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Yet another PyTorch implementation of Stable Diffusion (probably easy to read)
| Date | Stars |
|---|---|
| 2026-07-24 | 593 |
| 2026-07-25 | 593 |
| 2026-07-28 | 593 |
| 2026-07-30 | 593 |
| 2026-07-31 | 592 |
| 2026-08-06 | 592 |
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# stable-diffusion-pytorch
[](https://colab.research.google.com/github/kjsman/stable-diffusion-pytorch/blob/main/demo.ipynb)
Yet another PyTorch implementation of [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release).
I tried my best to make the codebase minimal, self-contained, consistent, hackable, and easy to read. Features are pruned if not needed in Stable Diffusion (e.g. Attention mask at CLIP tokenizer/encoder). Configs are hard-coded (based on Stable Diffusion v1.x). Loops are unrolled when that shape makes more sense.
Despite of my efforts, I feel like [I cooked another sphagetti](https://xkcd.com/927/). Well, help yourself!
Heavily referred to following repositories. Big kudos to them!
* [divamgupta/stable-diffusion-tensorflow](https://github.com/divamgupta/stable-diffusion-tensorflow)
* [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion)
* [huggingface/transformers](https://github.com/huggingface/transformers)
* [crowsonkb/k-diffusion](https://github.com/crowsonkb/k-diffusion)
* [karpathy/minGPT](https://github.com/karpathy/minGPT)
## Dependencies
* PyTorch
* Numpy
* Pillow
* regex
* tqdm
## How to Install
1. Clone or download this repository.
2. Install dependencies: Run `pip install torch numpy Pillow regex` or `pip install -r requirements.txt`.
3. Download `data.v20221029.tar` from [here](https://huggingface.co/jinseokim/stable-diffusion-pytorch-data/resolve/main/data.v20221029.tar) and unpack in the parent folder of `stable_diffusion_pytorch`. Your folders should be like this:
```
stable-diffusion-pytorch(-main)/
├─ data/
│ ├─ ckpt/
│ ├─ ...
├─ stable_diffusion_pytorch/
│ ├─ samplers/
└ ┴─ ...
```
*Note that checkpoint files included in `data.zip` [have different license](#license) -- you should agree to the license to use checkpoint files.*
## How to Use
Import `stable_diffusion_pytorch` as submodule.
Here's some example scripts. You can also read the docstring of `stable_diffusion_pytorch.pipeline.generate`.
Text-to-image generation:
```py
from stable_diffusion_pytorch import pipeline
prompts = ["a photograph of an astronaut riding a horse"]
images = pipeline.generate(prompts)
images[0].save('output.jpg')
```
...with multiple prompts:
```
prompts = [
"a photograph of an astronaut riding a horse",
""]
images = pipeline.generate(prompts)
```
...with unconditional(negative) prompts:
```py
prompts = ["a photograph of an astronaut riding a horse"]
uncond_prompts = ["low quality"]
images = pipeline.generate(prompts, uncond_prompts)
```
...with seed:
```py
prompts = ["a photograph of an astronaut riding a horse"]
images = pipeline.generate(prompts, uncond_prompts, seed=42)
```
Preload models (you will need enough VRAM):
```py
from stable_diffusion_pytorch import model_loader
models = model_loader.preload_models('cuda')
prompts = ["a photograph of an astronaut riding a horse"]
images = pipeline.generate(prompts, models=models)
```
If you get OOM with above code but have enough RAM (not VRAM), you can move models to GPU when needed
and move back to CPU when not needed:
```py
from stable_diffusion_pytorch import model_loader
models = model_loader.preload_models('cpu')
prompts = ["a photograph of an astronaut riding a horse"]
images = pipeline.generate(prompts, models=models, device='cuda', idle_device='cpu')
```
Image-to-image generation:
```py
from PIL import Image
prompts = ["a photograph of an astronaut riding a horse"]
input_images = [Image.open('space.jpg')]
images = pipeline.generate(prompts, input_images=images)
```
...with custom strength:
```py
prompts = ["a photograph of an astronaut riding a horse"]
input_images = [Image.open('space.jpg')]
images = pipeline.generate(prompts, input_images=images, strength=0.6)
```
Change [classifier-free guidance](https://arxiv.org/abs/2207.12598) scale:
```py
prompts = ["a photograph of an astronaut riding a horse"]
imagesExcerpt of 5,083 characters
Read on GitHub
11
Massimiliano Pronesti · IBM Research · Ireland
5
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f8fdf4a96f6fff06, topic:stable-diffusion, topic:image-generation, readme:text-to-image