Instructions to use MidnightRunner/Misc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use MidnightRunner/Misc with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf MidnightRunner/Misc:UD-Q4_K_S # Run inference directly in the terminal: llama cli -hf MidnightRunner/Misc:UD-Q4_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf MidnightRunner/Misc:UD-Q4_K_S # Run inference directly in the terminal: llama cli -hf MidnightRunner/Misc:UD-Q4_K_S
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf MidnightRunner/Misc:UD-Q4_K_S # Run inference directly in the terminal: ./build/bin/llama cli -hf MidnightRunner/Misc:UD-Q4_K_S
Use Docker
docker model run hf.co/MidnightRunner/Misc:UD-Q4_K_S
- Llama new
- LM Studio
- Jan
- Ollama
How to use MidnightRunner/Misc with Ollama:
ollama run hf.co/MidnightRunner/Misc:UD-Q4_K_S
- Unsloth Desktop
- Docker Model Runner
How to use MidnightRunner/Misc with Docker Model Runner:
docker model run hf.co/MidnightRunner/Misc:UD-Q4_K_S
- Lemonade
How to use MidnightRunner/Misc with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MidnightRunner/Misc:UD-Q4_K_S
Run and chat with the model
lemonade run user.Misc-UD-Q4_K_S
List all available models
lemonade list
- Atomic Chat
Create README.md
Browse files
README.md
ADDED
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---
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license: creativeml-openrail-m
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language:
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- en
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base_model: []
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pipeline_tag: other
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tags:
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- upscaler
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- denoiser
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- comfyui
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- automatic1111
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datasets: []
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metrics: []
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---
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# 🗂 MidnightRunner/Misc
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## Overview
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This repo is my **miscellaneous toolbox** — a collection of models, upscalers, denoisers, configs, and other bits I keep around for quick pulls.
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It’s not meant to be polished, just **fast standbys** that I can drop into workflows when needed.
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---
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## Contents
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- **Upscalers**
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- ESRGAN, RealESRGAN, AnimeSharp, UltraSharp
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- SwinIR, NMKD, Foolhardy
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- **Denoisers & Sharpeners**
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- ITF SkinDiff Detail Lite
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- Lexica Sharp series
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- DeNoise realplksr
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- **Experimental Checkpoints**
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- Astraali configs
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- OmniSR (x2, x3, x4)
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- SAM (Segment Anything) weights
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- Motion tests (bounceV, danceMax, etc.)
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- **Workflow Utilities**
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- FixFP16Errors
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- Oddball safetensors
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- “Just in case” helper models
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---
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## Quick Pulls
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Fetch files or the entire repo with Hugging Face tools:
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```bash
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# clone the whole repo
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git lfs install
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git clone https://huggingface.co/MidnightRunner/Misc
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# download a single file
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huggingface-cli download MidnightRunner/Misc 4x-UltraSharp.pth
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# pull from Python
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from huggingface_hub import hf_hub_download
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file = hf_hub_download(
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repo_id="MidnightRunner/Misc",
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filename="4x-UltraSharp.pth"
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)
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```
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---
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## Notes
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* **Disorganized on purpose**: this is a stash, not a showcase.
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* Everything here is tested, works, and has bailed me out more than once.
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* Licenses follow their original sources.
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