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14 seconds to generate an image on an M1 Max with the given instructions (`--n_samples 1 --n_iter 1`)
Also, interesting/curious small note: images generated with this script are "invisibly watermarked" i.e. steganographied!
See https://github.com/bfirsh/stable-diffusion/blob/main/scripts...
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.
Yeah. Try running with PYTORCH_ENABLE_MPS_FALLBACK=1 --full-precision
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are there benchmarks? I was following the github issue and the CPU bound one was at 4-5 minutes, the MDS one was at 30 seconds, then 18 seconds, and people were still calling that slow. What is it currently at now? and I don't know what "fast" is, to compare What are the Windows 10 with nice Nvidia chips w/ CUDA getting? Just curious whats comprehensive
Wait, what? On my M1 imac I’m getting about 25 minutes . What am i doing wrong?
I've been playing with Stable Diffusion a lot the past few days on a Dell R620 CPU (24 cores, 96 GB of RAM). With a little fiddling (not knowing any python or anything about machine learning) I was able to get img2img.py working by simply comparing that script to the txt2img.py CPU patch. Was only a few lines of tweaking. img2img takes ~2 minutes to generate an image with 1 sample and 50 iterations, txt2img takes abo…
With the 1.4 checkpoint, everything under 40 steps can't be used basically and you only get good fidelity with >75 steps. I usually use 100, that's a good middleground.
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Running lstein's fork with these requirements[0] but seeing this output[1]. Same steps as original guide otherwise. Anyone got any ideas? [0] https://github.com/bfirsh/stable-diffusion/blob/392cda328a69... [1] https://gist.github.com/bfirsh/594c50fd9b2e6b173e31de753a842...
Same output for me also. EDIT: https://github.com/lstein/stable-diffusion/issues/293#issuec... fixed it for me.
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.
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Unscientifically that puts the M1 Pro GPU at about 25% of the performance of a RTX 3080. Not too shabby... EDIT - this comment implies it's much faster: https://news.ycombinator.com/item?id=32679518 If that's correct then it's close to matching my 3080 (mobile).
It's likely that a significant fraction of the perf difference between Apple' GPUs and NVIDIA GPUs is due to NVIDIA's CUDA being high optimized, and Pytorch being tuned to work with CUDA. If Pytorch's metal support improves and Apple's Metal drivers improve (big ifs), it's likely that Apple's GPUs will perform better relatively to NVIDIA than they currently do.
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Wait, what? On my M1 imac I’m getting about 25 minutes . What am i doing wrong?
It's falling back to CPU. Follow the instructions to use a GPU version - sometimes it's even a completely different repo, depending on whose instructions you're following.
Running into this error `RuntimeError: expected scalar type BFloat16 but found Float` when I run `txt2img.py`
SOLUTION - append the command with `--precision full`
For reference the full command:
`python scripts/txt2img.py \ --prompt "a red juicy apple floating in outer space, like a planet" \ --n_samples 1 --n_iter 1 --plms --precision full`
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Yes, me too! Please post here if you find a solution for all the other people that come and find this by commmand-F'ing this error
I'm stuck on 'RuntimeError: expected scalar type BFloat16 but found Float' too. Most relevant links seems https://github.com/CompVis/stable-diffusion/pull/47 but I'm not sure. Please post when there is a solution.
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What kind of speed does this run at? Eg. How long to make a 512x512 image at standard settings?
I haven't installed from this link specifically, but I used one of the branches on which this is based a few days ago, so the results should be similar. On a first-gen M1 Mac mini with 8GB RAM, it takes 70-90 minutes for each image. Still feels like magic, but old-school magic.
It looks like memory is super-important for this (which isn't all that surprising, really...).