stylegan2-projecting-images
Projecting images to latent space with StyleGAN2. (by woctezuma)
Stable-textual-inversion_win | stylegan2-projecting-images | |
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15 | 135 | |
240 | 288 | |
- | - | |
10.0 | 10.0 | |
over 1 year ago | about 1 year ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stable-textual-inversion_win
Posts with mentions or reviews of Stable-textual-inversion_win.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-09-26.
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Using DreamBooth on SD on a 3090 w/24gb VRAM (about 1.5 hrs to train)
Would it be possible for you to add this new code in the "regular" textual inversion code? like in this one : https://github.com/nicolai256/Stable-textual-inversion_win - I'm using a 3090, batch size of 3, workers 10, size 384 - works pretty good but if your modification could reduce the VRAM, it could go faster.
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Question About Running Local Textual Inversion
Rinongal and nicolai256 versions, the latter of which is also the one explained in Nerdy Rodent's youtube video https://www.youtube.com/watch?v=WsDykBTjo20, work but they also have an issue of lacking editability in comparison to one made by huggingface's collab which is followed up in a very long issue on Rinongal's Github. You can add accumulate_grad_batches: 4 to the end of the finetune files like shown in Nerdy Rodent's video at this time stamp to try to alleviate this issue, but the quality isn't as good as one made in the online collab.
- NMKD Stable Diffusion GUI 1.4.0 is here! Now with support for inpainting, HuggingFace concepts, VRAM optimizations, and the model no longer needs to be reloaded for every prompt. Full changelog in comments!
- Useful link
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I like Disco Elysium so have been trying some Textual Inversion training + some internal prompt business to replicate the look of the portraits.
the prompt for this one was "a portrait of beautiful young \, painting by Michael Garmash and Kilian Eng, in the style of &",* after training * with pictures of my GF and & with all the Disco Elysium portrait pictures. using the stuff here: https://github.com/nicolai256/Stable-textual-inversion_win, also, thank you u/ExponentialCookie.
- My Stable Diffusion GUI update 1.3.0 is out now! Includes optimizedSD code, upscaling and face restoration, seamless mode, and a ton of fixes!
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Textual Inversion Help
Here is an alternate fork of the repo you talked about: https://github.com/nicolai256/Stable-textual-inversion_win
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Is there any info on how to finetune without using textual inversion?
From my understanding the only finetuning people are doing currently is using textual inversion (this https://github.com/nicolai256/Stable-textual-inversion_win/ and this https://www.reddit.com/r/StableDiffusion/comments/wvzr7s/tutorial_fine_tuning_stable_diffusion_using_only/), but this seems very different from the real finetuning Emad was talking about, and what others (like NovelAI) are doing?
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A user did an Arvalis / RJ Palmer fine-tune (textual inversion)
Cred. to florishdiffusion for showing these gens. I'm not knowledgeable on how to use text inversion but it is possible to do in Free Colab from this source
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Self Portrait, using SD and textual inversion trained on images of myself
what is your --init_word? also what is your prompt for generation? i have doing person training for 6 day and not getting a good results damn! i use https://github.com/nicolai256/Stable-textual-inversion_win
stylegan2-projecting-images
Posts with mentions or reviews of stylegan2-projecting-images.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-04-15.
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Getting Started with Gemma Models
A Colab notebook.
- Welcome to Colaboratory
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A playground to practice differential privacy - Antigranular
To play with the dataset, we first must create a Jupyter notebook, a powerful and popular tool among data engineers. I created mine on Google Colab.
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Topic and Subtopic Extraction with the Google Gemini Pro
Please head over to the Google Colab
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How do I begin building AI tools for myself?
But regardless of what you want to do, you'll probably use Python. In this context, a good way to work with Python is using Jupyter Notebooks. So you should start with installing Python and Jupyter and go from there. If you want to get started without installing anything, Google Colab gives you a remote Jupyter Notebook which runs in the browser for free.
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教程:使用 Google Colab 安全地转发 B 站视频
访问 Google Colab 。
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Journey into Jupyter Notebooks: A Beginner's Guide
Remember school days when you'd share notes with classmates? Jupyter takes that spirit and amplifies it. Once you've crafted your Notebook, you can share it with peers, collaborators, and the world. Platforms like GitHub and Google's Colab natively render Jupyter Notebooks. It's like penning an open letter to the world but in a delightful mix of code, text, and visuals.
- This feels like an obvious question, but if I load a pickle file that is 1GB in size, is it taking up 1GB of memory?
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Leveraging Google Colab to run Postgres: A Comprehensive Guide
Open your web browser and navigate to Google Colab.
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No excuses to start working with Python
Using Google Colab you can develop Python codes, similar to Jupyter Notebooks. You will have an environment prepared with various Python libraries. In addition you have tips on small codes for development, some tutorials, gihub connection, cloud -saved notebooks and more.
What are some alternatives?
When comparing Stable-textual-inversion_win and stylegan2-projecting-images you can also consider the following projects:
stable-diffusion
fast-stable-diffusion - fast-stable-diffusion + DreamBooth
textual_inversion
stable-diffusion-webui-colab - stable diffusion webui colab
stable-diffusion - A latent text-to-image diffusion model
gimp-stable-diffusion
sd-enable-textual-inversion - Copy these files to your stable-diffusion to enable text-inversion
discoart - 🪩 Create Disco Diffusion artworks in one line
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
quickstart-android - Firebase Quickstart Samples for Android
stable-diffusion
comfyui-colab - comfyui colabs templates new nodes
Stable-textual-inversion_win vs stable-diffusion
stylegan2-projecting-images vs fast-stable-diffusion
Stable-textual-inversion_win vs textual_inversion
stylegan2-projecting-images vs stable-diffusion-webui-colab
Stable-textual-inversion_win vs stable-diffusion
stylegan2-projecting-images vs gimp-stable-diffusion
Stable-textual-inversion_win vs sd-enable-textual-inversion
stylegan2-projecting-images vs discoart
Stable-textual-inversion_win vs bitsandbytes
stylegan2-projecting-images vs quickstart-android
Stable-textual-inversion_win vs stable-diffusion
stylegan2-projecting-images vs comfyui-colab