{"id":18785318,"url":"https://github.com/hemantkarya/hqstablediffusioncolab","last_synced_at":"2026-03-12T17:33:53.754Z","repository":{"id":150017426,"uuid":"537546159","full_name":"HemantKArya/HqStableDiffusionColab","owner":"HemantKArya","description":"A High Quality (HD / 2K / 4K) Image Generation Using Stable Diffusion and Real-ESR / SwinIR /GFPGAN","archived":false,"fork":false,"pushed_at":"2022-12-29T09:24:46.000Z","size":23648,"stargazers_count":35,"open_issues_count":0,"forks_count":5,"subscribers_count":4,"default_branch":"main","last_synced_at":"2025-04-13T12:45:06.243Z","etag":null,"topics":["artwork","colab","diffusion","diffusion-models","gfpgan","hd-images-using-stable-diffusion","high-quality-images","latent","real-esrgan","stable-diffusion","swinir"],"latest_commit_sha":null,"homepage":"https://www.instagram.com/iamhemantindia","language":"Jupyter 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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artwork","colab","diffusion","diffusion-models","gfpgan","hd-images-using-stable-diffusion","high-quality-images","latent","real-esrgan","stable-diffusion","swinir"],"created_at":"2024-11-07T20:46:18.088Z","updated_at":"2026-03-12T17:33:53.738Z","avatar_url":"https://github.com/HemantKArya.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# **High Quality Text to Image Generation using Stable Diffusion, GFPGAN,Real-ESR and Swin IR**\n\n\n[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/HemantKArya/HqStableDiffusionColab/blob/main/HighQuality_Text2Image_Stable_Diffusion_ls.ipynb)\n[![banner](./doc/bannerls.jpg)](https://www.instagram.com/iamhemantindia)\n\nGenerate 4K and FULL HD Images and Artworks for Free Using Stable Diffusion.\n\n## No Need to generate token key for genrating images from huggingface...\nLink to Coalb Notebook [![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/HemantKArya/HqStableDiffusionColab/blob/main/HighQuality_Text2Image_Stable_Diffusion_ls.ipynb)\n\n\nFor Upscale Only goto RealESR Notebook (4K Upscale)[![open in colabesr](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/HemantKArya/HqStableDiffusionColab/blob/main/RealESR_Upscale.ipynb)\n \nRun All the cell until you Reach your Prompt cell.\n ***In case if you have any human face in your images then it will restore Distorted figures(like eyes,nose,etc) in images, here is example.***  **In Stable Diffusin her Eyes and Lips are bit distorted.**\n ![indexface](./doc/indexface.jpg)\n\nTo upscale images to 2K or 4k using Real-ESR GAN. Note that after running Reasl-ESRGAN leave SwinIR until unless you are not satisfied with RealESR results.\n![scupesr](./doc/sc4.png)\nafter running this cell you will get a comparison matrix like this.\n\n**Input Images --\u003e Upscaled Images(Real-ESR)**\n![index3](./doc/index33.png)\n\nAfter Upscaling you images using Real-ESRGAN rest of the cell are optional to run and not recommended (Cause limited GPU RAM in Colab, After running these cell may be it will show you error like ``cuda out of memory``) to run until you are not satisfied with result of Upscaled images of Real-ESR.\nright Now I am going to show you difference b/w both Upscalers.\nUsing both Optional cell at the last of notebook. (It may full your current colab RAM)\n\n**Input Images ------ Upscaled Images(SwinIR) ----- Upscaled Images(RealESRGAN)**\n![index5](./doc/index35.png)\n\nVisit Logical Spot for Video Help:-\n\n [![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge\u0026logo=YouTube\u0026logoColor=white)](https://www.youtube.com/c/LogicalSpot)\n \n \n # **Stable Diffusion** 🎨 \n*...using `🧨diffusers`*\n\nStable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/) and [LAION](https://laion.ai/). It's trained on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.\nSee the [model card](https://huggingface.co/CompVis/stable-diffusion) for more information.\nThis Colab notebook shows how to use Stable Diffusion with the 🤗 Hugging Face [🧨 Diffusers library](https://github.com/huggingface/diffusers) . \nhttps://github.com/CompVis/stable-diffusion\n\norignal-link to colab https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb\n\n# **Real-ESRGAN**\n[![arXiv](https://img.shields.io/badge/arXiv-Paper-\u003cCOLOR\u003e.svg)](https://arxiv.org/abs/2107.10833)\n[![GitHub Stars](https://img.shields.io/github/stars/xinntao/Real-ESRGAN?style=social)](https://github.com/xinntao/Real-ESRGAN)\n[![download](https://img.shields.io/github/downloads/xinntao/Real-ESRGAN/total.svg)](https://github.com/xinntao/Real-ESRGAN/releases)\n[![open in colabesr](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/HemantKArya/HqStableDiffusionColab/blob/main/RealESR_Upscale.ipynb)\n \n\nReal-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.We extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data.\n\n# **SwinIR**\n[![arXiv](https://img.shields.io/badge/arXiv-Paper-\u003cCOLOR\u003e.svg)](https://arxiv.org/abs/2108.10257)\n[![GitHub Stars](https://img.shields.io/github/stars/JingyunLiang/SwinIR?style=social)](https://github.com/JingyunLiang/SwinIR)\n[![download](https://img.shields.io/github/downloads/JingyunLiang/SwinIR/total.svg)](https://github.com/JingyunLiang/SwinIR/releases)\n\nSwinIR achieves state-of-the-art performance on six tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image denoising (including grayscale and color image denoising) and JPEG compression artifact reduction. See our [paper](https://arxiv.org/abs/2108.10257) and [project page](https://github.com/JingyunLiang/SwinIR) for detailed results.\n\n### (No colorization; No CUDA extensions required)\n\n[![arXiv](https://img.shields.io/badge/arXiv-Paper-\u003cCOLOR\u003e.svg)](https://arxiv.org/abs/2101.04061)\n[![GitHub Stars](https://img.shields.io/github/stars/TencentARC/GFPGAN?style=social)](https://github.com/TencentARC/GFPGAN)\n[![download](https://img.shields.io/github/downloads/TencentARC/GFPGAN/total.svg)](https://github.com/TencentARC/GFPGAN/releases)\n\n## **GFPGAN** - Towards Real-World Blind Face Restoration with Generative Facial Prior\n\nGFPGAN is a blind face restoration algorithm towards real-world face images. \u003cbr\u003e\nIt leverages the generative face prior in a pre-trained GAN (*e.g.*, StyleGAN2) to restore realistic faces while precerving fidelity. \u003cbr\u003e\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhemantkarya%2Fhqstablediffusioncolab","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhemantkarya%2Fhqstablediffusioncolab","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhemantkarya%2Fhqstablediffusioncolab/lists"}