Prediction
genmoai/mochi-1-lora-trainer:170ea99fb48a30fef98cb1c9fb403a2882ab9d60c2ba15ad9383ace33c3fa385Input
- seed
- 42
- steps
- 500
- hf_token
- ████████████████████
This value was redacted after being sent to the model.
- optimizer
- adamw
- batch_size
- 1
- hf_repo_id
- lucataco/mochi-lora-vhs
- compile_dit
- input_videos
- vhs-4.zip
- learning_rate
- 0.0004
- trim_and_crop
- caption_dropout
- 0.1
{
"seed": 42,
"steps": 500,
"hf_token": "[REDACTED]",
"optimizer": "adamw",
"batch_size": 1,
"hf_repo_id": "lucataco/mochi-lora-vhs",
"compile_dit": true,
"input_videos": "https://replicate.delivery/pbxt/M7v7tzysZ9DiC0Pdla5rGUDW9tafeTOKzx9iS5fiqhehavkX/vhs-4.zip",
"learning_rate": 0.0004,
"trim_and_crop": true,
"caption_dropout": 0.1
}Install Replicate’s Node.js client library:
npm install replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:
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: {
seed: 42,
steps: 500,
hf_token: "[REDACTED]",
optimizer: "adamw",
batch_size: 1,
hf_repo_id: "lucataco/mochi-lora-vhs",
compile_dit: true,
input_videos: "https://replicate.delivery/pbxt/M7v7tzysZ9DiC0Pdla5rGUDW9tafeTOKzx9iS5fiqhehavkX/vhs-4.zip",
learning_rate: 0.0004,
trim_and_crop: true,
caption_dropout: 0.1
}
}
);
console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:
pip install replicateSet the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:
import replicateRun 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={
"seed": 42,
"steps": 500,
"hf_token": "[REDACTED]",
"optimizer": "adamw",
"batch_size": 1,
"hf_repo_id": "lucataco/mochi-lora-vhs",
"compile_dit": True,
"input_videos": "https://replicate.delivery/pbxt/M7v7tzysZ9DiC0Pdla5rGUDW9tafeTOKzx9iS5fiqhehavkX/vhs-4.zip",
"learning_rate": 0.0004,
"trim_and_crop": True,
"caption_dropout": 0.1
}
)
print(output)To learn more, take a look at the guide on getting started with Python.
Set the
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
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": {
"seed": 42,
"steps": 500,
"hf_token": "[REDACTED]",
"optimizer": "adamw",
"batch_size": 1,
"hf_repo_id": "lucataco/mochi-lora-vhs",
"compile_dit": true,
"input_videos": "https://replicate.delivery/pbxt/M7v7tzysZ9DiC0Pdla5rGUDW9tafeTOKzx9iS5fiqhehavkX/vhs-4.zip",
"learning_rate": 0.0004,
"trim_and_crop": true,
"caption_dropout": 0.1
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{
"completed_at": "2024-12-11T18:27:26.196131Z",
"created_at": "2024-12-11T17:55:06.813000Z",
"data_removed": false,
"error": null,
"id": "mxey6b9vqnrm80ckpveaqjqb9w",
"input": {
"seed": 42,
"steps": 500,
"hf_token": "[REDACTED]",
"optimizer": "adamw",
"batch_size": 1,
"hf_repo_id": "lucataco/mochi-lora-vhs",
"compile_dit": true,
"input_videos": "https://replicate.delivery/pbxt/M7v7tzysZ9DiC0Pdla5rGUDW9tafeTOKzx9iS5fiqhehavkX/vhs-4.zip",
"learning_rate": 0.0004,
"trim_and_crop": true,
"caption_dropout": 0.1
},
"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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2.67s/it, loss=0.783, lr=2.74e-7]\nSteps: 99%|█████████▉| 496/500 [31:42<00:10, 2.67s/it, loss=0.941, lr=1.75e-7]\nSteps: 99%|█████████▉| 497/500 [31:49<00:12, 4.18s/it, loss=0.941, lr=1.75e-7]\nSteps: 99%|█████████▉| 497/500 [31:49<00:12, 4.18s/it, loss=0.78, lr=9.87e-8] \nSteps: 100%|█████████▉| 498/500 [31:51<00:06, 3.49s/it, loss=0.78, lr=9.87e-8]\nSteps: 100%|█████████▉| 498/500 [31:51<00:06, 3.49s/it, loss=0.928, lr=4.39e-8]\nSteps: 100%|█████████▉| 499/500 [31:53<00:03, 3.00s/it, loss=0.928, lr=4.39e-8]\nSteps: 100%|█████████▉| 499/500 [31:53<00:03, 3.00s/it, loss=1.03, lr=1.1e-8] \nSteps: 100%|██████████| 500/500 [31:55<00:00, 2.66s/it, loss=1.03, lr=1.1e-8]\nSteps: 100%|██████████| 500/500 [31:55<00:00, 2.66s/it, loss=0.906, lr=0] \nSteps: 100%|██████████| 500/500 [31:59<00:00, 3.84s/it, loss=0.906, lr=0]\n---Tar up output directory---\nmochi-lora/\nmochi-lora/pytorch_lora_weights.safetensors\nUploading to Hugging Face: lucataco/mochi-lora-vhs\nHF Repo URL: 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",
"metrics": {
"predict_time": 1939.37619092,
"total_time": 1939.383131
},
"output": {
"weights": "https://replicate.delivery/xezq/eWTxCE13svWocSUepK0rWZdxJhthKmFtpE2SzDBfrSVdcxznA/trained_model.tar"
},
"started_at": "2024-12-11T17:55:06.819940Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/mxey6b9vqnrm80ckpveaqjqb9w",
"cancel": "https://api.replicate.com/v1/predictions/mxey6b9vqnrm80ckpveaqjqb9w/cancel"
},
"version": "170ea99fb48a30fef98cb1c9fb403a2882ab9d60c2ba15ad9383ace33c3fa385"
}Generated in
Cleaning up previous runs
Extracted 8 files from zip to videos_input
---Starting to Trim input videos---
Processing: videos_input/vhs1.mp4
Copied videos_input/vhs1.txt to videos_prepared/vhs1.txt
Moviepy - Building video videos_prepared/vhs1.mp4.
