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import gc
import logging
import os
from typing import List, Optional
import numpy as np
import torch
from diffusers.training_utils import set_seed
from depthcrafter.depth_crafter_ppl import DepthCrafterPipeline
from depthcrafter.unet import DiffusersUNetSpatioTemporalConditionModelDepthCrafter
from depthcrafter.utils import read_video_frames, save_video, vis_sequence_depth
logger = logging.getLogger(__name__)
class DepthCrafterInference:
"""
Inference class for DepthCrafter.
"""
def __init__(
self,
unet_path: str,
pre_train_path: str,
cpu_offload: Optional[str] = "model",
device: str = "cuda",
):
"""
Initialize the DepthCrafter inference pipeline.
Args:
unet_path (str): Path to the UNet model.
pre_train_path (str): Path to the pre-trained model.
cpu_offload (Optional[str]): CPU offload strategy ("model", "sequential", or None).
device (str): Device to run the model on ("cuda" or "cpu").
"""
# Determine dtype based on device
if device == "cpu":
dtype = torch.float32
variant = None
else:
dtype = torch.float16
variant = "fp16"
logger.info(f"Loading UNet from {unet_path}")
unet = DiffusersUNetSpatioTemporalConditionModelDepthCrafter.from_pretrained(
unet_path,
low_cpu_mem_usage=True,
torch_dtype=dtype,
)
logger.info(f"Loading pipeline from {pre_train_path}")
pipeline_kwargs = {
"unet": unet,
"torch_dtype": dtype,
}
if variant is not None:
pipeline_kwargs["variant"] = variant
self.pipe = DepthCrafterPipeline.from_pretrained(
pre_train_path,
**pipeline_kwargs
)
if cpu_offload is not None:
if cpu_offload == "sequential":
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(device)
try:
self.pipe.enable_xformers_memory_efficient_attention()
except (ImportError, ModuleNotFoundError, AttributeError) as e:
logger.warning(f"Xformers is not enabled: {e}")
self.pipe.enable_attention_slicing()
def infer(
self,
video_path: 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,
) -> List[str]:
"""
Run inference on a video.
Args:
video_path (str): Path to the input video.
num_denoising_steps (int): Number of denoising steps.
guidance_scale (float): Guidance scale.
save_folder (str): Folder to save output.
window_size (int): Window size for sliding window inference.
process_length (int): Maximum number of frames to process.
overlap (int): Overlap between windows.
max_res (int): Maximum resolution.
dataset (str): Dataset name for resolution settings.
target_fps (int): Target FPS for output video.
seed (int): Random seed.
track_time (bool): Whether to track execution time.
save_npz (bool): Whether to save depth map as .npz.
save_exr (bool): Whether to save depth map as .exr.
Returns:
List[str]: List of paths to saved files.
"""
set_seed(seed)
frames, target_fps = read_video_frames(
video_path,
process_length,
target_fps,
max_res,
dataset,
)
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]
res = res.sum(-1) / res.shape[-1]
res = (res - res.min()) / (res.max() - res.min())
vis = vis_sequence_depth(res)
save_path = os.path.join(
save_folder, os.path.splitext(os.path.basename(video_path))[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:
self._save_exr(res, save_path)
return [
save_path + "_input.mp4",
save_path + "_vis.mp4",
save_path + "_depth.mp4",
]
def _save_exr(self, res: np.ndarray, save_path: str):
"""
Save results as EXR files.
"""
try:
import OpenEXR
import Imath
except ImportError:
logger.error("OpenEXR or Imath not installed. Skipping EXR saving.")
return
os.makedirs(save_path, exist_ok=True)
logger.info(f"Saving EXR results to {save_path}")
for i, frame in enumerate(res):
output_exr = f"{save_path}/frame_{i:04d}.exr"
header = OpenEXR.Header(frame.shape[1], frame.shape[0])
header["channels"] = {
"Z": Imath.Channel(Imath.PixelType(Imath.PixelType.FLOAT))
}
exr_file = OpenEXR.OutputFile(output_exr, header)
exr_file.writePixels({"Z": frame.tobytes()})
exr_file.close()
def clear_cache(self):
"""Clear CUDA cache."""
gc.collect()
torch.cuda.empty_cache()
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