File size: 6,631 Bytes
9a7fe1f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 |
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as tvtrans
from einops import rearrange
import pytorch_lightning as pl
from . import get_model
from ..cfg_helper import model_cfg_bank
from ..common.utils import regularize_image, regularize_video, remove_duplicate_word
import warnings
warnings.filterwarnings("ignore")
class model_module(pl.LightningModule):
def __init__(self, model='codi', load_weights=True, data_dir='pretrained', pth=["CoDi_encoders.pth"], fp16=False):
super().__init__()
# import torch
# device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
cfgm = model_cfg_bank()(model)
net = get_model()(cfgm)
if fp16:
net = net.half()
if load_weights:
for path in pth:
net.load_state_dict(torch.load(os.path.join(data_dir, path), map_location='cpu'), strict=False)
print('Load pretrained weight from {}'.format(pth))
self.net = net
from core.models.ddim.ddim_vd import DDIMSampler_VD
self.sampler = DDIMSampler_VD(net)
def decode(self, z, xtype):
# import torch
# device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
device = z.device
net = self.net
z = z.to(device)
if xtype == 'image':
x = net.autokl_decode(z)
x = torch.clamp((x+1.0)/2.0, min=0.0, max=1.0)
# x = [tvtrans.ToPILImage()(xi) for xi in x]
return x
elif xtype == 'video':
num_frames = z.shape[2]
z = rearrange(z, 'b c f h w -> (b f) c h w')
x = net.autokl_decode(z)
x = rearrange(x, '(b f) c h w -> b f c h w', f=num_frames)
x = torch.clamp((x+1.0)/2.0, min=0.0, max=1.0)
video_list = []
for video in x:
video_list.append([tvtrans.ToPILImage()(xi) for xi in video])
return video_list
elif xtype == 'text':
prompt_temperature = 1.0
prompt_merge_same_adj_word = True
x = net.optimus_decode(z, temperature=prompt_temperature)
"""
if prompt_merge_same_adj_word:
xnew = []
for xi in x:
xi_split = xi.split()
xinew = []
for idxi, wi in enumerate(xi_split):
if idxi!=0 and wi==xi_split[idxi-1]:
continue
xinew.append(wi)
xnew.append(remove_duplicate_word(' '.join(xinew)))
x = xnew
"""
return x
elif xtype == 'audio':
x = net.audioldm_decode(z)
x = net.mel_spectrogram_to_waveform(x)
return x
def forward(self, xtype=[], condition=[], condition_types=[], n_samples=1, mix_weight={'video': 1, 'audio': 1, 'text': 1, 'image': 1}, image_size=256, ddim_steps=50, scale=7.5, num_frames=8):
# import torch
# device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
device = self.device
net = self.net
sampler = self.sampler
ddim_eta = 0.0
conditioning = []
assert len(set(condition_types)) == len(condition_types), "we don't support condition with same modalities yet."
assert len(condition) == len(condition_types)
for i, condition_type in enumerate(condition_types):
if condition_type == 'image':
print(condition[i].shape)
ctemp1 = regularize_image(condition[i]).squeeze().to(device)
print(ctemp1.shape)
ctemp1 = ctemp1[None].repeat(n_samples, 1, 1, 1)
cim = net.clip_encode_vision(ctemp1).to(device)
uim = None
if scale != 1.0:
dummy = torch.zeros_like(ctemp1).to(device)
uim = net.clip_encode_vision(dummy).to(device)
conditioning.append(torch.cat([uim, cim]))
elif condition_type == 'video':
ctemp1 = regularize_video(condition[i]).to(device)
ctemp1 = ctemp1[None].repeat(n_samples, 1, 1, 1, 1)
cim = net.clip_encode_vision(ctemp1).to(device)
uim = None
if scale != 1.0:
dummy = torch.zeros_like(ctemp1).to(device)
uim = net.clip_encode_vision(dummy).to(device)
conditioning.append(torch.cat([uim, cim]))
elif condition_type == 'audio':
ctemp = condition[i][None].repeat(n_samples, 1, 1)
cad = net.clap_encode_audio(ctemp)
uad = None
if scale != 1.0:
dummy = torch.zeros_like(ctemp)
uad = net.clap_encode_audio(dummy)
conditioning.append(torch.cat([uad, cad]))
elif condition_type == 'text':
ctx = net.clip_encode_text(n_samples * [condition[i]]).to(device)
utx = None
if scale != 1.0:
utx = net.clip_encode_text(n_samples * [""]).to(device)
conditioning.append(torch.cat([utx, ctx]))
shapes = []
for xtype_i in xtype:
if xtype_i == 'image':
h, w = [image_size, image_size]
shape = [n_samples, 4, h//8, w//8]
elif xtype_i == 'video':
h, w = [image_size, image_size]
shape = [n_samples, 4, num_frames, h//8, w//8]
elif xtype_i == 'text':
n = 768
shape = [n_samples, n]
elif xtype_i == 'audio':
h, w = [256, 16]
shape = [n_samples, 8, h, w]
else:
raise
shapes.append(shape)
z, _ = sampler.sample(
steps=ddim_steps,
shape=shapes,
condition=conditioning,
unconditional_guidance_scale=scale,
xtype=xtype,
condition_types=condition_types,
eta=ddim_eta,
verbose=False,
mix_weight=mix_weight)
out_all = []
for i, xtype_i in enumerate(xtype):
z[i] = z[i].to(device)
x_i = self.decode(z[i], xtype_i)
out_all.append(x_i)
return out_all
|