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Readme update (#253)
* check if torch.backends has mps before calling it * Updated Mac Readme with latest debugging info Co-authored-by: James Reynolds <magnsuviri@me.com>
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@ -1,20 +1,19 @@
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# Apple Silicon Mac Users
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Several people have gotten Stable Diffusion to work on Apple Silicon
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Macs using Anaconda. I've gathered up most of their instructions and
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put them in this fork (and readme). I haven't tested anything besides
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Anaconda, and I've read about issues with things like miniforge, so if
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you have an issue that isn't dealt with in this fork then head on over
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to the [Apple
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Silicon](https://github.com/CompVis/stable-diffusion/issues/25) issue
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on GitHub (that page is so long that GitHub hides most of it by
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default, so you need to find the hidden part and expand it to view the
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whole thing). This fork would not have been possible without the work
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done by the people on that issue.
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Macs using Anaconda, miniforge, etc. I've gathered up most of their instructions and
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put them in this fork (and readme). Things have moved really fast and so these
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instructions change often. Hopefully things will settle down a little.
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There's several places where people are discussing Apple
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MPS functionality: [the original CompVis
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issue](https://github.com/CompVis/stable-diffusion/issues/25), and generally on
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[lstein's fork](https://github.com/lstein/stable-diffusion/).
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You have to have macOS 12.3 Monterey or later. Anything earlier than that won't work.
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BTW, I haven't tested any of this on Intel Macs.
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BTW, I haven't tested any of this on Intel Macs but I have read that one person
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got it to work.
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How to:
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@ -27,38 +26,41 @@ ln -s /path/to/ckpt/sd-v1-1.ckpt models/ldm/stable-diffusion-v1/model.ckpt
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conda env create -f environment-mac.yaml
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conda activate ldm
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python scripts/preload_models.py
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python scripts/orig_scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms
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```
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These instructions are identical to the main repo except I added
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environment-mac.yaml because Mac doesn't have cudatoolkit.
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We have not gotten lstein's dream.py to work yet.
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After you follow all the instructions and run txt2img.py you might get several errors. Here's the errors I've seen and found solutions for.
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### Is it slow?
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Be sure to specify 1 sample and 1 iteration.
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python ./scripts/txt2img.py --prompt "ocean" --ddim_steps 5 --n_samples 1 --n_iter 1
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### Doesn't work anymore?
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We are using PyTorch nightly, which includes support for MPS. I don't
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know exactly how Anaconda does updates, but I woke up one morning and
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Stable Diffusion crashed and I couldn't think of anything I did that
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would've changed anything the night before, when it worked. A day and
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a half later I finally got it working again. I don't know what changed
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overnight. PyTorch-nightly changes overnight but I'm pretty sure I
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didn't manually update it. Either way, things are probably going to be
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bumpy on Apple Silicon until PyTorch releases a firm version that we
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can lock to.
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PyTorch nightly includes support for MPS. Because of this, this setup is
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inherently unstable. One morning I woke up and it no longer worked no matter
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what I did until I switched to miniforge. However, I have another Mac that works
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just fine with Anaconda. If you can't get it to work, please search a little
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first because many of the errors will get posted and solved. If you can't find
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a solution please [create an issue](https://github.com/lstein/stable-diffusion/issues).
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To manually update to the latest version of PyTorch nightly (which could fix issues), run this command.
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One debugging step is to update to the latest version of PyTorch nightly.
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conda install pytorch torchvision torchaudio -c pytorch-nightly
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## Debugging?
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Or you can clean everything up.
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Tired of waiting for your renders to finish before you can see if it
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works? Reduce the steps! The picture wont look like anything but if it
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finishes, hey, it works! This could also help you figure out if you've
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got a memory problem, because I'm betting 1 step doesn't use much
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memory.
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conda clean --yes --all
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python ./scripts/txt2img.py --prompt "ocean" --ddim_steps 1
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Or you can reset Anaconda.
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conda update --force-reinstall -y -n base -c defaults conda
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### "No module named cv2" (or some other module)
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@ -83,6 +85,23 @@ globally.
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You might also need to install Rust (I mention this again below).
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### Debugging?
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Tired of waiting for your renders to finish before you can see if it
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works? Reduce the steps! The image quality will be horrible but at least you'll
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get quick feedback.
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python ./scripts/txt2img.py --prompt "ocean" --ddim_steps 5 --n_samples 1 --n_iter 1
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### MAC: torch._C' has no attribute '_cuda_resetPeakMemoryStats' #234
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We haven't fixed gotten dream.py to work on Mac yet.
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### OSError: Can't load tokenizer for 'openai/clip-vit-large-patch14'...
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python scripts/preload_models.py
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### "The operator [name] is not current implemented for the MPS device." (sic)
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Example error.
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@ -92,9 +111,7 @@ Example error.
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NotImplementedError: The operator 'aten::index.Tensor' is not current implemented for the MPS device. If you want this op to be added in priority during the prototype phase of this feature, please comment on [https://github.com/pytorch/pytorch/issues/77764](https://github.com/pytorch/pytorch/issues/77764). As a temporary fix, you can set the environment variable `PYTORCH_ENABLE_MPS_FALLBACK=1` to use the CPU as a fallback for this op. WARNING: this will be slower than running natively on MPS.
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```
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Just do what it says:
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export PYTORCH_ENABLE_MPS_FALLBACK=1
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The lstein branch includes this fix in [environment-mac.yaml](https://github.com/lstein/stable-diffusion/blob/main/environment-mac.yaml).
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### "Could not build wheels for tokenizers"
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@ -104,6 +121,8 @@ I have not seen this error because I had Rust installed on my computer before I
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### How come `--seed` doesn't work?
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First this:
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> Completely reproducible results are not guaranteed across PyTorch
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releases, individual commits, or different platforms. Furthermore,
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results may not be reproducible between CPU and GPU executions, even
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@ -111,7 +130,8 @@ when using identical seeds.
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[PyTorch docs](https://pytorch.org/docs/stable/notes/randomness.html)
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There is an [open issue](https://github.com/pytorch/pytorch/issues/78035) (as of August 2022) in pytorch regarding gradient inconsistency. I am guessing that's what is causing this.
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Second, we might have a fix that at least gets a consistent seed sort of. We're
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still working on it.
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### libiomp5.dylib error?
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@ -137,6 +157,8 @@ sort). [There's more
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suggestions](https://stackoverflow.com/questions/53014306/error-15-initializing-libiomp5-dylib-but-found-libiomp5-dylib-already-initial),
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like uninstalling tensorflow and reinstalling. I haven't tried them.
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Since I switched to miniforge I haven't seen the error.
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### Not enough memory.
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This seems to be a common problem and is probably the underlying
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@ -174,10 +196,10 @@ Actually, this could be happening because there's not enough RAM. You could try
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### My images come out black
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I haven't solved this issue. I just throw away my black
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images. There's a [similar
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issue](https://github.com/CompVis/stable-diffusion/issues/69) on CUDA
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GPU's where the images come out green. Maybe it's the same issue?
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We might have this fixed, we are still testing.
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There's a [similar issue](https://github.com/CompVis/stable-diffusion/issues/69)
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on CUDA GPU's where the images come out green. Maybe it's the same issue?
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Someone in that issue says to use "--precision full", but this fork
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actually disables that flag. I don't know why, someone else provided
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that code and I don't know what it does. Maybe the `model.half()`
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@ -204,25 +226,4 @@ What? Intel? On an Apple Silicon?
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The processor must support the Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) instructions.
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The processor must support the Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.
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This fixed it for me:
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conda clean --yes --all
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### Still slow?
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I changed the defaults of n_samples and n_iter to 1 so that it uses
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less RAM and makes less images so it will be faster the first time you
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use it. I don't actually know what n_samples does internally, but I
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know it consumes a lot more RAM. The n_iter flag just loops around the
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image creation code, so it shouldn't consume more RAM (it should be
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faster if you're going to do multiple images because the libraries and
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model will already be loaded--use a prompt file to get this speed
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boost).
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These flags are the default sample and iter settings in this fork/branch:
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~~~~
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python scripts/txt2img.py --prompt "ocean" --n_samples=1 --n_iter=1
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~~~
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This was actually the issue that I couldn't solve until I switched to miniforge.
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