76 lines
2.5 KiB
Markdown
76 lines
2.5 KiB
Markdown
# Stable-Diffusion-Burn
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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.
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## How To Use
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### Step 0: Install libtorch v2.4.1
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### Step 1: Download the Model and Set Environment Variables
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Start by downloading the SDv1-4 model provided on HuggingFace.
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```bash
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wget https://huggingface.co/Gadersd/Stable-Diffusion-Burn/resolve/main/SDv1-4.mpk
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```
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### Step 2: Run the Sample Binary
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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.
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```bash
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# torch (at least 6 GB VRAM, possibly less)
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# Arguments: <model_type(burn or dump)> <model_name> <unconditional_guidance_scale> <n_diffusion_steps> <prompt> <output_image_name> [cuda, mps, cpu]
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# Cuda
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cargo run --release --bin sample burn SDv1-4 7.5 20 "An ancient mossy stone." img cuda
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# Mps(Mac)
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cargo run --release --bin sample burn SDv1-4 7.5 20 "An ancient mossy stone." img mps
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# wgpu (UNSTABLE)
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# Arguments: <model_type(burn or dump)> <model> <unconditional_guidance_scale> <n_diffusion_steps> <prompt> <output_image>
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cargo run --release --features wgpu-backend --bin sample burn SDv1-4 7.5 20 "An ancient mossy stone." img
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```
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This command will generate an image according to the provided prompt, which will be saved as 'img0.png'.
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### Optional: Extract and Convert a Fine-Tuned Model
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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.
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```bash
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# Step into the Python directory
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cd python
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# Download the model, this is just the base v1.4 model as an example
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wget https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
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# Install tinygrad
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pip install -r requirements.txt
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# Extract the weights
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CPU=1 python3 dump.py sd-v1-4.ckpt
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# Move the extracted weight folder out
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mv params ..
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# Step out of the Python directory
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cd ..
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# Convert the weights into a usable form
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cargo run --release --bin convert params SDv1-4
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```
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The binaries 'convert' and 'sample' are contained in Rust. Convert works on CPU whereas sample needs CUDA.
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Remember, `convert` should be used if you're planning on using the fine-tuned version of the stable diffusion.
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## License
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This project is licensed under MIT license.
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We wish you a productive time using this project. Enjoy!
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