@@ -70,28 +70,16 @@ We provide a first script for sampling from our unconditional models. Start it v
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70 | 70 | 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> |
71 | 71 | ``` |
72 | 72 | |
73 | | -# Inpainting |
74 | | - |
75 | | - |
76 | | -Download the pre-trained weights |
77 | | -``` |
78 | | -wget XXX |
79 | | -``` |
| 73 | +## Coming Soon... |
80 | 74 | |
81 | | -and sample with |
82 | | -``` |
83 | | -python scripts/inpaint.py --indir data/inpainting_examples/ --outdir outputs/inpainting_results |
84 | | -``` |
85 | | -`indir` should contain images `*.png` and masks `<image_fname>_mask.png` like |
86 | | -the examples provided in `data/inpainting_examples`. |
87 | | - |
88 | | - |
89 | | -## Comin Soon... |
| 75 | + |
90 | 76 | |
91 | 77 | * Code for training LDMs and the corresponding compression models. |
92 | 78 | * Inference scripts for conditional LDMs for various conditioning modalities. |
93 | 79 | * In the meantime, you can play with our colab notebook https://colab.research.google.com/drive/1xqzUi2iXQXDqXBHQGP9Mqt2YrYW6cx-J?usp=sharing |
94 | 80 | * We will also release some further pretrained models. |
| 81 | + |
| 82 | + |
95 | 83 | ## Comments |
96 | 84 | |
97 | 85 | - Our codebase for the diffusion models builds heavily on [OpenAI's codebase](https://github.com/openai/guided-diffusion) |
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