Create tangoflux

tangoflux

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+from diffusers import AutoencoderOobleck
+import torch
+from transformers  import T5EncoderModel,T5TokenizerFast
+from diffusers import   FluxTransformer2DModel
+from torch import nn
+from typing import List
+from diffusers import FlowMatchEulerDiscreteScheduler
+from diffusers.training_utils import compute_density_for_timestep_sampling
+import copy
+import torch.nn.functional as F
+import numpy as np
+from src.model import TangoFlux
+from huggingface_hub import snapshot_download
+from tqdm import tqdm
+from typing import Optional,Union,List
+from datasets import load_dataset, Audio
+from math import pi
+import json
+import inspect
+import yaml
+from safetensors.torch import load_file
+
+
+class TangoFluxInference:
+
+    def __init__(self,name='declare-lab/TangoFlux',device="cuda"):
+        
+
+        self.vae = AutoencoderOobleck()
+
+        paths = snapshot_download(repo_id=name) 
+        vae_weights = load_file("{}/vae.safetensors".format(paths))
+        self.vae.load_state_dict(vae_weights)
+        weights = load_file("{}/tangoflux.safetensors".format(paths))
+
+        with open('{}/config.json'.format(paths),'r') as f:
+            config = json.load(f)
+        self.model = TangoFlux(config)
+        self.model.load_state_dict(weights,strict=False)
+        # _IncompatibleKeys(missing_keys=['text_encoder.encoder.embed_tokens.weight'], unexpected_keys=[]) this behaviour is expected 
+        self.vae.to(device)
+        self.model.to(device)
+    
+    def generate(self,prompt,steps=25,duration=10,guidance_scale=4.5):
+        
+        with torch.no_grad():
+            latents = self.model.inference_flow(prompt,
+            duration=duration,
+            num_inference_steps=steps,
+            guidance_scale=guidance_scale)
+
+
+
+            wave = self.vae.decode(latents.transpose(2,1)).sample.cpu()[0]
+        waveform_end = int(duration * self.vae.config.sampling_rate)
+        wave =  wave[:, :waveform_end]
+        return wave
+
+
+
+