File size: 7,051 Bytes
b14067d |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 |
import gc
import os
import numpy as np
import torch
from diffusers.training_utils import set_seed
from fire import Fire
from depthcrafter.depth_crafter_ppl import DepthCrafterPipeline
from depthcrafter.unet import DiffusersUNetSpatioTemporalConditionModelDepthCrafter
from depthcrafter.utils import vis_sequence_depth, save_video, read_video_frames
class DepthCrafterDemo:
def __init__(
self,
unet_path: str,
pre_train_path: str,
cpu_offload: str = "model",
):
unet = DiffusersUNetSpatioTemporalConditionModelDepthCrafter.from_pretrained(
unet_path,
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
)
# load weights of other components from the provided checkpoint
self.pipe = DepthCrafterPipeline.from_pretrained(
pre_train_path,
unet=unet,
torch_dtype=torch.float16,
variant="fp16",
)
# for saving memory, we can offload the model to CPU, or even run the model sequentially to save more memory
if cpu_offload is not None:
if cpu_offload == "sequential":
# This will slow, but save more memory
self.pipe.enable_sequential_cpu_offload()
elif cpu_offload == "model":
self.pipe.enable_model_cpu_offload()
else:
raise ValueError(f"Unknown cpu offload option: {cpu_offload}")
else:
self.pipe.to("cuda")
# enable attention slicing and xformers memory efficient attention
try:
self.pipe.enable_xformers_memory_efficient_attention()
except Exception as e:
print(e)
print("Xformers is not enabled")
self.pipe.enable_attention_slicing()
def infer(
self,
video: str,
num_denoising_steps: int,
guidance_scale: float,
save_folder: str = "./demo_output",
window_size: int = 110,
process_length: int = 195,
overlap: int = 25,
max_res: int = 1024,
dataset: str = "open",
target_fps: int = 15,
seed: int = 42,
track_time: bool = True,
save_npz: bool = False,
save_exr: bool = False,
):
set_seed(seed)
frames, target_fps = read_video_frames(
video,
process_length,
target_fps,
max_res,
dataset,
)
# inference the depth map using the DepthCrafter pipeline
with torch.inference_mode():
res = self.pipe(
frames,
height=frames.shape[1],
width=frames.shape[2],
output_type="np",
guidance_scale=guidance_scale,
num_inference_steps=num_denoising_steps,
window_size=window_size,
overlap=overlap,
track_time=track_time,
).frames[0]
# convert the three-channel output to a single channel depth map
res = res.sum(-1) / res.shape[-1]
# normalize the depth map to [0, 1] across the whole video
res = (res - res.min()) / (res.max() - res.min())
# visualize the depth map and save the results
vis = vis_sequence_depth(res)
# save the depth map and visualization with the target FPS
save_path = os.path.join(
save_folder, os.path.splitext(os.path.basename(video))[0]
)
os.makedirs(os.path.dirname(save_path), exist_ok=True)
save_video(res, save_path + "_depth.mp4", fps=target_fps)
save_video(vis, save_path + "_vis.mp4", fps=target_fps)
save_video(frames, save_path + "_input.mp4", fps=target_fps)
if save_npz:
np.savez_compressed(save_path + ".npz", depth=res)
if save_exr:
import OpenEXR
import Imath
os.makedirs(save_path, exist_ok=True)
print(f"==> saving EXR results to {save_path}")
# Iterate over each frame and save as a separate EXR file
for i, frame in enumerate(res):
output_exr = f"{save_path}/frame_{i:04d}.exr"
# Prepare EXR header for each frame
header = OpenEXR.Header(frame.shape[1], frame.shape[0])
header["channels"] = {
"Z": Imath.Channel(Imath.PixelType(Imath.PixelType.FLOAT))
}
# Create EXR file and write the frame
exr_file = OpenEXR.OutputFile(output_exr, header)
exr_file.writePixels({"Z": frame.tobytes()})
exr_file.close()
return [
save_path + "_input.mp4",
save_path + "_vis.mp4",
save_path + "_depth.mp4",
]
def run(
self,
input_video,
num_denoising_steps,
guidance_scale,
max_res=1024,
process_length=195,
):
res_path = self.infer(
input_video,
num_denoising_steps,
guidance_scale,
max_res=max_res,
process_length=process_length,
)
# clear the cache for the next video
gc.collect()
torch.cuda.empty_cache()
return res_path[:2]
def main(
video_path: str,
save_folder: str = "./demo_output",
unet_path: str = "tencent/DepthCrafter",
pre_train_path: str = "stabilityai/stable-video-diffusion-img2vid-xt",
process_length: int = -1,
cpu_offload: str = "model",
target_fps: int = -1,
seed: int = 42,
num_inference_steps: int = 5,
guidance_scale: float = 1.0,
window_size: int = 110,
overlap: int = 25,
max_res: int = 1024,
dataset: str = "open",
save_npz: bool = False,
save_exr: bool = False,
track_time: bool = False,
):
depthcrafter_demo = DepthCrafterDemo(
unet_path=unet_path,
pre_train_path=pre_train_path,
cpu_offload=cpu_offload,
)
# process the videos, the video paths are separated by comma
video_paths = video_path.split(",")
for video in video_paths:
depthcrafter_demo.infer(
video,
num_inference_steps,
guidance_scale,
save_folder=save_folder,
window_size=window_size,
process_length=process_length,
overlap=overlap,
max_res=max_res,
dataset=dataset,
target_fps=target_fps,
seed=seed,
track_time=track_time,
save_npz=save_npz,
save_exr=save_exr,
)
# clear the cache for the next video
gc.collect()
torch.cuda.empty_cache()
if __name__ == "__main__":
# running configs
# the most important arguments for memory saving are `cpu_offload`, `enable_xformers`, `max_res`, and `window_size`
# the most important arguments for trade-off between quality and speed are
# `num_inference_steps`, `guidance_scale`, and `max_res`
Fire(main)
|