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69 lines
2.2 KiB
Markdown
69 lines
2.2 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 1: Download the Model and Set Environment Variables
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Start by downloading the SDv1-4.bin model provided on HuggingFace.
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```bash
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wget https://huggingface.co/Gadersd/Stable-Diffusion-Burn/resolve/main/V1/SDv1-4.bin
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```
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Next, set the appropriate CUDA version.
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```bash
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export TORCH_CUDA_VERSION=cu113
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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, as shown below. Loading the burn model file
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is currently very slow, but hopefully that will be rectified soon. You can also dump a torch model's weights and load that
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which is currently much faster.
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```bash
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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 --bin sample burn SDv1-4 7.5 20 "A half-eaten apple sitting on a desk." apple
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```
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This command will generate an image according to the provided prompt, which will be saved as 'apple.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.
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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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# Extract the weights
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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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## Example Inference
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INSER IMAGE HERE
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We wish you a productive time using this project. Enjoy! |