textual_inversion
textual_inversion | Stable-textual-inversion_win | |
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30 | 15 | |
2,743 | 240 | |
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0.8 | 10.0 | |
about 1 year ago | over 1 year ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | MIT License |
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textual_inversion
- FLiP Stack Weekly for 06 February 2023
- Loading textual inversion embeddings in vanilla SD library?
- Embeddings without using AUTO1111
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How to use embeddings with PyTorch
Checking out https://github.com/rinongal/textual_inversion, which has some possibly informative examples and scripts.
- Textual Inversion
- Advice on Automatic1111 textual inversion tuning?
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Hi. Is training my own textual inversion feasible on one 1070? &how long does it take?
I think currently you will need about 20GB VRam..., options are: 1. https://github.com/rinongal/textual_inversion - localy
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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.
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How close are we to full movie generation from a technical standpoint?
That may mostly solve that but it’s too early right now: https://github.com/rinongal/textual_inversion
For fun I tried to make an entire animated music video but it took over one week of processing and basically fell apart coherently by 30 seconds so just did one third:
https://youtu.be/f3GfUKJBUYA
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Easy Textual Inversion tutorial. How To Train Stable Diffusion With Your Own Art.
The huggingface models don't work with the local stable diffusion, only the models trained locally with this repo https://github.com/rinongal/textual_inversion can be installed, at least for now.
Stable-textual-inversion_win
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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
What are some alternatives?
stable-diffusion - A latent text-to-image diffusion model
stable-diffusion
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
InvokeAI - InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.
sd-enable-textual-inversion - Copy these files to your stable-diffusion to enable text-inversion
stable-diffusion-webui - Stable Diffusion web UI
stable-diffusion - This version of CompVis/stable-diffusion features an interactive command-line script that combines text2img and img2img functionality in a "dream bot" style interface, a WebGUI, and multiple features and other enhancements. [Moved to: https://github.com/invoke-ai/InvokeAI]
stylegan2-projecting-images - Projecting images to latent space with StyleGAN2.
VideoX - VideoX: a collection of video cross-modal models
stable-diffusion