stable-diffusion
sd-akashic
| stable-diffusion | sd-akashic | |
|---|---|---|
| 20 | 37 | |
| 338 | 1,595 | |
| - | - | |
| 0.0 | 1.4 | |
| over 1 year ago | about 1 year ago | |
| Jupyter Notebook | ||
| GNU General Public License v3.0 or later | The Unlicense |
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stable-diffusion
- [Machine Learning] [P] Exécutez une diffusion stable sur le GPU de votre M1 Mac
- High-performance image generation using Stable Diffusion in KerasCV
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Charl-e: “Stable Diffusion on your Mac in 1 click”
SD on an Intel mac with Vega graphics runs pretty well though — I think it ran at something like ~3-5 iterations/s for me, which is decent. I ran either https://github.com/magnusviri/stable-diffusion or https://github.com/lstein/stable-diffusion which have MPS support
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Stable Diffusion PR optimizes VRAM, generate 576x1280 images with 6 GB VRAM
https://github.com/magnusviri/stable-diffusion/commit/d0b168...
Copying this change fixed seeds on M1 for me.
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Intel Mac User, How do I start?
You should be able to run it on a CPU. Maybe try this version. If MPS is supported on your Mac you can check this out.
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[P] Run Stable Diffusion on your M1 Mac’s GPU
A group of open source hackers forked Stable Diffusion on GitHub and optimized the model to run on Apple's M1 chip, enabling images to be generated in ~ 15 seconds (512x512 pixels, 50 diffusion steps).
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Run Stable Diffusion on Your M1 Mac’s GPU
Magnusviro [0], the original author of the SD M1 repo credited in this article, has merged his fork into the Lstein Stable Diffusion repo [1], and you can now run Lstein fork with M1 as of a few hours ago.
This adds a ton of functionality - GUI, Upscaling & Facial improvements, weighted subprompts etc.
This has been a big undertaking over the last few days, and I highly recommend checking it out.
[0] https://github.com/magnusviri/stable-diffusion
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How are Mac people using Windows for A.I. stuff?
You can run it on an M1. Using a macbook M1 pro max with 32Gb I get 512x512 in about 50 seconds. use this branch https://github.com/magnusviri/stable-diffusion/tree/apple-mps-support
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ResolvePackageNotFound
I had this error too, and I tried a ton of things to get cudatoolkit to install, without any luck. This fork has an environment-mac.yml file that actually got it working on my M1 Max: https://github.com/magnusviri/stable-diffusion/tree/apple-silicon-mps-support
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If I set a seed value and re-run using the exact same settings, should I get the same image back each time?
But when I run it (locally, using the Mac M1 port), every time I run it creates a different image.
sd-akashic
- [Stable Diffusion] La longueur maximale utilisable d'une invite de texte de diffusion stable est prétendument 77 jetons. Voici ce que cela signifie et comment tester le nombre de jetons dans votre invite de texte.
- [Stablediffusion] Que font exactement les guidance scale ?
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Model Testing - Realistic portraits with a study of various artists (A's)
The very study you linked to also did this same thing, as have multiple others, and they have a visible, attributable, and even measurable effect exactly as I pointed out. Meanwhile yours doesn't, to the point of being attributable to noise on many. Therefor, "Don't be fooled."
- [Stablediffusion] Que fait exactement la Guidance Scale ?
- Is this tutorial legit?
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Hi folks, thought I'd ask for help here because I'm quite a noob when it comes to stable diffusion. I have the problem that I want to generate landscapes and every time I get double mountains. I used negative (((Duplicate mountain))), double mountains,(((extra mountain))) but it doesn't help. Though
I remember referencing this image a lot - https://github.com/Maks-s/sd-akashic/blob/master/img/brbbbq-dimensions.png
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Improving old 3D renders with AI and SD
I've found the keywords to improve the results in this repository along with a lot of usefull info.
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What kind of limits would I have with an RTX 2070?
Picture Ratios
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List of SD Tutorials & Resources
Stable Diffusion Akashic Records
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Intro to Stable Diffusion: Resources and Tutorials
I think the current best list is at https://github.com/Maks-s/sd-akashic. There are now many such lists floating around. (I'm currently starting the hoarding of data and see if I can add to that list the many new links.)
What are some alternatives?
openvino - OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
Dreambooth-Stable-Diffusion - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) by way of Textual Inversion (https://arxiv.org/abs/2208.01618) for Stable Diffusion (https://arxiv.org/abs/2112.10752). Tweaks focused on training faces, objects, and styles.
stable-diffusion-webui-docker - Easy Docker setup for Stable Diffusion with user-friendly UI
rocm-build - build scripts for ROCm
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/sd-webui/stable-diffusion-webui]
stable-diffusion - Optimized Stable Diffusion modified to run on lower GPU VRAM
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
stable-diffusion - A latent text-to-image diffusion model
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]
stable-diffusion-webui - Stable Diffusion web UI