Moviepy - Writing video videos_prepared/vhs1.mp4
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Moviepy - Done !
Moviepy - video ready videos_prepared/vhs1.mp4
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Processing: videos_input/vhs2.mp4
Copied videos_input/vhs2.txt to videos_prepared/vhs2.txt
Moviepy - Building video videos_prepared/vhs2.mp4.
Moviepy - Writing video videos_prepared/vhs2.mp4
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Moviepy - Done !
Moviepy - video ready videos_prepared/vhs2.mp4
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Processing: videos_input/vhs3.mp4
Copied videos_input/vhs3.txt to videos_prepared/vhs3.txt
Moviepy - Building video videos_prepared/vhs3.mp4.
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Moviepy - Done !
Moviepy - video ready videos_prepared/vhs3.mp4
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Processing: videos_input/vhs4.mp4
Copied videos_input/vhs4.txt to videos_prepared/vhs4.txt
Moviepy - Building video videos_prepared/vhs4.mp4.
Moviepy - Writing video videos_prepared/vhs4.mp4
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Moviepy - Done !
Moviepy - video ready videos_prepared/vhs4.mp4
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---Starting to Embed videos---
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Processing videos_prepared/vhs1.mp4
Trimmed video from 40 to first 37 frames
0it [00:00, ?it/s]
Processing videos_prepared/vhs2.mp4
Trimmed video from 40 to first 37 frames
1it [00:01, 1.38s/it]
Processing videos_prepared/vhs3.mp4
Trimmed video from 40 to first 37 frames
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Processing videos_prepared/vhs4.mp4
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---Starting training---
Found 4 training videos in videos_prepared
Loaded 4/4 valid file pairs.
===== Memory before training =====
memory_allocated=18.780 GB
max_memory_allocated=18.780 GB
max_memory_reserved=19.250 GB
***** Running training *****
Num trainable parameters = 19005440
Num examples = 4
Num batches each epoch = 4
Num epochs = 125
Instantaneous batch size per device = 1
Total train batch size (w. parallel, distributed & accumulation) = 1
Total optimization steps = 500
Steps: 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.
W1211 17:57:31.089000 135012609543680 torch/fx/experimental/symbolic_shapes.py:4449] [0/0] xindex is not in var_ranges, defaulting to unknown range.
W1211 17:57:31.224000 135012609543680 torch/fx/experimental/symbolic_shapes.py:4449] [0/0] xindex is not in var_ranges, defaulting to unknown range.
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---Tar up output directory---
mochi-lora/
mochi-lora/pytorch_lora_weights.safetensors
Uploading to Hugging Face: lucataco/mochi-lora-vhs
HF Repo URL: https://huggingface.co/lucataco/mochi-lora-vhs
pytorch_lora_weights.safetensors: 0%| | 0.00/76.1M [00:00<?, ?B/s]
pytorch_lora_weights.safetensors: 10%|▉ | 7.34M/76.1M [00:00<00:00, 73.4MB/s]
pytorch_lora_weights.safetensors: 21%|██ | 16.0M/76.1M [00:00<00:01, 42.3MB/s]
pytorch_lora_weights.safetensors: 42%|████▏ | 32.0M/76.1M [00:00<00:00, 46.4MB/s]
pytorch_lora_weights.safetensors: 63%|██████▎ | 48.0M/76.1M [00:00<00:00, 54.6MB/s]
pytorch_lora_weights.safetensors: 84%|████████▍ | 64.0M/76.1M [00:01<00:00, 57.3MB/s]
pytorch_lora_weights.safetensors: 100%|██████████| 76.1M/76.1M [00:01<00:00, 54.6MB/s]
Successfully uploaded model to https://huggingface.co/lucataco/mochi-lora-vhs