stable-diffusion
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GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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stable-diffusion
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DALL·E Now Available Without Waitlist
No, sorry, but there's a whole bunch of one-click things now, I think?
I'm running it on Windows 10 using (a modified version of) https://github.com/bfirsh/stable-diffusion.git and Anaconda to create the environment from their `environment.yaml` (all of which was done using the normal `cmd` shell). Then to use it, I activate that env from `cmd` and switch into cygwin `bash` to run the `txt2img.py` script (because it's easier to script, etc.)
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How do I save the arguments for images I create when using the terminal? (Apple M1 Pro)
I am using the bfirsh version. And yes, I run "pyhthon scripts/txt2imp.py" to generate an image.
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Current canonical way to install Stable Diffusion on Apple Silicon?
Specifically regarding the first option above, I see that the procedure clones the repository from: https://github.com/bfirsh/stable-diffusion.git
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One-Click Install Stable Diffusion GUI App for M1 Mac. No Dependencies Needed
Just done a run on my 3080 under Windows using https://github.com/bfirsh/stable-diffusion.git and it's about 8 iterations/sec when nothing else is using CPU or GPU.
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Using the same seed and same prompt is still resulting in two different images?
I've cloned this repository on my M1 Mac: https://github.com/bfirsh/stable-diffusion/tree/apple-silicon-mps-support
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Run Stable Diffusion on Your M1 Mac’s GPU
Boom - nice. Here's a fork with that: https://github.com/bfirsh/stable-diffusion/tree/lstein
Requirements are "requirements-mac.txt" which'll need subbing in the guide.
We're testing this out with a few people in Discord before shipping to the blog post.
stable-diffusion
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Go is bigger than crab!
Which is a 1-click install of Stable Diffusion with an alternative web interface. You can choose a different approach but this one is pretty simple and I am new to this stuff.
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Why & How to check Invisible Watermark
an invisible watermarking of the outputs, to help viewers identify the images as machine-generated.
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How to create an Image generating AI?
It sounds like you just want to set up Stable Diffusion to run locally. I don't think your computer's specs will be able to do it. You need a graphics card with a decent amount of VRAM. Stable diffusion is in Python as is almost every AI open source project I've seen. If you can get your hands on a system with an Nvidia RTX card with as much VRAM as possible, you're in business. I have an RTX 3060 with 12 gigs of VRAM and I can run stable diffusion and a whole variety of open source LLMs as well as other projects like face swap, Roop, tortoise TTS, sadtalker, etc...
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Two video cards...one dedicated to Stable Diffusion...the other for everything else on my PC?
Use specific GPU on multi GPU systems · Issue #87 · CompVis/stable-diffusion · GitHub
- Automatic1111 - Multiple GPUs
- Ist Google inzwischen einfach unbrauchbar?
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Why are people so against compensation for artists?
I dealt with this in one of my posts. At least SD 1.1 till 1.5 are all trained on a batch size of 2048. The version pretty much everyone uses (1.5) is first pretrained at a resolution of 256x256 for 237K steps on laion2B-en, at the end of those training steps it will have seen roughly 500M images in laion2B-en. After that it is pre-trained for 194K steps on laion-high-resolution at a resolution of 512x512, which is a subset of 170M images from laion5B. Finally it is trained for 1.110K steps on LAION aesthetic v2 5+. This is easily verified by taking a glance at the model card of SD 1.5. Though that one doesn't specify for part of the training exactly which aesthetic set was used for part of the training, for that you have to look at the CompVis github repo. Thus at the end of it all both the most recent images and the majority of images will have come from LAION aesthetic v2 5+ (seeing every image approx 4 times). Realistically a lot of the weights obtained from pretraining on 2B will have been lost, and only provided a good starting point for the weights.
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Is SDXL really open-source?
stable diffusion · CompVis/stable-diffusion@2ff270f · GitHub
- I want to ask the AI to draw me as a Pokemon anime character then draw six of Pokemon of my choice next to me. What are my best free, 15$ or under and 30$ or under choices?
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how can i create my own ai image model
Here for example --> https://github.com/CompVis/stable-diffusion
What are some alternatives?
stable_diffusion.openvino
GFPGAN - GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
sd-webui-colab - A repo for the maintenance of the Colab version of stable-diffusion-webui repo
diffusers-uncensored - Uncensored fork of diffusers
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]
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
invisible-watermark - python library for invisible image watermark (blind image watermark)
VQGAN-CLIP - Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.
stable-diffusion-rocm
onnx - Open standard for machine learning interoperability