BLIP
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BLIP | Stable-textual-inversion_win | |
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14 | 15 | |
4,242 | 240 | |
5.5% | - | |
0.0 | 10.0 | |
7 months ago | over 1 year ago | |
Jupyter Notebook | Jupyter Notebook | |
BSD 3-clause "New" or "Revised" License | MIT License |
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BLIP
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MetaCLIP – Meta AI Research
I suggest trying BLIP for this. I've had really good results from that.
https://github.com/salesforce/BLIP
I built a tiny Python CLI wrapper for it to make it easier to try: https://github.com/simonw/blip-caption
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Is there a website where you can upload a photo and get the description in a paragraph?
You can download the source and run it yourself from here: https://github.com/salesforce/BLIP
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Stable Diffusion v2-1-unCLIP model released
Then there's also BLIP (Bootstrapping Language-Image Pre-training).
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GPT-4 shows emergent Theory of Mind on par with an adult. It scored in the 85+ percentile for a lot of major college exams. It can also do taxes and create functional websites from a simple drawing
Or BLIP
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meme
GitHub - salesforce/BLIP: PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
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Object Recognition for Photo Metadata
From what I understand, what's most important to you is having a model that's already trained on something, rather than the architecture. Yolo is probably fine, as would be some of the older ones. You should be able to find a model that's been pretrained on COCO - you can look at see what classes are included. I don't know if there are other broadly trained models available that will serve your purpose. What I'd do is just run your picture through a COCO trained object detection model and see if the annotations do what you want.
Though backing up a bit, there are also image captioning models that may better do what you want to do for organizing your photos. I'm not really familiar with any - though I did come across BLIP the other day but I haven't used it: https://github.com/salesforce/BLIP
This may be a better way to get at what you want
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I have a problem with the "interrogate" function of Automatic1111's fork. Can someone help me?
git clone https://github.com/salesforce/BLIP.git repositories/BLIP
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Stable-diffusion in Nix
# Copy models as described in README cp ~/Downloads/model.ckpt . cp ~/Downloads/GFPGANv1.3.pth . # Clone other repos as mentioned in README mkdir repositories git clone https://github.com/CompVis/stable-diffusion.git repositories/stable-diffusion git clone https://github.com/CompVis/taming-transformers.git repositories/taming-transformers git clone https://github.com/sczhou/CodeFormer.git repositories/CodeFormer git clone https://github.com/salesforce/BLIP.git repositories/BLIP export NIXPKGS_ALLOW_UNFREE=1 nix-shell default.nix pip install torch --extra-index-url https://download.pytorch.org/whl/cu113 # Also from linux instructions. Can probably be added to default.nix python webui.py
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My easy-to-install Windows GUI for Stable Diffusion is ready for a beta release! It supports img2img as well, various samplers, can run multiple scales per image automatically, and more!
Also check img2text (basically to prompt): https://github.com/salesforce/BLIP
- [D] Author Interview - BLIP: Bootstrapping Language-Image Pre-training (Video)
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?
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
stable-diffusion
a-PyTorch-Tutorial-to-Image-Captioning - Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning
textual_inversion
CodeFormer - [NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer
stable-diffusion - A latent text-to-image diffusion model
virtex - [CVPR 2021] VirTex: Learning Visual Representations from Textual Annotations
sd-enable-textual-inversion - Copy these files to your stable-diffusion to enable text-inversion
nix-stable-diffusion - Nix-friendly fork of: Optimized Stable Diffusion modified to run on lower GPU VRAM
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
taming-transformers - Taming Transformers for High-Resolution Image Synthesis
stylegan2-projecting-images - Projecting images to latent space with StyleGAN2.