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| # Latent Diffusion Models | |
| ## Requirements | |
| A suitable [conda](https://conda.io/) environment named `ldm` can be created | |
| and activated with: | |
| ``` | |
| conda env create -f environment.yaml | |
| conda activate ldm | |
| ``` | |
| # Model Zoo | |
| ## Pretrained Autoencoding Models | |
|  | |
| | Model | FID vs val | PSNR | PSIM | Link | Comments | |
| |-------------------------|------------|----------------|---------------|-------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------| | |
| | f=4, VQ (Z=8192, d=3) | 0.58 | 27.43 +/- 4.26 | 0.53 +/- 0.21 | https://ommer-lab.com/files/latent-diffusion/vq-f4.zip | | | |
| | f=4, VQ (Z=8192, d=3) | 1.06 | 25.21 +/- 4.17 | 0.72 +/- 0.26 | https://heibox.uni-heidelberg.de/f/9c6681f64bb94338a069/?dl=1 | no attention | | |
| | f=8, VQ (Z=16384, d=4) | 1.14 | 23.07 +/- 3.99 | 1.17 +/- 0.36 | https://ommer-lab.com/files/latent-diffusion/vq-f8.zip | | | |
| | f=8, VQ (Z=256, d=4) | 1.49 | 22.35 +/- 3.81 | 1.26 +/- 0.37 | https://ommer-lab.com/files/latent-diffusion/vq-f8-n256.zip | | |
| | f=16, VQ (Z=16384, d=8) | 5.15 | 20.83 +/- 3.61 | 1.73 +/- 0.43 | https://heibox.uni-heidelberg.de/f/0e42b04e2e904890a9b6/?dl=1 | | | |
| | | | | | | | | |
| | f=4, KL | 0.27 | 27.53 +/- 4.54 | 0.55 +/- 0.24 | https://ommer-lab.com/files/latent-diffusion/kl-f4.zip | | | |
| | f=8, KL | 0.90 | 24.19 +/- 4.19 | 1.02 +/- 0.35 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | | | |
| | f=16, KL (d=16) | 0.87 | 24.08 +/- 4.22 | 1.07 +/- 0.36 | https://ommer-lab.com/files/latent-diffusion/kl-f16.zip | | | |
| | f=32, KL (d=64) | 2.04 | 22.27 +/- 3.93 | 1.41 +/- 0.40 | https://ommer-lab.com/files/latent-diffusion/kl-f32.zip | | | |
| ### Get the models | |
| Running the following script downloads und extracts all available pretrained autoencoding models. | |
| ```shell script | |
| bash scripts/download_first_stages.sh | |
| ``` | |
| The first stage models can then be found in `models/first_stage_models/<model_spec>` | |
| ## Pretrained LDMs | |
| | Datset | Task | Model | FID | IS | Prec | Recall | Link | Comments | |
| |---------------------------------|------|--------------|---------------|-----------------|------|------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------| | |
| | CelebA-HQ | Unconditional Image Synthesis | LDM-VQ-4 (200 DDIM steps, eta=0)| 5.11 (5.11) | 3.29 | 0.72 | 0.49 | https://ommer-lab.com/files/latent-diffusion/celeba.zip | | | |
| | FFHQ | Unconditional Image Synthesis | LDM-VQ-4 (200 DDIM steps, eta=1)| 4.98 (4.98) | 4.50 (4.50) | 0.73 | 0.50 | https://ommer-lab.com/files/latent-diffusion/ffhq.zip | | | |
| | LSUN-Churches | Unconditional Image Synthesis | LDM-KL-8 (400 DDIM steps, eta=0)| 4.02 (4.02) | 2.72 | 0.64 | 0.52 | https://ommer-lab.com/files/latent-diffusion/lsun_churches.zip | | | |
| | LSUN-Bedrooms | Unconditional Image Synthesis | LDM-VQ-4 (200 DDIM steps, eta=1)| 2.95 (3.0) | 2.22 (2.23)| 0.66 | 0.48 | https://ommer-lab.com/files/latent-diffusion/lsun_bedrooms.zip | | | |
| | ImageNet | Class-conditional Image Synthesis | LDM-VQ-8 (200 DDIM steps, eta=1) | 7.77(7.76)* /15.82** | 201.56(209.52)* /78.82** | 0.84* / 0.65** | 0.35* / 0.63** | https://ommer-lab.com/files/latent-diffusion/cin.zip | *: w/ guiding, classifier_scale 10 **: w/o guiding, scores in bracket calculated with script provided by [ADM](https://github.com/openai/guided-diffusion) | | |
| | Conceptual Captions | Text-conditional Image Synthesis | LDM-VQ-f4 (100 DDIM steps, eta=0) | 16.79 | 13.89 | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/text2img.zip | finetuned from LAION | | |
| | OpenImages | Super-resolution | N/A | N/A | N/A | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/sr_bsr.zip | BSR image degradation | | |
| | OpenImages | Layout-to-Image Synthesis | LDM-VQ-4 (200 DDIM steps, eta=0) | 32.02 | 15.92 | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/layout2img_model.zip | | | |
| | Landscapes (finetuned 512) | Semantic Image Synthesis | LDM-VQ-4 (100 DDIM steps, eta=1) | N/A | N/A | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/semantic_synthesis.zip | | | |
| ### Get the models | |
| The LDMs listed above can jointly be downloaded and extracted via | |
| ```shell script | |
| bash scripts/download_models.sh | |
| ``` | |
| The models can then be found in `models/ldm/<model_spec>`. | |
| ### Sampling with unconditional models | |
| We provide a first script for sampling from our unconditional models. Start it via | |
| ```shell script | |
| 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 | |
|  | |
| Download the pre-trained weights | |
| ``` | |
| wget -O models/ldm/inpainting_big/last.ckpt https://heibox.uni-heidelberg.de/f/4d9ac7ea40c64582b7c9/?dl=1 | |
| ``` | |
| 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`. | |
| ## Coming 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) | |
| and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch). | |
| Thanks for open-sourcing! | |
| - The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories). | |