# 🖼️ Python Bindings for [`stable-diffusion.cpp`](https://github.com/leejet/stable-diffusion.cpp)
Simple Python bindings for **@leejet's** [`stable-diffusion.cpp`](https://github.com/leejet/stable-diffusion.cpp) library.
[](https://opensource.org/licenses/MIT)
[](https://pypi.org/project/stable-diffusion-cpp-python/)
[](https://pepy.tech/project/stable-diffusion-cpp-python)
This package provides:
- Low-level access to C API via `ctypes` interface.
- High-level Python API for Stable Diffusion, FLUX and Wan image/video generation.
## Installation
Requirements:
- Python 3.8+
- C compiler
- Linux: gcc or clang
- Windows: Visual Studio or MinGW
- MacOS: Xcode
To install the package, run:
```bash
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](https://github.com/leejet/stable-diffusion.cpp#build) 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.
<details open>
<summary>Environment Variables</summary>
```bash
# Linux and Mac
CMAKE_ARGS="-DSD_CUDA=ON" pip install stable-diffusion-cpp-python
```
```powershell
# Windows
$env:CMAKE_ARGS="-DSD_CUDA=ON"
pip install stable-diffusion-cpp-python
```
</details>
<details>
<summary>CLI / requirements.txt</summary>
They can also be set via `pip install -C / --config-settings` command and saved to a `requirements.txt` file:
```bash
pip install --upgrade pip # ensure pip is up to date
pip install stable-diffusion-cpp-python -C cmake.args="-DSD_CUDA=ON"
```
```txt
# requirements.txt
stable-diffusion-cpp-python -C cmake.args="-DSD_CUDA=ON"
```
</details>
### Supported Backends
Below are some common backends, their build commands and any additional environment variables required.
<!-- CUDA -->
<details>
<summary>Using CUDA (CUBLAS)</summary>
This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure you have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). You can check your installed CUDA toolkit version by running `nvcc --version`.
- It is recommended you have at least 4 GB of VRAM.
```bash
CMAKE_ARGS="-DSD_CUDA=ON" pip install stable-diffusion-cpp-python
```
</details>
<!-- HIPBLAS -->
<details>
<summary>Using HIPBLAS (ROCm)</summary>
This provides BLAS acceleration using the ROCm cores of your AMD GPU. Make sure you have the ROCm toolkit installed and that you replace the `$GFX_NAME` value with that of your GPU architecture (`gfx1030` for consumer RDNA2 cards for example).Windows users refer to [docs/hipBLAS_on_Windows.md](docs%2FhipBLAS_on_Windows.md) for a comprehensive guide and troubleshooting tips.
```bash
if command -v rocminfo; then export GFX_NAME=$(rocminfo | awk '/ *Name: +gfx[1-9]/ {print $2; exit}'); else echo "rocminfo missing!"; fi
if [ -z "${GFX_NAME}" ]; then echo "Error: Couldn't detect GPU!"; else echo "Building for GPU: ${GFX_NAME}"; fi
CMAKE_ARGS="-G Ninja -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DGPU_TARGETS=$GFX_NAME -DAMDGPU_TARGETS=$GFX_NAME -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON -DCMAKE_POSITION_INDEPENDENT_CODE=ON" pip install stable-diffusion-cpp-python
```
</details>
<!-- Metal -->
<details>
<summary>Using Metal</summary>
Using Metal runs the computation on Apple Silicon. Currently, there are some issues with Metal when performing operations on very large matrices, making it highly inefficient. Performance improvements are expected in the near future.
```bash
CMAKE_ARGS="-DSD_METAL=ON" pip install stable-diffusion-cpp-python
```
</details>
<!-- Vulkan -->
<details>
<summary>Using Vulkan</summary>
Install Vulkan SDK from https://www.lunarg.com/vulkan-sdk/.
```bash
CMAKE_ARGS="-DSD_VULKAN=ON" pip install stable-diffusion-cpp-python
```
</details>
<!-- SYCL -->
<details>
<summary>Using SYCL</summary>
Using SYCL runs the computation on an Intel GPU. Please make sure you have installed the related driver and [Intel® oneAPI Base toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) before starting. For more details refer to [llama.cpp SYCL backend](https://github.com/ggerganov/llama.cpp/blob/master/docs/backend/SYCL.md#linux).
```bash
# 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
```
</details>
<!-- OpenBLAS -->
<details>
<summary>Using OpenBLAS</summary>
```bash
CMAKE_ARGS="-DGGML_OPENBLAS=ON" pip install stable-diffusion-cpp-python
```
</details>
<!-- MUSA -->
<details>
<summary>Using MUSA</summary>
This provides BLAS acceleration using the MUSA cores of your Moore Threads GPU. Make sure to have the MUSA toolkit installed.
```bash
CMAKE_ARGS="-DCMAKE_C_COMPILER=/usr/local/musa/bin/clang -DCMAKE_CXX_COMPILER=/usr/local/musa/bin/clang++ -DSD_MUSA=ON -DCMAKE_BUILD_TYPE=Release" pip install stable-diffusion-cpp-python
```
</details>
<!-- OpenCL -->
<details>
<summary>Using OpenCL (Adreno GPU)</summary>
Currently, it only supports Adreno GPUs and is primarily optimized for Q4_0 type.
To build for Windows ARM please refers to [Windows 11 Arm64](https://github.com/ggml-org/llama.cpp/blob/master/docs/backend/OPENCL.md#windows-11-arm64)
Building for Android:
Android NDK:
- Download and install the Android NDK from the [official Android developer site](https://developer.android.com/ndk/downloads).
Setup OpenCL Dependencies for NDK:
You need to provide OpenCL headers and the ICD loader library to your NDK sysroot.
- OpenCL Headers:
```bash
# In a temporary working directory
git clone https://github.com/KhronosGroup/OpenCL-Headers
cd OpenCL-Headers
# Replace <YOUR_NDK_PATH> with your actual NDK installation path
# e.g., cp -r CL /path/to/android-ndk-r26c/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include
sudo cp -r CL <YOUR_NDK_PATH>/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include
cd ..
```
- OpenCL ICD Loader:
```bash
# In the same temporary working directory
git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader
cd OpenCL-ICD-Loader
mkdir build_ndk && cd build_ndk
# Replace <YOUR_NDK_PATH> in the CMAKE_TOOLCHAIN_FILE and OPENCL_ICD_LOADER_HEADERS_DIR
cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release \
-DCMAKE_TOOLCHAIN_FILE=<YOUR_NDK_PATH>/build/cmake/android.toolchain.cmake \
-DOPENCL_ICD_LOADER_HEADERS_DIR=<YOUR_NDK_PATH>/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include \
-DANDROID_ABI=arm64-v8a \
-DANDROID_PLATFORM=24 \
-DANDROID_STL=c++_shared
ninja
# Replace <YOUR_NDK_PATH>
# e.g., cp libOpenCL.so /path/to/android-ndk-r26c/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android
sudo cp libOpenCL.so <YOUR_NDK_PATH>/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android
cd ../..
```
Build `stable-diffusion-cpp-python` for Android with (untested):
```bash
# Replace <YOUR_NDK_PATH> with your actual NDK installation path
# e.g., -DCMAKE_TOOLCHAIN_FILE=/path/to/android-ndk-r26c/build/cmake/android.toolchain.cmake
CMAKE_ARGS="-G Ninja -DCMAKE_TOOLCHAIN_FILE=<YOUR_NDK_PATH>/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-28 -DGGML_OPENMP=OFF -DSD_OPENCL=ON
```
_(Note: Don't forget to include `LD_LIBRARY_PATH=/vendor/lib64` in your command line before running the binary)_
</details>
### 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.
### Using Flash Attention
Enabling flash attention for the diffusion model reduces memory usage by varying amounts of MB, e.g.:
- **flux 768x768** ~600mb
- **SD2 768x768** ~1400mb
For most backends, it slows things down, but for cuda it generally speeds it up too.
At the moment, it is only supported for some models and some backends (like `cpu`, `cuda/rocm` and `metal`).
