From af660163caf286f80fcced98e55ca047b838b204 Mon Sep 17 00:00:00 2001 From: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com> Date: Wed, 13 Mar 2024 14:15:29 +0530 Subject: [PATCH] chore: cleanup DepthAnything code --- .../controlnet_image_processors.py | 5 ++-- .../image_util/depth_anything/__init__.py | 30 +++++++++---------- 2 files changed, 16 insertions(+), 19 deletions(-) diff --git a/invokeai/app/invocations/controlnet_image_processors.py b/invokeai/app/invocations/controlnet_image_processors.py index 7b6cfaaaf1..8d7d771434 100644 --- a/invokeai/app/invocations/controlnet_image_processors.py +++ b/invokeai/app/invocations/controlnet_image_processors.py @@ -574,7 +574,7 @@ DEPTH_ANYTHING_MODEL_SIZES = Literal["large", "base", "small"] title="Depth Anything Processor", tags=["controlnet", "depth", "depth anything"], category="controlnet", - version="1.0.0", + version="1.0.1", ) class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation): """Generates a depth map based on the Depth Anything algorithm""" @@ -583,13 +583,12 @@ class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation): default="small", description="The size of the depth model to use" ) resolution: int = InputField(default=512, ge=64, multiple_of=64, description=FieldDescriptions.image_res) - offload: bool = InputField(default=False) def run_processor(self, image: Image.Image): depth_anything_detector = DepthAnythingDetector() depth_anything_detector.load_model(model_size=self.model_size) - processed_image = depth_anything_detector(image=image, resolution=self.resolution, offload=self.offload) + processed_image = depth_anything_detector(image=image, resolution=self.resolution) return processed_image diff --git a/invokeai/backend/image_util/depth_anything/__init__.py b/invokeai/backend/image_util/depth_anything/__init__.py index fcd600b99e..af30f538a8 100644 --- a/invokeai/backend/image_util/depth_anything/__init__.py +++ b/invokeai/backend/image_util/depth_anything/__init__.py @@ -13,9 +13,11 @@ from invokeai.app.services.config.config_default import InvokeAIAppConfig 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 choose_torch_device +from invokeai.backend.util.logging import InvokeAILogger from invokeai.backend.util.util import download_with_progress_bar config = InvokeAIAppConfig.get_config() +logger = InvokeAILogger.get_logger(config=config) DEPTH_ANYTHING_MODELS = { "large": { @@ -54,8 +56,9 @@ class DepthAnythingDetector: def __init__(self) -> None: self.model = None self.model_size: Union[Literal["large", "base", "small"], None] = None + self.device = choose_torch_device() - def load_model(self, model_size=Literal["large", "base", "small"]): + def load_model(self, model_size: Literal["large", "base", "small"] = "small"): DEPTH_ANYTHING_MODEL_PATH = pathlib.Path(config.models_path / DEPTH_ANYTHING_MODELS[model_size]["local"]) if not DEPTH_ANYTHING_MODEL_PATH.exists(): download_with_progress_bar(DEPTH_ANYTHING_MODELS[model_size]["url"], DEPTH_ANYTHING_MODEL_PATH) @@ -71,8 +74,6 @@ class DepthAnythingDetector: 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]) - case _: - raise TypeError("Not a supported model") self.model.load_state_dict(torch.load(DEPTH_ANYTHING_MODEL_PATH.as_posix(), map_location="cpu")) self.model.eval() @@ -80,20 +81,20 @@ class DepthAnythingDetector: self.model.to(choose_torch_device()) return self.model - def to(self, device): - self.model.to(device) - return self + 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 - def __call__(self, image, resolution=512, offload=False): - image = np.array(image, dtype=np.uint8) - image = image[:, :, ::-1] / 255.0 + np_image = np.array(image, dtype=np.uint8) + np_image = np_image[:, :, ::-1] / 255.0 - image_height, image_width = image.shape[:2] - image = transform({"image": image})["image"] - image = torch.from_numpy(image).unsqueeze(0).to(choose_torch_device()) + image_height, image_width = np_image.shape[:2] + np_image = transform({"image": image})["image"] + tensor_image = torch.from_numpy(image).unsqueeze(0).to(choose_torch_device()) with torch.no_grad(): - depth = self.model(image) + 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 @@ -103,7 +104,4 @@ class DepthAnythingDetector: new_height = int(image_height * (resolution / image_width)) depth_map = depth_map.resize((resolution, new_height)) - if offload: - del self.model - return depth_map