Img condition (#1)
* update reqs * add image variations * update readme
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# Latent Diffusion Models
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[arXiv](https://arxiv.org/abs/2112.10752) | [BibTeX](#bibtex)
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# Experiments with Stable Diffusion
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<p align="center">
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<img src=assets/results.gif />
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</p>
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## Image variations
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[](https://twitter.com/Buntworthy/status/1561703483316781057)
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_TODO describe in more detail_
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[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)<br/>
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[Robin Rombach](https://github.com/rromb)\*,
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[Andreas Blattmann](https://github.com/ablattmann)\*,
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[Dominik Lorenz](https://github.com/qp-qp)\,
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[Patrick Esser](https://github.com/pesser),
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[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/>
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\* equal contribution
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<p align="center">
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<img src=assets/modelfigure.png />
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</p>
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## News
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### April 2022
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- Thanks to [Katherine Crowson](https://github.com/crowsonkb), classifier-free guidance received a ~2x speedup and the [PLMS sampler](https://arxiv.org/abs/2202.09778) is available. See also [this PR](https://github.com/CompVis/latent-diffusion/pull/51).
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- Our 1.45B [latent diffusion LAION model](#text-to-image) was integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo: [](https://huggingface.co/spaces/multimodalart/latentdiffusion)
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- More pre-trained LDMs are available:
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- A 1.45B [model](#text-to-image) trained on the [LAION-400M](https://arxiv.org/abs/2111.02114) database.
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- A class-conditional model on ImageNet, achieving a FID of 3.6 when using [classifier-free guidance](https://openreview.net/pdf?id=qw8AKxfYbI) Available via a [colab notebook](https://colab.research.google.com/github/CompVis/latent-diffusion/blob/main/scripts/latent_imagenet_diffusion.ipynb) [![][colab]][colab-cin].
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## Requirements
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A suitable [conda](https://conda.io/) environment named `ldm` can be created
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and activated with:
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```
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conda env create -f environment.yaml
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conda activate ldm
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```
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# Pretrained Models
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A general list of all available checkpoints is available in via our [model zoo](#model-zoo).
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If you use any of these models in your work, we are always happy to receive a [citation](#bibtex).
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## Text-to-Image
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Download the pre-trained weights (5.7GB)
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```
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mkdir -p models/ldm/text2img-large/
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wget -O models/ldm/text2img-large/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/txt2img-f8-large/model.ckpt
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```
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and sample with
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```
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python scripts/txt2img.py --prompt "a virus monster is playing guitar, oil on canvas" --ddim_eta 0.0 --n_samples 4 --n_iter 4 --scale 5.0 --ddim_steps 50
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```
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This will save each sample individually as well as a grid of size `n_iter` x `n_samples` at the specified output location (default: `outputs/txt2img-samples`).
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Quality, sampling speed and diversity are best controlled via the `scale`, `ddim_steps` and `ddim_eta` arguments.
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As a rule of thumb, higher values of `scale` produce better samples at the cost of a reduced output diversity.
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Furthermore, increasing `ddim_steps` generally also gives higher quality samples, but returns are diminishing for values > 250.
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Fast sampling (i.e. low values of `ddim_steps`) while retaining good quality can be achieved by using `--ddim_eta 0.0`.
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Faster sampling (i.e. even lower values of `ddim_steps`) while retaining good quality can be achieved by using `--ddim_eta 0.0` and `--plms` (see [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778)).
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#### Beyond 256²
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For certain inputs, simply running the model in a convolutional fashion on larger features than it was trained on
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can sometimes result in interesting results. To try it out, tune the `H` and `W` arguments (which will be integer-divided
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by 8 in order to calculate the corresponding latent size), e.g. run
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```
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python scripts/txt2img.py --prompt "a sunset behind a mountain range, vector image" --ddim_eta 1.0 --n_samples 1 --n_iter 1 --H 384 --W 1024 --scale 5.0
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```
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to create a sample of size 384x1024. Note, however, that controllability is reduced compared to the 256x256 setting.
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The example below was generated using the above command.