Run by passing `diffusion_flash_attn=True` to the `StableDiffusion` class and watch for:
```log
[INFO] stable-diffusion.cpp:312 - Using flash attention in the diffusion model
```
and the compute buffer shrink in the debug log:
```log
[DEBUG] ggml_extend.hpp:1004 - flux compute buffer size: 650.00 MB(VRAM)
```
## 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:
### <u>Text to Image</u>
```python
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 (e.g. "q8_0", "f16", etc) (The "default" setting is automatically applied and determines the weight type of a model file)
)
output = stable_diffusion.generate_image(
prompt="a lovely cat",
width=512,
height=512,
progress_callback=callback,
# seed=1337, # Uncomment to set a specific seed (use -1 for a random seed)
)
output[0].save("output.png") # Output returned as list of PIL Images
# Model and generation paramaters accessible via .info
print(output[0].info)
```
#### <u>With LoRA (Stable Diffusion)</u>
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](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#lora). (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:
```python
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.generate_image(
prompt="a lovely cat<lora:marblesh:1>",
)
```
- The `lora_model_dir` argument is used in the same way for FLUX image generation.
---
### <u>FLUX Image Generation</u>
FLUX models should be run using the same implementation as the [stable-diffusion.cpp FLUX documentation](https://github.com/leejet/stable-diffusion.cpp/blob/master/docs/flux.md) 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 (for most models).
Download the weights from the links below:
- Preconverted gguf weights from [FLUX.1-dev-gguf](https://huggingface.co/leejet/FLUX.1-dev-gguf) or [FLUX.1-schnell](https://huggingface.co/leejet/FLUX.1-schnell-gguf), 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
```python
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",
vae_decode_only=True, # Can be True if not generating image to image
keep_clip_on_cpu=True, # Prevents black images when using some T5 models
)
output = stable_diffusion.generate_image(
prompt="a lovely cat holding a sign says 'flux.cpp'",
cfg_scale=1.0, # a cfg_scale of 1 is recommended for FLUX
# sample_method="euler", # euler is recommended for FLUX, set automatically if "default" is specified
)
```
#### <u>With LoRA (FLUX)</u>
LoRAs can be used with FLUX models in the same way as Stable Diffusion models ([as shown above](#with-lora-stable-diffusion)).
Note that:
- It is recommended you use LoRAs with naming formats compatible with ComfyUI.
- LoRAs will only work with `Flux-dev q8_0`.
- You can download FLUX LoRA models from https://huggingface.co/XLabs-AI/flux-lora-collection/tree/main (you must use a comfy converted version!!!).
#### <u>Kontext (FLUX)</u>
Download the weights from the links below:
- Preconverted gguf model from [FLUX.1-Kontext-dev-GGUF](https://huggingface.co/QuantStack/FLUX.1-Kontext-dev-GGUF)
- Otherwise, download FLUX.1-Kontext-dev from [black-forest-labs/FLUX.1-Kontext-dev](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev/blob/main/flux1-kontext-dev.safetensors)
- The `vae`, `clip_l`, and `t5xxl` models are the same as for FLUX image generation linked above.
```python
from stable_diffusion_cpp import StableDiffusion
stable_diffusion = StableDiffusion(
diffusion_model_path="../models/flux1-kontext-dev-Q5_K_S.gguf", # In place of model_path
clip_l_path="../models/clip_l.safetensors",
t5xxl_path="../models/t5xxl_fp16.safetensors",
vae_path="../models/ae.safetensors",
vae_decode_only=False, # Must be False for FLUX Kontext
keep_clip_on_cpu=True, # Prevents black images when using some T5 models
)
output = stable_diffusion.generate_image(
prompt="make the cat blue",
ref_images=["input.png"],
cfg_scale=1.0, # a cfg_scale of 1 is recommended for FLUX
)
```
#### <u>Chroma (FLUX)</u>
Download the weights from the links below:
- Preconverted gguf model from [silveroxides/Chroma1-Flash-GGUF](https://huggingface.co/silveroxides/Chroma1-Flash-GGUF), [silveroxides/Chroma1-Base-GGUF](https://huggingface.co/silveroxides/Chroma1-Base-GGUF) or [silveroxides/Chroma1-HD-GGUF](https://huggingface.co/silveroxides/Chroma1-HD-GGUF) ([silveroxides/Chroma-GGUF](https://huggingface.co/silveroxides/Chroma-GGUF) is DEPRECATED)
- Otherwise, download chroma's safetensors from [lodestones/Chroma1-Flash](https://huggingface.co/lodestones/Chroma1-Flash), [lodestones/Chroma1-Base](https://huggingface.co/lodestones/Chroma1-Base) or [lodestones/Chroma1-HD](https://huggingface.co/lodestones/Chroma1-HD) ([lodestones/Chroma](https://huggingface.co/lodestones/Chroma) is DEPRECATED)
- The `vae` and `t5xxl` models are the same as for FLUX image generation linked above (`clip_l` not required).
```python
from stable_diffusion_cpp import StableDiffusion
stable_diffusion = StableDiffusion(
diffusion_model_path="../models/Chroma1-HD-Flash-Q4_0.gguf", # In place of model_path
t5xxl_path="../models/t5xxl_fp16.safetensors",
vae_path="../models/ae.safetensors",
vae_decode_only=True, # Can be True if we are not generating image to image
chroma_use_dit_mask=False,
keep_clip_on_cpu=True, # Prevents black images when using some T5 models
)
output = stable_diffusion.generate_image(
prompt="a lovely cat holding a sign says 'chroma.cpp'",
cfg_scale=4.0, # a cfg_scale of 4 is recommended for Chroma
)
```
---
### <u>SD3.5 Image Generation</u>
Download the weights from the links below:
- Download `sd3.5_large` from https://huggingface.co/stabilityai/stable-diffusion-3.5-large/blob/main/sd3.5_large.safetensors
- Download `clip_g` from https://huggingface.co/Comfy-Org/stable-diffusion-3.5-fp8/blob/main/text_encoders/clip_g.safetensors
- Download `clip_l` from https://huggingface.co/Comfy-Org/stable-diffusion-3.5-fp8/blob/main/text_encoders/clip_l.safetensors
- Download `t5xxl` from https://huggingface.co/Comfy-Org/stable-diffusion-3.5-fp8/blob/main/text_encoders/t5xxl_fp16.safetensors
```python
from stable_diffusion_cpp import StableDiffusion
stable_diffusion = StableDiffusion(
model_path="../models/sd3.5_large.safetensors",
clip_l_path="../models/clip_l.safetensors",
clip_g_path="../models/clip_g.safetensors",
t5xxl_path="../models/t5xxl_fp16.safetensors",
keep_clip_on_cpu=True, # Prevents black images when using some T5 models
)
output = stable_diffusion.generate_image(
prompt="a lovely cat holding a sign says 'Stable diffusion 3.5 Large'",
height=1024,
width=1024,
cfg_scale=4.5,
sample_method="euler",
)
```
---
### <u>Image to Image</u>
```python
from stable_diffusion_cpp import StableDiffusion
# from PIL import Image
INPUT_IMAGE = "../input.png"
# INPUT_IMAGE = Image.open("../input.png") # or alternatively, pass as PIL Image
stable_diffusion = StableDiffusion(model_path="../models/v1-5-pruned-emaonly.safetensors")
output = stable_diffusion.generate_image(
prompt="blue eyes",
init_image=INPUT_IMAGE, # Note: The input image will be automatically resized to the match the width and height arguments (default: 512x512)
strength=0.4,
)
```
---
### <u>Inpainting</u>
```python
from stable_diffusion_cpp import StableDiffusion
# Note: Inpainting with a base model gives poor results. A model fine-tuned for inpainting is recommended.
stable_diffusion = StableDiffusion(model_path="../models/v1-5-pruned-emaonly.safetensors")
output = stable_diffusion.generate_image(
prompt="blue eyes",
init_image="../input.png",
mask_image="../mask.png", # A grayscale image where 0 is masked and 255 is unmasked
strength=0.4,
)
```
---
### <u>PhotoMaker</u>
You can use [PhotoMaker](https://github.com/TencentARC/PhotoMaker) to personalize generated images with your own ID.
