ControlNet: Text to Image
ControlNet is a neural framework designed to enhance control over generative models by incorporating conditional inputs like edge maps, depth data, or semantic segmentation. Proposed by Lvmin Zhang and Maneesh Agrawala, it integrates with diffusion models to enable precise control over image composition, object placement, and stylistic details via sketches, pose cues, or structural constraints. Widely used in digital art, design prototyping, film previsualization, and photo editing, it trains conditional encoders alongside base models to harmonize creative flexibility with structural guidance. The framework supports multi-modal inputs and real-time interaction, though challenges include stabilizing complex condition integration, optimizing computational overhead, and preventing overfitting.
The source model can be found here
Performance Reference
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Inference & Model Conversion
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License
Source Model: APACHE-2.0
Deployable Model: APLUX-MODEL-FARM-LICENSE