| --- |
| license: creativeml-openrail-m |
| language: |
| - en |
| base_model: [] |
| pipeline_tag: other |
| tags: |
| - upscaler |
| - denoiser |
| - comfyui |
| - automatic1111 |
| datasets: [] |
| metrics: [] |
| --- |
| |
| # Model Card for MidnightRunner/ControlNet |
|
|
| This repository provides a **ready-to-use collection of ControlNet models** for SDXL, ComfyUI, and Automatic1111. |
| These models include edge detectors, pose estimators, depth mappers, lineart adapters, tilers, and experimental adapters for advanced conditioning and structure control in AI art generation. |
| All models are tested, practical, and selected for reliable integration into custom creative workflows. |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| A curated toolbox of ControlNet models for high-precision structure control, pose transfer, lineart extraction, depth estimation, segmentation, inpainting, recoloring, and more. |
| This set enables rapid workflow iteration for generative AI artists, illustrators, and researchers seeking robust conditioning tools for SDXL-based systems. |
|
|
| - **Developed by:** MidnightRunner and open-source contributors |
| - **Model type:** ControlNet Adapters (edge, depth, pose, etc.) |
| - **License:** creativeml-openrail-m |
| - **Language(s) (NLP):** N/A (image processing only) |
| - **Finetuned from model:** ControlNet base models, original authors noted per file |
|
|
| ### Model Sources |
|
|
| - **Repository:** https://huggingface.co/MidnightRunner/ControlNet |
|
|
| ## Uses |
|
|
| ### Direct Use |
|
|
| Integrate with ComfyUI, Automatic1111, SDXL workflows, and other diffusion UIs for: |
| - pose-to-pose transformation |
| - edge/lineart guidance |
| - depth-aware rendering |
| - mask-based editing, recoloring, and inpainting |
| - seamless tiling and upscaling |
|
|
| ### Downstream Use |
|
|
| May be included in chained pipelines for creative tools, batch image post-processing, or AI-driven illustration tools. |
|
|
| ### Out-of-Scope Use |
|
|
| Not for medical imaging, biometric authentication, or other critical inference domains. |
|
|
| ## Bias, Risks, and Limitations |
|
|
| - All models inherit the limitations and biases of their upstream datasets and architectures. |
| - May produce artifacts or degrade image quality in edge cases. |
| - Outputs should be reviewed in all sensitive, safety-critical, or NSFW scenarios. |
|
|
| ### Recommendations |
|
|
| Outputs should be manually reviewed before deployment in professional or public-facing applications. |
|
|
| ## How to Get Started with the Model |
|
|
| ```bash |
| git lfs install |
| git clone https://huggingface.co/MidnightRunner/ControlNet |
| ``` |
|
|
| # Download a single file |
| huggingface-cli download MidnightRunner/ControlNet controlnetxlCNXL_xinsirOpenpose.safetensors |
| |
| # Python example |
| ```bash |
| from huggingface_hub import hf_hub_download |
|
|
| file = hf_hub_download( |
| repo_id="MidnightRunner/ControlNet", |
| filename="controlnetxlCNXL_xinsirOpenpose.safetensors" |
| ) |
| ``` |
| # Results |
| Models selected based on strongest visual fidelity and lowest artifact rate in practical SDXL workflows. |
| |
| # Summary |
| This ControlNet toolbox provides high success rates and reliability for AI-driven image control and conditioning tasks, based on both quantitative metrics and extensive real-world user testing. |
|
|
| # Environmental Impact |
| Hardware Type: Consumer and research GPUs (NVIDIA A100, RTX 3090, Apple Silicon, etc.) |
|
|
| Carbon Emitted: Minimal for inference; training costs depend on model size and upstream provider. |
|
|
| # Technical Specifications |
| Model Architecture and Objective |
| All models follow the ControlNet architecture paradigm, adapted for specific guidance (edge, pose, depth, etc.) |
| Objectives are structure preservation, fidelity, and seamless integration with diffusion image synthesis. |
|
|
| # Compute Infrastructure |
| Hardware: NVIDIA GPUs (A100, 3090, etc.), Apple M1/M2 |
|
|
| Software: Python 3.10+, PyTorch 2.x, ComfyUI, Automatic1111, HuggingFace Hub tools |
|
|
| # Citation |
| If you use these models in your research or product, please cite the original ControlNet paper and any upstream sources referenced per file. |
|
|
| ## More Information |
| For more details, licensing, or integration tips, visit https://huggingface.co/MidnightRunner/ControlNet or contact MidnightRunner via HuggingFace. |
