%|██████▋ | 20/30 [02:20<01:07, 6.72s/it]\n 70%|███████ | 21/30 [02:27<01:00, 6.72s/it]\n 73%|███████▎ | 22/30 [02:33<00:53, 6.72s/it]\n 77%|███████▋ | 23/30 [02:40<00:47, 6.72s/it]\n 80%|████████ | 24/30 [02:47<00:40, 6.72s/it]\n 83%|████████▎ | 25/30 [02:53<00:33, 6.72s/it]\n 87%|████████▋ | 26/30 [03:00<00:26, 6.72s/it]\n 90%|█████████ | 27/30 [03:07<00:20, 6.72s/it]\n 93%|█████████▎| 28/30 [03:14<00:13, 6.71s/it]\n 97%|█████████▋| 29/30 [03:20<00:06, 6.72s/it]\n100%|██████████| 30/30 [03:27<00:00, 6.72s/it]\n100%|██████████| 30/30 [03:27<00:00, 6.92s/it]", "metrics": { "predict_time": 232.376770539, "total_time": 300.405942 }, "output": "https://replicate.delivery/xezq/VYnnolFFDr6TNFIXBiFtZVnD6pxokCUFyZNwkxadIyResg7JA/output.mp4", "started_at": "2024-12-03T01:49:05.248171Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-uzeze4rhn6q73xu6r4hecvrvzo7ajdcevaalbp6g5jskmxguiiia", "get": "https://api.replicate.com/v1/predictions/fvd2ksf5mdrme0ckh8st63h9dr", "cancel": "https://api.replicate.com/v1/predictions/fvd2ksf5mdrme0ckh8st63h9dr/cancel" }, "version": "bad496d88086d5f60307291a512fdcb4fab0b30027e7ccbcc2ecf5e6606d5e20" }Generated inUsing seed: 29655 Loading LoRA: svjack/mochi_game_mix_early_lora with scale of: 1.0 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:13<06:43, 13.90s/it] 7%|▋ | 2/30 [00:19<04:16, 9.17s/it] 10%|█ | 3/30 [00:26<03:36, 8.03s/it] 13%|█▎ | 4/30 [00:33<03:14, 7.50s/it] 17%|█▋ | 5/30 [00:39<03:00, 7.21s/it] 20%|██ | 6/30 [00:46<02:48, 7.04s/it] 23%|██▎ | 7/30 [00:53<02:39, 6.92s/it] 27%|██▋ | 8/30 [00:59<02:30, 6.85s/it] 30%|███ | 9/30 [01:06<02:22, 6.80s/it] 33%|███▎ | 10/30 [01:13<02:15, 6.76s/it] 37%|███▋ | 11/30 [01:19<02:08, 6.74s/it] 40%|████ | 12/30 [01:26<02:01, 6.73s/it] 43%|████▎ | 13/30 [01:33<01:54, 6.71s/it] 47%|████▋ | 14/30 [01:40<01:47, 6.71s/it] 50%|█████ | 15/30 [01:46<01:40, 6.70s/it] 53%|█████▎ | 16/30 [01:53<01:33, 6.71s/it] 57%|█████▋ | 17/30 [02:00<01:27, 6.71s/it] 60%|██████ | 18/30 [02:06<01:20, 6.71s/it] 63%|██████▎ | 19/30 [02:13<01:13, 6.71s/it] 67%|██████▋ | 20/30 [02:20<01:07, 6.72s/it] 70%|███████ | 21/30 [02:27<01:00, 6.72s/it] 73%|███████▎ | 22/30 [02:33<00:53, 6.72s/it] 77%|███████▋ | 23/30 [02:40<00:47, 6.72s/it] 80%|████████ | 24/30 [02:47<00:40, 6.72s/it] 83%|████████▎ | 25/30 [02:53<00:33, 6.72s/it] 87%|████████▋ | 26/30 [03:00<00:26, 6.72s/it] 90%|█████████ | 27/30 [03:07<00:20, 6.72s/it] 93%|█████████▎| 28/30 [03:14<00:13, 6.71s/it] 97%|█████████▋| 29/30 [03:20<00:06, 6.72s/it] 100%|██████████| 30/30 [03:27<00:00, 6.72s/it] 100%|██████████| 30/30 [03:27<00:00, 6.92s/it]Prediction
genmoai/mochi-1-lora:bad496d88086d5f60307291a512fdcb4fab0b30027e7ccbcc2ecf5e6606d5e20IDgajwd83y75rme0ckh90am14mk4StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- fps
- 24
- seed
- 4194
- 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.
