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import os
import sys
import math
import docx
try:
import utils
from diffusion import create_diffusion
except:
# sys.path.append(os.getcwd())
sys.path.append(os.path.split(sys.path[0])[0])
# sys.path[0]
# os.path.split(sys.path[0])
import utils
from diffusion import create_diffusion
import torch
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
import argparse
import torchvision
from einops import rearrange
from models import get_models
from torchvision.utils import save_image
from diffusers.models import AutoencoderKL
from models.clip import TextEmbedder
from omegaconf import OmegaConf
from PIL import Image
import numpy as np
from torchvision import transforms
sys.path.append("..")
from datasets import video_transforms
from utils import mask_generation_before
from natsort import natsorted
from diffusers.utils.import_utils import is_xformers_available
config_path = "configs/sample_i2v.yaml"
args = OmegaConf.load(config_path)
device = "cuda" if torch.cuda.is_available() else "cpu"
print(args)
def model_i2v_fun(args):
if args.seed:
torch.manual_seed(args.seed)
torch.set_grad_enabled(False)
if args.ckpt is None:
raise ValueError("Please specify a checkpoint path using --ckpt <path>")
latent_h = args.image_size[0] // 8
latent_w = args.image_size[1] // 8
args.image_h = args.image_size[0]
args.image_w = args.image_size[1]
args.latent_h = latent_h
args.latent_w = latent_w
print("loading model")
model = get_models(args).to(device)
if args.use_compile:
model = torch.compile(model)
ckpt_path = args.ckpt
state_dict = torch.load(ckpt_path, map_location=lambda storage, loc: storage)['ema']
model.load_state_dict(state_dict)
print('loading success')
model.eval()
pretrained_model_path = args.pretrained_model_path
diffusion = create_diffusion(str(args.num_sampling_steps))
vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae").to(device)
text_encoder = TextEmbedder(pretrained_model_path).to(device)
# if args.use_fp16:
# print('Warning: using half precision for inference')
# vae.to(dtype=torch.float16)
# model.to(dtype=torch.float16)
# text_encoder.to(dtype=torch.float16)
return vae, model, text_encoder, diffusion
def auto_inpainting(args, video_input, masked_video, mask, prompt, vae, text_encoder, diffusion, model, device,):
b,f,c,h,w=video_input.shape
latent_h = args.image_size[0] // 8
latent_w = args.image_size[1] // 8
# prepare inputs
if args.use_fp16:
z = torch.randn(1, 4, args.num_frames, args.latent_h, args.latent_w, dtype=torch.float16, device=device) # b,c,f,h,w
masked_video = masked_video.to(dtype=torch.float16)
mask = mask.to(dtype=torch.float16)
else:
z = torch.randn(1, 4, args.num_frames, args.latent_h, args.latent_w, device=device) # b,c,f,h,w
masked_video = rearrange(masked_video, 'b f c h w -> (b f) c h w').contiguous()
masked_video = vae.encode(masked_video).latent_dist.sample().mul_(0.18215)
masked_video = rearrange(masked_video, '(b f) c h w -> b c f h w', b=b).contiguous()
mask = torch.nn.functional.interpolate(mask[:,:,0,:], size=(latent_h, latent_w)).unsqueeze(1)
# classifier_free_guidance
if args.do_classifier_free_guidance:
masked_video = torch.cat([masked_video] * 2)
mask = torch.cat([mask] * 2)
z = torch.cat([z] * 2)
prompt_all = [prompt] + [args.negative_prompt]
else:
masked_video = masked_video
mask = mask
z = z
prompt_all = [prompt]
text_prompt = text_encoder(text_prompts=prompt_all, train=False)
model_kwargs = dict(encoder_hidden_states=text_prompt,
class_labels=None,
cfg_scale=args.cfg_scale,
use_fp16=args.use_fp16,) # tav unet
# Sample images:
if args.sample_method == 'ddim':
samples = diffusion.ddim_sample_loop(
