stable-diffusion-cpp-python 0.1.8

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 and FLUX 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="-DSD_CUBLAS=ON" pip install stable-diffusion-cpp-python

# Windows
$env:CMAKE_ARGS="-DSD_CUBLAS=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="-DSD_CUBLAS=ON"

# requirements.txt

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

Supported Backends

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

Using CUBLAS (CUDA)

This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure you 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. You can check your installed CUDA toolkit version by running nvcc --version.

• It is recommended you 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 you 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 Vulkan

Install Vulkan SDK from https://www.lunarg.com/vulkan-sdk/.

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

Using SYCL

Using SYCL makes the computation run on the Intel GPU. Please make sure you have installed the related driver and Intel® oneAPI Base toolkit before start. More details and steps can refer to llama.cpp SYCL backend.

# Export relevant ENV variables
source /opt/intel/oneapi/setvars.sh

# Option 1: Use FP32 (recommended for better performance in most cases)
CMAKE_ARGS="-DSD_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx" pip install stable-diffusion-cpp-python

# Option 2: Use FP16
CMAKE_ARGS="-DSD_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=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

Using OpenBLAS

CMAKE_ARGS="-DGGML_OPENBLAS=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:

Text to Image

from stable_diffusion_cpp import StableDiffusion

def callback(step: int, steps: int, time: float):
    print("Completed step: {} of {}".format(step, steps))

stable_diffusion = StableDiffusion(
      model_path="../models/v1-5-pruned-emaonly.safetensors",
      wtype="default", # Weight type (default: automatically determines weight type of model file)
)
output = stable_diffusion.txt_to_img(
      prompt="a lovely cat",
      width=512, # Must be a multiple of 64
      height=512, # Must be a multiple of 64
      progress_callback=callback,
      # seed=1337, # Uncomment to set a specific seed
)
output[0].save("output.png") # Image returned as list of PIL Images

With LoRA (Stable Diffusion)

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/", # This should point to folder where LoRA weights are stored (not an individual file)
)
output = stable_diffusion.txt_to_img(
      prompt="a lovely cat<lora:marblesh:1>",
)

• The lora_model_dir argument is used in the same way for FLUX image generation.

FLUX Image Generation

FLUX models should be run using the same implementation as the stable-diffusion.cpp FLUX documentation where the diffusion_model_path argument is used in place of the model_path. The clip_l_path, t5xxl_path, and vae_path arguments are also required for inference to function.

Download the weights from the links below:

• Preconverted gguf weights from FLUX.1-dev-gguf or FLUX.1-schnell, this way you don't have to do the conversion yourself.
• Download vae from https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/ae.safetensors
• Download clip_l from https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/clip_l.safetensors
• Download t5xxl from https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp16.safetensors

from stable_diffusion_cpp import StableDiffusion

stable_diffusion = StableDiffusion(
    diffusion_model_path="../models/flux1-schnell-q3_k.gguf", # In place of model_path
    clip_l_path="../models/clip_l.safetensors",
    t5xxl_path="../models/t5xxl_fp16.safetensors",
    vae_path="../models/ae.safetensors",
)
output = stable_diffusion.txt_to_img(
      prompt="a lovely cat holding a sign says 'flux.cpp'",
      sample_steps=4,
      cfg_scale=1.0, # a cfg_scale of 1 is recommended for FLUX
      sample_method="euler", # euler is recommended for FLUX
)

With LoRA (FLUX)

LoRAs can be used with FLUX models in the same way as Stable Diffusion models (as shown above).

Note that:

• It is recommended to use LoRA with naming formats compatible with ComfyUI.
• Only the Flux-dev q8_0 will work with LoRAs.
• You can download FLUX LoRA models from https://huggingface.co/XLabs-AI/flux-lora-collection/tree/main (you must use a comfy converted version!!!).

Other High-level API Examples

Other examples for the high-level API (such as image to image, upscaling and model conversion) can be found in the tests directory.

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.