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.
Start by downloading the SDv1-4.bin model provided on HuggingFace.
wget https://huggingface.co/Gadersd/Stable-Diffusion-Burn/resolve/main/V1/SDv1-4.binInvoke the sample binary provided in the rust code. By default, torch is used. The WGPU backend is unstable for SD but may work well in the future as burn-wpu is optimized.
# torch (at least 6 GB VRAM, possibly less)
export TORCH_CUDA_VERSION=cu113
# Arguments: <model_type(burn or dump)> <model> <unconditional_guidance_scale> <n_diffusion_steps> <prompt> <output_image>
cargo run --release --bin sample burn SDv1-4 7.5 20 "An ancient mossy stone." img
# wgpu (UNSTABLE)
# Arguments: <model_type(burn or dump)> <model> <unconditional_guidance_scale> <n_diffusion_steps> <prompt> <output_image>
cargo run --release --features wgpu-backend --bin sample burn SDv1-4 7.5 20 "An ancient mossy stone." imgThis command will generate an image according to the provided prompt, which will be saved as 'img0.png'.
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. Note: the tinygrad dependency should be installed from source rather than with pip.
# 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
# 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 SDv1-4The 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.
This project is licensed under MIT license.
We wish you a productive time using this project. Enjoy!