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## Inpainting
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Download the pre-trained weights
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```
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wget -O models/ldm/inpainting_big/last.ckpt https://heibox.uni-heidelberg.de/f/4d9ac7ea40c64582b7c9/?dl=1
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```
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and sample with
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```
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python scripts/inpaint.py --indir data/inpainting_examples/ --outdir outputs/inpainting_results
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```
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`indir` should contain images `*.png` and masks `<image_fname>_mask.png` like
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the examples provided in `data/inpainting_examples`.
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## Class-Conditional ImageNet
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Available via a [notebook](scripts/latent_imagenet_diffusion.ipynb) [![][colab]][colab-cin].
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[colab]: <https://colab.research.google.com/assets/colab-badge.svg>
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[colab-cin]: <https://colab.research.google.com/github/CompVis/latent-diffusion/blob/main/scripts/latent_imagenet_diffusion.ipynb>
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## Unconditional Models
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We also provide a script for sampling from unconditional LDMs (e.g. LSUN, FFHQ, ...). Start it via
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```shell script
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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>
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```
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# Train your own LDMs
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## Data preparation
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### Faces
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For downloading the CelebA-HQ and FFHQ datasets, proceed as described in the [taming-transformers](https://github.com/CompVis/taming-transformers#celeba-hq)
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repository.
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### LSUN
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The LSUN datasets can be conveniently downloaded via the script available [here](https://github.com/fyu/lsun).
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We performed a custom split into training and validation images, and provide the corresponding filenames
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at [https://ommer-lab.com/files/lsun.zip](https://ommer-lab.com/files/lsun.zip).
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After downloading, extract them to `./data/lsun`. The beds/cats/churches subsets should
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also be placed/symlinked at `./data/lsun/bedrooms`/`./data/lsun/cats`/`./data/lsun/churches`, respectively.
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### ImageNet
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The code will try to download (through [Academic
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Torrents](http://academictorrents.com/)) and prepare ImageNet the first time it
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is used. However, since ImageNet is quite large, this requires a lot of disk
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space and time. If you already have ImageNet on your disk, you can speed things
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up by putting the data into
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`${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/` (which defaults to
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`~/.cache/autoencoders/data/ILSVRC2012_{split}/data/`), where `{split}` is one
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of `train`/`validation`. It should have the following structure:
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```
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${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/
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├── n01440764
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│ ├── n01440764_10026.JPEG
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│ ├── n01440764_10027.JPEG
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│ ├── ...
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├── n01443537
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│ ├── n01443537_10007.JPEG
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│ ├── n01443537_10014.JPEG
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│ ├── ...
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├── ...
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```
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If you haven't extracted the data, you can also place
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`ILSVRC2012_img_train.tar`/`ILSVRC2012_img_val.tar` (or symlinks to them) into
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`${XDG_CACHE}/autoencoders/data/ILSVRC2012_train/` /
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`${XDG_CACHE}/autoencoders/data/ILSVRC2012_validation/`, which will then be
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extracted into above structure without downloading it again. Note that this
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will only happen if neither a folder
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`${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/data/` nor a file
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`${XDG_CACHE}/autoencoders/data/ILSVRC2012_{split}/.ready` exist. Remove them
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if you want to force running the dataset preparation again.
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## Model Training
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Logs and checkpoints for trained models are saved to `logs/<START_DATE_AND_TIME>_<config_spec>`.
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### Training autoencoder models
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Configs for training a KL-regularized autoencoder on ImageNet are provided at `configs/autoencoder`.
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Training can be started by running
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```
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CUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/autoencoder/<config_spec>.yaml -t --gpus 0,
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```
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where `config_spec` is one of {`autoencoder_kl_8x8x64`(f=32, d=64), `autoencoder_kl_16x16x16`(f=16, d=16),
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`autoencoder_kl_32x32x4`(f=8, d=4), `autoencoder_kl_64x64x3`(f=4, d=3)}.
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For training VQ-regularized models, see the [taming-transformers](https://github.com/CompVis/taming-transformers)
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repository.
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### Training LDMs
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In ``configs/latent-diffusion/`` we provide configs for training LDMs on the LSUN-, CelebA-HQ, FFHQ and ImageNet datasets.
