129 lines
5.9 KiB
Markdown
129 lines
5.9 KiB
Markdown
# Local Stable Diffusion
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Stable diffusion (SD) is an AI technique for generating images from text prompts.
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Similar to DALL-E, which drives the popular [craiyon](https://www.craiyon.com/), SD is available as an [online tool](https://huggingface.co/spaces/stabilityai/stable-diffusion).
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These web tools are amazing, and easy to use, but can be frustrating - they're often under high load, and impose long waiting times.
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They use a good chunk of computational resources, specifically GPUs and so have generally been out of reach for even people with powerful personal machines.
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Now, however, SD has reached the point it can be run using (admittedly, high-end) consumer video cards.
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Stability AI - the model's developers - recently [published a blog post](https://stability.ai/blog/stable-diffusion-v2-release) open-sourcing SD 2.
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There's a README for getting started [here](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/README.md), but it has a couple of gotchas and assumptions which plenty of people (like myself) won't have known if they're not already familiar with the technologies in use, such as Python and CUDA.
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This post is descibes my experience setting up SD 2 on my local workstation.
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For hardware, I have an i7-6700k, RTX 2080 Super and 48GB of RAM.
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If you have an AMD video card, you won't be able to use CUDA, but you may be able to use GPU acceleration regardless using something ROCm.
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In this post I'm using Arch Linux, but I have successfully set it up on Windows too.
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Python is an exceedingly portable language, so it should work wherever you're able to get a Python installation.
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This post assumes that you already have a working Python installation.
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## Install CUDA
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CUDA needs to be installed separately from Python dependencies.
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It is quite large, and as with all NVIDIA driver installations, can be a bit confusing.
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On Linux, it's straightforward to install it from your distribution's package manager.
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```bash
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sudo pacman -Syu
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sudo pacman -S cuda
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```
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On Windows, you will need to go to NVIDIA's site to download the correct version of CUDA.
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At time of writing, the SD 2 script expects CUDA 11.7, and will not work if you install the latest 12.0 version.
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To get older versions, go to their [download archive](https://developer.nvidia.com/cuda-toolkit-archive) and select the appropriate one.
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## Set up a virtual environment an PyTorch
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Python can be installed at a system level, but it's usually a good idea to set up a virtual environment for your project.
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This isolates the project dependencies from the wider system, and makes your setup reproducible.
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I will use [`pipenv`](https://pipenv.pypa.io/en/latest/index.html) as it's what I'm familiar with.
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PyTorch is a deep-learning framework, used to put together machine learning pipelines.
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To get a command to install the relevant dependencies, go to [PyTorch's site](https://pytorch.org/get-started/locally/) and choose the options for your setup.
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In my case, I replaced `pip3` with `pipenv` as I want to install dependencies to a new virtual environment instead of to the system.
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```bash
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mkdir stable-diffusion && cd stable-diffusion
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pipenv install torch torchvision torchaudio
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```
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## Install Stable Diffusion
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SD 2 is provided by the `diffusers` package.
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We can install it in our virtual environment as follows:
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```bash
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pipenv shell
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pip3 install git+https://github.com/huggingface/diffusers.git transformers accelerate scipy
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exit
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```
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We use `pipenv shell` to enter a shell using the virtual environment, before using the `pip3` command described on their README.
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After installing dependencies, we can leave the virtual environment shell and return to our original one.
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`transformers` and `accelerate` are optional, but used to reduce memory usage and so are recommended.
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## Create a Python script
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Python does have an interactive envronment, but so save our fingers let's use a `stable-diffusion.py` script to contain and run our Python code.
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Here I'll mostly copy the Python included in their README:
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```python
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import torch
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from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
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model_id = "stabilityai/stable-diffusion-2"
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# Use the Euler scheduler here instead
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scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
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pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, revision="fp16", torch_dtype=torch.float16)
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pipe = pipe.to("cuda")
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pipe.enable_attention_slicing()
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prompt = "a photo of an astronaut riding a horse on mars"
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image = pipe(prompt, height=768, width=768).images[0]
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image.save("astronaut_rides_horse.png")
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```
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I've made two additions here.
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First, I've added `import torch` at the top - I'm not sure why the code in the README omits this, but it's needed to work.
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I've also added `pipe.enable_attention_slicing()` - this is a more memory-efficient running mode, which is less intensive at the cost of taking longer.
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If you have a monster video card, this may not be necessary.
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At this point, we're done - after running the script successfully, you should have a new picture of an astronaut riding a horse on mars.
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## Some nice-to-haves
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In this basic script we only have the one, hardcoded prompt.
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To change it, we need to update the file itself.
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Instead, we can change how `prompt` is set, and have it read from command-line parameters instead.
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```python
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# at the top of the file
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import sys
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...
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prompt = " ".join(sys.argv[1:])
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```
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While we're at it, we can also base the filename on the input prompt:
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```python
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image.save(f'{prompt.replace(" ", "_")}.png')
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```
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## Wrapping up
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And that's it!
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Enjoy making some generative art.
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My favourites so far have been prefixing "psychedelic" to things.
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I've also been enjoying generating descriptions with [ChatGPT](https://chat.openai.com/chat) and plugging them into SD, for some zero-effort creativity.
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As always, if anything's out of place of if you'd like to get in touch, please [send me an email!](mailto:me@ktyl.dev).
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