stable-diffusion
txt2imghd
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stable-diffusion | txt2imghd | |
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142 | 22 | |
2,438 | 679 | |
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9.8 | 0.0 | |
over 1 year ago | over 1 year ago | |
Jupyter Notebook | Python | |
GNU General Public License v3.0 or later | MIT License |
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stable-diffusion
- [Machine Learning] [P] Exécutez une diffusion stable sur le GPU de votre M1 Mac
- Its time!
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Anybody running SD on a Macbook Pro? What are you using and how did you install it?
Yes, you can install it with Python! https://github.com/lstein/stable-diffusion works with macOS, and you can control all the common parameter via their WebUI or CLI :)
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How do I save the arguments for images I create when using the terminal? (Apple M1 Pro)
I'm using lstein fork ("dream") and when I create an image from the terminal, it also writes back to the terminal like this:
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I Resurrected “Ugly Sonic” with Stable Diffusion Textual Inversion
Stable Diffusion is wild - the space has been quickly developing and watching the pace of development makes me reconsider what I consider "staggering". I've been blown away. The accessibility of this technology is even more incredible - there's even a fork that is working on M1 Macs (https://github.com/lstein/stable-diffusion)
We are in for some interesting times. Whatever the next iteration of Textual Inversion is will be extremely disruptive, especially if the concepts continue to be developed collectively.
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AI Seamless Texture Generator Built-In to Blender
Oh, it generates from a text prompt, not a sample texture. I thought this was just a tool to generate wrapped textures from non-wrapped ones.
The licensing is a mess. The Blender plug-in is GPL 3, the stable diffusion code is MIT, and the weights for the model have a very restrictive custom license.[1] Whether the weights, which are program-generated, are copyrightable is a serious legal question.
[1] https://github.com/lstein/stable-diffusion/blob/61f46cac31b5...
> Whenever I ask for something like ‘seamless tiling xxxxxx’ it kinda sorta gets the idea, but the resulting texture doesn’t quite tile right.
Getting seamless tiling requires more than just have "seamless tiling" in the prompt. It also depends on if the fork you're using has that feature at all.
https://github.com/lstein/stable-diffusion has the feature, but you need to pass it outside the prompt. So if you use the `dream.py` prompt cli, you can pass it `"Hats on the ground" --seamless` and it should be perfectly tilable.
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Auto SD Workflow - Update 0.2.0 - "Collections", Password Protection, Brand new UI + more
From https://github.com/lstein/stable-diffusion
Yes, works perfectly fine as of 1e8e5245ebca5211e271f35a3a849dee8f4793d2 which contains the performance improvements. Probably works fine with later commits too but I haven't personally tested them so won't vouch for it.
txt2imghd
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What do the "Scale Latent" and "Denoising strength" settings (under Highres. fix) do in the Automatic1111 repo?
The dictionary definition of upscale is to increase the size or improve the quality of. or convert (an image or video) so that it displays correctly in a higher resolution format. It's just scaling in one direction, the opposite of downscaling. Even the original author of high res fix uses word upscaling because the original library uses Real-ESRGAN to upscale transient images. A1111 repo provides an option to use an upscaling model or just bilinear interpolation for this step.
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Upscale to huge sizes and add detail with SD Upscale, it's easy!
(4) What the SD upscaler does is chop the image into tiles, re-run SD on the tiles, and then stitch them back together. img2imgHD, and this custom script are other options that do a similar thing. You can also do it manually and combine the tiles in photoshop/gimp/krita, but it's a lot more work.
- What should I even call this?
- Obama Biden is pretty cursed...
- Landscapes, both scifi and fantasy, are incredible in this model. Made a few new wallpapers using a combination of img2imghd + RealESRGan with some great results. Things really took off once I learned how to use the --generated option to upscale other finds! Original pics at 150 steps, 768x512
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A better (?) way of doing img2img by finding the noise which reconstructs the original image
https://github.com/jquesnelle/txt2imghd The link was literally in the Auto's code's comments.
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any go big solution for stable diffusion?
This exact thing DOES exist... kinda. https://github.com/jquesnelle/txt2imghd Not sure if there's a Colab notebook for it yet, though. I'd use it in a heartbeat if there was one.
- txt2imghd: Generate high-res images with Stable Diffusion
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StableDiffusion RUNS on M1 chips.
I was looking for a new script that could implement this process directly with the prompt and I found this: https://github.com/jquesnelle/txt2imghd/blob/master/README.md
What are some alternatives?
waifu-diffusion - stable diffusion finetuned on weeb stuff
taming-transformers - Taming Transformers for High-Resolution Image Synthesis
stable-diffusion-webui - Stable Diffusion web UI
diffusers-uncensored - Uncensored fork of diffusers
dream-textures - Stable Diffusion built-in to Blender
diffusionbee-stable-diffusion-ui - Diffusion Bee is the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed.
stable-diffusion - A latent text-to-image diffusion model
stable-diffusion-gobig-txt2imghd - Stable diffusion using a standalone implementation of txt2imghd, which is similar to GoBig
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/sd-webui/stable-diffusion-webui]
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
stable-diffusion - Optimized Stable Diffusion modified to run on lower GPU VRAM