replicate demo

README.md

@@ -12,7 +12,8 @@ TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching a
 
 <br/>
 
-[![arXiv](https://img.shields.io/badge/Read_the_Paper-blue?link=https%3A%2F%2Fopenreview.net%2Fattachment%3Fid%3DtpJPlFTyxd%26name%3Dpdf)](https://arxiv.org/abs/2412.21037) [![Static Badge](https://img.shields.io/badge/TangoFlux-Huggingface-violet?logo=huggingface&link=https%3A%2F%2Fhuggingface.co%2Fdeclare-lab%2FTangoFlux)](https://huggingface.co/declare-lab/TangoFlux) [![Static Badge](https://img.shields.io/badge/Demos-declare--lab-brightred?style=flat)](https://tangoflux.github.io/) [![Static Badge](https://img.shields.io/badge/TangoFlux-Huggingface_Space-8A2BE2?logo=huggingface&link=https%3A%2F%2Fhuggingface.co%2Fspaces%2Fdeclare-lab%2FTangoFlux)](https://huggingface.co/spaces/declare-lab/TangoFlux) [![Static Badge](https://img.shields.io/badge/TangoFlux_Dataset-Huggingface-red?logo=huggingface&link=https%3A%2F%2Fhuggingface.co%2Fdatasets%2Fdeclare-lab%2FTangoFlux)](https://huggingface.co/datasets/declare-lab/CRPO)
+[![arXiv](https://img.shields.io/badge/Read_the_Paper-blue?link=https%3A%2F%2Fopenreview.net%2Fattachment%3Fid%3DtpJPlFTyxd%26name%3Dpdf)](https://arxiv.org/abs/2412.21037) [![Static Badge](https://img.shields.io/badge/TangoFlux-Huggingface-violet?logo=huggingface&link=https%3A%2F%2Fhuggingface.co%2Fdeclare-lab%2FTangoFlux)](https://huggingface.co/declare-lab/TangoFlux) [![Static Badge](https://img.shields.io/badge/Demos-declare--lab-brightred?style=flat)](https://tangoflux.github.io/) [![Static Badge](https://img.shields.io/badge/TangoFlux-Huggingface_Space-8A2BE2?logo=huggingface&link=https%3A%2F%2Fhuggingface.co%2Fspaces%2Fdeclare-lab%2FTangoFlux)](https://huggingface.co/spaces/declare-lab/TangoFlux) [![Static Badge](https://img.shields.io/badge/TangoFlux_Dataset-Huggingface-red?logo=huggingface&link=https%3A%2F%2Fhuggingface.co%2Fdatasets%2Fdeclare-lab%2FTangoFlux)](https://huggingface.co/datasets/declare-lab/CRPO) [![Replicate](https://replicate.com/chenxwh/tangoflux/badge)](https://replicate.com/chenxwh/tangoflux)
+
 
 
 

replicate_demo/cog.yaml

+# Configuration for Cog ⚙️
+# Reference: https://cog.run/yaml
+
+build:
+  # set to true if your model requires a GPU
+  gpu: true
+
+  # a list of ubuntu apt packages to install
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+
+  # python version in the form '3.11' or '3.11.4'
+  python_version: "3.11"
+
+  # a list of packages in the format <package-name>==<version>
+  python_packages:
+    - torch==2.4.0
+    - torchaudio==2.4.0
+    - torchlibrosa==0.1.0
+    - torchvision==0.19.0
+    - transformers==4.44.0
+    - diffusers==0.30.0
+    - accelerate==0.34.2
+    - datasets==2.21.0
+    - librosa
+    - ipython
+
+  run:
+    - curl -o /usr/local/bin/pget -L "https://github.com/replicate/pget/releases/download/v0.6.0/pget_linux_x86_64" && chmod +x /usr/local/bin/pget
+predict: "predict.py:Predictor"

replicate_demo/predict.py

+# Prediction interface for Cog ⚙️
+# https://cog.run/python
+
+import os
+import subprocess
+import time
+import json
+from cog import BasePredictor, Input, Path
+from diffusers import AutoencoderOobleck
+import soundfile as sf
+from safetensors.torch import load_file
+from huggingface_hub import snapshot_download
+from src.model import TangoFlux
+from tangoflux import TangoFluxInference
+
+MODEL_CACHE = "model_cache"
+MODEL_URL = "https://weights.replicate.delivery/default/declare-lab/TangoFlux/model_cache.tar"
+
+
+class CachedTangoFluxInference(TangoFluxInference):
+    ## load the weights from replicate.delivery for faster booting
+    def __init__(self, name="declare-lab/TangoFlux", device="cuda", cached_paths=None):
+        if cached_paths:
+            paths = cached_paths
+        else:
+            paths = snapshot_download(repo_id=name)
+
+        self.vae = AutoencoderOobleck()
+        vae_weights = load_file(f"{paths}/vae.safetensors")
+        self.vae.load_state_dict(vae_weights)
+        weights = load_file(f"{paths}/tangoflux.safetensors")
+
+        with open(f"{paths}/config.json", "r") as f:
+            config = json.load(f)
+        self.model = TangoFlux(config)
+        self.model.load_state_dict(weights, strict=False)
+        self.vae.to(device)
+        self.model.to(device)
+
+
+def download_weights(url, dest):
+    start = time.time()
+    print("downloading url: ", url)
+    print("downloading to: ", dest)
+    subprocess.check_call(["pget", "-x", url, dest], close_fds=False)
+    print("downloading took: ", time.time() - start)
+
+
+class Predictor(BasePredictor):
+    def setup(self) -> None:
+        """Load the model into memory to make running multiple predictions efficient"""
+
+        if not os.path.exists(MODEL_CACHE):
+            print("downloading")
+            download_weights(MODEL_URL, MODEL_CACHE)
+
+        self.model = CachedTangoFluxInference(
+            cached_paths=f"{MODEL_CACHE}/declare-lab/TangoFlux"
+        )
+
+    def predict(
+        self,
+        prompt: str = Input(
+            description="Input prompt", default="Hammer slowly hitting the wooden table"
+        ),
+        duration: int = Input(
+            description="Duration of the output audio in seconds", default=10
+        ),
+        steps: int = Input(
+            description="Number of inference steps", ge=1, le=200, default=25
+        ),
+        guidance_scale: float = Input(
+            description="Scale for classifier-free guidance", ge=1, le=20, default=4.5
+        ),
+    ) -> Path:
+        """Run a single prediction on the model"""
+
+        audio = self.model.generate(
+            prompt,
+            steps=steps,
+            guidance_scale=guidance_scale,
+            duration=duration,
+        )
+        audio_numpy = audio.numpy()
+        out_path = "/tmp/out.wav"
+
+        sf.write(
+            out_path, audio_numpy.T, samplerate=self.model.vae.config.sampling_rate
+        )
+        return Path(out_path)