**NOTE**, currently PhotoMaker **ONLY** works with **SDXL** (any SDXL model files will work).
Download PhotoMaker model file (in safetensor format) [here](https://huggingface.co/bssrdf/PhotoMaker). The official release of the model file (in .bin format) does not work with `stablediffusion.cpp`.
In prompt, make sure you have a class word followed by the trigger word `"img"` (hard-coded for now). The class word could be one of `"man, woman, girl, boy"`. If input ID images contain asian faces, add `Asian` before the class word.
```python
import os
from stable_diffusion_cpp import StableDiffusion
stable_diffusion = StableDiffusion(
model_path="../models/sdxl.vae.safetensors",
vae_path="../models/sdxl.vae.safetensors",
photo_maker_path="../models/photomaker-v1.safetensors",
# keep_vae_on_cpu=True, # If on low memory GPUs (<= 8GB), setting this to True is recommended to get artifact free images
)
INPUT_ID_IMAGES_DIR = "../assets/newton_man"
output = stable_diffusion.generate_image(
cfg_scale=5.0, # a cfg_scale of 5.0 is recommended for PhotoMaker
height=1024,
width=1024,
pm_style_strength=10, # (0-100)% Default is 20 and 10-20 typically gets good results. Lower ratio means more faithfully following input ID (not necessarily better quality).
sample_method="euler",
prompt="a man img, retro futurism, retro game art style but extremely beautiful, intricate details, masterpiece, best quality, space-themed, cosmic, celestial, stars, galaxies, nebulas, planets, science fiction, highly detailed",
negative_prompt="realistic, photo-realistic, worst quality, greyscale, bad anatomy, bad hands, error, text",
pm_id_images=[
os.path.join(INPUT_ID_IMAGES_DIR, f)
for f in os.listdir(INPUT_ID_IMAGES_DIR)
if f.lower().endswith((".png", ".jpg", ".jpeg", ".bmp"))
],
)
```
#### <u>PhotoMaker Version 2</u>
[PhotoMaker Version 2 (PMV2)](https://github.com/TencentARC/PhotoMaker/blob/main/README_pmv2.md) has some key improvements. Unfortunately it has a very heavy dependency which makes running it a bit involved.
Running PMV2 Requires running a python script `face_detect.py` (found here [stable-diffusion.cpp/face_detect.py](https://github.com/leejet/stable-diffusion.cpp/blob/master/face_detect.py)) to obtain `id_embeds` for the given input images.
```bash
python face_detect.py <input_image_dir>
```
An `id_embeds.bin` file will be generated in `input_images_dir`.
**Note: This step only needs to be run once — the resulting `id_embeds` can be reused.**
- Run the same command as in version 1 but replacing `photomaker-v1.safetensors` with `photomaker-v2.safetensors` and pass the `id_embeds.bin` path into the `pm_id_embed_path` parameter.
Download `photomaker-v2.safetensors` from [bssrdf/PhotoMakerV2](https://huggingface.co/bssrdf/PhotoMakerV2).
- All other parameters from Version 1 remain the same for Version 2.
---
### <u>QWEN Image</u>
Download the weights from the links below:
- Download `Qwen Image`
- safetensors: https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/diffusion_models
- gguf: https://huggingface.co/QuantStack/Qwen-Image-GGUF/tree/main
- Download `vae`
- safetensors: https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/vae
- Download `qwen_2.5_vl 7b`
- safetensors: https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/text_encoders
- gguf: https://huggingface.co/mradermacher/Qwen2.5-VL-7B-Instruct-GGUF/tree/main
```python
from stable_diffusion_cpp import StableDiffusion
stable_diffusion = StableDiffusion(
diffusion_model_path="../models/qwen-image-Q8_0.gguf",
qwen2vl_path="../models/Qwen2.5-VL-7B-Instruct.Q8_0.gguf",
vae_path="../models/qwen_image_vae.safetensors",
offload_params_to_cpu=True,
flow_shift=3,
)
output = stable_diffusion.generate_image(
prompt='一个穿着"QWEN"标志的T恤的中国美女正拿着黑色的马克笔面相镜头微笑。她身后的玻璃板上手写体写着 “一、Qwen-Image的技术路线： 探索视觉生成基础模型的极限，开创理解与生成一体化的未来。二、Qwen-Image的模型特色：1、复杂文字渲染。支持中英渲染、自动布局； 2、精准图像编辑。支持文字编辑、物体增减、风格变换。三、Qwen-Image的未来愿景：赋能专业内容创作、助力生成式AI发展。”',
cfg_scale=2.5,
sample_method='euler',
)
```
#### <u>QWEN Image Edit</u>
Download the weights from the links below:
- Download `Qwen Image Edit`
- Qwen Image Edit
- safetensors: https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/tree/main/split_files/diffusion_models
- gguf: https://huggingface.co/QuantStack/Qwen-Image-Edit-GGUF/tree/main
- Qwen Image Edit 2509
- safetensors: https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/tree/main/split_files/diffusion_models
- gguf: https://huggingface.co/QuantStack/Qwen-Image-Edit-2509-GGUF/tree/main
- Download `vae`
- safetensors: https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/vae
- Download `qwen_2.5_vl 7b`
- safetensors: https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/text_encoders
- gguf: https://huggingface.co/mradermacher/Qwen2.5-VL-7B-Instruct-GGUF/tree/main
```python
from stable_diffusion_cpp import StableDiffusion
stable_diffusion = StableDiffusion(
diffusion_model_path="../models/Qwen_Image_Edit-Q8_0.gguf",
qwen2vl_path="../models/Qwen2.5-VL-7B-Instruct.Q8_0.gguf",
vae_path="../models/qwen_image_vae.safetensors",
offload_params_to_cpu=True,
flow_shift=3,
)
output = stable_diffusion.generate_image(
prompt="make the cat blue",
ref_images=["input.png"],
cfg_scale=2.5,
sample_method='euler',
)
```
---
### <u>Wan Video Generation</u>
See [stable-diffusion.cpp Wan download weights](https://github.com/leejet/stable-diffusion.cpp/blob/master/docs/wan.md#download-weights) for a complete list of Wan models.
```python
from stable_diffusion_cpp import StableDiffusion
stable_diffusion = StableDiffusion(
diffusion_model_path="../models/wan2.1_t2v_1.3B_fp16.safetensors", # In place of model_path
t5xxl_path="../models/umt5-xxl-encoder-Q8_0.gguf",
vae_path="../models/wan_2.1_vae.safetensors",
flow_shift=3.0,
keep_clip_on_cpu=True, # Prevents black images when using some T5 models
)
output = stable_diffusion.generate_video(
prompt="a cute dog jumping",
negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
height=832,
width=480,
cfg_scale=6.0,
sample_method="euler",
video_frames=33,
) # Output is a list of PIL Images (video frames)
```
As the output is simply a list of images (video frames), you can convert it into a video using any library you prefer. The example below uses `ffmpeg-python`. Alternatively, libraries such **OpenCV** or **MoviePy** can also be used.
> **Note**
>
> - You'll require **Python bindings for FFmpeg**, `python-ffmpeg` (`pip install ffmpeg-python`) in addition to an **FFmpeg installation on your system**, accessible in your PATH. Check with `ffmpeg -version`.
```python
from typing import List
from PIL import Image
import numpy as np
import ffmpeg
def save_video_ffmpeg(frames: List[Image.Image], fps: int, out_path: str) -> None:
if not frames:
raise ValueError("No frames provided")
width, height = frames[0].size
# Concatenate frames into raw RGB bytes
raw_bytes = b"".join(np.array(frame.convert("RGB"), dtype=np.uint8).tobytes() for frame in frames)
(
ffmpeg.input(
"pipe:",
format="rawvideo",
pix_fmt="rgb24",
s=f"{width}x{height}",
r=fps,
)
.output(
out_path,
vcodec="libx264",
pix_fmt="yuv420p",
r=fps,
movflags="+faststart",
)
.overwrite_output()
.run(input=raw_bytes)
)
save_video_ffmpeg(output, fps=16, out_path="output.mp4")
```
#### <u>Wan VACE</u>
Use FFmpeg to extract frames from a video to use as control frames for Wan VACE.
