stable-diffusion.cpp

Inference of Stable Diffusion in pure C/C++

Features

Plain C/C++ implementation based on ggml, working in the same way as llama.cpp
Super lightweight and without external dependencies.
16-bit, 32-bit float support
4-bit, 5-bit and 8-bit integer quantization support
Accelerated memory-efficient CPU inference
- Only requires ~2.3GB when using txt2img with fp16 precision to generate a 512x512 image, enabling Flash Attention just requires ~1.8GB.
AVX, AVX2 and AVX512 support for x86 architectures
SD1.x and SD2.x support
Full CUDA backend for GPU acceleration, for now just for float16 and float32 models. There are some issues with quantized models and CUDA; it will be fixed in the future.
Flash Attention for memory usage optimization (only cpu for now).
Original txt2img and img2img mode
Negative prompt
stable-diffusion-webui style tokenizer (not all the features, only token weighting for now)
LoRA support, same as stable-diffusion-webui
Latent Consistency Models support (LCM/LCM-LoRA)
Sampling method
- Euler A
- Euler
- Heun
- DPM2
- DPM++ 2M
- DPM++ 2M v2
- DPM++ 2S a
- LCM
Cross-platform reproducibility (--rng cuda, consistent with the stable-diffusion-webui GPU RNG)
Embedds generation parameters into png output as webui-compatible text string
Supported platforms
- Linux
- Mac OS
- Windows
- Android (via Termux)

TODO

More sampling methods
Make inference faster
- The current implementation of ggml_conv_2d is slow and has high memory usage
Continuing to reduce memory usage (quantizing the weights of ggml_conv_2d)
Implement BPE Tokenizer
Add TAESD for faster VAE decoding
k-quants support

Usage

Get the Code

git clone --recursive https://github.com/leejet/stable-diffusion.cpp
cd stable-diffusion.cpp

If you have already cloned the repository, you can use the following command to update the repository to the latest code.

cd stable-diffusion.cpp
git pull origin master
git submodule init
git submodule update

Convert weights

download original weights(.ckpt or .safetensors). For example

Stable Diffusion v1.4 from https://huggingface.co/CompVis/stable-diffusion-v-1-4-original
Stable Diffusion v1.5 from https://huggingface.co/runwayml/stable-diffusion-v1-5
Stable Diffuison v2.1 from https://huggingface.co/stabilityai/stable-diffusion-2-1

curl -L -O https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
# curl -L -O https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors
# curl -L -O https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-nonema-pruned.safetensors

convert weights to gguf model format
```
./bin/convert sd-v1-4.ckpt -t f16
```

Quantization

You can specify the output model format using the --type or -t parameter

f16 for 16-bit floating-point
f32 for 32-bit floating-point
q8_0 for 8-bit integer quantization
q5_0 or q5_1 for 5-bit integer quantization
q4_0 or q4_1 for 4-bit integer quantization

Build

Build from scratch

mkdir build
cd build
cmake ..
cmake --build . --config Release

Using OpenBLAS

cmake .. -DGGML_OPENBLAS=ON
cmake --build . --config Release

Using CUBLAS

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 .. -DSD_CUBLAS=ON
cmake --build . --config Release

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 .. -DSD_FLASH_ATTN=ON
cmake --build . --config Release

Run

usage: ./bin/sd [arguments]

arguments:
  -h, --help                         show this help message and exit
  -M, --mode [txt2img or img2img]    generation mode (default: txt2img)
  -t, --threads N                    number of threads to use during computation (default: -1).
                                     If threads <= 0, then threads will be set to the number of CPU physical cores
  -m, --model [MODEL]                path to model
  --lora-model-dir [DIR]             lora model directory
  -i, --init-img [IMAGE]             path to the input image, required by img2img
  -o, --output OUTPUT                path to write result image to (default: .\output.png)
  -p, --prompt [PROMPT]              the prompt to render
  -n, --negative-prompt PROMPT       the negative prompt (default: "")
  --cfg-scale SCALE                  unconditional guidance scale: (default: 7.0)
  --strength STRENGTH                strength for noising/unnoising (default: 0.75)
                                     1.0 corresponds to full destruction of information in init image
  -H, --height H                     image height, in pixel space (default: 512)
  -W, --width W                      image width, in pixel space (default: 512)
  --sampling-method {euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, lcm}
                                     sampling method (default: "euler_a")
  --steps  STEPS                     number of sample steps (default: 20)
  --rng {std_default, cuda}          RNG (default: cuda)
  -s SEED, --seed SEED               RNG seed (default: 42, use random seed for < 0)
  -b, --batch-count COUNT            number of images to generate.
  --schedule {discrete, karras}      Denoiser sigma schedule (default: discrete)
  -v, --verbose                      print extra info

txt2img example

./bin/sd -m ../sd-v1-4-f16.gguf -p "a lovely cat"

Using formats of different precisions will yield results of varying quality.

f32	f16	q8_0	q5_0	q5_1	q4_0	q4_1

img2img example

./output.png is the image generated from the above txt2img pipeline

./bin/sd --mode img2img -m ../models/sd-v1-4-f16.gguf -p "cat with blue eyes" -i ./output.png -o ./img2img_output.png --strength 0.4

with LoRA

convert lora weights to gguf model format

bin/convert [lora path] -t f16
# For example,  bin/convert marblesh.safetensors -t f16

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.

Here's a simple example:

./bin/sd -m ../models/v1-5-pruned-emaonly-f16.gguf -p "a lovely cat<lora:marblesh:1>" --lora-model-dir ../models

../models/marblesh.gguf will be applied to the model

LCM/LCM-LoRA

Download LCM-LoRA form https://huggingface.co/latent-consistency/lcm-lora-sdv1-5
Specify LCM-LoRA by adding <lora:lcm-lora-sdv1-5:1> to prompt
It's advisable to set --cfg-scale to 1.0 instead of the default 7.0. For --steps, a range of 2-8 steps is recommended. For --sampling-method, lcm/euler_a is recommended.