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Stable diffusion fine-tune repo for Paris shopfront experiment (This Place Does Exist)
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README.md

Latent Diffusion Models

Requirements

A suitable conda environment named ldm can be created and activated with:

conda env create -f environment.yaml
conda activate ldm

Model Zoo

Pretrained Autoencoding Models

rec2

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.

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
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

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

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

inpainting

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.

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