```bash
mkdir assets/frames
ffmpeg -i assets/test.mp4 -qscale:v 1 -vf fps=8 assets/frames/frame_%04d.jpg
```
```python
output = stable_diffusion.generate_video(
...
# Add control frames for VACE (PIL Images or file paths)
control_frames=[
os.path.join('assets/frames', f)
for f in os.listdir('assets/frames')
if f.lower().endswith((".png", ".jpg", ".jpeg", ".bmp"))
],
)
```
---
### <u>GGUF Model Conversion</u>
You can convert models to GGUF format using the `convert` method.
```python
from stable_diffusion_cpp import StableDiffusion
stable_diffusion = StableDiffusion()
stable_diffusion.convert(
input_path="../models/v1-5-pruned-emaonly.safetensors",
output_path="new_model.gguf",
output_type="q8_0",
)
```
---
### <u>Listing GGML model and RNG types, schedulers and sample methods</u>
Access the GGML model and RNG types, schedulers, and sample methods via the following maps:
```python
from stable_diffusion_cpp import GGML_TYPE_MAP, RNG_TYPE_MAP, SCHEDULER_MAP, SAMPLE_METHOD_MAP
print("GGML model types:", list(GGML_TYPE_MAP))
print("RNG types:", list(RNG_TYPE_MAP))
print("Schedulers:", list(SCHEDULER_MAP))
print("Sample methods:", list(SAMPLE_METHOD_MAP))
```
---
### <u>Other High-level API Examples</u>
Other examples for the high-level API (such as upscaling and model conversion) can be found in the [tests](tests) directory.
## Low-level API
The low-level API is a direct [`ctypes`](https://docs.python.org/3/library/ctypes.html) 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](https://github.com/william-murray1204/stable-diffusion-cpp-python/blob/main/stable_diffusion_cpp/stable_diffusion_cpp.py) and directly mirrors the C API in [stable-diffusion.h](https://github.com/leejet/stable-diffusion.cpp/blob/master/stable-diffusion.h).
Below is a short example demonstrating low-level API usage:
```python
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
```
## Development
To get started, clone the repository and install the package in editable / development mode.
```bash
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.
## References
- [stable-diffusion.cpp](https://github.com/leejet/stable-diffusion.cpp)
- [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
- [llama.cpp](https://github.com/ggerganov/llama.cpp)
- [whisper-cpp-python](https://github.com/carloscdias/whisper-cpp-python)
- [Golang stable-diffusion](https://github.com/seasonjs/stable-diffusion)
- [StableDiffusion.NET](https://github.com/DarthAffe/StableDiffusion.NET)
## License
This project is licensed under the terms of the MIT license. See [LICENSE](LICENSE) for details.
Raw data
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"name": "stable-diffusion-cpp-python",
"maintainer": "William Murray",
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"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "diffusion, stable-diffusion, python, cpp-bindings, artificial-intelligence, machine-learning",
"author": "William Murray",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/df/7d/6f8351e1366b2baa16864ee8aeae682dfb4c937bd87b0452df9790f122ca/stable_diffusion_cpp_python-0.3.6.tar.gz",
"platform": null,
"description": "# \ud83d\uddbc\ufe0f Python Bindings for [`stable-diffusion.cpp`](https://github.com/leejet/stable-diffusion.cpp)\n\nSimple Python bindings for **@leejet's** [`stable-diffusion.cpp`](https://github.com/leejet/stable-diffusion.cpp) library.\n\n[](https://opensource.org/licenses/MIT)\n[](https://pypi.org/project/stable-diffusion-cpp-python/)\n[](https://pepy.tech/project/stable-diffusion-cpp-python)\n\nThis package provides:\n\n- Low-level access to C API via `ctypes` interface.\n- High-level Python API for Stable Diffusion, FLUX and Wan image/video generation.\n\n## Installation\n\nRequirements:\n\n- Python 3.8+\n- C compiler\n - Linux: gcc or clang\n - Windows: Visual Studio or MinGW\n - MacOS: Xcode\n\nTo install the package, run:\n\n```bash\npip install stable-diffusion-cpp-python\n```\n\nThis will also build `stable-diffusion.cpp` from source and install it alongside this python package.\n\nIf this fails, add `--verbose` to the `pip install` to see the full cmake build log.\n\n### Installation Configuration\n\n`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](https://github.com/leejet/stable-diffusion.cpp#build) for a full list.\n\nAll `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.\n\n<details open>\n<summary>Environment Variables</summary>\n\n```bash\n# Linux and Mac\nCMAKE_ARGS=\"-DSD_CUDA=ON\" pip install stable-diffusion-cpp-python\n```\n\n```powershell\n# Windows\n$env:CMAKE_ARGS=\"-DSD_CUDA=ON\"\npip install stable-diffusion-cpp-python\n```\n\n</details>\n\n<details>\n<summary>CLI / requirements.txt</summary>\n\nThey can also be set via `pip install -C / --config-settings` command and saved to a `requirements.txt` file:\n\n```bash\npip install --upgrade pip # ensure pip is up to date\npip install stable-diffusion-cpp-python -C cmake.args=\"-DSD_CUDA=ON\"\n```\n\n```txt\n# requirements.txt\n\nstable-diffusion-cpp-python -C cmake.args=\"-DSD_CUDA=ON\"\n```\n\n</details>\n\n### Supported Backends\n\nBelow are some common backends, their build commands and any additional environment variables required.\n\n<!-- CUDA -->\n<details>\n<summary>Using CUDA (CUBLAS)</summary>\n\nThis 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](https://developer.nvidia.com/cuda-downloads). You can check your installed CUDA toolkit version by running `nvcc --version`.\n\n- It is recommended you have at least 4 GB of VRAM.\n\n```bash\nCMAKE_ARGS=\"-DSD_CUDA=ON\" pip install stable-diffusion-cpp-python\n```\n\n</details>\n\n<!-- HIPBLAS -->\n<details>\n<summary>Using HIPBLAS (ROCm)</summary>\n\nThis provides BLAS acceleration using the ROCm cores of your AMD GPU. Make sure you have the ROCm toolkit installed and that you replace the `$GFX_NAME` value with that of your GPU architecture (`gfx1030` for consumer RDNA2 cards for example).Windows users refer to [docs/hipBLAS_on_Windows.md](docs%2FhipBLAS_on_Windows.md) for a comprehensive guide and troubleshooting tips.\n\n```bash\nif command -v rocminfo; then export GFX_NAME=$(rocminfo | awk '/ *Name: +gfx[1-9]/ {print $2; exit}'); else echo \"rocminfo missing!\"; fi\nif [ -z \"${GFX_NAME}\" ]; then echo \"Error: Couldn't detect GPU!\"; else echo \"Building for GPU: ${GFX_NAME}\"; fi\n\nCMAKE_ARGS=\"-G Ninja -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DGPU_TARGETS=$GFX_NAME -DAMDGPU_TARGETS=$GFX_NAME -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON -DCMAKE_POSITION_INDEPENDENT_CODE=ON\" pip install stable-diffusion-cpp-python\n```\n\n</details>\n\n<!