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
git lfs install
git clone https://huggingface.co/MidnightRunner/ControlNet
Download a single file
huggingface-cli download MidnightRunner/ControlNet controlnetxlCNXL_xinsirOpenpose.safetensors
Python example
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.