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Clarity AI | AI Image Upscaler & Enhancer - free and open-source Magnific Alternative

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Clarity AI | AI Image Upscaler & Enhancer - free and open-source Magnific Alternative

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👋 Hello

I build open source AI apps. To finance my work i also build paid versions of my code. But feel free to use the free code. I post features and new projects on https://twitter.com/philz1337x

🗞️ Updates

🚀 Options to use Clarity-Upscaler

Note that this repository is an implementation for cog. If you are not familiar with cog, I recommend the easier solutions. The free options are ComfyUI and A1111, while the paid but easy-to-use options are my app ClarityAI.co and the ComfyUI API Node.

🧑‍💻 App

The simplest option to use Clarity is with the app at ClarityAI.co

🐰 ComfyUI

1. API node

  1. Open ComfyUI Manager, search for Clarity AI, and install the node.
  2. Create an API key at: ClarityAI.co/ComfyUI
  3. Add the API key to the node as a) envirement variable CAI_API_KEY OR b) to a cai_platform_key.txt text file OR c) in api_key_override field of the node.

Full instructions: https://github.com/philz1337x/ComfyUI-ClarityAI

2. Free workflow

  1. Download the repo https://github.com/philz1337x/ComfyUI-ClarityAI and use the file free-wokflow.json

⚙️ API

Use the API at: ClarityAI.co/API

Cog

If you are not familiar with cog read: cog docs

  • run download_weights.py

  • predict with cog:

cog predict -i image="link-to-image"

A1111 webUI

For a detailed explanation, use the tutorial in this post: https://x.com/philz1337x/status/1830504764389380466

https://github.com/AUTOMATIC1111/stable-diffusion-webui

  • Use these params:
masterpiece, best quality, highres, <lora:more_details:0.5> <lora:SDXLrender_v2.0:1> Negative prompt: (worst quality, low quality, normal quality:2) JuggernautNegative-neg Steps: 18, Sampler: DPM++ 3M SDE Karras, CFG scale: 6.0, Seed: 1337, Size: 1024x1024, Model hash: 338b85bc4f, Model: juggernaut_reborn, Denoising strength: 0.35, Tiled Diffusion upscaler: 4x-UltraSharp, Tiled Diffusion scale factor: 2, Tiled Diffusion: {"Method": "MultiDiffusion", "Tile tile width": 112, "Tile tile height": 144, "Tile Overlap": 4, "Tile batch size": 8, "Upscaler": "4x-UltraSharp", "Upscale factor": 2, "Keep input size": true}, ControlNet 0: "Module: tile_resample, Model: control_v11f1e_sd15_tile, Weight: 0.6, Resize Mode: 1, Low Vram: False, Processor Res: 512, Threshold A: 1, Threshold B: 1, Guidance Start: 0.0, Guidance End: 1.0, Pixel Perfect: True, Control Mode: 1, Hr Option: HiResFixOption.BOTH, Save Detected Map: False", Lora hashes: "more_details: 3b8aa1d351ef, SDXLrender_v2.0: 3925cf4759af"