-- Metal -->\n<details>\n<summary>Using Metal</summary>\n\nUsing Metal runs the computation on Apple Silicon. Currently, there are some issues with Metal when performing operations on very large matrices, making it highly inefficient. Performance improvements are expected in the near future.\n\n```bash\nCMAKE_ARGS=\"-DSD_METAL=ON\" pip install stable-diffusion-cpp-python\n```\n\n</details>\n\n<!-- Vulkan -->\n<details>\n<summary>Using Vulkan</summary>\nInstall Vulkan SDK from https://www.lunarg.com/vulkan-sdk/.\n\n```bash\nCMAKE_ARGS=\"-DSD_VULKAN=ON\" pip install stable-diffusion-cpp-python\n```\n\n</details>\n\n<!-- SYCL -->\n<details>\n<summary>Using SYCL</summary>\n\nUsing SYCL runs the computation on an Intel GPU. Please make sure you have installed the related driver and [Intel\u00ae oneAPI Base toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) before starting. For more details refer to [llama.cpp SYCL backend](https://github.com/ggerganov/llama.cpp/blob/master/docs/backend/SYCL.md#linux).\n\n```bash\n# Export relevant ENV variables\nsource /opt/intel/oneapi/setvars.sh\n\n# Option 1: Use FP32 (recommended for better performance in most cases)\nCMAKE_ARGS=\"-DSD_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx\" pip install stable-diffusion-cpp-python\n\n# Option 2: Use FP16\nCMAKE_ARGS=\"-DSD_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON\" pip install stable-diffusion-cpp-python\n```\n\n</details>\n\n<!-- OpenBLAS -->\n<details>\n<summary>Using OpenBLAS</summary>\n\n```bash\nCMAKE_ARGS=\"-DGGML_OPENBLAS=ON\" pip install stable-diffusion-cpp-python\n```\n\n</details>\n\n<!-- MUSA -->\n<details>\n<summary>Using MUSA</summary>\n\nThis provides BLAS acceleration using the MUSA cores of your Moore Threads GPU. Make sure to have the MUSA toolkit installed.\n\n```bash\nCMAKE_ARGS=\"-DCMAKE_C_COMPILER=/usr/local/musa/bin/clang -DCMAKE_CXX_COMPILER=/usr/local/musa/bin/clang++ -DSD_MUSA=ON -DCMAKE_BUILD_TYPE=Release\" pip install stable-diffusion-cpp-python\n```\n\n</details>\n\n<!-- OpenCL -->\n<details>\n<summary>Using OpenCL (Adreno GPU)</summary>\n\nCurrently, it only supports Adreno GPUs and is primarily optimized for Q4_0 type.\n\nTo build for Windows ARM please refers to [Windows 11 Arm64](https://github.com/ggml-org/llama.cpp/blob/master/docs/backend/OPENCL.md#windows-11-arm64)\n\nBuilding for Android:\n\nAndroid NDK:\n\n- Download and install the Android NDK from the [official Android developer site](https://developer.android.com/ndk/downloads).\n\nSetup OpenCL Dependencies for NDK:\nYou need to provide OpenCL headers and the ICD loader library to your NDK sysroot.\n\n- OpenCL Headers:\n\n ```bash\n # In a temporary working directory\n git clone https://github.com/KhronosGroup/OpenCL-Headers\n cd OpenCL-Headers\n\n # Replace <YOUR_NDK_PATH> with your actual NDK installation path\n # e.g., cp -r CL /path/to/android-ndk-r26c/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include\n sudo cp -r CL <YOUR_NDK_PATH>/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include\n cd ..\n ```\n\n- OpenCL ICD Loader:\n\n ```bash\n # In the same temporary working directory\n git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader\n cd OpenCL-ICD-Loader\n mkdir build_ndk && cd build_ndk\n\n # Replace <YOUR_NDK_PATH> in the CMAKE_TOOLCHAIN_FILE and OPENCL_ICD_LOADER_HEADERS_DIR\n cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release \\\n -DCMAKE_TOOLCHAIN_FILE=<YOUR_NDK_PATH>/build/cmake/android.toolchain.cmake \\\n -DOPENCL_ICD_LOADER_HEADERS_DIR=<YOUR_NDK_PATH>/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include \\\n -DANDROID_ABI=arm64-v8a \\\n -DANDROID_PLATFORM=24 \\\n -DANDROID_STL=c++_shared\n\n ninja\n # Replace <YOUR_NDK_PATH>\n # e.g., cp libOpenCL.so /path/to/android-ndk-r26c/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android\n sudo cp libOpenCL.so <YOUR_NDK_PATH>/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android\n cd ../..\n ```\n\nBuild `stable-diffusion-cpp-python` for Android with (untested):\n\n```bash\n# Replace <YOUR_NDK_PATH> with your actual NDK installation path\n# e.g., -DCMAKE_TOOLCHAIN_FILE=/path/to/android-ndk-r26c/build/cmake/android.toolchain.cmake\nCMAKE_ARGS=\"-G Ninja -DCMAKE_TOOLCHAIN_FILE=<YOUR_NDK_PATH>/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-28 -DGGML_OPENMP=OFF -DSD_OPENCL=ON\n```\n\n_(Note: Don't forget to include `LD_LIBRARY_PATH=/vendor/lib64` in your command line before running the binary)_\n\n</details>\n\n### Upgrading and Reinstalling\n\nTo 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.\n\n### Using Flash Attention\n\nEnabling flash attention for the diffusion model reduces memory usage by varying amounts of MB, e.g.:\n\n- **flux 768x768** ~600mb\n- **SD2 768x768** ~1400mb\n\nFor most backends, it slows things down, but for cuda it generally speeds it up too.\nAt the moment, it is only supported for some models and some backends (like `cpu`, `cuda/rocm` and `metal`).\n\nRun by passing `diffusion_flash_attn=True` to the `StableDiffusion` class and watch for:\n\n```log\n[INFO] stable-diffusion.cpp:312 - Using flash attention in the diffusion model\n```\n\nand the compute buffer shrink in the debug log:\n\n```log\n[DEBUG] ggml_extend.hpp:1004 - flux compute buffer size: 650.00 MB(VRAM)\n```\n\n## High-level API\n\nThe high-level API provides a simple managed interface through the `StableDiffusion` class.\n\nBelow is a short example demonstrating how to use the high-level API to generate a simple image:\n\n### <u>Text to Image</u>\n\n```python\nfrom stable_diffusion_cpp import StableDiffusion\n\ndef callback(step: int, steps: int, time: float):\n print(\"Completed step: {} of {}\".format(step, steps))\n\nstable_diffusion = StableDiffusion(\n model_path=\"../models/v1-5-pruned-emaonly.safetensors\",\n # wtype=\"default\", # Weight type (e.g. \"q8_0\", \"f16\", etc) (The \"default\" setting is automatically applied and determines the weight type of a model file)\n)\noutput = stable_diffusion.generate_image(\n prompt=\"a lovely cat\",\n width=512,\n height=512,\n progress_callback=callback,\n # seed=1337, # Uncomment to set a specific seed (use -1 for a random seed)\n)\noutput[0].save(\"output.png\") # Output returned as list of PIL Images\n\n# Model and generation paramaters accessible via .info\nprint(output[0].info)\n```\n\n#### <u>With LoRA (Stable Diffusion)</u>\n\nYou can specify the directory where the lora weights are stored via `lora_model_dir`. If not specified, the default is the current working directory.\n\n- LoRA is specified via prompt, just like [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#lora). (e.g. `<lora:marblesh:1>`)\n- LoRAs will not work when using quantized models. You must instead use a full precision `.safetensors` model.