mirror of
https://github.com/invoke-ai/InvokeAI.git
synced 2026-02-01 15:04:57 -05:00
e93f4d632d
* introduce new abstraction layer for GPU devices * add unit test for device abstraction * fix ruff * convert TorchDeviceSelect into a stateless class * move logic to select context-specific execution device into context API * add mock hardware environments to pytest * remove dangling mocker fixture * fix unit test for running on non-CUDA systems * remove unimplemented get_execution_device() call * remove autocast precision * Multiple changes: 1. Remove TorchDeviceSelect.get_execution_device(), as well as calls to context.models.get_execution_device(). 2. Rename TorchDeviceSelect to TorchDevice 3. Added back the legacy public API defined in `invocation_api`, including choose_precision(). 4. Added a config file migration script to accommodate removal of precision=autocast. * add deprecation warnings to choose_torch_device() and choose_precision() * fix test crash * remove app_config argument from choose_torch_device() and choose_torch_dtype() --------- Co-authored-by: Lincoln Stein <lstein@gmail.com>
111 lines
4.3 KiB
Python
111 lines
4.3 KiB
Python
import pathlib
|
|
from typing import Literal, Union
|
|
|
|
import cv2
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from einops import repeat
|
|
from PIL import Image
|
|
from torchvision.transforms import Compose
|
|
|
|
from invokeai.app.services.config.config_default import get_config
|
|
from invokeai.app.util.download_with_progress import download_with_progress_bar
|
|
from invokeai.backend.image_util.depth_anything.model.dpt import DPT_DINOv2
|
|
from invokeai.backend.image_util.depth_anything.utilities.util import NormalizeImage, PrepareForNet, Resize
|
|
from invokeai.backend.util.devices import TorchDevice
|
|
from invokeai.backend.util.logging import InvokeAILogger
|
|
|
|
config = get_config()
|
|
logger = InvokeAILogger.get_logger(config=config)
|
|
|
|
DEPTH_ANYTHING_MODELS = {
|
|
"large": {
|
|
"url": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vitl14.pth?download=true",
|
|
"local": "any/annotators/depth_anything/depth_anything_vitl14.pth",
|
|
},
|
|
"base": {
|
|
"url": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vitb14.pth?download=true",
|
|
"local": "any/annotators/depth_anything/depth_anything_vitb14.pth",
|
|
},
|
|
"small": {
|
|
"url": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vits14.pth?download=true",
|
|
"local": "any/annotators/depth_anything/depth_anything_vits14.pth",
|
|
},
|
|
}
|
|
|
|
|
|
transform = Compose(
|
|
[
|
|
Resize(
|
|
width=518,
|
|
height=518,
|
|
resize_target=False,
|
|
keep_aspect_ratio=True,
|
|
ensure_multiple_of=14,
|
|
resize_method="lower_bound",
|
|
image_interpolation_method=cv2.INTER_CUBIC,
|
|
),
|
|
NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
|
|
PrepareForNet(),
|
|
]
|
|
)
|
|
|
|
|
|
class DepthAnythingDetector:
|
|
def __init__(self) -> None:
|
|
self.model = None
|
|
self.model_size: Union[Literal["large", "base", "small"], None] = None
|
|
self.device = TorchDevice.choose_torch_device()
|
|
|
|
def load_model(self, model_size: Literal["large", "base", "small"] = "small"):
|
|
DEPTH_ANYTHING_MODEL_PATH = config.models_path / DEPTH_ANYTHING_MODELS[model_size]["local"]
|
|
download_with_progress_bar(
|
|
pathlib.Path(DEPTH_ANYTHING_MODELS[model_size]["url"]).name,
|
|
DEPTH_ANYTHING_MODELS[model_size]["url"],
|
|
DEPTH_ANYTHING_MODEL_PATH,
|
|
)
|
|
|
|
if not self.model or model_size != self.model_size:
|
|
del self.model
|
|
self.model_size = model_size
|
|
|
|
match self.model_size:
|
|
case "small":
|
|
self.model = DPT_DINOv2(encoder="vits", features=64, out_channels=[48, 96, 192, 384])
|
|
case "base":
|
|
self.model = DPT_DINOv2(encoder="vitb", features=128, out_channels=[96, 192, 384, 768])
|
|
case "large":
|
|
self.model = DPT_DINOv2(encoder="vitl", features=256, out_channels=[256, 512, 1024, 1024])
|
|
|
|
self.model.load_state_dict(torch.load(DEPTH_ANYTHING_MODEL_PATH.as_posix(), map_location="cpu"))
|
|
self.model.eval()
|
|
|
|
self.model.to(self.device)
|
|
return self.model
|
|
|
|
def __call__(self, image: Image.Image, resolution: int = 512) -> Image.Image:
|
|
if not self.model:
|
|
logger.warn("DepthAnything model was not loaded. Returning original image")
|
|
return image
|
|
|
|
np_image = np.array(image, dtype=np.uint8)
|
|
np_image = np_image[:, :, ::-1] / 255.0
|
|
|
|
image_height, image_width = np_image.shape[:2]
|
|
np_image = transform({"image": np_image})["image"]
|
|
tensor_image = torch.from_numpy(np_image).unsqueeze(0).to(self.device)
|
|
|
|
with torch.no_grad():
|
|
depth = self.model(tensor_image)
|
|
depth = F.interpolate(depth[None], (image_height, image_width), mode="bilinear", align_corners=False)[0, 0]
|
|
depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
|
|
|
|
depth_map = repeat(depth, "h w -> h w 3").cpu().numpy().astype(np.uint8)
|
|
depth_map = Image.fromarray(depth_map)
|
|
|
|
new_height = int(image_height * (resolution / image_width))
|
|
depth_map = depth_map.resize((resolution, new_height))
|
|
|
|
return depth_map
|