Prediction

Model

gougouccnu/stable-audio-open-1.0:0f69810d

vsaagqva15rg80cm8jfaafk59r

Status

Succeeded

Source

Web

Hardware

Total duration

6m 11s

Created

11 months ago by gougouccnu

Webhook

–

Input

prompt: A toilet flushing.
negative_prompt: Empty string
seconds_start: 0
seconds_total: 8
cfg_scale: 6
steps: 100
seed: -1
sampler_type: dpmpp-3m-sde
sigma_min: 0.03
sigma_max: 500
init_noise_level: 1
batch_size: 1

{
  "batch_size": 1,
  "cfg_scale": 6,
  "init_noise_level": 1,
  "negative_prompt": "",
  "prompt": "A toilet flushing.",
  "sampler_type": "dpmpp-3m-sde",
  "seconds_start": 0,
  "seconds_total": 8,
  "seed": -1,
  "sigma_max": 500,
  "sigma_min": 0.03,
  "steps": 100
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_KMw**********************************

This is your API token. Keep it to yourself.

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 gougouccnu/stable-audio-open-1.0 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "gougouccnu/stable-audio-open-1.0:0f69810d42dd9098b8d20d0c8db087fbfd5ccd1704eef0a99a48c7325ab7fc3a",
  {
    input: {
      batch_size: 1,
      cfg_scale: 6,
      init_noise_level: 1,
      negative_prompt: "",
      prompt: "A toilet flushing.",
      sampler_type: "dpmpp-3m-sde",
      seconds_start: 0,
      seconds_total: 8,
      seed: -1,
      sigma_max: 500,
      sigma_min: 0.03,
      steps: 100
    }
  }
);

// 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

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_KMw**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run gougouccnu/stable-audio-open-1.0 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "gougouccnu/stable-audio-open-1.0:0f69810d42dd9098b8d20d0c8db087fbfd5ccd1704eef0a99a48c7325ab7fc3a",
    input={
        "batch_size": 1,
        "cfg_scale": 6,
        "init_noise_level": 1,
        "negative_prompt": "",
        "prompt": "A toilet flushing.",
        "sampler_type": "dpmpp-3m-sde",
        "seconds_start": 0,
        "seconds_total": 8,
        "seed": -1,
        "sigma_max": 500,
        "sigma_min": 0.03,
        "steps": 100
    }
)

# To access the file URL:
print(output.url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output.read())

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=r8_KMw**********************************

This is your API token. Keep it to yourself.

