update gitignore

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
-
-
-
-