sd-parseq
Dreambooth-Stable-Diffusion
sd-parseq | Dreambooth-Stable-Diffusion | |
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8 | 100 | |
338 | 3,170 | |
- | - | |
8.9 | 6.8 | |
7 months ago | 4 months ago | |
TypeScript | Jupyter Notebook | |
MIT License | MIT License |
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sd-parseq
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Subject rotation with deforum
This is how it was done https://github.com/rewbs/sd-parseq/discussions/106
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Google researchers achieve performance breakthrough, rendering Stable Diffusion images in sub-12 seconds on a mobile phone. Generative AI models running on your mobile phone is nearing reality.
rewbs/sd-parseq (github.com)
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Music visualisation + stable diffusion + lots of animation parameter tweaking (method in comments)
sd-parseq for parameter control / keyframing.
- List of SD Tutorials & Resources
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RĂªverie, variation 91: revisiting a nightmare (Stable Diffusion using Deforum + Parseq, no editing)
I need to modify Parseq to add a way for you to be able to specify absolute values and then render deltas/slopes for use in loopback-based animations. Otherwise it's too hard to say e.g. "I want a 180 degree rotation over 16 beats": you have to figure out what increments will add up to 180 manually.
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Good news: VAE prevents the loopback magenta skew!
Cool! Correct me if I'm wrong, but isn't that kind of animation essentially loopback with incremental transforms on each frame before feeding back into SD? If so, that's why I'm so excited about the colour improvements too (I'm the author of sd-parseq, a tool for SD animations: https://github.com/rewbs/sd-parseq)! :)
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Quick demo of sd-parseq for a1111: cycling through some famous faces with oscillating prompt weights, denoising strength and zoom using sd-parseq (details in comment)
As described in this post, I've been working on a script for automatic1111 UI with its own separate companion UI for "sequencing" parameter changes over multiple generations, resulting in interesting videos that you can control precisely. This video gives a quick & dirty demo to give you a better idea of what it does. The output described in that video looks like this. If you're feeling adventurous you can check out the param flows directly in the parseq UI here (NB: only tested in Chrome so far, uses massive URLs which some browsers don't like). The code is here: https://github.com/rewbs/sd-parseq
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"Parameter Sequencer" for Automatic1111 WebUI
I've been playing with the idea of a custom script + companion UI to give fine grained control over various parameters when generating videos. It's still very rough but I think it's just about ready to share: https://github.com/rewbs/sd-parseq
Dreambooth-Stable-Diffusion
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Will there be comprehensive tutorials for fine-tuning SD XL when it comes out?
Tons of stuff here, no? https://github.com/JoePenna/Dreambooth-Stable-Diffusion/
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Useful Links
Joe Penna's Dreambooth (Tutorial|24GB) Most popular DB repo with great results.
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Dreambooth / Custom Training / Model - what's the state of the art?
1) The https://github.com/JoePenna/Dreambooth-Stable-Diffusion instructions say to use the 1.5 checkpoints - is that the latest? Can I use the 2+ models or?
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My Experience with Training Real-Person Models: A Summary
I quickly turned to the second library, https://github.com/JoePenna/Dreambooth-Stable-Diffusion, because its readme was very encouraging, and its results were the best. Unfortunately, to use it on Colab, you need to sign up for Colab Pro to use advanced GPUs (at least 24GB of VRAM), and training a model requires at least 14 compute units. As a poor Chinese person, I could only buy Colab Pro from a proxy. The results from JoePenna/Dreambooth-Stable-Diffusion were fantastic, and the preparation was straightforward, requiring only <=20 512*512 photos without writing captions. I used it to create many beautiful photos.
- I Used Stable Diffusion and Dreambooth to Create an Art Portrait of My Dog
- training
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Training a model on Iwanaga Kotoko (from in/spectre), which step do you guys think the model is at its best?
I've found EveryDream to be brilliant and have switched from JoePenna's Dreambooth because I've found I get better results so long as I provide good captions for all the images, even if preparing the dataset takes 3x as long (took me 2 hours to crop and label the 54 images).
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Dreambooth training results for face, object and style datasets with various prior regularization settings.
From what I know you can train with whatever size you want. But you need software that will support it. For example, ShivamShrirao/diffusers repo seems to allow a change of dimension. Also, you need HW that would support the training, because bigger images need more VRAM, for example,Joe Penna repo is using ~23GB with 512x512px so probably it's not a valid option. But the ShivamShrirao repo has optimizations that allow to run it with less VRAM.
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Starting to get quite good results with Dreambooth. What do you think? (Follow @RokStrnisa on Twitter for more.)
This is a good starting place: https://github.com/JoePenna/Dreambooth-Stable-Diffusion
- I'm a N00b with training stuff. Trying to get runpod with Dreambooth training some images (80 total) and I'm getting this error. Help?
What are some alternatives?
deforum-for-automatic1111-webui - Deforum extension script for AUTOMATIC1111's Stable Diffusion webui [Moved to: https://github.com/deforum-art/sd-webui-deforum]
Dreambooth-SD-optimized - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion
stable-diffusion-loopback-color-correction-script - A script for AUTOMATIC1111/stable-diffusion-webui that allows advanced color correction options for img2img loopback
Stable-Diffusion-Regularization-Images - For use with fine-tuning, especially the current implementation of "Dreambooth".
unprompted - Templating language written for Stable Diffusion workflows. Available as an extension for the Automatic1111 WebUI.
A1111-Web-UI-Installer - Complete installer for Automatic1111's infamous Stable Diffusion WebUI
StylePile - A prompt generation helper script for AUTOMATIC1111/stable-diffusion-webui and compatible forks
civitai - A repository of models, textual inversions, and more
artbot-for-stable-diffusion - A front-end GUI for interacting with the AI Horde / Stable Diffusion distributed cluster
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/Sygil-Dev/sygil-webui]
sd-akashic - A compendium of informations regarding Stable Diffusion (SD)
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.