typefile
{
"batch_size": 1,
"caption_dropout": 0.1,
"compile_dit": true,
"hf_repo_id": "lucataco/mochi-lora-vhs",
"hf_token": "[REDACTED]",
"input_videos": "https://replicate.delivery/pbxt/M7v7tzysZ9DiC0Pdla5rGUDW9tafeTOKzx9iS5fiqhehavkX/vhs-4.zip",
"learning_rate": 0.0004,
"optimizer": "adamw",
"seed": 42,
"steps": 500,
"trim_and_crop": true
}npm install replicate
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_aTy**********************************
This is your API token. Keep it to yourself.
import Replicate from "replicate";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});
Run genmoai/mochi-1-lora-trainer 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-trainer:170ea99fb48a30fef98cb1c9fb403a2882ab9d60c2ba15ad9383ace33c3fa385",
{
input: {
batch_size: 1,
caption_dropout: 0.1,
compile_dit: true,
hf_repo_id: "lucataco/mochi-lora-vhs",
hf_token: "[REDACTED]",
input_videos: "https://replicate.delivery/pbxt/M7v7tzysZ9DiC0Pdla5rGUDW9tafeTOKzx9iS5fiqhehavkX/vhs-4.zip",
learning_rate: 0.0004,
optimizer: "adamw",
seed: 42,
steps: 500,
trim_and_crop: true
}
}
);
console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_aTy**********************************
This is your API token. Keep it to yourself.
import replicate
Run genmoai/mochi-1-lora-trainer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"genmoai/mochi-1-lora-trainer:170ea99fb48a30fef98cb1c9fb403a2882ab9d60c2ba15ad9383ace33c3fa385",
input={
"batch_size": 1,
"caption_dropout": 0.1,
"compile_dit": True,
"hf_repo_id": "lucataco/mochi-lora-vhs",
"hf_token": "[REDACTED]",
"input_videos": "https://replicate.delivery/pbxt/M7v7tzysZ9DiC0Pdla5rGUDW9tafeTOKzx9iS5fiqhehavkX/vhs-4.zip",
"learning_rate": 0.0004,
"optimizer": "adamw",
"seed": 42,
"steps": 500,
"trim_and_crop": True
}
)
print(output)
To learn more, take a look at the guide on getting started with Python.
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=r8_aTy**********************************
This is your API token. Keep it to yourself.
Run genmoai/mochi-1-lora-trainer 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-trainer:170ea99fb48a30fef98cb1c9fb403a2882ab9d60c2ba15ad9383ace33c3fa385",
"input": {
"batch_size": 1,
"caption_dropout": 0.1,
"compile_dit": true,
"hf_repo_id": "lucataco/mochi-lora-vhs",
"hf_token": "[REDACTED]",
"input_videos": "https://replicate.delivery/pbxt/M7v7tzysZ9DiC0Pdla5rGUDW9tafeTOKzx9iS5fiqhehavkX/vhs-4.zip",
"learning_rate": 0.0004,
"optimizer": "adamw",
"seed": 42,
"steps": 500,
"trim_and_crop": true
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Object output with 1 properties
{
"id": "mxey6b9vqnrm80ckpveaqjqb9w",
"model": "genmoai/mochi-1-lora-trainer",
"version": "170ea99fb48a30fef98cb1c9fb403a2882ab9d60c2ba15ad9383ace33c3fa385",
"input": {
"batch_size": 1,
"caption_dropout": 0.1,
"compile_dit": true,
"hf_repo_id": "lucataco/mochi-lora-vhs",
"hf_token": "[REDACTED]",
"input_videos": "https://replicate.delivery/pbxt/M7v7tzysZ9DiC0Pdla5rGUDW9tafeTOKzx9iS5fiqhehavkX/vhs-4.zip",
"learning_rate": 0.0004,
"optimizer": "adamw",
"seed": 42,
"steps": 500,
"trim_and_crop": true
},
