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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.
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.
Works with M1 Macs too.
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.
I generate one image in about ~3 seconds with the DDIM sampler, 20 steps, on a RTX 2080Ti (~8it/s). The video on the Patreon page is sped up as it's not very interesting to sit and watch renders haha.
Although, some of the users who started using my UI weren't using the fork my app connects to, and were surprised it was a bit faster than what they were using before, so maybe you can give it a try. The repository is https://github.com/lstein/stable-diffusion
I've been running webui [1] on M1 MacBook Air 16GB RAM: 512x512, 50 steps takes almost 300 seconds. I'm suspecting that it is running on CPU, because the script says "Max VRAM used for this generation: 0.00G" and Activity Monitor says that it's using lots of CPU % and no GPU % at all. When M1 users are running stable diffusion, does the Activity Monitor show the GPU usage correctly?
The original txt2img and img2img scripts are a bit wonky and not all of the samplers work, but as long as you stick to dream.py and use a working sampler, I have had good luck with k_lms, then it works great and runs way faster than the cpu version.
Works great on 32gb ram but I'm honestly tempted to sell this one and get a 64gb model once the m2 pros come around. This is capable of eating up all the ram you can throw at it to do multiple pictures simultaneously.
You can now run the Lstein fork[1] 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. See the mac m1 readme [3]
[0] https://github.com/magnusviri/stable-diffusion
[1] https://github.com/lstein/stable-diffusion
[2] https://github.com/lstein/stable-diffusion/blob/main/README-...
[0]: https://github.com/lstein/stable-diffusion
Keep in mind to have the batch-size low (equal to 1, probably), that was my main issue when I first installed this.
Then, there's lot's of great forks already which add an interactive repl or web ui [0][1]. They also run with half-precision which saves a few bytes. Additionally, they optionally integrate with upscaling neural networks, which means you can generate 512x512 images with stable diffusion and then scale them up to 1024x1024 easily. Moreover, they optionally integrate with face-fixing neural networks, which can also drastically improve the quality of images.
There's also this ultra-optimized repo, but it's a fair bit slower [2].
[0]: https://github.com/lstein/stable-diffusion