model.forward_with_cfg, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=True, device=device, \
mask=mask, x_start=masked_video, use_concat=args.use_mask
)
elif args.sample_method == 'ddpm':
samples = diffusion.p_sample_loop(
model.forward_with_cfg, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=True, device=device, \
mask=mask, x_start=masked_video, use_concat=args.use_mask
)
samples, _ = samples.chunk(2, dim=0) # [1, 4, 16, 32, 32]
if args.use_fp16:
samples = samples.to(dtype=torch.float16)
video_clip = samples[0].permute(1, 0, 2, 3).contiguous() # [16, 4, 32, 32]
video_clip = vae.decode(video_clip / 0.18215).sample # [16, 3, 256, 256]
return video_clip
def get_input(path,args):
input_path = path
# input_path = args.input_path
transform_video = transforms.Compose([
video_transforms.ToTensorVideo(), # TCHW
video_transforms.ResizeVideo((args.image_h, args.image_w)),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)
])
temporal_sample_func = video_transforms.TemporalRandomCrop(args.num_frames * args.frame_interval)
if input_path is not None:
print(f'loading image from {input_path}')
if os.path.isdir(input_path):
file_list = os.listdir(input_path)
video_frames = []
if args.mask_type.startswith('onelast'):
num = int(args.mask_type.split('onelast')[-1])
# get first and last frame
first_frame_path = os.path.join(input_path, natsorted(file_list)[0])
last_frame_path = os.path.join(input_path, natsorted(file_list)[-1])
first_frame = torch.as_tensor(np.array(Image.open(first_frame_path), dtype=np.uint8, copy=True)).unsqueeze(0)
last_frame = torch.as_tensor(np.array(Image.open(last_frame_path), dtype=np.uint8, copy=True)).unsqueeze(0)
for i in range(num):
video_frames.append(first_frame)
# add zeros to frames
num_zeros = args.num_frames-2*num
for i in range(num_zeros):
zeros = torch.zeros_like(first_frame)
video_frames.append(zeros)
for i in range(num):
video_frames.append(last_frame)
n = 0
video_frames = torch.cat(video_frames, dim=0).permute(0, 3, 1, 2) # f,c,h,w
video_frames = transform_video(video_frames)
else:
for file in file_list:
if file.endswith('jpg') or file.endswith('png'):
image = torch.as_tensor(np.array(Image.open(os.path.join(input_path,file)), dtype=np.uint8, copy=True)).unsqueeze(0)
video_frames.append(image)
else:
continue
n = 0
video_frames = torch.cat(video_frames, dim=0).permute(0, 3, 1, 2) # f,c,h,w
video_frames = transform_video(video_frames)
return video_frames, n
elif os.path.isfile(input_path):
_, full_file_name = os.path.split(input_path)
file_name, extention = os.path.splitext(full_file_name)
if extention == '.jpg' or extention == '.png':
# raise TypeError('a single image is not supported yet!!')
print("reading video from a image")
video_frames = []
num = int(args.mask_type.split('first')[-1])
first_frame = torch.as_tensor(np.array(Image.open(input_path), dtype=np.uint8, copy=True)).unsqueeze(0)
for i in range(num):
video_frames.append(first_frame)
num_zeros = args.num_frames-num
for i in range(num_zeros):
zeros = torch.zeros_like(first_frame)
video_frames.append(zeros)
n = 0
video_frames = torch.cat(video_frames, dim=0).permute(0, 3, 1, 2) # f,c,h,w
video_frames = transform_video(video_frames)
return video_frames, n
else:
raise TypeError(f'{extention} is not supported !!')
else:
raise ValueError('Please check your path input!!')
else:
raise ValueError('Need to give a video or some images')
# print('given video is None, using text to video')
# video_frames = torch.zeros(16,3,args.latent_h,args.latent_w,dtype=torch.uint8)
# args.mask_type = 'first1'
# video_frames = transform_video(video_frames)
# n = 0
# return video_frames, n
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
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