Run gougouccnu/stable-audio-open-1.0 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": "gougouccnu/stable-audio-open-1.0:0f69810d42dd9098b8d20d0c8db087fbfd5ccd1704eef0a99a48c7325ab7fc3a",
    "input": {
      "batch_size": 1,
      "cfg_scale": 6,
      "init_noise_level": 1,
      "negative_prompt": "",
      "prompt": "A toilet flushing.",
      "sampler_type": "dpmpp-3m-sde",
      "seconds_start": 0,
      "seconds_total": 8,
      "seed": -1,
      "sigma_max": 500,
      "sigma_min": 0.03,
      "steps": 100
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "id": "vsaagqva15rg80cm8jfaafk59r",
  "model": "gougouccnu/stable-audio-open-1.0",
  "version": "0f69810d42dd9098b8d20d0c8db087fbfd5ccd1704eef0a99a48c7325ab7fc3a",
  "input": {
    "batch_size": 1,
    "cfg_scale": 6,
    "init_noise_level": 1,
    "negative_prompt": "",
    "prompt": "A toilet flushing.",
    "sampler_type": "dpmpp-3m-sde",
    "seconds_start": 0,
    "seconds_total": 8,
    "seed": -1,
    "sigma_max": 500,
    "sigma_min": 0.03,
    "steps": 100
  },
  "logs": "Prompt: A toilet flushing.\n2887216820\n/src/stable_audio_tools/models/conditioners.py:314: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\nwith torch.cuda.amp.autocast(dtype=torch.float16) and torch.set_grad_enabled(self.enable_grad):\n/src/stable_audio_tools/inference/sampling.py:177: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\nwith torch.cuda.amp.autocast():\n  0%|          | 0/100 [00:00<?, ?it/s]/root/.pyenv/versions/3.10.15/lib/python3.10/contextlib.py:103: FutureWarning: `torch.backends.cuda.sdp_kernel()` is deprecated. In the future, this context manager will be removed. Please see `torch.nn.attention.sdpa_kernel()` for the new context manager, with updated signature.\nself.gen = func(*args, **kwds)\n  1%|          | 1/100 [00:00<00:23,  4.27it/s]\n  3%|▎         | 3/100 [00:00<00:11,  8.17it/s]\n  5%|▌         | 5/100 [00:00<00:09, 10.50it/s]\n  7%|▋         | 7/100 [00:00<00:07, 11.79it/s]\n  9%|▉         | 9/100 [00:00<00:07, 12.69it/s]\n 11%|█         | 11/100 [00:00<00:06, 13.32it/s]\n 13%|█▎        | 13/100 [00:01<00:06, 13.46it/s]\n 15%|█▌        | 15/100 [00:01<00:06, 13.65it/s]\n 17%|█▋        | 17/100 [00:01<00:05, 14.15it/s]\n 19%|█▉        | 19/100 [00:01<00:05, 14.50it/s]\n 21%|██        | 21/100 [00:01<00:05, 14.73it/s]\n 23%|██▎       | 23/100 [00:01<00:05, 14.91it/s]\n 25%|██▌       | 25/100 [00:01<00:05, 14.90it/s]\n 27%|██▋       | 27/100 [00:02<00:04, 14.98it/s]\n 29%|██▉       | 29/100 [00:02<00:04, 14.95it/s]\n 31%|███       | 31/100 [00:02<00:04, 15.03it/s]\n 33%|███▎      | 33/100 [00:02<00:04, 15.05it/s]\n 35%|███▌      | 35/100 [00:02<00:04, 15.13it/s]\n 37%|███▋      | 37/100 [00:02<00:04, 15.13it/s]\n 39%|███▉      | 39/100 [00:02<00:04, 15.15it/s]\n 41%|████      | 41/100 [00:02<00:03, 15.20it/s]\n 43%|████▎     | 43/100 [00:03<00:03, 15.31it/s]\n 45%|████▌     | 45/100 [00:03<00:03, 15.23it/s]\n 47%|████▋     | 47/100 [00:03<00:03, 15.33it/s]\n 49%|████▉     | 49/100 [00:03<00:03, 15.30it/s]\n 51%|█████     | 51/100 [00:03<00:03, 15.38it/s]\n 53%|█████▎    | 53/100 [00:03<00:03, 15.29it/s]\n 55%|█████▌    | 55/100 [00:03<00:02, 15.31it/s]\n 57%|█████▋    | 57/100 [00:03<00:02, 15.25it/s]\n 59%|█████▉    | 59/100 [00:04<00:02, 15.20it/s]\n 61%|██████    | 61/100 [00:04<00:02, 15.03it/s]\n 63%|██████▎   | 63/100 [00:04<00:02, 15.17it/s]\n 65%|██████▌   | 65/100 [00:04<00:02, 14.64it/s]\n 67%|██████▋   | 67/100 [00:04<00:02, 14.60it/s]\n 69%|██████▉   | 69/100 [00:04<00:02, 14.43it/s]\n 71%|███████   | 71/100 [00:04<00:01, 14.57it/s]\n 73%|███████▎  | 73/100 [00:05<00:01, 14.57it/s]\n 75%|███████▌  | 75/100 [00:05<00:01, 14.81it/s]\n 77%|███████▋  | 77/100 [00:05<00:01, 14.81it/s]\n 79%|███████▉  | 79/100 [00:05<00:01, 14.98it/s]\n 81%|████████  | 81/100 [00:05<00:01, 15.16it/s]\n 83%|████████▎ | 83/100 [00:05<00:01, 15.24it/s]\n 85%|████████▌ | 85/100 [00:05<00:00, 15.34it/s]\n 87%|████████▋ | 87/100 [00:06<00:00, 15.44it/s]\n 89%|████████▉ | 89/100 [00:06<00:00, 15.35it/s]\n 91%|█████████ | 91/100 [00:06<00:00, 15.42it/s]\n 93%|█████████▎| 93/100 [00:06<00:00, 15.37it/s]\n 95%|█████████▌| 95/100 [00:06<00:00, 15.34it/s]\n 97%|█████████▋| 97/100 [00:06<00:00, 15.43it/s]/root/.pyenv/versions/3.10.15/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:599: UserWarning: Should have ta>=t0 but got ta=0.029999999329447746 and t0=0.03.\nwarnings.warn(f\"Should have ta>=t0 but got ta={ta} and t0={self._start}.\")\n 99%|█████████▉| 99/100 [00:06<00:00, 15.43it/s]\n100%|██████████| 100/100 [00:06<00:00, 14.61it/s]",
  "output": "https://replicate.delivery/czjl/S1zDW6lofGTxHKaGE4iQPjY1wMnZkeEB4ExfAfIebMfhtPvAF/output.wav",
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2025-01-08T06:33:40.105Z",
  "started_at": "2025-01-08T06:39:43.159566Z",
  "completed_at": "2025-01-08T06:39:50.890854Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/vsaagqva15rg80cm8jfaafk59r/cancel",
    "get": "https://api.replicate.com/v1/predictions/vsaagqva15rg80cm8jfaafk59r",
    "stream": "https://stream.replicate.com/v1/files/fddq-krtvscrikk4erqado76o2g3jca7oki47cfvkprqrwzuaaq6k4ztq",
    "web": "https://replicate.com/p/vsaagqva15rg80cm8jfaafk59r"
  },
  "metrics": {
    "predict_time": 7.731288225,
    "total_time": 370.785854
  }
}

