---
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.