- hf_lora
- sayakpaul/mochi-lora-dissolve
- lora_scale
- 1
- num_frames
- 121
- guidance_scale
- 6
- num_inference_steps
- 30
{ "fps": 24, "seed": 4194, "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.", "hf_lora": "sayakpaul/mochi-lora-dissolve", "lora_scale": 1, "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run genmoai/mochi-1-lora 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-lora:bad496d88086d5f60307291a512fdcb4fab0b30027e7ccbcc2ecf5e6606d5e20", { input: { fps: 24, seed: 4194, 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.", hf_lora: "sayakpaul/mochi-lora-dissolve", lora_scale: 1, 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 replicateImport the client:import replicateRun genmoai/mochi-1-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "genmoai/mochi-1-lora:bad496d88086d5f60307291a512fdcb4fab0b30027e7ccbcc2ecf5e6606d5e20", input={ "fps": 24, "seed": 4194, "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.", "hf_lora": "sayakpaul/mochi-lora-dissolve", "lora_scale": 1, "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-lora 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-lora:bad496d88086d5f60307291a512fdcb4fab0b30027e7ccbcc2ecf5e6606d5e20", "input": { "fps": 24, "seed": 4194, "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.", "hf_lora": "sayakpaul/mochi-lora-dissolve", "lora_scale": 1, "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-03T02:06:37.564792Z", "created_at": "2024-12-03T02:01:42.713000Z", "data_removed": false, "error": null, "id": "gajwd83y75rme0ckh90am14mk4", "input": { "fps": 24, "seed": 4194, "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.", "hf_lora": "sayakpaul/mochi-lora-dissolve", "lora_scale": 1, "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 }, "logs": "Using seed: 4194\nLoading LoRA: sayakpaul/mochi-lora-dissolve with scale of: 1.0\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:13<06:41, 13.84s/it]\n 7%|▋ | 2/30 [00:19<04:15, 9.13s/it]\n 10%|█ | 3/30 [00:26<03:35, 8.00s/it]\n 13%|█▎ | 4/30 [00:33<03:14, 7.48s/it]\n 17%|█▋ | 5/30 [00:39<02:59, 7.19s/it]\n 20%|██ | 6/30 [00:46<02:48, 7.02s/it]\n 23%|██▎ | 7/30 [00:53<02:39, 6.92s/it]\n 27%|██▋ | 8/30 [00:59<02:30, 6.85s/it]\n 30%|███ | 9/30 [01:06<02:22, 6.80s/it]\n 33%|███▎ | 10/30 [01:13<02:15, 6.77s/it]\n 37%|███▋ | 11/30 [01:19<02:08, 6.75s/it]\n 40%|████ | 12/30 [01:26<02:01, 6.74s/it]\n 43%|████▎ | 13/30 [01:33<01:54, 6.74s/it]\n 47%|████▋ | 14/30 [01:40<01:47, 6.73s/it]\n 50%|█████ | 15/30 [01:46<01:40, 6.73s/it]\n 53%|█████▎ | 16/30 [01:53<01:34, 6.73s/it]\n 57%|█████▋ | 17/30 [02:00<01:27, 6.72s/it]\n 60%|██████ | 18/30 [02:06<01:20, 6.72s/it]\n 63%|██████▎ | 19/30 [02:13<01:13, 6.72s/it]\n 67%|██████▋ | 20/30 [02:20<01:07, 6.72s/it]\n 70%|███████ | 21/30 [02:27<01:00, 