added readme

inference.py

+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 tqdm import tqdm
+from typing import Optional,Union,List
+from datasets import load_dataset, Audio
+from math import pi
+import inspect
+import yaml
+from safetensors.torch import load_file
+
+
+class TangoFluxInference:
+
+    def __init__(self,path='declare-lab/TangoFlux',device="cuda"):
+        
+
+        self.vae = AutoencoderOobleck.from_pretrained("stabilityai/stable-audio-open-1.0",subfolder='vae')
+        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]
+        return wave
+
+
+
+

requirements.txt

+torch==2.4.1
+torchaudio==2.4.1
+torchlibrosa==0.1.0
+torchvision==0.19.1
+transformers==4.44.0
+diffusers==0.30.0
+accelerate==0.34.2
+datasets==2.21.0
+librosa
+tqdm
+wandb
+