control_v1p_sd15_brightness - A ControlNet model for brightness control in Stable Diffusion 1.5

## Primary Function of control_v1p_sd15_brightness The **control_v1p_sd15_brightness** model is designed to provide **brightness control** for Stable Diffusion 1.5. It allows users to: - **Colorize grayscale images** (e.g., historical photo restoration). - **Recolor generated images** by adjusting brightness and enhancing visual effects. ## Stable Diffusion Compatibility The model is fully compatible with **Stable Diffusion 1.5** and can be integrated into existing Stable Diffusion pipelines. ## Technical Specifications - **License**: `creativeml-openrail-m` (permits research and commercial use with restrictions). - **Dataset**: `ioclab/grayscale_image_aesthetic_3M` (3 million images derived from laion2B-en-aesthetic). - **Tags**: `image-to-image`, `controlnet`. - **Library**: `diffusers` (Hugging Face's diffusion model library). - **Training Hardware**: A6000 GPU and TPU v4-8 (training time: 13–25 hours). - **Resolution**: 512, **Learning Rate**: 1e-5. ## Model Demonstration Users can test the model via the **[Hugging Face Space demo](https://huggingface.co/spaces/ioclab/brightness-controlnet)**. The demo showcases practical applications of brightness control and recoloring. ## Weight Configuration While the model's official documentation does not specify exact weights, user suggestions include: - **Input weight**: Below 0.5 (e.g., 0.3). - **Output weight**: Adjustable (e.g., 0.5). These settings may vary based on desired results and should be fine-tuned experimentally. ## Training Process Details The **[AIGC All in One page](https://aigc.ioclab.com/sd-showcase/brightness-controlnet.html)** provides a **Chinese-language guide** on the model's training process, including: - Dataset preparation. - Hardware specifications. - Training parameters (e.g., epochs, batch size). ## Artistic QR Code Generation While the model's primary focus is **brightness control**, it may indirectly support artistic QR code generation by adjusting image brightness. However, this is not an explicitly documented feature. Users are advised to experiment with weight settings and consult community resources for optimal results. ## Integration Steps 1. Load the model into a **ControlNet-enabled Stable Diffusion 1.5 environment**. 2. Provide a **grayscale or condition image** as input. 3. Adjust brightness parameters and weights (e.g., input: 0.3, output: 0.5). 4. Generate and refine outputs based on visual feedback. ### Citation sources: - [control_v1p_sd15_brightness](https://huggingface.co/ViscoseBean/control_v1p_sd15_brightness) - Official URL Updated: 2025-04-01