worker_runpod.py

import os, json, requests, runpod

import torch
import torchaudio
from einops import rearrange
from stable_audio_tools import get_pretrained_model
from stable_audio_tools.inference.generation import generate_diffusion_cond

discord_token = os.getenv('com_camenduru_discord_token')
web_uri = os.getenv('com_camenduru_web_uri')
web_token = os.getenv('com_camenduru_web_token')

with torch.inference_mode():
    device = "cuda" if torch.cuda.is_available() else "cpu"
    model, model_config = get_pretrained_model("audo/stable-audio-open-1.0")
    sample_rate = model_config["sample_rate"]
    sample_size = model_config["sample_size"]
    model = model.to(device)

@torch.inference_mode()
def generate(input):
    values = input["input"]

    prompt = values['prompt']
    seconds_start = values['seconds_start']
    seconds_total = values['seconds_total']
    steps = values['steps']
    cfg_scale = values['cfg_scale']
    sigma_min = values['sigma_min']
    sigma_max = values['sigma_max']
    sampler_type = values['sampler_type']
    
    conditioning = [{
        "prompt": prompt,
        "seconds_start": seconds_start, 
        "seconds_total": seconds_total
    }]
    output = generate_diffusion_cond(
        model,
        steps=steps,
        cfg_scale=cfg_scale,
        conditioning=conditioning,
        sample_size=sample_size,
        sigma_min=sigma_min,
        sigma_max=sigma_max,
        sampler_type=sampler_type,
        device=device
    )
    output = rearrange(output, "b d n -> d (b n)")
    output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
    max_length = sample_rate * seconds_total
    if output.shape[1] > max_length:
        output = output[:, :max_length]
    torchaudio.save("/content/output.wav", output, sample_rate)

    result = "/content/output.wav"
    try:
        notify_uri = values['notify_uri']
        del values['notify_uri']
        notify_token = values['notify_token']
        del values['notify_token']
        discord_id = values['discord_id']
        del values['discord_id']
        if(discord_id == "discord_id"):
            discord_id = os.getenv('com_camenduru_discord_id')
        discord_channel = values['discord_channel']
        del values['discord_channel']
        if(discord_channel == "discord_channel"):
            discord_channel = os.getenv('com_camenduru_discord_channel')
        discord_token = values['discord_token']
        del values['discord_token']
        if(discord_token == "discord_token"):
            discord_token = os.getenv('com_camenduru_discord_token')
        job_id = values['job_id']
        del values['job_id']
        # default_filename = os.path.basename(result)
        # with open(result, "rb") as file:
        #     files = {default_filename: file.read()}
        # payload = {"content": f"{json.dumps(values)} <@{discord_id}>"}
        # response = requests.post(
        #     f"https://discord.com/api/v9/channels/{discord_channel}/messages",
        #     data=payload,
        #     headers={"Authorization": f"Bot {discord_token}"},
        #     files=files
        # )
        # response.raise_for_status()
        # result_url = response.json()['attachments'][0]['url']
        with open(result, 'rb') as file:
            response = requests.post("https://upload.tost.ai/api/v1", files={'file': file})
        response.raise_for_status()
        result_url = response.text
        notify_payload = {"jobId": job_id, "result": result_url, "status": "DONE"}
        web_notify_uri = os.getenv('com_camenduru_web_notify_uri')
        web_notify_token = os.getenv('com_camenduru_web_notify_token')
        if(notify_uri == "notify_uri"):
            requests.post(web_notify_uri, data=json.dumps(notify_payload), headers={'Content-Type': 'application/json', "Authorization": web_notify_token})
        else:
            requests.post(web_notify_uri, data=json.dumps(notify_payload), headers={'Content-Type': 'application/json', "Authorization": web_notify_token})
            requests.post(notify_uri, data=json.dumps(notify_payload), headers={'Content-Type': 'application/json', "Authorization": notify_token})
        return {"jobId": job_id, "result": result_url, "status": "DONE"}
    except Exception as e:
        error_payload = {"jobId": job_id, "status": "FAILED"}
        try:
            if(notify_uri == "notify_uri"):
                requests.post(web_notify_uri, data=json.dumps(error_payload), headers={'Content-Type': 'application/json', "Authorization": web_notify_token})
            else:
                requests.post(web_notify_uri, data=json.dumps(error_payload), headers={'Content-Type': 'application/json', "Authorization": web_notify_token})
                requests.post(notify_uri, data=json.dumps(error_payload), headers={'Content-Type': 'application/json', "Authorization": notify_token})
        except:
            pass
        return {"jobId": job_id, "result": f"FAILED: {str(e)}", "status": "FAILED"}
    finally:
        if os.path.exists(result):
            os.remove(result)

runpod.serverless.start({"handler": generate})