stable-diffusion
Stable-textual-inversion_win | stable-diffusion | |
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15 | 26 | |
240 | 203 | |
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10.0 | 0.0 | |
over 1 year ago | over 1 year ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | GNU General Public License v3.0 or later |
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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
stable-diffusion
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Trying to merge model checkpoints and getting an error
Looks like Doggettx is a fork of CompVis/stable-diffusion, as a proof of concept:
- Stable Diffusion links from around September 11, 2022 that I collected for further processing
- Stable Diffusion for AMD GPUs on Windows using DirectML (Txt2Img, Img2Img & Inpainting) easy to setup (Python + Git)
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Has anyone made a commandline client to use Automatic1111's version of Stable Diffusion over the network?
Don't use a UI if you want terminal access. Use a project meant for terminal. https://github.com/Doggettx/stable-diffusion/tree/autocast-improvements
- Looking at cheap high VRAM old tesla cards to run stable diffusion at high res!
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Looking for a script I saw mentioned but can't find. Prompt Editing over Steps
The feature is just called prompt editing or prompt2prompt. It is also implemented in the Automatic1111 webui.
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Any way to fix this?
Depends on what fork you are using but its just means you are running out of vram since it states you only have 4gb of it. You may need to use the optimizedsd scripts and use the Doggettx's attention.py, you can find this in ldm/modules/attention.py (I personally have 2 of those in my own folder since I need to switch them but typically you require 6gb min for sd.
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Jabba The Hutt as a newborn
I installed SD from the CompVis GitHub repo and then swapped in modifications (namely attention.py and main.py) done by u/Doggettx that can be found here to overcome CUDA Out Of Memory issues. Going to try larger image sizes next. I wish you all good luck with concentrating on real work with this imaginatron around! ðŸ¤
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Stable Diffusion Gui Benchmark Results: Loading... Generated 1 image in 5.58s (20/20)
using optimized attention.py and model.py from this github issue.
- This community continues to blow me away. 8 days ago I was amazed by my 1408 x 960 resolution image. With all the new features I'm now doing 6 megapixel native output (3072x2048). That's 24 times more pixels than 512x512. Full workflow in comments.
What are some alternatives?
stable-diffusion
stable-diffusion
textual_inversion
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.
stable-diffusion - A latent text-to-image diffusion model
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
sd-enable-textual-inversion - Copy these files to your stable-diffusion to enable text-inversion
stable-diffusion-webui - Stable Diffusion web UI
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
Dreambooth-Stable-Diffusion - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion
stylegan2-projecting-images - Projecting images to latent space with StyleGAN2.
dream-textures - Stable Diffusion built-in to Blender