Generated in

7.7 seconds

Tweak it Report

Prompt: A toilet flushing. 2887216820 /src/stable_audio_tools/models/conditioners.py:314: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(dtype=torch.float16) and torch.set_grad_enabled(self.enable_grad): /src/stable_audio_tools/inference/sampling.py:177: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead. with torch.cuda.amp.autocast(): 0%| | 0/100 [00:00<?, ?it/s]/root/.pyenv/versions/3.10.15/lib/python3.10/contextlib.py:103: FutureWarning: `torch.backends.cuda.sdp_kernel()` is deprecated. In the future, this context manager will be removed. Please see `torch.nn.attention.sdpa_kernel()` for the new context manager, with updated signature. self.gen = func(*args, **kwds) 1%| | 1/100 [00:00<00:23, 4.27it/s] 3%|▎ | 3/100 [00:00<00:11, 8.17it/s] 5%|▌ | 5/100 [00:00<00:09, 10.50it/s] 7%|▋ | 7/100 [00:00<00:07, 11.79it/s] 9%|▉ | 9/100 [00:00<00:07, 12.69it/s] 11%|█ | 11/100 [00:00<00:06, 13.32it/s] 13%|█▎ | 13/100 [00:01<00:06, 13.46it/s] 15%|█▌ | 15/100 [00:01<00:06, 13.65it/s] 17%|█▋ | 17/100 [00:01<00:05, 14.15it/s] 19%|█▉ | 19/100 [00:01<00:05, 14.50it/s] 21%|██ | 21/100 [00:01<00:05, 14.73it/s] 23%|██▎ | 23/100 [00:01<00:05, 14.91it/s] 25%|██▌ | 25/100 [00:01<00:05, 14.90it/s] 27%|██▋ | 27/100 [00:02<00:04, 14.98it/s] 29%|██▉ | 29/100 [00:02<00:04, 14.95it/s] 31%|███ | 31/100 [00:02<00:04, 15.03it/s] 33%|███▎ | 33/100 [00:02<00:04, 15.05it/s] 35%|███▌ | 35/100 [00:02<00:04, 15.13it/s] 37%|███▋ | 37/100 [00:02<00:04, 15.13it/s] 39%|███▉ | 39/100 [00:02<00:04, 15.15it/s] 41%|████ | 41/100 [00:02<00:03, 15.20it/s] 43%|████▎ | 43/100 [00:03<00:03, 15.31it/s] 45%|████▌ | 45/100 [00:03<00:03, 15.23it/s] 47%|████▋ | 47/100 [00:03<00:03, 15.33it/s] 49%|████▉ | 49/100 [00:03<00:03, 15.30it/s] 51%|█████ | 51/100 [00:03<00:03, 15.38it/s] 53%|█████▎ | 53/100 [00:03<00:03, 15.29it/s] 55%|█████▌ | 55/100 [00:03<00:02, 15.31it/s] 57%|█████▋ | 57/100 [00:03<00:02, 15.25it/s] 59%|█████▉ | 59/100 [00:04<00:02, 15.20it/s] 61%|██████ | 61/100 [00:04<00:02, 15.03it/s] 63%|██████▎ | 63/100 [00:04<00:02, 15.17it/s] 65%|██████▌ | 65/100 [00:04<00:02, 14.64it/s] 67%|██████▋ | 67/100 [00:04<00:02, 14.60it/s] 69%|██████▉ | 69/100 [00:04<00:02, 14.43it/s] 71%|███████ | 71/100 [00:04<00:01, 14.57it/s] 73%|███████▎ | 73/100 [00:05<00:01, 14.57it/s] 75%|███████▌ | 75/100 [00:05<00:01, 14.81it/s] 77%|███████▋ | 77/100 [00:05<00:01, 14.81it/s] 79%|███████▉ | 79/100 [00:05<00:01, 14.98it/s] 81%|████████ | 81/100 [00:05<00:01, 15.16it/s] 83%|████████▎ | 83/100 [00:05<00:01, 15.24it/s] 85%|████████▌ | 85/100 [00:05<00:00, 15.34it/s] 87%|████████▋ | 87/100 [00:06<00:00, 15.44it/s] 89%|████████▉ | 89/100 [00:06<00:00, 15.35it/s] 91%|█████████ | 91/100 [00:06<00:00, 15.42it/s] 93%|█████████▎| 93/100 [00:06<00:00, 15.37it/s] 95%|█████████▌| 95/100 [00:06<00:00, 15.34it/s] 97%|█████████▋| 97/100 [00:06<00:00, 15.43it/s]/root/.pyenv/versions/3.10.15/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:599: UserWarning: Should have ta>=t0 but got ta=0.029999999329447746 and t0=0.03. warnings.warn(f"Should have ta>=t0 but got ta={ta} and t0={self._start}.") 99%|█████████▉| 99/100 [00:06<00:00, 15.43it/s] 100%|██████████| 100/100 [00:06<00:00, 14.61it/s]