stable-diffusion-cpp-python 0.1.7

Python bindings for the stable-diffusion.cpp library

🖼️ Python Bindings for stable-diffusion.cpp

Simple Python bindings for @leejet's stable-diffusion.cpp library.

This package provides:

• Low-level access to C API via ctypes interface.
• High-level Python API for stable diffusion image generation.

Installation

Requirements:

• Python 3.8+
• C compiler
  • Linux: gcc or clang
  • Windows: Visual Studio or MinGW
  • MacOS: Xcode

To install the package, run:

pip install stable-diffusion-cpp-python

This will also build stable-diffusion.cpp from source and install it alongside this python package.

If this fails, add --verbose to the pip install to see the full cmake build log.

Installation Configuration

stable-diffusion.cpp supports a number of hardware acceleration backends to speed up inference as well as backend specific options. See the stable-diffusion.cpp README for a full list.

All stable-diffusion.cpp cmake build options can be set via the CMAKE_ARGS environment variable or via the --config-settings / -C cli flag during installation.

Environment Variables

# Linux and Mac
CMAKE_ARGS="-DGGML_OPENBLAS=ON" \
  pip install stable-diffusion-cpp-python

# Windows
$env:CMAKE_ARGS = "-DGGML_OPENBLAS=ON"
pip install stable-diffusion-cpp-python

CLI / requirements.txt

They can also be set via pip install -C / --config-settings command and saved to a requirements.txt file:

pip install --upgrade pip # ensure pip is up to date
pip install stable-diffusion-cpp-python \
  -C cmake.args="-DGGML_OPENBLAS=ON"

# requirements.txt

stable-diffusion-cpp-python -C cmake.args="-DGGML_OPENBLAS=ON"

Supported Backends

Below are some common backends, their build commands and any additional environment variables required.

Using OpenBLAS (CPU)

CMAKE_ARGS="-DGGML_OPENBLAS=ON" pip install stable-diffusion-cpp-python

Using cuBLAS (CUDA)

This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. apt install nvidia-cuda-toolkit) or from here: CUDA Toolkit. Recommended to have at least 4 GB of VRAM.

CMAKE_ARGS="-DSD_CUBLAS=on" pip install stable-diffusion-cpp-python

Using hipBLAS (ROCm)

This provides BLAS acceleration using the ROCm cores of your AMD GPU. Make sure to have the ROCm toolkit installed. Windows Users Refer to docs/hipBLAS_on_Windows.md for a comprehensive guide.

CMAKE_ARGS="-G Ninja -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS=gfx1101" pip install stable-diffusion-cpp-python

Using Metal

Using Metal makes the computation run on the GPU. Currently, there are some issues with Metal when performing operations on very large matrices, making it highly inefficient at the moment. Performance improvements are expected in the near future.

CMAKE_ARGS="-DSD_METAL=ON" pip install stable-diffusion-cpp-python

Using Flash Attention

Enabling flash attention reduces memory usage by at least 400 MB. At the moment, it is not supported when CUBLAS is enabled because the kernel implementation is missing.

CMAKE_ARGS="-DSD_FLASH_ATTN=ON" pip install stable-diffusion-cpp-python

Upgrading and Reinstalling

To upgrade and rebuild stable-diffusion-cpp-python add --upgrade --force-reinstall --no-cache-dir flags to the pip install command to ensure the package is rebuilt from source.

High-level API

The high-level API provides a simple managed interface through the StableDiffusion class.

Below is a short example demonstrating how to use the high-level API to generate a simple image:

>>> from stable_diffusion_cpp import StableDiffusion
>>> stable_diffusion = StableDiffusion(
      model_path="../models/v1-5-pruned-emaonly.safetensors",
      wtype="default", # Weight type (options: default, f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0)
)
>>> output = stable_diffusion.txt_to_img(
      "a lovely cat", # Prompt
      # seed=1337, # Uncomment to set a specific seed
)

• Other examples for the high-level API can be found in the tests directory.

With LoRA

You can specify the directory where the lora weights are stored via lora_model_dir. If not specified, the default is the current working directory.

• LoRA is specified via prompt, just like stable-diffusion-webui. (e.g. <lora:marblesh:1>)
• LoRAs will not work when using quantized models. You must instead use a full precision .safetensors model.

Here's a simple example:

>>> from stable_diffusion_cpp import StableDiffusion
>>> stable_diffusion = StableDiffusion(
      model_path="../models/v1-5-pruned-emaonly.safetensors",
      lora_model_dir="../models/",
)
>>> output = stable_diffusion.txt_to_img(
      "a lovely cat<lora:marblesh:1>", # Prompt
)

Low-level API

The low-level API is a direct ctypes binding to the C API provided by stable-diffusion.cpp. The entire low-level API can be found in stable_diffusion_cpp/stable_diffusion_cpp.py and directly mirrors the C API in stable-diffusion.h.

Below is a short example demonstrating how to use the low-level API:

>>> import stable_diffusion_cpp as sd_cpp
>>> import ctypes
>>> from PIL import Image

>>> img = Image.open("path/to/image.png")
>>> img_bytes = img.tobytes()

>>> c_image = sd_cpp.sd_image_t(
      width=img.width,
      height=img.height,
      channel=channel,
      data=ctypes.cast(
            (ctypes.c_byte * len(img_bytes))(*img_bytes),
            ctypes.POINTER(ctypes.c_uint8),
      ),
) # Create a new C sd_image_t

>>> img = sd_cpp.upscale(
      self.upscaler,
      image_bytes,
      upscale_factor,
) # Upscale the image

>>> sd_cpp.free_image(c_image)

Development

To get started, clone the repository and install the package in editable / development mode.

git clone --recurse-submodules https://github.com/william-murray1204/stable-diffusion-cpp-python.git
cd stable-diffusion-cpp-python

# Upgrade pip (required for editable mode)
pip install --upgrade pip

# Install with pip
pip install -e .

Now you can make changes to the code within the stable_diffusion_cpp directory and test them in your python environment.

Cleanup

To clear the cache.

make clean

References

• stable-diffusion.cpp
• llama-cpp-python
• llama.cpp
• whisper-cpp-python
• Golang stable-diffusion
• StableDiffusion.NET

License

This project is licensed under the terms of the MIT license. See LICENSE for details.