61–70 of 415 posts
How fast is it on a m1?
It takes 1-2 mins for a 512x512 image. It's been a lot of fun since I did this last night.
If you extrapolate the power consumption (3070 @~300w vs M1 Pro GPU@~30-50w) the metrics make a lot of sense.
Earlier quoted context omitted.
That was fast. I'm only getting 5.26s/iter on an M1 Pro MBP with 16GB RAM. EDIT: Speed increased to 2.3s/iter after a reboot
Depends what fork you're running... Some seem to be using CPU-based generation, others use the MPS device backend correctly which is MUCH faster. I have another comment floating around about lstein's fork, but it takes some massaging to get it to run happily. https://github.com/lstein/stable-diffusion/
Hm, when I run the example, I get this error: > expected scalar type BFloat16 but found Float Has anyone seen this error? It's pretty hard to google for.
Does running it locally give you anything over using the web version?
Is there a good set of benchmarks available for Stable Diffusion? I was able to run a custom Stable Diffusion build on a GCE A100 instance (~$1/hour) at around 1Mpix per 10 seconds. I.e, I could create a 512x512 image in 2.5 seconds with some batching optimizations. A consumer GPU like a 3090 runs at ~1Mpix per 20 seconds. I'm wondering what the price floor of stock art will be when someone can use https://lexica.art…
(DreamStudio is charging a bit over one cent per generated image at default settings, depending on exchange rates.)
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-...
Are we being pranked? I just followed the steps but the image output from my prompt is just a single frame of Rick Astley... EDIT: It was a false-positive (honest!) on the NSFW filter. To disable it, edit txt2img.py around line 325. Comment this line out: x_checked_image, has_nsfw_concept = check_safety(x_samples_ddim) And replace it with: x_checked_image = x_samples_ddim
When the model detects NSFW content it replaces the output with the frame of Rick Astley.
I think it would be awesome to update the rickroll feature to the following:
Auto Re-run the img2img with some text prompt: "all of the people are now Rick Astley" with low strength so it can adjust the faces, but not change the nudity!!!1