Update README.md

README.md

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----
-license: cc
----
+---
+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.