6.72s/it]\n 73%|███████▎ | 22/30 [02:33<00:53, 6.72s/it]\n 77%|███████▋ | 23/30 [02:40<00:47, 6.72s/it]\n 80%|████████ | 24/30 [02:47<00:40, 6.73s/it]\n 83%|████████▎ | 25/30 [02:54<00:33, 6.72s/it]\n 87%|████████▋ | 26/30 [03:00<00:26, 6.72s/it]\n 90%|█████████ | 27/30 [03:07<00:20, 6.72s/it]\n 93%|█████████▎| 28/30 [03:14<00:13, 6.72s/it]\n 97%|█████████▋| 29/30 [03:20<00:06, 6.72s/it]\n100%|██████████| 30/30 [03:27<00:00, 6.72s/it]\n100%|██████████| 30/30 [03:27<00:00, 6.92s/it]", "metrics": { "predict_time": 232.892389769, "total_time": 294.851792 }, "output": "https://replicate.delivery/xezq/pLweyV4jH9XnRCDneeWMuthRsimAMF5gQ06HHKfeUuIq1M4eE/output.mp4", "started_at": "2024-12-03T02:02:44.672402Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-ludkjklx4bkmdjgaoh7er6vz6u2i4rakvzbvl7ff3ul2z6fsc6wq", "get": "https://api.replicate.com/v1/predictions/gajwd83y75rme0ckh90am14mk4", "cancel": "https://api.replicate.com/v1/predictions/gajwd83y75rme0ckh90am14mk4/cancel" }, "version": "bad496d88086d5f60307291a512fdcb4fab0b30027e7ccbcc2ecf5e6606d5e20" }Generated inUsing seed: 4194 Loading LoRA: sayakpaul/mochi-lora-dissolve with scale of: 1.0 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:13<06:41, 13.84s/it] 7%|▋ | 2/30 [00:19<04:15, 9.13s/it] 10%|█ | 3/30 [00:26<03:35, 8.00s/it] 13%|█▎ | 4/30 [00:33<03:14, 7.48s/it] 17%|█▋ | 5/30 [00:39<02:59, 7.19s/it] 20%|██ | 6/30 [00:46<02:48, 7.02s/it] 23%|██▎ | 7/30 [00:53<02:39, 6.92s/it] 27%|██▋ | 8/30 [00:59<02:30, 6.85s/it] 30%|███ | 9/30 [01:06<02:22, 6.80s/it] 33%|███▎ | 10/30 [01:13<02:15, 6.77s/it] 37%|███▋ | 11/30 [01:19<02:08, 6.75s/it] 40%|████ | 12/30 [01:26<02:01, 6.74s/it] 43%|████▎ | 13/30 [01:33<01:54, 6.74s/it] 47%|████▋ | 14/30 [01:40<01:47, 6.73s/it] 50%|█████ | 15/30 [01:46<01:40, 6.73s/it] 53%|█████▎ | 16/30 [01:53<01:34, 6.73s/it] 57%|█████▋ | 17/30 [02:00<01:27, 6.72s/it] 60%|██████ | 18/30 [02:06<01:20, 6.72s/it] 63%|██████▎ | 19/30 [02:13<01:13, 6.72s/it] 67%|██████▋ | 20/30 [02:20<01:07, 6.72s/it] 70%|███████ | 21/30 [02:27<01:00, 6.72s/it] 73%|███████▎ | 22/30 [02:33<00:53, 6.72s/it] 77%|███████▋ | 23/30 [02:40<00:47, 6.72s/it] 80%|████████ | 24/30 [02:47<00:40, 6.73s/it] 83%|████████▎ | 25/30 [02:54<00:33, 6.72s/it] 87%|████████▋ | 26/30 [03:00<00:26, 6.72s/it] 90%|█████████ | 27/30 [03:07<00:20, 6.72s/it] 93%|█████████▎| 28/30 [03:14<00:13, 6.72s/it] 97%|█████████▋| 29/30 [03:20<00:06, 6.72s/it] 100%|██████████| 30/30 [03:27<00:00, 6.72s/it] 100%|██████████| 30/30 [03:27<00:00, 6.92s/it]Prediction
genmoai/mochi-1-lora:bad496d88086d5f60307291a512fdcb4fab0b30027e7ccbcc2ecf5e6606d5e20Input
- fps
- 24
- seed
- 8520
- prompt
- a parrot flying in the blue skies, a grainy or noisy video effect in the background
- hf_lora
- lucataco/mochi-lora-vhs
- lora_scale
- 1
