Visit ComfyUI Online for ready-to-use ComfyUI environment
Marigold depth estimation in ComfyUI is a wrapper node utilizing the same diffusers pipeline as the original Marigold implementation. It requires a model in diffusers format to function effectively within ComfyUI.
ComfyUI-Marigold is an extension designed to integrate the powerful Marigold depth estimation model into the ComfyUI environment. This extension allows AI artists to generate high-quality depth maps from monocular images, leveraging the advanced capabilities of diffusion-based image generators. By using ComfyUI-Marigold, you can enhance your visual projects with accurate depth information, which is particularly useful for 3D modeling, visual effects (VFX), and other creative applications.
ComfyUI-Marigold works by wrapping the Marigold depth estimation model into a user-friendly node within the ComfyUI framework. The Marigold model, originally designed for monocular depth estimation, repurposes the visual knowledge stored in modern generative image models to predict depth information from a single image. This process involves several steps:
ComfyUI-Marigold offers several features to customize and optimize the depth estimation process:
denoise_steps
): This parameter controls the number of steps taken to refine the depth map. Increasing the number of steps can improve accuracy but will also increase processing time.n_repeat
): This determines how many times the depth estimation process is repeated and ensembled into a single depth map. More iterations can lead to better accuracy at the cost of longer processing times.n_repeat_batch_size
): This sets how many iterations are processed simultaneously. If you have sufficient VRAM, matching this to the number of repeats can speed up processing.ComfyUI-Marigold uses the Marigold model, which is available in different versions optimized for various performance needs:
The extension is regularly updated to improve performance and add new features. Here are some recent updates:
Here are some common issues and their solutions:
n_repeat
or n_repeat_batch_size
parameters. Using the fp16
option can also help by halving the memory usage.denoise_steps
and n_repeat
parameters gradually to find a balance between accuracy and processing time.invert
option if the depth map appears inverted for your application.What is the optimal resolution for input images? Marigold performs best at a resolution of around 768p. Resizing your images to this resolution before processing can yield better results.
How can I save the depth map in a high-quality format? Use the OpenEXR node to save the depth map, as it preserves the full range of depth information, which is beneficial for VFX and 3D modeling.
For additional resources, tutorials, and community support, you can visit the following links:
© Copyright 2024 RunComfy. All Rights Reserved.