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