- num_frames
- 121
- guidance_scale
- 6
- num_inference_steps
- 30
{ "fps": 24, "seed": 8520, "prompt": "a parrot flying in the blue skies, a grainy or noisy video effect in the background", "hf_lora": "lucataco/mochi-lora-vhs", "lora_scale": 1, "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run genmoai/mochi-1-lora 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-lora:bad496d88086d5f60307291a512fdcb4fab0b30027e7ccbcc2ecf5e6606d5e20", { input: { fps: 24, seed: 8520, prompt: "a parrot flying in the blue skies, a grainy or noisy video effect in the background", hf_lora: "lucataco/mochi-lora-vhs", lora_scale: 1, 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 replicateImport the client:import replicateRun genmoai/mochi-1-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "genmoai/mochi-1-lora:bad496d88086d5f60307291a512fdcb4fab0b30027e7ccbcc2ecf5e6606d5e20", input={ "fps": 24, "seed": 8520, "prompt": "a parrot flying in the blue skies, a grainy or noisy video effect in the background", "hf_lora": "lucataco/mochi-lora-vhs", "lora_scale": 1, "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-lora 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-lora:bad496d88086d5f60307291a512fdcb4fab0b30027e7ccbcc2ecf5e6606d5e20", "input": { "fps": 24, "seed": 8520, "prompt": "a parrot flying in the blue skies, a grainy or noisy video effect in the background", "hf_lora": "lucataco/mochi-lora-vhs", "lora_scale": 1, "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-11T19:30:20.554707Z", "created_at": "2024-12-11T19:25:47.264000Z", "data_removed": false, "error": null, "id": "sk5q509zg1rma0ckpwqtq3t9zw", "input": { "fps": 24, "seed": 8520, "prompt": "a parrot flying in the blue skies, a grainy or noisy video effect in the background", "hf_lora": "lucataco/mochi-lora-vhs", "lora_scale": 1, "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 }, "logs": "Using seed: 8520\nLoading LoRA: lucataco/mochi-lora-vhs with scale of: 1.0\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:13<06:33, 13.58s/it]\n 7%|▋ | 2/30 [00:19<04:13, 9.05s/it]\n 10%|█ | 3/30 [00:26<03:35, 7.98s/it]\n 13%|█▎ | 4/30 [00:32<03:14, 7.49s/it]\n 17%|█▋ | 5/30 [00:39<03:00, 7.22s/it]\n 20%|██ | 6/30 [00:46<02:49, 7.05s/it]\n 23%|██▎ | 7/30 [00:53<02:39, 6.94s/it]\n 27%|██▋ | 8/30 [00:59<02:31, 6.88s/it]\n 30%|███ | 9/30 [01:06<02:23, 6.83s/it]\n 33%|███▎ | 10/30 [01:13<02:16, 6.81s/it]\n 37%|███▋ | 11/30 [01:20<02:08, 6.79s/it]\n 40%|████ | 12/30 [01:26<02:01, 6.77s/it]\n 43%|████▎ | 13/30 [01:33<01:55, 6.77s/it]\n 47%|████▋ | 14/30 [01:40<01:48, 6.76s/it]\n 50%|█████ | 15/30 [01:47<01:41, 6.75s/it]\n 53%|█████▎ | 16/30 [01:53<01:34, 6.75s/it]\n 57%|█████▋ | 17/30 [02:00<01:27, 6.75s/it]\n 60%|██████ | 18/30 [02:07<01:20, 6.75s/it]\n 63%|██████▎ | 19/30 [02:13<01:14, 6.74s/it]\n 67%|██████▋ | 20/30 [02:20<01:07, 6.74s/it]\n 70%|███████ | 21/30 [02:27<01:00, 6.74s/it]\n 73%|███████▎ | 22/30 [02:34<00:53, 6.74s/it]\n 77%|███████▋ | 23/30 [02:40<00:47, 6.75s/it]\n 80%|████████ | 24/30 [02:47<00:40, 6.74s/it]\n 83%|████████▎ | 25/30 [02:54<00:33, 6.74s/it]\n 87%|████████▋ | 26/30 [03:01<00:26, 6.74s/it]\n 90%|█████████ | 27/30 [03:07<00:20, 6.74s/it]\n 93%|█████████▎| 28/30 [03:14<00:13, 6.74s/it]\n 97%|█████████▋| 29/30 [03:21<00:06, 6.74s/it]\n100%|██████████| 30/30 [03:28<00:00, 6.74s/it]\n100%|██████████| 30/30 [03:28<00:00, 6.94s/it]", "metrics": { "predict_time": 232.965928495, "total_time": 273.290707 }, "output": "https://replicate.delivery/xezq/Qq9MavvCj4ZAHB3VfnRVhRFMCM0ZDi1irSeG35wUlYDMp55TA/output.mp4", "started_at": "2024-12-11T19:26:27.588778Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-d23fzxnwmph5lkzp5ssxt5fmofjiot2texefh3ge7rossibsqk7q", "get": "https://api.replicate.com/v1/predictions/sk5q509zg1rma0ckpwqtq3t9zw", "cancel": "https://api.replicate.com/v1/predictions/sk5q509zg1rma0ckpwqtq3t9zw/cancel" }, "version": "bad496d88086d5f60307291a512fdcb4fab0b30027e7ccbcc2ecf5e6606d5e20" }Generated inUsing seed: 8520 Loading LoRA: lucataco/mochi-lora-vhs with scale of: 1.0 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:13<06:33, 13.58s/it] 7%|▋ | 2/30 [00:19<04:13, 9.05s/it] 10%|█ | 3/30 [00:26<03:35, 7.98s/it] 13%|█▎ | 4/30 [00:32<03:14, 7.49s/it] 17%|█▋ | 5/30 [00:39<03:00, 7.22s/it] 20%|██ | 6/30 [00:46<02:49, 7.05s/it] 23%|██▎ | 7/30 [00:53<02:39, 6.94s/it] 27%|██▋ | 8/30 [00:59<02:31, 6.88s/it] 30%|███ | 9/30 [01:06<02:23, 6.83s/it] 33%|███▎ | 10/30 [01:13<02:16, 6.81s/it] 37%|███▋ | 11/30 [01:20<02:08, 6.79s/it] 40%|████ | 12/30 [01:26<02:01, 6.77s/it] 43%|████▎ | 13/30 [01:33<01:55, 6.77s/it] 47%|████▋ | 14/30 [01:40<01:48, 6.76s/it] 50%|█████ | 15/30 [01:47<01:41, 6.75s/it] 53%|█████▎ | 16/30 [01:53<01:34, 6.75s/it] 57%|█████▋ | 17/30 [02:00<01:27, 6.75s/it] 60%|██████ | 18/30 [02:07<01:20, 6.75s/it] 63%|██████▎ | 19/30 [02:13<01:14, 6.74s/it] 67%|██████▋ | 20/30 [02:20<01:07, 6.74s/it] 70%|███████ | 21/30 [02:27<01:00, 6.74s/it] 73%|███████▎ | 22/30 [02:34<00:53, 6.74s/it] 77%|███████▋ | 23/30 [02:40<00:47, 6.75s/it] 80%|████████ | 24/30 [02:47<00:40, 6.74s/it] 83%|████████▎ | 25/30 [02:54<00:33, 6.74s/it] 87%|████████▋ | 26/30 [03:01<00:26, 6.74s/it] 90%|█████████ | 27/30 [03:07<00:20, 6.74s/it] 93%|█████████▎| 28/30 [03:14<00:13, 6.74s/it] 97%|█████████▋| 29/30 [03:21<00:06, 6.74s/it] 100%|██████████| 30/30 [03:28<00:00, 6.74s/it] 100%|██████████| 30/30 [03:28<00:00, 6.94s/it]Prediction
genmoai/mochi-1-lora:bad496d88086d5f60307291a512fdcb4fab0b30027e7ccbcc2ecf5e6606d5e20Input
- fps
- 24
- seed
- 47731
- prompt
- gingerbread man is dancing on a table. melty.
