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Ben_Kosytorz 90e0f4c985 feat: migrate to pure Rust backend with WGPU/Vulkan support

- Remove libtorch dependency and related features
- Switch to ndarray backend as default for CPU execution
- Update README to reflect new WGPU/Vulkan backend usage
- Simplify device selection to use only CPU backend
- Enable WGPU backend via feature flag for GPU acceleration

2026-03-03 22:17:25 +01:00

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Stable-Diffusion-Burn

Stable-Diffusion-Burn is a Rust-based project which ports the V1 stable diffusion model into the deep learning framework, Burn. This repository is licensed under the MIT Licence.

How To Use

Step 1: Download the Model and Set Environment Variables

Start by downloading the SDv1-4 model provided on HuggingFace.

wget https://huggingface.co/Gadersd/Stable-Diffusion-Burn/resolve/main/SDv1-4.mpk

Step 2: Run the Sample Binary

Invoke the sample binary provided in the rust code. The application now uses a pure Rust backend (WGPU/Vulkan) instead of libtorch. The WGPU backend is unstable for SD but may work well in the future as burn-wpu is optimized.

# WGPU/Vulkan backend (GPU accelerated, requires Vulkan-compatible GPU)
# Arguments: <model_type(burn or dump)> <model_name> <unconditional_guidance_scale> <n_diffusion_steps> <prompt> <output_image_name>

# GPU (Vulkan)
cargo run --release --features wgpu-backend --bin sample burn SDv1-4 7.5 20 "An ancient mossy stone." img

# CPU (UNSTABLE - fallback if GPU not available)
cargo run --release --bin sample burn SDv1-4 7.5 20 "An ancient mossy stone." img

This command will generate an image according to the provided prompt, which will be saved as 'img0.png'.

![An image of an ancient mossy stone](img0.png)

### Optional: Extract and Convert a Fine-Tuned Model

If users are interested in using a fine-tuned version of stable diffusion, the Python scripts provided in this project can be used to transform a weight dump into a Burn model file. This does not work on Windows.

```bash
# Step into the Python directory
cd python

# Download the model, this is just the base v1.4 model as an example
wget https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt

# Install tinygrad
pip install -r requirements.txt

# Extract the weights
CPU=1 python3 dump.py sd-v1-4.ckpt

# Move the extracted weight folder out
mv params ..

# Step out of the Python directory
cd ..

# Convert the weights into a usable form
cargo run --release --bin convert params SDv1-4

The binaries 'convert' and 'sample' are contained in Rust. Convert works on CPU whereas sample needs CUDA.

Remember, convert should be used if you're planning on using the fine-tuned version of the stable diffusion.

License

This project is licensed under MIT license.

We wish you a productive time using this project. Enjoy!