Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,661 Bytes
a3a3ae4 cb40006 a3a3ae4 |
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 |
from typing import List
from PIL import Image
import numpy as np
import math
import random
import cv2
from typing import List
import torch
import einops
from pytorch_lightning import seed_everything
from transparent_background import Remover
from dataset.opencv_transforms.functional import to_tensor, center_crop
from vtdm.model import create_model
from vtdm.util import tensor2vid
remover = Remover(jit=False)
def pil_to_cv2(pil_image: Image.Image) -> np.ndarray:
cv_image = np.array(pil_image)
cv_image = cv2.cvtColor(cv_image, cv2.COLOR_RGB2BGR)
return cv_image
def prepare_white_image(input_image: Image.Image) -> Image.Image:
# remove bg
output = remover.process(input_image, type='rgba')
# expand image
width, height = output.size
max_side = max(width, height)
white_image = Image.new('RGBA', (max_side, max_side), (0, 0, 0, 0))
x_offset = (max_side - width) // 2
y_offset = (max_side - height) // 2
white_image.paste(output, (x_offset, y_offset))
return white_image
class MultiViewGenerator:
def __init__(self, checkpoint_path, config_path="inference.yaml"):
self.models = {}
denoising_model = create_model(config_path).cpu()
denoising_model.init_from_ckpt(checkpoint_path)
denoising_model = denoising_model.cuda().half()
self.models["denoising_model"] = denoising_model
def denoising(self, frames, args):
with torch.no_grad():
C, T, H, W = frames.shape
batch = {"video": frames.unsqueeze(0)}
batch["elevation"] = (
torch.Tensor([args["elevation"]]).to(torch.int64).to(frames.device)
)
batch["fps_id"] = torch.Tensor([7]).to(torch.int64).to(frames.device)
batch["motion_bucket_id"] = (
torch.Tensor([127]).to(torch.int64).to(frames.device)
)
batch = self.models["denoising_model"].add_custom_cond(batch, infer=True)
with torch.autocast(device_type="cuda", dtype=torch.float16):
c, uc = self.models[
"denoising_model"
].conditioner.get_unconditional_conditioning(
batch,
force_uc_zero_embeddings=["cond_frames", "cond_frames_without_noise"],
)
additional_model_inputs = {
"image_only_indicator": torch.zeros(2, T).to(
self.models["denoising_model"].device
),
"num_video_frames": batch["num_video_frames"],
}
def denoiser(input, sigma, c):
return self.models["denoising_model"].denoiser(
self.models["denoising_model"].model,
input,
sigma,
c,
**additional_model_inputs
)
with torch.autocast(device_type="cuda", dtype=torch.float16):
randn = torch.randn(
[T, 4, H // 8, W // 8], device=self.models["denoising_model"].device
)
samples = self.models["denoising_model"].sampler(denoiser, randn, cond=c, uc=uc)
samples = self.models["denoising_model"].decode_first_stage(samples.half())
samples = einops.rearrange(samples, "(b t) c h w -> b c t h w", t=T)
return tensor2vid(samples)
def video_pipeline(self, frames, args) -> List[Image.Image]:
num_iter = args["num_iter"]
out_list = []
for _ in range(num_iter):
with torch.no_grad():
results = self.denoising(frames, args)
if len(out_list) == 0:
out_list = out_list + results
else:
out_list = out_list + results[1:]
img = out_list[-1]
img = to_tensor(img)
img = (img - 0.5) * 2.0
frames[:, 0] = img
result = []
for i, frame in enumerate(out_list):
input_image = Image.fromarray(frame)
output_image = remover.process(input_image, type='rgba')
result.append(output_image)
return result
def process(self, white_image: Image.Image, args) -> List[Image.Image]:
img = pil_to_cv2(white_image)
frame_list = [img] * args["clip_size"]
h, w = frame_list[0].shape[0:2]
rate = max(
args["input_resolution"][0] * 1.0 / h, args["input_resolution"][1] * 1.0 / w
)
frame_list = [
cv2.resize(f, [math.ceil(w * rate), math.ceil(h * rate)]) for f in frame_list
]
frame_list = [
center_crop(f, [args["input_resolution"][0], args["input_resolution"][1]])
for f in frame_list
]
frame_list = [cv2.cvtColor(f, cv2.COLOR_BGR2RGB) for f in frame_list]
frame_list = [to_tensor(f) for f in frame_list]
frame_list = [(f - 0.5) * 2.0 for f in frame_list]
frames = torch.stack(frame_list, 1)
frames = frames.cuda()
self.models["denoising_model"].num_samples = args["clip_size"]
self.models["denoising_model"].image_size = args["input_resolution"]
return self.video_pipeline(frames, args)
def infer(self, white_image: Image.Image) -> List[Image.Image]:
seed = random.randint(0, 65535)
seed_everything(seed)
params = {
"clip_size": 25,
"input_resolution": [512, 512],
"num_iter": 1,
"aes": 6.0,
"mv": [0.0, 0.0, 0.0, 10.0],
"elevation": 0,
}
return self.process(white_image, params)
|