\n\nHere's a simple example:\n\n```python\nfrom stable_diffusion_cpp import StableDiffusion\n\nstable_diffusion = StableDiffusion(\n model_path=\"../models/v1-5-pruned-emaonly.safetensors\",\n lora_model_dir=\"../models/\", # This should point to folder where LoRA weights are stored (not an individual file)\n)\noutput = stable_diffusion.generate_image(\n prompt=\"a lovely cat<lora:marblesh:1>\",\n)\n```\n\n- The `lora_model_dir` argument is used in the same way for FLUX image generation.\n\n---\n\n### <u>FLUX Image Generation</u>\n\nFLUX models should be run using the same implementation as the [stable-diffusion.cpp FLUX documentation](https://github.com/leejet/stable-diffusion.cpp/blob/master/docs/flux.md) 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 (for most models).\n\nDownload the weights from the links below:\n\n- Preconverted gguf weights from [FLUX.1-dev-gguf](https://huggingface.co/leejet/FLUX.1-dev-gguf) or [FLUX.1-schnell](https://huggingface.co/leejet/FLUX.1-schnell-gguf), this way you don't have to do the conversion yourself.\n- Download `vae` from https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/ae.safetensors\n- Download `clip_l` from https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/clip_l.safetensors\n- Download `t5xxl` from https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp16.safetensors\n\n```python\nfrom stable_diffusion_cpp import StableDiffusion\n\nstable_diffusion = StableDiffusion(\n diffusion_model_path=\"../models/flux1-schnell-q3_k.gguf\", # In place of model_path\n clip_l_path=\"../models/clip_l.safetensors\",\n t5xxl_path=\"../models/t5xxl_fp16.safetensors\",\n vae_path=\"../models/ae.safetensors\",\n vae_decode_only=True, # Can be True if not generating image to image\n keep_clip_on_cpu=True, # Prevents black images when using some T5 models\n)\noutput = stable_diffusion.generate_image(\n prompt=\"a lovely cat holding a sign says 'flux.cpp'\",\n cfg_scale=1.0, # a cfg_scale of 1 is recommended for FLUX\n # sample_method=\"euler\", # euler is recommended for FLUX, set automatically if \"default\" is specified\n)\n```\n\n#### <u>With LoRA (FLUX)</u>\n\nLoRAs can be used with FLUX models in the same way as Stable Diffusion models ([as shown above](#with-lora-stable-diffusion)).\n\nNote that:\n\n- It is recommended you use LoRAs with naming formats compatible with ComfyUI.\n- LoRAs will only work with `Flux-dev q8_0`.\n- You can download FLUX LoRA models from https://huggingface.co/XLabs-AI/flux-lora-collection/tree/main (you must use a comfy converted version!!!).\n\n#### <u>Kontext (FLUX)</u>\n\nDownload the weights from the links below:\n\n- Preconverted gguf model from [FLUX.1-Kontext-dev-GGUF](https://huggingface.co/QuantStack/FLUX.1-Kontext-dev-GGUF)\n- Otherwise, download FLUX.1-Kontext-dev from [black-forest-labs/FLUX.1-Kontext-dev](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev/blob/main/flux1-kontext-dev.safetensors)\n- The `vae`, `clip_l`, and `t5xxl` models are the same as for FLUX image generation linked above.\n\n```python\nfrom stable_diffusion_cpp import StableDiffusion\n\nstable_diffusion = StableDiffusion(\n diffusion_model_path=\"../models/flux1-kontext-dev-Q5_K_S.gguf\", # In place of model_path\n clip_l_path=\"../models/clip_l.safetensors\",\n t5xxl_path=\"../models/t5xxl_fp16.safetensors\",\n vae_path=\"../models/ae.safetensors\",\n vae_decode_only=False, # Must be False for FLUX Kontext\n keep_clip_on_cpu=True, # Prevents black images when using some T5 models\n)\noutput = stable_diffusion.generate_image(\n prompt=\"make the cat blue\",\n ref_images=[\"input.png\"],\n cfg_scale=1.0, # a cfg_scale of 1 is recommended for FLUX\n)\n```\n\n#### <u>Chroma (FLUX)</u>\n\nDownload the weights from the links below:\n\n- Preconverted gguf model from [silveroxides/Chroma1-Flash-GGUF](https://huggingface.co/silveroxides/Chroma1-Flash-GGUF), [silveroxides/Chroma1-Base-GGUF](https://huggingface.co/silveroxides/Chroma1-Base-GGUF) or [silveroxides/Chroma1-HD-GGUF](https://huggingface.co/silveroxides/Chroma1-HD-GGUF) ([silveroxides/Chroma-GGUF](https://huggingface.co/silveroxides/Chroma-GGUF) is DEPRECATED)\n- Otherwise, download chroma's safetensors from [lodestones/Chroma1-Flash](https://huggingface.co/lodestones/Chroma1-Flash), [lodestones/Chroma1-Base](https://huggingface.co/lodestones/Chroma1-Base) or [lodestones/Chroma1-HD](https://huggingface.co/lodestones/Chroma1-HD) ([lodestones/Chroma](https://huggingface.co/lodestones/Chroma) is DEPRECATED)\n- The `vae` and `t5xxl` models are the same as for FLUX image generation linked above (`clip_l` not required).\n\n```python\nfrom stable_diffusion_cpp import StableDiffusion\n\nstable_diffusion = StableDiffusion(\n diffusion_model_path=\"../models/Chroma1-HD-Flash-Q4_0.gguf\", # In place of model_path\n t5xxl_path=\"../models/t5xxl_fp16.safetensors\",\n vae_path=\"../models/ae.safetensors\",\n vae_decode_only=True, # Can be True if we are not generating image to image\n chroma_use_dit_mask=False,\n keep_clip_on_cpu=True, # Prevents black images when using some T5 models\n)\noutput = stable_diffusion.generate_image(\n prompt=\"a lovely cat holding a sign says 'chroma.cpp'\",\n cfg_scale=4.0, # a cfg_scale of 4 is recommended for Chroma\n)\n```\n\n---\n\n### <u>SD3.5 Image Generation</u>\n\nDownload the weights from the links below:\n\n- Download `sd3.5_large` from https://huggingface.co/stabilityai/stable-diffusion-3.5-large/blob/main/sd3.5_large.safetensors\n- Download `clip_g` from https://huggingface.co/Comfy-Org/stable-diffusion-3.5-fp8/blob/main/text_encoders/clip_g.safetensors\n- Download `clip_l` from https://huggingface.co/Comfy-Org/stable-diffusion-3.5-fp8/blob/main/text_encoders/clip_l.safetensors\n- Download `t5xxl` from https://huggingface.co/Comfy-Org/stable-diffusion-3.5-fp8/blob/main/text_encoders/t5xxl_fp16.safetensors\n\n```python\nfrom stable_diffusion_cpp import StableDiffusion\n\nstable_diffusion = StableDiffusion(\n model_path=\"../models/sd3.5_large.safetensors\",\n clip_l_path=\"../models/clip_l.safetensors\",\n clip_g_path=\"../models/clip_g.safetensors\",\n t5xxl_path=\"../models/t5xxl_fp16.safetensors\",\n keep_clip_on_cpu=True, # Prevents black images when using some T5 models\n)\noutput = stable_diffusion.generate_image(\n prompt=\"a lovely cat holding a sign says 'Stable diffusion 3.5 Large'\",\n height=1024,\n width=1024,\n cfg_scale=4.5,\n sample_method=\"euler\",\n)\n```\n\n---\n\n### <u>Image to Image</u>\n\n```python\nfrom stable_diffusion_cpp import StableDiffusion\n# from PIL import Image\n\nINPUT_IMAGE = \"../input.png\"\n# INPUT_IMAGE = Image.open(\"../input.png\") # or alternatively, pass as PIL Image\n\nstable_diffusion = StableDiffusion(model_path=\"../models/v1-5-pruned-emaonly.safetensors\")\n\noutput = stable_diffusion.generate_image(\n prompt=\"blue eyes\",\n init_image=INPUT_IMAGE, # Note: The input image will be automatically resized to the match the width and height arguments (default: 512x512)\n strength=0.4,\n)\n```\n\n---\n\n### <u>Inpainting</u>\n\n```python\nfrom stable_diffusion_cpp import StableDiffusion\n\n# Note: Inpainting with a base model gives poor results. A model fine-tuned for inpainting is recommended.