"logs": "Cleaning up previous runs\nExtracted 8 files from zip to videos_input\n---Starting to Trim input videos---\nProcessing: videos_input/vhs1.mp4\nCopied videos_input/vhs1.txt to videos_prepared/vhs1.txt\nMoviepy - Building video videos_prepared/vhs1.mp4.\nMoviepy - Writing video videos_prepared/vhs1.mp4\n 0%| | 0/4 [00:00<?, ?it/s]\n0%| | 0/4 [00:00<?, ?it/s]\n 0%| | 0/4 [00:00<?, ?it/s]\nt: 0%| | 0/40 [00:00<?, ?it/s, now=None]\u001b[A\n \u001b[A\n0%| | 0/4 [00:00<?, ?it/s]\nMoviepy - Done !\nMoviepy - video ready videos_prepared/vhs1.mp4\n 0%| | 0/4 [00:00<?, ?it/s]\nProcessing: videos_input/vhs2.mp4\nCopied videos_input/vhs2.txt to videos_prepared/vhs2.txt\nMoviepy - Building video videos_prepared/vhs2.mp4.\nMoviepy - Writing video videos_prepared/vhs2.mp4\n 25%|██▌ | 1/4 [00:00<00:00, 3.16it/s]\n25%|██▌ | 1/4 [00:00<00:00, 3.16it/s]\n 25%|██▌ | 1/4 [00:00<00:00, 3.16it/s]\nt: 0%| | 0/40 [00:00<?, ?it/s, now=None]\u001b[A\n \u001b[A\n25%|██▌ | 1/4 [00:00<00:00, 3.16it/s]\nMoviepy - Done !\nMoviepy - video ready videos_prepared/vhs2.mp4\n 25%|██▌ | 1/4 [00:00<00:00, 3.16it/s]\nProcessing: videos_input/vhs3.mp4\nCopied videos_input/vhs3.txt to videos_prepared/vhs3.txt\nMoviepy - Building video videos_prepared/vhs3.mp4.\n 50%|█████ | 2/4 [00:00<00:00, 3.05it/s]\n50%|█████ | 2/4 [00:00<00:00, 3.05it/s]\nMoviepy - Writing video videos_prepared/vhs3.mp4\n 50%|█████ | 2/4 [00:00<00:00, 3.05it/s]\nt: 0%| | 0/40 [00:00<?, ?it/s, now=None]\u001b[A\n \u001b[A\n50%|█████ | 2/4 [00:00<00:00, 3.05it/s]\nMoviepy - Done !\nMoviepy - video ready videos_prepared/vhs3.mp4\n 50%|█████ | 2/4 [00:00<00:00, 3.05it/s]\nProcessing: videos_input/vhs4.mp4\nCopied videos_input/vhs4.txt to videos_prepared/vhs4.txt\nMoviepy - Building video videos_prepared/vhs4.mp4.\nMoviepy - Writing video videos_prepared/vhs4.mp4\n 75%|███████▌ | 3/4 [00:00<00:00, 3.05it/s]\n75%|███████▌ | 3/4 [00:01<00:00, 3.05it/s]\n 75%|███████▌ | 3/4 [00:01<00:00, 3.05it/s]\nt: 0%| | 0/40 [00:00<?, ?it/s, now=None]\u001b[A\n \u001b[A\n75%|███████▌ | 3/4 [00:01<00:00, 3.05it/s]\nMoviepy - Done !\nMoviepy - video ready videos_prepared/vhs4.mp4\n 75%|███████▌ | 3/4 [00:01<00:00, 3.05it/s]\n100%|██████████| 4/4 [00:01<00:00, 3.07it/s]\n100%|██████████| 4/4 [00:01<00:00, 3.07it/s]\n---Starting to Embed videos---\nLoading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]\nLoading checkpoint shards: 50%|█████ | 1/2 [00:00<00:00, 1.67it/s]\nLoading checkpoint shards: 100%|██████████| 2/2 [00:01<00:00, 1.78it/s]\nLoading checkpoint shards: 100%|██████████| 2/2 [00:01<00:00, 1.76it/s]\nLoading pipeline components...: 0%| | 0/3 [00:00<?, ?it/s]\nLoading pipeline components...: 100%|██████████| 3/3 [00:00<00:00, 681.59it/s]\nProcessing videos_prepared/vhs1.mp4\nTrimmed video from 40 to first 37 frames\n0it [00:00, ?it/s]\nProcessing videos_prepared/vhs2.mp4\nTrimmed video from 40 to first 37 frames\n1it [00:01, 1.38s/it]\nProcessing videos_prepared/vhs3.mp4\nTrimmed video from 40 to first 37 frames\n2it [00:02, 1.15s/it]\nProcessing videos_prepared/vhs4.mp4\nTrimmed video from 40 to first 37 frames\n3it [00:03, 1.07s/it]\n4it [00:04, 1.03s/it]\n4it [00:04, 1.08s/it]\n---Starting training---\nFound 4 training videos in videos_prepared\nLoaded 4/4 valid file pairs.