stable-diffusion.cpp/README.md
2023-08-13 19:47:53 +08:00

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<p align="center">
<img src="./assets/a%20lovely%20cat.png" width="256x">
</p>
# stable-diffusion.cpp
Inference of [Stable Diffusion](https://github.com/CompVis/stable-diffusion) in pure C/C++
## Features
- Plain C/C++ implementation based on [ggml](https://github.com/ggerganov/ggml), working in the same way as [llama.cpp](https://github.com/ggerganov/llama.cpp)
- 16-bit, 32-bit float support
- 4-bit, 5-bit and 8-bit integer quantization support
- Accelerated memory-efficient CPU inference
- AVX, AVX2 and AVX512 support for x86 architectures
- Original `txt2img` mode
- Negative prompt
- Sampling method
- `Euler A`
- Supported platforms
- Linux
- Mac OS
- Windows
### TODO
- [ ] Original `img2img` mode
- [ ] More sampling methods
- [ ] GPU support
- [ ] 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)
- [ ] [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) style tokenizer (eg: token weighting, ...)
- [ ] LoRA support
- [ ] k-quants support
## Usage
### Get the Code
```
git clone --recursive https://github.com/leejet/stable-diffusion.cpp
cd stable-diffusion.cpp
```
### 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
```shell
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
```
- convert weights to ggml model format
```shell
cd models
pip install -r requirements.txt
python convert.py [path to weights] --out_type [output precision]
# For example, python convert.py sd-v1-4.ckpt --out_type f16
```
### Quantization
You can specify the output model format using the --out_type 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
```shell
mkdir build
cd build
cmake ..
cmake --build . --config Release
```
#### Using OpenBLAS
```
cmake .. -DGGML_OPENBLAS=ON
cmake --build . --config Release
```
### Run
```
usage: ./sd [arguments]
arguments:
-h, --help show this help message and exit
-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 cores
-m, --model [MODEL] path to model
-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)
-H, --height H image height, in pixel space (default: 512)
-W, --width W image width, in pixel space (default: 512)
--sample-method SAMPLE_METHOD sample method (default: "eular a")
--steps STEPS number of sample steps (default: 20)
-s SEED, --seed SEED RNG seed (default: 42, use random seed for < 0)
-v, --verbose print extra info
```
For example
```
./sd -m ../models/sd-v1-4-ggml-model-f16.bin -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 |
| ---- |---- |---- |---- |---- |---- |---- |
| ![](./assets/f32.png) |![](./assets/f16.png) |![](./assets/q8_0.png) |![](./assets/q5_0.png) |![](./assets/q5_1.png) |![](./assets/q4_0.png) |![](./assets/q4_1.png) |
## Memory/Disk Requirements
| precision | f32 | f16 |q8_0 |q5_0 |q5_1 |q4_0 |q4_1 |
| ---- | ---- |---- |---- |---- |---- |---- |---- |
| **Disk** | 2.8G | 2.0G | 1.7G | 1.6G | 1.6G | 1.5G | 1.5G |
| **Memory**(txt2img - 512 x 512) | ~4.9G | ~4.1G | ~3.8G | ~3.7G | ~3.7G | ~3.6G | ~3.6G |
## References
- [ggml](https://github.com/ggerganov/ggml)
- [stable-diffusion](https://github.com/CompVis/stable-diffusion)
- [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
- [k-diffusion](https://github.com/crowsonkb/k-diffusion)