OzzyGT / depth_anything_custom_block

Depth Anything V2 Estimator Block

A custom Modular Diffusers block for monocular depth estimation using Depth Anything V2 . Supports both images and videos.

Features

• Relative depth estimation using Depth Anything V2 (Large variant, 335M params)
• Image and video input support
• Grayscale or turbo colormap visualization

Installation

# Using uv
uv sync

# Using pip
pip install -r requirements.txt

Quick Start

Load the block

from diffusers import ModularPipelineBlocks
import torch

blocks = ModularPipelineBlocks.from_pretrained(
    "your-username/depth-anything-v2-estimator",  # or local path "."
    trust_remote_code=True,
)
pipeline = blocks.init_pipeline()
pipeline.load_components(torch_dtype=torch.float16)
pipeline.to("cuda")

Single image - grayscale depth

from PIL import Image

image = Image.open("photo.jpg")
output = pipeline(image=image)

# Save depth map
output.depth_image.save("photo_depth.png")

# Access raw relative depth tensor
print(output.predicted_depth.shape)  # (H, W)

Single image - turbo colormap

output = pipeline(image=image, colormap="turbo")
output.depth_image.save("photo_depth_turbo.png")

Video - grayscale depth

from block import save_video

output = pipeline(video_path="input.mp4", colormap="grayscale")
save_video(output.depth_frames, output.fps, "output_depth.mp4")

Video - turbo colormap

output = pipeline(video_path="input.mp4", colormap="turbo")
save_video(output.depth_frames, output.fps, "output_depth_turbo.mp4")

Inputs

Outputs

Image mode

Video mode

Depth Normalization

Depth values are min-max normalized and inverted so that bright areas represent nearby surfaces and dark areas represent distant ones.

• Bright = close , dark = far (grayscale)
• Warm (red/yellow) = close , cool (blue) = far (turbo)

Model Variants

The block defaults to depth-anything/Depth-Anything-V2-Large-hf . Other available variants: