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README.md
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README.md
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@ -4,25 +4,31 @@ _Google Colab notebooks ported to standard Jupiter for Paperspace._
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Don't post bug reports on the Kazakhstan rock collection forum. Create an issue here.
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Don't post bug reports on the Kazakhstan rock collection forum. Create an issue here.
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### Getting Started
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### Getting Started
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[Docs/Paperspace Guide for Idiots.md](https://github.com/Engineer-of-Stuff/stable-diffusion-paperspace/blob/main/Docs/Paperspace%20Guide%20for%20Retards.md)
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[Docs/Paperspace Guide for Idiots.md](https://github.com/Engineer-of-Stuff/stable-diffusion-paperspace/blob/main/Docs/Paperspace%20Guide%20for%20Retards.md)
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### Notebook Descriptions
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### Notebook Descriptions
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**StableDiffusionUI_Voldemort_paperspace.ipynb**
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#### StableDiffusionUI_Voldemort_paperspace.ipynb
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Voldemort's webUI. His repository is updated frequently so the notebook will update the local copy if it's already installed. Can run the normal model or waifu-diffusion.
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AUTOMATIC1111's webUI. Designed to get a newbie set up, features lots of error checking and automation.
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#### Cyberes_Textual_Inversion_Training.ipynb
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All the commands you need to do create a textual inversion embedding. Does not hold your hand or guide you through the process. Any issues related to this notebook will be closed.
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**CodeFormer_Inference_Simplified.ipynb**
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#### lfs/latent-diffusion
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CodeFormer face fixer standalone. Can process images in bulk.
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Holds stuff for latent-diffusion. Probably not needed but latent-diffusion had them stored on some random file hosting service.
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#### /other Directory
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Other notebooks and code.
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**stable_diffusion_webui_hlky_paperspace_09-07-2022.ipynb**
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- CodeFormer_Inference_Simplified.ipynb
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- stable_diffusion_webui_hlky_paperspace_09-07-2022.ipynb
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hlky's webui
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- sd-concepts-library_Exporter.ipynb
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- Huggingface Textual Inversion Training.ipynb
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- download_sd-concepts-library.py
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- Misc. old stuff
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# Getting Started on Paperspace for Retards <3
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**What is Paperspace?**
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Basically, a cloud compute service for AI development. You run your code on their hardware.
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**Why Paperspace?**
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Paperspace is convenient, free, and you probably don't have a computer good enough to run the AI. They're aimed at professionals whereas Colab is literally for children.
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You'll run your code on their powerful GPUs which is much faster and easier than locally on your computer.
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**Do I have to pay?**
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Only if you feel limited by their free tier. You should have everything you need to generate ~~porn~~ really neat images. I'm pretty Paperspace is cheaper than Google Colab Pro.
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**Why not Google Colab?**
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Google Colab is an alternative, but their free tier is more restrictive than Paperspace's and has less powerful hardware. And, knowing Google, they probably record what you generate.
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**But I want to run it on Google Colab!**
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Ok fine. [Here's the official colab notebook by Voldy himself.](https://colab.research.google.com/drive/1Iy-xW9t1-OQWhb0hNxueGij8phCyluOh)
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**But I'd rather run it on my own computer!**
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Running it in the cloud is much easier and you're less likely to mess up your OS, but since you insist here's a Docker container to make is simpler: [AbdBarho/stable-diffusion-webui-docker](https://github.com/AbdBarho/stable-diffusion-webui-docker)
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## Let's do it!
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1. [Create an Account](https://console.paperspace.com/signup) You will be asked for your phone number. **You can use a VOIP number such as Google Voice, they don't block it!**
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2. Click this button and fill out the form that pops up
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3. Click this button to create a notebook
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4. Under "Runtime" select "Start from Stratch"
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5. Download [StableDiffusionUI_Voldemort_paperspace.ipynb](https://github.com/Engineer-of-Stuff/stable-diffusion-paperspace/blob/main/StableDiffusionUI_Voldemort_paperspace.ipynb)
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6. Upload that file to your notebook
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7. Follow directions
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## Launch the Jupyter Notebook
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Click this symbol in the left vertical menubar.
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You can access the terminal in the Jupyter Notebook.
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## I need more help :(
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Here's some other guides.
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- [FINAL GUI RETARD GUIDE](https://rentry.org/voldy)
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- [Using the WebUI](https://rentry.org/voldy)
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- [Using the Inpainter](https://rentry.org/drfar)
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- [Textual Inversion](https://rentry.org/aikgx)
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- [Crowd-Sourced Prompts](https://lexica.art/)
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- [Artist Name Prompts](https://sgreens.notion.site/sgreens/4ca6f4e229e24da6845b6d49e6b08ae7?v=fdf861d1c65d456e98904fe3f3670bd3)
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[Images hosted on Github](https://github.com/Engineer-of-Stuff/stable-diffusion-paperspace)
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[Github Mirror](https://github.com/Engineer-of-Stuff/stable-diffusion-paperspace/blob/main/docs/archives/Getting%20Started%20on%20Paperspace.pdf)
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# Other
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#### CodeFormer_Inference_Simplified.ipynb
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CodeFormer face fixer standalone. Can process images in bulk.
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#### stable_diffusion_webui_hlky_paperspace_09-07-2022.ipynb
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An alternative to AUTOMATIC1111's WebUI from a guy named hlky. Ported to Paperspace.
