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Control V11f1p Sd15 Depth

Developed by frankjoshua
ControlNet v1.1 is the successor model to ControlNet v1.0, controlling Stable Diffusion image generation through depth image conditions.
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Release Time : 7/27/2023

Model Overview

ControlNet is a neural network structure that controls diffusion models by adding additional conditions. This checkpoint corresponds to the ControlNet based on depth image conditions and can be used in conjunction with Stable Diffusion.

Model Features

Depth Condition Control
Uses depth images as conditional input to precisely control the geometric structure and spatial relationships of generated images.
Improved Training Data
Fixed issues in the v1.0 training dataset, reducing bias and improving model robustness.
Data Augmentation
Applied data augmentation techniques such as random left-right flipping to enhance model generalization.
Compatibility with Multiple Depth Estimation Methods
Supports different preprocessor resolutions and multiple depth estimation methods (e.g., Midas, leres, and zoe).

Model Capabilities

Depth map-based image generation
Image-to-image translation
Geometric structure preservation
3D scene generation

Use Cases

Artistic Creation
3D Scene Generation
Generates 3D scene images with correct perspective and spatial relationships based on depth maps.
Generated images maintain the geometric structure of the input depth map.
Architectural Design
Architectural Visualization
Generates detailed architectural renderings from simple depth sketches.
Quickly transforms conceptual designs into realistic images.
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