\nstable_diffusion = StableDiffusion(model_path=\"../models/v1-5-pruned-emaonly.safetensors\")\n\noutput = stable_diffusion.generate_image(\n prompt=\"blue eyes\",\n init_image=\"../input.png\",\n mask_image=\"../mask.png\", # A grayscale image where 0 is masked and 255 is unmasked\n strength=0.4,\n)\n```\n\n---\n\n### <u>PhotoMaker</u>\n\nYou can use [PhotoMaker](https://github.com/TencentARC/PhotoMaker) to personalize generated images with your own ID.\n\n**NOTE**, currently PhotoMaker **ONLY** works with **SDXL** (any SDXL model files will work).\n\nDownload PhotoMaker model file (in safetensor format) [here](https://huggingface.co/bssrdf/PhotoMaker). The official release of the model file (in .bin format) does not work with `stablediffusion.cpp`.\n\nIn prompt, make sure you have a class word followed by the trigger word `\"img\"` (hard-coded for now). The class word could be one of `\"man, woman, girl, boy\"`. If input ID images contain asian faces, add `Asian` before the class word.\n\n```python\nimport os\nfrom stable_diffusion_cpp import StableDiffusion\n\nstable_diffusion = StableDiffusion(\n model_path=\"../models/sdxl.vae.safetensors\",\n vae_path=\"../models/sdxl.vae.safetensors\",\n photo_maker_path=\"../models/photomaker-v1.safetensors\",\n # keep_vae_on_cpu=True, # If on low memory GPUs (<= 8GB), setting this to True is recommended to get artifact free images\n)\n\nINPUT_ID_IMAGES_DIR = \"../assets/newton_man\"\n\noutput = stable_diffusion.generate_image(\n cfg_scale=5.0, # a cfg_scale of 5.0 is recommended for PhotoMaker\n height=1024,\n width=1024,\n pm_style_strength=10, # (0-100)% Default is 20 and 10-20 typically gets good results. Lower ratio means more faithfully following input ID (not necessarily better quality).\n sample_method=\"euler\",\n prompt=\"a man img, retro futurism, retro game art style but extremely beautiful, intricate details, masterpiece, best quality, space-themed, cosmic, celestial, stars, galaxies, nebulas, planets, science fiction, highly detailed\",\n negative_prompt=\"realistic, photo-realistic, worst quality, greyscale, bad anatomy, bad hands, error, text\",\n pm_id_images=[\n os.path.join(INPUT_ID_IMAGES_DIR, f)\n for f in os.listdir(INPUT_ID_IMAGES_DIR)\n if f.lower().endswith((\".png\", \".jpg\", \".jpeg\", \".bmp\"))\n ],\n)\n```\n\n#### <u>PhotoMaker Version 2</u>\n\n[PhotoMaker Version 2 (PMV2)](https://github.com/TencentARC/PhotoMaker/blob/main/README_pmv2.md) has some key improvements. Unfortunately it has a very heavy dependency which makes running it a bit involved.\n\nRunning PMV2 Requires running a python script `face_detect.py` (found here [stable-diffusion.cpp/face_detect.py](https://github.com/leejet/stable-diffusion.cpp/blob/master/face_detect.py)) to obtain `id_embeds` for the given input images.\n\n```bash\npython face_detect.py <input_image_dir>\n```\n\nAn `id_embeds.bin` file will be generated in `input_images_dir`.\n\n**Note: This step only needs to be run once \u2014 the resulting `id_embeds` can be reused.**\n\n- Run the same command as in version 1 but replacing `photomaker-v1.safetensors` with `photomaker-v2.safetensors` and pass the `id_embeds.bin` path into the `pm_id_embed_path` parameter.\n Download `photomaker-v2.safetensors` from [bssrdf/PhotoMakerV2](https://huggingface.co/bssrdf/PhotoMakerV2).\n- All other parameters from Version 1 remain the same for Version 2.\n\n---\n\n### <u>QWEN Image</u>\n\nDownload the weights from the links below:\n\n- Download `Qwen Image`\n - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/diffusion_models\n - gguf: https://huggingface.co/QuantStack/Qwen-Image-GGUF/tree/main\n- Download `vae`\n - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/vae\n- Download `qwen_2.5_vl 7b`\n - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/text_encoders\n - gguf: https://huggingface.co/mradermacher/Qwen2.5-VL-7B-Instruct-GGUF/tree/main\n\n```python\nfrom stable_diffusion_cpp import StableDiffusion\n\nstable_diffusion = StableDiffusion(\n diffusion_model_path=\"../models/qwen-image-Q8_0.gguf\",\n qwen2vl_path=\"../models/Qwen2.5-VL-7B-Instruct.Q8_0.gguf\",\n vae_path=\"../models/qwen_image_vae.safetensors\",\n offload_params_to_cpu=True,\n flow_shift=3,\n)\n\noutput = stable_diffusion.generate_image(\n prompt='\u4e00\u4e2a\u7a7f\u7740\"QWEN\"\u6807\u5fd7\u7684T\u6064\u7684\u4e2d\u56fd\u7f8e\u5973\u6b63\u62ff\u7740\u9ed1\u8272\u7684\u9a6c\u514b\u7b14\u9762\u76f8\u955c\u5934\u5fae\u7b11\u3002\u5979\u8eab\u540e\u7684\u73bb\u7483\u677f\u4e0a\u624b\u5199\u4f53\u5199\u7740 \u201c\u4e00\u3001Qwen-Image\u7684\u6280\u672f\u8def\u7ebf\uff1a \u63a2\u7d22\u89c6\u89c9\u751f\u6210\u57fa\u7840\u6a21\u578b\u7684\u6781\u9650\uff0c\u5f00\u521b\u7406\u89e3\u4e0e\u751f\u6210\u4e00\u4f53\u5316\u7684\u672a\u6765\u3002\u4e8c\u3001Qwen-Image\u7684\u6a21\u578b\u7279\u8272\uff1a1\u3001\u590d\u6742\u6587\u5b57\u6e32\u67d3\u3002\u652f\u6301\u4e2d\u82f1\u6e32\u67d3\u3001\u81ea\u52a8\u5e03\u5c40\uff1b 2\u3001\u7cbe\u51c6\u56fe\u50cf\u7f16\u8f91\u3002\u652f\u6301\u6587\u5b57\u7f16\u8f91\u3001\u7269\u4f53\u589e\u51cf\u3001\u98ce\u683c\u53d8\u6362\u3002\u4e09\u3001Qwen-Image\u7684\u672a\u6765\u613f\u666f\uff1a\u8d4b\u80fd\u4e13\u4e1a\u5185\u5bb9\u521b\u4f5c\u3001\u52a9\u529b\u751f\u6210\u5f0fAI\u53d1\u5c55\u3002\u201d',\n cfg_scale=2.5,\n sample_method='euler',\n)\n```\n\n#### <u>QWEN Image Edit</u>\n\nDownload the weights from the links below:\n\n- Download `Qwen Image Edit`\n - Qwen Image Edit\n - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/tree/main/split_files/diffusion_models\n - gguf: https://huggingface.co/QuantStack/Qwen-Image-Edit-GGUF/tree/main\n - Qwen Image Edit 2509\n - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/tree/main/split_files/diffusion_models\n - gguf: https://huggingface.co/QuantStack/Qwen-Image-Edit-2509-GGUF/tree/main\n- Download `vae`\n - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/vae\n- Download `qwen_2.5_vl 7b`\n - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/text_encoders\n - gguf: https://huggingface.co/mradermacher/Qwen2.5-VL-7B-Instruct-GGUF/tree/main\n\n```python\nfrom stable_diffusion_cpp import StableDiffusion\n\nstable_diffusion = StableDiffusion(\n diffusion_model_path=\"../models/Qwen_Image_Edit-Q8_0.gguf\",\n qwen2vl_path=\"../models/Qwen2.5-VL-7B-Instruct.Q8_0.gguf\",\n vae_path=\"../models/qwen_image_vae.safetensors\",\n offload_params_to_cpu=True,\n flow_shift=3,\n)\n\noutput = stable_diffusion.generate_image(\n prompt=\"make the cat blue\",\n ref_images=[\"input.png\"],\n cfg_scale=2.5,\n sample_method='euler',\n)\n```\n\n---\n\n### <u>Wan Video Generation</u>\n\nSee [stable-diffusion.cpp Wan download weights](https://github.com/leejet/stable-diffusion.cpp/blob/master/docs/wan.md#download-weights) for a complete list of Wan models.