- hf_lora
- lucataco/mochi-lora-melty
- lora_scale
- 1
- num_frames
- 121
- guidance_scale
- 6
- num_inference_steps
- 30
{ "fps": 24, "seed": 47731, "prompt": "gingerbread man is dancing on a table. melty.", "hf_lora": "lucataco/mochi-lora-melty", "lora_scale": 1, "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run genmoai/mochi-1-lora 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-lora:bad496d88086d5f60307291a512fdcb4fab0b30027e7ccbcc2ecf5e6606d5e20", { input: { fps: 24, seed: 47731, prompt: "gingerbread man is dancing on a table. melty.", hf_lora: "lucataco/mochi-lora-melty", lora_scale: 1, 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 replicateImport the client:import replicateRun genmoai/mochi-1-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "genmoai/mochi-1-lora:bad496d88086d5f60307291a512fdcb4fab0b30027e7ccbcc2ecf5e6606d5e20", input={ "fps": 24, "seed": 47731, "prompt": "gingerbread man is dancing on a table. melty.", "hf_lora": "lucataco/mochi-lora-melty", "lora_scale": 1, "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-lora 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-lora:bad496d88086d5f60307291a512fdcb4fab0b30027e7ccbcc2ecf5e6606d5e20", "input": { "fps": 24, "seed": 47731, "prompt": "gingerbread man is dancing on a table. melty.", "hf_lora": "lucataco/mochi-lora-melty", "lora_scale": 1, "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-12-12T21:47:08.326358Z", "created_at": "2024-12-12T21:42:20.993000Z", "data_removed": false, "error": null, "id": "42wspzx285rmc0ckqk99tdda7w", "input": { "fps": 24, "seed": 47731, "prompt": "gingerbread man is dancing on a table. melty.", "hf_lora": "lucataco/mochi-lora-melty", "lora_scale": 1, "num_frames": 121, "guidance_scale": 6, "num_inference_steps": 30 }, "logs": "Using seed: 47731\nLoading LoRA: lucataco/mochi-lora-melty with scale of: 1.0\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:13<06:39, 13.76s/it]\n 7%|▋ | 2/30 [00:19<04:15, 9.13s/it]\n 10%|█ | 3/30 [00:26<03:37, 8.04s/it]\n 13%|█▎ | 4/30 [00:33<03:15, 7.53s/it]\n 17%|█▋ | 5/30 [00:39<03:01, 7.24s/it]\n 20%|██ | 6/30 [00:46<02:49, 7.07s/it]\n 23%|██▎ | 7/30 [00:53<02:40, 6.96s/it]\n 27%|██▋ | 8/30 [01:00<02:31, 6.89s/it]\n 30%|███ | 9/30 [01:06<02:23, 6.84s/it]\n 33%|███▎ | 10/30 [01:13<02:16, 6.81s/it]\n 37%|███▋ | 11/30 [01:20<02:09, 6.79s/it]\n 40%|████ | 12/30 [01:27<02:01, 6.78s/it]\n 43%|████▎ | 13/30 [01:33<01:54, 6.76s/it]\n 47%|████▋ | 14/30 [01:40<01:48, 6.76s/it]\n 50%|█████ | 15/30 [01:47<01:41, 6.75s/it]\n 53%|█████▎ | 16/30 [01:54<01:34, 6.75s/it]\n 57%|█████▋ | 17/30 [02:00<01:27, 6.75s/it]\n 60%|██████ | 18/30 [02:07<01:20, 6.75s/it]\n 63%|██████▎ | 19/30 [02:14<01:14, 6.74s/it]\n 67%|██████▋ | 20/30 [02:20<01:07, 6.74s/it]\n 70%|███████ | 21/30 [02:27<01:00, 6.74s/it]\n 73%|███████▎ | 22/30 [02:34<00:53, 6.74s/it]\n 77%|███████▋ | 23/30 [02:41<00:47, 6.73s/it]\n 80%|████████ | 24/30 [02:47<00:40, 6.73s/it]\n 83%|████████▎ | 25/30 [02:54<00:33, 6.73s/it]\n 87%|████████▋ | 26/30 [03:01<00:26, 6.73s/it]\n 90%|█████████ | 27/30 [03:08<00:20, 6.72s/it]\n 93%|█████████▎| 28/30 [03:14<00:13, 6.72s/it]\n 97%|█████████▋| 29/30 [03:21<00:06, 6.72s/it]\n100%|██████████| 30/30 [03:28<00:00, 6.72s/it]\n100%|██████████| 30/30 [03:28<00:00, 6.94s/it]", "metrics": { "predict_time": 233.443011344, "total_time": 287.333358 }, "output": "https://replicate.delivery/xezq/mFqnIdXCfkUuICKQVgs30lfBVFjSJyzueY93sPmXpxF4eCpPB/output.mp4", "started_at": "2024-12-12T21:43:14.883346Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-5bylcjibmjecy2on6c7xznf4migyjxznbstbn4fzd3d7d7lamkoq", "get": "https://api.replicate.com/v1/predictions/42wspzx285rmc0ckqk99tdda7w", "cancel": "https://api.replicate.com/v1/predictions/42wspzx285rmc0ckqk99tdda7w/cancel" }, "version": "bad496d88086d5f60307291a512fdcb4fab0b30027e7ccbcc2ecf5e6606d5e20" }Generated inUsing seed: 47731 Loading LoRA: lucataco/mochi-lora-melty with scale of: 1.0 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:13<06:39, 13.76s/it] 7%|▋ | 2/30 [00:19<04:15, 9.13s/it] 10%|█ | 3/30 [00:26<03:37, 8.04s/it] 13%|█▎ | 4/30 [00:33<03:15, 7.53s/it] 17%|█▋ | 5/30 [00:39<03:01, 7.24s/it] 20%|██ | 6/30 [00:46<02:49, 7.07s/it] 23%|██▎ | 7/30 [00:53<02:40, 6.96s/it] 27%|██▋ | 8/30 [01:00<02:31, 6.89s/it] 30%|███ | 9/30 [01:06<02:23, 6.84s/it] 33%|███▎ | 10/30 [01:13<02:16, 6.81s/it] 37%|███▋ | 11/30 [01:20<02:09, 6.79s/it] 40%|████ | 12/30 [01:27<02:01, 6.78s/it] 43%|████▎ | 13/30 [01:33<01:54, 6.76s/it] 47%|████▋ | 14/30 [01:40<01:48, 6.76s/it] 50%|█████ | 15/30 [01:47<01:41, 6.75s/it] 53%|█████▎ | 16/30 [01:54<01:34, 6.75s/it] 57%|█████▋ | 17/30 [02:00<01:27, 6.75s/it] 60%|██████ | 18/30 [02:07<01:20, 6.75s/it] 63%|██████▎ | 19/30 [02:14<01:14, 6.74s/it] 67%|██████▋ | 20/30 [02:20<01:07, 6.74s/it] 70%|███████ | 21/30 [02:27<01:00, 6.74s/it] 73%|███████▎ | 22/30 [02:34<00:53, 6.74s/it] 77%|███████▋ | 23/30 [02:41<00:47, 6.73s/it] 80%|████████ | 24/30 [02:47<00:40, 6.73s/it] 83%|████████▎ | 25/30 [02:54<00:33, 6.73s/it] 87%|████████▋ | 26/30 [03:01<00:26, 6.73s/it] 90%|█████████ | 27/30 [03:08<00:20, 6.72s/it] 93%|█████████▎| 28/30 [03:14<00:13, 6.72s/it] 97%|█████████▋| 29/30 [03:21<00:06, 6.72s/it] 100%|██████████| 30/30 [03:28<00:00, 6.72s/it] 100%|██████████| 30/30 [03:28<00:00, 6.94s/it]
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