File size: 3,275 Bytes
436b829 | 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 | from PIL import Image
import numpy as np
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import cv2
import random
from omegaconf import DictConfig
from ppd.utils.diffusion.timesteps import Timesteps
from ppd.utils.diffusion.schedule import LinearSchedule
from ppd.utils.diffusion.sampler import EulerSampler
from ppd.utils.transform import image2tensor, resize_1024, resize_1024_crop, resize_keep_aspect
from ppd.models.depth_anything_v2.dpt import DepthAnythingV2
from ppd.models.dit import DiT
class PixelPerfectDepth(nn.Module):
def __init__(
self,
semantics_model='MoGe2',
semantics_pth='checkpoints/moge2.pt',
sampling_steps=10,
):
super().__init__()
self.sampling_steps = sampling_steps
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
self.device = DEVICE
if semantics_model == 'MoGe2':
from ppd.moge.model.v2 import MoGeModel
self.sem_encoder = MoGeModel.from_pretrained(semantics_pth)
else:
self.sem_encoder = DepthAnythingV2(
encoder='vitl',
features=256,
out_channels=[256, 512, 1024, 1024]
)
self.sem_encoder.load_state_dict(torch.load(semantics_pth, map_location='cpu'), strict=False)
self.sem_encoder = self.sem_encoder.to(self.device).eval()
self.sem_encoder.requires_grad_(False)
self.configure_diffusion()
self.dit = DiT()
def configure_diffusion(self):
self.schedule = LinearSchedule(T=1000)
self.sampling_timesteps = Timesteps(
T=self.schedule.T,
steps=self.sampling_steps,
device=self.device,
)
self.sampler = EulerSampler(
schedule=self.schedule,
timesteps=self.sampling_timesteps,
prediction_type='velocity'
)
@torch.no_grad()
def infer_image(self, image, use_fp16: bool = True):
# Resize the image to match the training resolution area while keeping the original aspect ratio.
resize_image = resize_keep_aspect(image)
image = image2tensor(resize_image)
image = image.to(self.device)
autocast_dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
with torch.autocast(device_type=self.device.type, dtype=autocast_dtype):
depth = self.forward_test(image)
return depth, resize_image
@torch.no_grad()
def forward_test(self, image):
semantics = self.semantics_prompt(image)
cond = image - 0.5
latent = torch.randn(size=[cond.shape[0], 1, cond.shape[2], cond.shape[3]]).to(self.device)
for timestep in self.sampling_timesteps:
input = torch.cat([latent, cond], dim=1)
pred = self.dit(x=input, semantics=semantics, timestep=timestep)
latent = self.sampler.step(pred=pred, x_t=latent, t=timestep)
return latent + 0.5
@torch.no_grad()
def semantics_prompt(self, image):
with torch.no_grad():
semantics = self.sem_encoder.forward_semantics(image)
return semantics
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