| | --- |
| | 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. |
