# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Sample new images from a pre-trained DiT.
"""
import os
import sys
import math
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
from vlogger.STEB.model_transform import ip_scale_set, ip_transform_model, tca_transform_model
from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
def get_input(args):
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)
])
if input_path is not None:
print(f'loading video 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(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':
print("loading the input 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')
def auto_inpainting(args,
video_input,
masked_video,
mask,
prompt,
image,
vae,
text_encoder,
image_encoder,
diffusion,
model,
device,
):
image_prompt_embeds = None
if prompt is None:
prompt = ""
if image is not None:
clip_image = CLIPImageProcessor()(images=image, return_tensors="pt").pixel_values
clip_image_embeds = image_encoder(clip_image.to(device)).image_embeds
uncond_clip_image_embeds = torch.zeros_like(clip_image_embeds).to(device)
image_prompt_embeds = torch.cat([clip_image_embeds, uncond_clip_image_embeds], dim=0)
image_prompt_embeds = rearrange(image_prompt_embeds, '(b n) c -> b n c', b=2).contiguous()
model = ip_scale_set(model, args.ref_cfg_scale)
if args.use_fp16:
image_prompt_embeds = image_prompt_embeds.to(dtype=torch.float16)
b, f, c, h, w = video_input.shape
latent_h = video_input.shape[-2] // 8
latent_w = video_input.shape[-1] // 8
if args.use_fp16:
z = torch.randn(1, 4, 16, latent_h, 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, 16, latent_h, 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)
masked_video = torch.cat([masked_video] * 2)
mask = torch.cat([mask] * 2)
z = torch.cat([z] * 2)
prompt_all = [prompt] + [args.negative_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,
ip_hidden_states=image_prompt_embeds)
# Sample images:
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=True
)
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 main(args):
# Setup PyTorch:
if args.seed:
torch.manual_seed(args.seed)
torch.set_grad_enabled(False)
device = "cuda" if torch.cuda.is_available() else "cpu"
# device = "cpu"
if args.ckpt is None:
raise ValueError("Please specify a checkpoint path using --ckpt <path>")
# Load model:
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)
model = tca_transform_model(model).to(device)
model = ip_transform_model(model).to(device)
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
model.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
# load model
ckpt_path = args.ckpt
state_dict = torch.load(ckpt_path, map_location=lambda storage, loc: storage)['ema']
model_dict = model.state_dict()
pretrained_dict = {}
for k, v in state_dict.items():
if k in model_dict:
pretrained_dict[k] = v
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
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)
image_encoder = CLIPVisionModelWithProjection.from_pretrained(args.image_encoder_path).to(device)
if args.use_fp16:
print('Warnning: using half percision for inferencing!')
vae.to(dtype=torch.float16)
model.to(dtype=torch.float16)
text_encoder.to(dtype=torch.float16)
# prompt:
prompt = args.text_prompt
if prompt ==[]:
prompt = args.input_path.split('/')[-1].split('.')[0].replace('_', ' ')
else:
prompt = prompt[0]
prompt_base = prompt.replace(' ','_')
prompt = prompt + args.additional_prompt
if not os.path.exists(os.path.join(args.save_path)):
os.makedirs(os.path.join(args.save_path))
video_input, researve_frames = get_input(args) # f,c,h,w
video_input = video_input.to(device).unsqueeze(0) # b,f,c,h,w
mask = mask_generation_before(args.mask_type, video_input.shape, video_input.dtype, device) # b,f,c,h,w
masked_video = video_input * (mask == 0)
pil_image = Image.open(args.ref_path)
pil_image.resize((256, 256))
video_clip = auto_inpainting(args,
video_input,
masked_video,
mask,
prompt,
pil_image,
vae,
text_encoder,
image_encoder,
diffusion,
model,
device,
)
video_ = ((video_clip * 0.5 + 0.5) * 255).add_(0.5).clamp_(0, 255).to(dtype=torch.uint8).cpu().permute(0, 2, 3, 1)
save_video_path = os.path.join(args.save_path, prompt_base+ '.mp4')
torchvision.io.write_video(save_video_path, video_, fps=8)
print(f'save in {save_video_path}')
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default="configs/with_mask_ref_sample.yaml")
args = parser.parse_args()
omega_conf = OmegaConf.load(args.config)
main(omega_conf)