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stable-diffusion-finetune
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Update README.md

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@ -70,28 +70,16 @@ We provide a first script for sampling from our unconditional models. Start it v
CUDA_VISIBLE_DEVICES=<GPU_ID> python scripts/sample_diffusion.py -r models/ldm/<model_spec>/model.ckpt -l <logdir> -n <\#samples> --batch_size <batch_size> -c <\#ddim steps> -e <\#eta>
```
# Inpainting
## Coming Soon...
![inpainting](assets/inpainting.png)
Download the pre-trained weights
```
wget XXX
```
and sample with
```
python scripts/inpaint.py --indir data/inpainting_examples/ --outdir outputs/inpainting_results
```
`indir` should contain images `*.png` and masks `<image_fname>_mask.png` like
the examples provided in `data/inpainting_examples`.
## Comin Soon...
* Code for training LDMs and the corresponding compression models.
* Inference scripts for conditional LDMs for various conditioning modalities.
* In the meantime, you can play with our colab notebook https://colab.research.google.com/drive/1xqzUi2iXQXDqXBHQGP9Mqt2YrYW6cx-J?usp=sharing
* We will also release some further pretrained models.
## Comments
- Our codebase for the diffusion models builds heavily on [OpenAI's codebase](https://github.com/openai/guided-diffusion)