\n===== Memory before training =====\nmemory_allocated=18.780 GB\nmax_memory_allocated=18.780 GB\nmax_memory_reserved=19.250 GB\n***** Running training *****\nNum trainable parameters = 19005440\nNum examples = 4\nNum batches each epoch = 4\nNum epochs = 125\nInstantaneous batch size per device = 1\nTotal train batch size (w. parallel, distributed & accumulation) = 1\nTotal optimization steps = 500\nSteps: 0%| | 0/500 [00:00<?, ?it/s]W1211 17:57:31.075000 135012609543680 torch/fx/experimental/symbolic_shapes.py:4449] [0/0] xindex is not in var_ranges, defaulting to unknown range.\nW1211 17:57:31.089000 135012609543680 torch/fx/experimental/symbolic_shapes.py:4449] [0/0] xindex is not in var_ranges, defaulting to unknown range.\nW1211 17:57:31.224000 135012609543680 torch/fx/experimental/symbolic_shapes.py:4449] [0/0] xindex is not in var_ranges, defaulting to unknown range.\nSteps: 0%| | 1/500 [04:16<35:33:30, 256.53s/it]\nSteps: 0%| | 1/500 [04:16<35:33:30, 256.53s/it, loss=0.933, lr=2e-6]\nSteps: 0%| | 2/500 [04:18<14:45:44, 106.72s/it, loss=0.933, lr=2e-6]\nSteps: 0%| | 2/500 [04:18<14:45:44, 106.72s/it, loss=1.05, lr=4e-6] \nSteps: 1%| | 3/500 [04:20<8:07:22, 58.84s/it, loss=1.05, lr=4e-6] \nSteps: 1%| | 3/500 [04:20<8:07:22, 58.84s/it, loss=0.864, lr=6e-6]\nSteps: 1%| | 4/500 [04:22<5:00:27, 36.34s/it, loss=0.864, lr=6e-6]\nSteps: 1%| | 4/500 [04:22<5:00:27, 36.34s/it, loss=1.06, lr=8e-6] \nSteps: 1%| | 5/500 [04:29<3:34:15, 25.97s/it, loss=1.06, lr=8e-6]\nSteps: 1%| | 5/500 [04:29<3:34:15, 25.97s/it, loss=0.874, lr=1e-5]\nSteps: 1%| | 6/500 [04:31<2:26:21, 17.78s/it, loss=0.874, lr=1e-5]\nSteps: 1%| | 6/500 [04:31<2:26:21, 17.78s/it, loss=1.02, lr=1.2e-5]\nSteps: 1%|▏ | 7/500 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https://huggingface.co/lucataco/mochi-lora-vhs\npytorch_lora_weights.safetensors: 0%| | 0.00/76.1M [00:00<?, ?B/s]\npytorch_lora_weights.safetensors: 10%|▉ | 7.34M/76.1M [00:00<00:00, 73.4MB/s]\npytorch_lora_weights.safetensors: 21%|██ | 16.0M/76.1M [00:00<00:01, 42.3MB/s]\npytorch_lora_weights.safetensors: 42%|████▏ | 32.0M/76.1M [00:00<00:00, 46.4MB/s]\npytorch_lora_weights.safetensors: 63%|██████▎ | 48.0M/76.1M [00:00<00:00, 54.6MB/s]\npytorch_lora_weights.safetensors: 84%|████████▍ | 64.0M/76.1M [00:01<00:00, 57.3MB/s]\npytorch_lora_weights.safetensors: 100%|██████████| 76.1M/76.1M [00:01<00:00, 54.6MB/s]\nSuccessfully uploaded model to https://huggingface.co/lucataco/mochi-lora-vhs",
"output": {
"weights": "https://replicate.delivery/xezq/eWTxCE13svWocSUepK0rWZdxJhthKmFtpE2SzDBfrSVdcxznA/trained_model.tar"
},
"data_removed": false,
"error": null,
"source": "web",
"status": "succeeded",
"created_at": "2024-12-11T17:55:06.813Z",
"started_at": "2024-12-11T17:55:06.81994Z",
"completed_at": "2024-12-11T18:27:26.196131Z",
"urls": {
"cancel": "https://api.replicate.com/v1/predictions/mxey6b9vqnrm80ckpveaqjqb9w/cancel",
"get": "https://api.replicate.com/v1/predictions/mxey6b9vqnrm80ckpveaqjqb9w",
"web": "https://replicate.com/p/mxey6b9vqnrm80ckpveaqjqb9w"
},
"metrics": {
"predict_time": 1939.37619092,
"total_time": 1939.383131
}
}