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Nap/
depth_anything_v2_vitg

Name: depth_anything_v2_vitg
Availability: InStock

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apache-2.0

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Depth Anything V2 Giant - 1.3B params - FP32 - Converted from .pth to .safetensors

The model was previously published under apache-2.0 license and later removed. See the commit in the official GitHub repo: https://github.com/DepthAnything/Depth-Anything-V2/commit/0a7e2b58a7e378c7863bd7486afc659c41f9ef99

A copy of the original .pth model is available in this Hugging Face repo: https://huggingface.co/likeabruh/depth_anything_v2_vitg/tree/main

This is simply the same available model in .safetensors format.

If you want to use it in ComfyUI, you can use Kijai's custom nodes (https://github.com/kijai/ComfyUI-DepthAnythingV2), select the model and it will be downloaded automatically.

You may get OOM using the gigant model depending on your VRAM and the size of the image you're processing. In these cases, try to reduce the input image size. I can get 1024x1024 depth maps just fine with 24GB VRAM (uses about 56% of available VRAM).

~~If you want to use it in ComfyUI, you have two options:~~

1. (Recommended) Use the .safetensors file with the modified version of Kijai's custom nodes (https://github.com/kijai/ComfyUI-DepthAnythingV2). Just replace the ComfyUI/custom_nodes/comfyui-depthanythingv2/nodes.py file with the nodes.py file in this repo and ensure depth_anything_v2_vitg_fp32.safetensors is in the ComfyUI/models/depthanything/ folder, as it will not be downloaded automatically.

2. Use depth_anything_v2_vitg.pth directly with Fannovel16's custom nodes (https://github.com/Fannovel16/comfyui_controlnet_aux). Use a node called Depth Anything V2 - Relative and select depth_anything_v2_vitg.pth. Ensure the file is in the folder ComfyUI/custom_nodes/comfyui_controlnet_aux/ckpts/depth-anything/Depth-Anything-V2-Giant/ folder, as it will not be downloaded automatically.

Kijai's nodes produce more detailed depth maps. However, you will likely get OOM using the gigant model depending on your VRAM and the size of the image you're processing. I can get 1024x1024 depth maps just fine with 24GB VRAM.

Nap/depth_anything_v2_vitg

license: apache-2.0

Nap/
depth_anything_v2_vitg