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#### sd-concepts-library_Exporter.ipynb
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Download all textual inversion embeddings in huggingface.co/sd-concepts-library. May or may not work.
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#### Huggingface Textual Inversion Training.ipynb
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Train textual inversion embeddings using the HuggingFace API. Doesn't work as well as my `Textual Inversion Training.ipynb` notebook.
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#### download_sd-concepts-library.py
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Basically `sd-concepts-library_Exporter.ipynb` but a Python script. Can download restricted models.
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# Old
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#### voldy's_colab_paperspace_09-07-2022.ipynb
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First iteration of the WebUI for Paperspace.
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#### waifu-diffusion_quick_n_dirty_09-07-2022.ipynb
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Quick 'n dirty notebook made to quickly test Waifu Diffusion the day it was released.
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import argparse
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import datetime
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import os
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import shutil
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import sys
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from urllib import request as ulreq
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import requests
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from huggingface_hub import HfApi
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from PIL import ImageFile
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def getsizes(uri):
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# https://stackoverflow.com/a/37709319
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# get file size *and* image size (None if not known)
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file = ulreq.urlopen(uri)
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size = file.headers.get("content-length")
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if size:
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size = int(size)
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p = ImageFile.Parser()
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while True:
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data = file.read(1024)
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if not data:
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break
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p.feed(data)
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if p.image:
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return size, p.image.size
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break
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file.close()
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return (size, None)
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parser = argparse.ArgumentParser()
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parser.add_argument('out_file', nargs='?', help='file to save to')
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args = parser.parse_args()
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print('Will save to file:', args.out_file)
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# Get list of models under the sd-concepts-library organization
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print('Getting list of models...')
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api = HfApi()
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models_list = []
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for model in api.list_models(author="sd-concepts-library"):
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models_list.append(model.modelId.replace('sd-concepts-library/', ''))
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models_list.sort()
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html_struct = """<!DOCTYPE html>
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<html lang="en">
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<head>
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<title>Stable Diffusion Texual Inversion Models</title>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1">
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<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.2.1/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-iYQeCzEYFbKjA/T2uDLTpkwGzCiq6soy8tYaI1GyVh/UjpbCx/TYkiZhlZB6+fzT" crossorigin="anonymous">
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</head>
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<body>
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<style>
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.img-wrapper {
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display: flex;
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margin-top: 40px;
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}
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.img1,
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.img2,
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.img3 {}
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.thumbnail {
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height: 185px;
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}
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.model-link {}
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.model-title {
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margin-top: 100px;
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}
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.model-link-wrapper {}
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</style>
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<div class="container" style="margin-bottom: 180px;">
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<div class="jumbotron text-center" style="margin: 45px;"><h1>Stable Diffusion Texual Inversion Models</h1></div>
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<p><i>Page updates daily. Last updated {datetime.datetime.now().strftime("%A, %B %d %Y")}.</i></p>
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<p>
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Generated from <a href="https://huggingface.co/sd-concepts-library">huggingface.co/sd-concepts-library</a>
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</p>
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<p>
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Downloaded models are straight from the HuggingFace repositories and are named learned_embeds.bin. Rename to model_name.pt
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</p>
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<br><hr>
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"""
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i = 1
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for model_name in models_list:
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if i == 3:
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break
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print(f'{i}/{len(models_list)} -> {model_name}')
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# if os.path.exists(f'{model_name}/learned_embeds.bin'): # double check the file exists since sometimes it hasn't been uploaded yet
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# shutil.move(f'{model_name}/learned_embeds.bin', f'{model_name}/{model_name}.pt')
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# pass
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# else:
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# continue
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# Images can be in a few different formats, figure out which one it's in
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img_type = None
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img_width = None
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for type in ['jpeg', 'png', 'jpg']:
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r = requests.head(f'https://huggingface.co/sd-concepts-library/{model_name}/resolve/main/concept_images/0.{type}', allow_redirects=True)
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if r.status_code == 200:
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img_type = type
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img_width = getsizes(f'https://huggingface.co/sd-concepts-library/{model_name}/resolve/main/concept_images/0.{type}')[1][0]
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break
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html_struct = html_struct + f"""<h3 class="model-title">{model_name}</h3>
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<p><a class="model-link" href="https://huggingface.co/sd-concepts-library/{model_name}/resolve/main/learned_embeds.bin">Download {model_name}</a></p>
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<p><a class="model-link" href="https://huggingface.co/sd-concepts-library/{model_name}/">View Repository</a></p>
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<div class="img-wrapper">
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<div class="img1" style="width:{img_width}px;">
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<img class="thumbnail" src="https://huggingface.co/sd-concepts-library/{model_name}/resolve/main/concept_images/0.{img_type}">
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</div>
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<div class="img2" style="width:{img_width}px;">
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<img class="thumbnail" src="https://huggingface.co/sd-concepts-library/{model_name}/resolve/main/concept_images/1.{img_type}">
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</div>
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<div class="img3" style="width:{img_width}px;">
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<img class="thumbnail" src="https://huggingface.co/sd-concepts-library/{model_name}/resolve/main/concept_images/2.{img_type}">
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</div>
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</div>"""
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i = i + 1
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html_struct = html_struct + '</div></body></html>'
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f = open(args.out_file, 'w')
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f.write(html_struct)
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f.close()
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