mirror of
https://github.com/sd-webui/stable-diffusion-webui.git
synced 2024-12-13 18:02:31 +03:00
added cloudflared tunnel.
This commit is contained in:
aedh carrick
parent
5432b73fb8
commit
70e6d12451
@ -49,7 +49,7 @@
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"\n",
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"## Installation instructions for:\n",
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"\n",
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"- **[Windows](https://sygil-dev.github.io/sygil-webui/docs/1.windows-installation.html)** \n",
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"- **[Windows](https://sygil-dev.github.io/sygil-webui/docs/1.windows-installation.html)**\n",
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"- **[Linux](https://sygil-dev.github.io/sygil-webui/docs/2.linux-installation.html)**\n",
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"\n",
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"### Want to ask a question or request a feature?\n",
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@ -172,7 +172,7 @@
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"\n",
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"If you want to use GFPGAN to improve generated faces, you need to install it separately.\n",
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"Download [GFPGANv1.4.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth) and put it\n",
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"into the `/sygil-webui/models/gfpgan` directory. \n",
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"into the `/sygil-webui/models/gfpgan` directory.\n",
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"\n",
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"### RealESRGAN\n",
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"\n",
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@ -182,7 +182,7 @@
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"There is also a separate tab for using RealESRGAN on any picture.\n",
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"\n",
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"Download [RealESRGAN_x4plus.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth) and [RealESRGAN_x4plus_anime_6B.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth).\n",
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"Put them into the `sygil-webui/models/realesrgan` directory. \n",
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"Put them into the `sygil-webui/models/realesrgan` directory.\n",
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"\n",
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"\n",
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"\n",
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@ -219,8 +219,8 @@
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"\n",
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"[Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion\n",
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"model.\n",
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"Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database. \n",
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"Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487), \n",
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"Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database.\n",
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"Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487),\n",
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"this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.\n",
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"With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.\n",
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"See [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion).\n",
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@ -229,26 +229,26 @@
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"\n",
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"Stable Diffusion v1 refers to a specific configuration of the model\n",
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"architecture that uses a downsampling-factor 8 autoencoder with an 860M UNet\n",
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"and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and \n",
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"and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and\n",
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"then finetuned on 512x512 images.\n",
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"\n",
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"*Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are present\n",
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"in its training data. \n",
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"in its training data.\n",
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"Details on the training procedure and data, as well as the intended use of the model can be found in the corresponding [model card](https://huggingface.co/CompVis/stable-diffusion).\n",
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"\n",
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"## Comments\n",
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"\n",
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"- Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)\n",
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" and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch). \n",
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" and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch).\n",
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" Thanks for open-sourcing!\n",
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"\n",
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"- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories). \n",
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"- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories).\n",
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"\n",
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"## BibTeX\n",
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"\n",
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"```\n",
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"@misc{rombach2021highresolution,\n",
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" title={High-Resolution Image Synthesis with Latent Diffusion Models}, \n",
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" title={High-Resolution Image Synthesis with Latent Diffusion Models},\n",
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" author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},\n",
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" year={2021},\n",
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" eprint={2112.10752},\n",
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@ -502,7 +502,7 @@
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" file_url = file_info[file]['download_link']\n",
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" if 'save_location' in file_info[file]:\n",
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" file_path = file_info[file]['save_location']\n",
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" else: \n",
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" else:\n",
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" file_path = models[model]['save_location']\n",
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" download_file(file_name, file_path, file_url)\n",
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"\n",
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@ -580,6 +580,54 @@
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"Run Streamlit through cloudflare."
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],
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"metadata": {
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"id": "QhazvrFG97zX"
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "jjjjjjjjjjjjjj"
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},
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"outputs": [],
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"source": [
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"#@title Run Streamlit through cloudflare.\n",
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"!wget https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64.deb\n",
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"!dpkg -i cloudflared-linux-amd64.deb\n",
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"\n",
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"import subprocess\n",
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"import threading\n",
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"import time\n",
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"import socket\n",
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"import urllib.request\n",
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"\n",
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"def iframe_thread(port):\n",
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" while True:\n",
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" time.sleep(0.5)\n",
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" sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n",
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" result = sock.connect_ex(('127.0.0.1', port))\n",
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" if result == 0:\n",
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" break\n",
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" sock.close()\n",
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"\n",
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" p = subprocess.Popen([\"cloudflared\", \"tunnel\", \"--url\", \"http://127.0.0.1:{}\".format(port)], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
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" for line in p.stderr:\n",
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" l = line.decode()\n",
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" if \"trycloudflare.com \" in l:\n",
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" print(\"This is the URL to access Sygil WebUI:\", l[l.find(\"http\"):], end='')\n",
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"\n",
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"\n",
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"threading.Thread(target=iframe_thread, daemon=True, args=(8501)).start()\n",
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"\n",
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"!streamlit run scripts/webui_streamlit.py --theme.base dark --server.headless true"
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]
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}
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]
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}
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}
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