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Stable Diffusion v1.4 ported to Rust's burn framework
| Date | Stars |
|---|---|
| 2026-07-31 | 349 |
| 2026-08-03 | 349 |
| 2026-08-06 | 349 |
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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 0: Install libtorch v2.4.1 ### Step 1: Download the Model and Set Environment Variables Start by downloading the SDv1-4 model provided on HuggingFace. ```bash 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. By default, torch is used. The WGPU backend is unstable for SD but may work well in the future as burn-wpu is optimized. ```bash # torch (at least 6 GB VRAM, possibly less) # Arguments: <model_type(burn or dump)> <model_name> <unconditional_guidance_scale> <n_diffusion_steps> <prompt> <output_image_name> [cuda, mps, cpu] # Cuda cargo run --release --bin sample burn SDv1-4 7.5 20 "An ancient mossy stone." img cuda # Mps(Mac) cargo run --release --bin sample burn SDv1-4 7.5 20 "An ancient mossy stone." img mps # 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." img ``` This command will generate an image according to the provided prompt, which will be saved as '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!
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:25147bdc36a41b16, name:stable diffusion, desc:stable diffusion