\n\n```python\nfrom stable_diffusion_cpp import StableDiffusion\n\nstable_diffusion = StableDiffusion(\n diffusion_model_path=\"../models/wan2.1_t2v_1.3B_fp16.safetensors\", # In place of model_path\n t5xxl_path=\"../models/umt5-xxl-encoder-Q8_0.gguf\",\n vae_path=\"../models/wan_2.1_vae.safetensors\",\n flow_shift=3.0,\n keep_clip_on_cpu=True, # Prevents black images when using some T5 models\n)\n\noutput = stable_diffusion.generate_video(\n prompt=\"a cute dog jumping\",\n negative_prompt=\"\u8272\u8c03\u8273\u4e3d\uff0c\u8fc7\u66dd\uff0c\u9759\u6001\uff0c\u7ec6\u8282\u6a21\u7cca\u4e0d\u6e05\uff0c\u5b57\u5e55\uff0c\u98ce\u683c\uff0c\u4f5c\u54c1\uff0c\u753b\u4f5c\uff0c\u753b\u9762\uff0c\u9759\u6b62\uff0c\u6574\u4f53\u53d1\u7070\uff0c\u6700\u5dee\u8d28\u91cf\uff0c\u4f4e\u8d28\u91cf\uff0cJPEG\u538b\u7f29\u6b8b\u7559\uff0c\u4e11\u964b\u7684\uff0c\u6b8b\u7f3a\u7684\uff0c\u591a\u4f59\u7684\u624b\u6307\uff0c\u753b\u5f97\u4e0d\u597d\u7684\u624b\u90e8\uff0c\u753b\u5f97\u4e0d\u597d\u7684\u8138\u90e8\uff0c \u7578\u5f62\u7684\uff0c\u6bc1\u5bb9\u7684\uff0c\u5f62\u6001\u7578\u5f62\u7684\u80a2\u4f53\uff0c\u624b\u6307\u878d\u5408\uff0c\u9759\u6b62\u4e0d\u52a8\u7684\u753b\u9762\uff0c\u6742\u4e71\u7684\u80cc\u666f\uff0c\u4e09\u6761\u817f\uff0c\u80cc\u666f\u4eba\u5f88\u591a\uff0c\u5012\u7740\u8d70\",\n height=832,\n width=480,\n cfg_scale=6.0,\n sample_method=\"euler\",\n video_frames=33,\n) # Output is a list of PIL Images (video frames)\n```\n\nAs the output is simply a list of images (video frames), you can convert it into a video using any library you prefer. The example below uses `ffmpeg-python`. Alternatively, libraries such **OpenCV** or **MoviePy** can also be used.\n\n> **Note**\n>\n> - You'll require **Python bindings for FFmpeg**, `python-ffmpeg` (`pip install ffmpeg-python`) in addition to an **FFmpeg installation on your system**, accessible in your PATH. Check with `ffmpeg -version`.\n\n```python\nfrom typing import List\nfrom PIL import Image\nimport numpy as np\nimport ffmpeg\n\ndef save_video_ffmpeg(frames: List[Image.Image], fps: int, out_path: str) -> None:\n if not frames:\n raise ValueError(\"No frames provided\")\n\n width, height = frames[0].size\n\n # Concatenate frames into raw RGB bytes\n raw_bytes = b\"\".join(np.array(frame.convert(\"RGB\"), dtype=np.uint8).tobytes() for frame in frames)\n (\n ffmpeg.input(\n \"pipe:\",\n format=\"rawvideo\",\n pix_fmt=\"rgb24\",\n s=f\"{width}x{height}\",\n r=fps,\n )\n .output(\n out_path,\n vcodec=\"libx264\",\n pix_fmt=\"yuv420p\",\n r=fps,\n movflags=\"+faststart\",\n )\n .overwrite_output()\n .run(input=raw_bytes)\n )\n\nsave_video_ffmpeg(output, fps=16, out_path=\"output.mp4\")\n```\n\n#### <u>Wan VACE</u>\n\nUse FFmpeg to extract frames from a video to use as control frames for Wan VACE.\n\n```bash\nmkdir assets/frames\nffmpeg -i assets/test.mp4 -qscale:v 1 -vf fps=8 assets/frames/frame_%04d.jpg\n```\n\n```python\noutput = stable_diffusion.generate_video(\n ...\n # Add control frames for VACE (PIL Images or file paths)\n control_frames=[\n os.path.join('assets/frames', f)\n for f in os.listdir('assets/frames')\n if f.lower().endswith((\".png\", \".jpg\", \".jpeg\", \".bmp\"))\n ],\n)\n```\n\n---\n\n### <u>GGUF Model Conversion</u>\n\nYou can convert models to GGUF format using the `convert` method.\n\n```python\nfrom stable_diffusion_cpp import StableDiffusion\n\nstable_diffusion = StableDiffusion()\n\nstable_diffusion.convert(\n input_path=\"../models/v1-5-pruned-emaonly.safetensors\",\n output_path=\"new_model.gguf\",\n output_type=\"q8_0\",\n)\n```\n\n---\n\n### <u>Listing GGML model and RNG types, schedulers and sample methods</u>\n\nAccess the GGML model and RNG types, schedulers, and sample methods via the following maps:\n\n```python\nfrom stable_diffusion_cpp import GGML_TYPE_MAP, RNG_TYPE_MAP, SCHEDULER_MAP, SAMPLE_METHOD_MAP\n\nprint(\"GGML model types:\", list(GGML_TYPE_MAP))\nprint(\"RNG types:\", list(RNG_TYPE_MAP))\nprint(\"Schedulers:\", list(SCHEDULER_MAP))\nprint(\"Sample methods:\", list(SAMPLE_METHOD_MAP))\n```\n\n---\n\n### <u>Other High-level API Examples</u>\n\nOther examples for the high-level API (such as upscaling and model conversion) can be found in the [tests](tests) directory.\n\n## Low-level API\n\nThe low-level API is a direct [`ctypes`](https://docs.python.org/3/library/ctypes.html) binding to the C API provided by `stable-diffusion.cpp`.\nThe entire low-level API can be found in [stable_diffusion_cpp/stable_diffusion_cpp.py](https://github.com/william-murray1204/stable-diffusion-cpp-python/blob/main/stable_diffusion_cpp/stable_diffusion_cpp.py) and directly mirrors the C API in [stable-diffusion.h](https://github.com/leejet/stable-diffusion.cpp/blob/master/stable-diffusion.h).\n\nBelow is a short example demonstrating low-level API usage:\n\n```python\nimport stable_diffusion_cpp as sd_cpp\nimport ctypes\nfrom PIL import Image\n\nimg = Image.open(\"path/to/image.png\")\nimg_bytes = img.tobytes()\n\nc_image = sd_cpp.sd_image_t(\n width=img.width,\n height=img.height,\n channel=channel,\n data=ctypes.cast(\n (ctypes.c_byte * len(img_bytes))(*img_bytes),\n ctypes.POINTER(ctypes.c_uint8),\n ),\n) # Create a new C sd_image_t\n```\n\n## Development\n\nTo get started, clone the repository and install the package in editable / development mode.\n\n```bash\ngit clone --recurse-submodules https://github.com/william-murray1204/stable-diffusion-cpp-python.git\ncd stable-diffusion-cpp-python\n\n# Upgrade pip (required for editable mode)\npip install --upgrade pip\n\n# Install with pip\npip install -e .\n```\n\nNow you can make changes to the code within the `stable_diffusion_cpp` directory and test them in your python environment.\n\n## References\n\n- [stable-diffusion.cpp](https://github.com/leejet/stable-diffusion.cpp)\n- [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)\n- [llama.cpp](https://github.com/ggerganov/llama.cpp)\n- [whisper-cpp-python](https://github.com/carloscdias/whisper-cpp-python)\n- [Golang stable-diffusion](https://github.com/seasonjs/stable-diffusion)\n- [StableDiffusion.NET](https://github.com/DarthAffe/StableDiffusion.NET)\n\n## License\n\nThis project is licensed under the terms of the MIT license. See [LICENSE](LICENSE) for details.\n",
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