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Training can be started by running
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```shell script
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CUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/latent-diffusion/<config_spec>.yaml -t --gpus 0,
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```
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where ``<config_spec>`` is one of {`celebahq-ldm-vq-4`(f=4, VQ-reg. autoencoder, spatial size 64x64x3),`ffhq-ldm-vq-4`(f=4, VQ-reg. autoencoder, spatial size 64x64x3),
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`lsun_bedrooms-ldm-vq-4`(f=4, VQ-reg. autoencoder, spatial size 64x64x3),
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`lsun_churches-ldm-vq-4`(f=8, KL-reg. autoencoder, spatial size 32x32x4),`cin-ldm-vq-8`(f=8, VQ-reg. autoencoder, spatial size 32x32x4)}.
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# Model Zoo
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## Pretrained Autoencoding Models
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All models were trained until convergence (no further substantial improvement in rFID).
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| Model | rFID vs val | train steps |PSNR | PSIM | Link | Comments
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|-------------------------|------------|----------------|----------------|---------------|-------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------|
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| f=4, VQ (Z=8192, d=3) | 0.58 | 533066 | 27.43 +/- 4.26 | 0.53 +/- 0.21 | https://ommer-lab.com/files/latent-diffusion/vq-f4.zip | |
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| f=4, VQ (Z=8192, d=3) | 1.06 | 658131 | 25.21 +/- 4.17 | 0.72 +/- 0.26 | https://heibox.uni-heidelberg.de/f/9c6681f64bb94338a069/?dl=1 | no attention |
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| f=8, VQ (Z=16384, d=4) | 1.14 | 971043 | 23.07 +/- 3.99 | 1.17 +/- 0.36 | https://ommer-lab.com/files/latent-diffusion/vq-f8.zip | |
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| f=8, VQ (Z=256, d=4) | 1.49 | 1608649 | 22.35 +/- 3.81 | 1.26 +/- 0.37 | https://ommer-lab.com/files/latent-diffusion/vq-f8-n256.zip |
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| f=16, VQ (Z=16384, d=8) | 5.15 | 1101166 | 20.83 +/- 3.61 | 1.73 +/- 0.43 | https://heibox.uni-heidelberg.de/f/0e42b04e2e904890a9b6/?dl=1 | |
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| | | | | | | |
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| f=4, KL | 0.27 | 176991 | 27.53 +/- 4.54 | 0.55 +/- 0.24 | https://ommer-lab.com/files/latent-diffusion/kl-f4.zip | |
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| f=8, KL | 0.90 | 246803 | 24.19 +/- 4.19 | 1.02 +/- 0.35 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | |
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| f=16, KL (d=16) | 0.87 | 442998 | 24.08 +/- 4.22 | 1.07 +/- 0.36 | https://ommer-lab.com/files/latent-diffusion/kl-f16.zip | |
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| f=32, KL (d=64) | 2.04 | 406763 | 22.27 +/- 3.93 | 1.41 +/- 0.40 | https://ommer-lab.com/files/latent-diffusion/kl-f32.zip | |
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### Get the models
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Running the following script downloads und extracts all available pretrained autoencoding models.
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```shell script
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bash scripts/download_first_stages.sh
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```
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The first stage models can then be found in `models/first_stage_models/<model_spec>`
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## Pretrained LDMs
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| Datset | Task | Model | FID | IS | Prec | Recall | Link | Comments
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|---------------------------------|------|--------------|---------------|-----------------|------|------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------|
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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) |
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| 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 |
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| OpenImages | Super-resolution | LDM-VQ-4 | N/A | N/A | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/sr_bsr.zip | BSR image degradation |
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| 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 | |
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| Landscapes | Semantic Image Synthesis | LDM-VQ-4 | N/A | N/A | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/semantic_synthesis256.zip | |
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| Landscapes | Semantic Image Synthesis | LDM-VQ-4 | N/A | N/A | N/A | N/A | https://ommer-lab.com/files/latent-diffusion/semantic_synthesis.zip | finetuned on resolution 512x512 |
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### Get the models
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The LDMs listed above can jointly be downloaded and extracted via
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```shell script
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bash scripts/download_models.sh
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```
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The models can then be found in `models/ldm/<model_spec>`.
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## Coming Soon...
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* More inference scripts for conditional LDMs.
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* In the meantime, you can play with our colab notebook https://colab.research.google.com/drive/1xqzUi2iXQXDqXBHQGP9Mqt2YrYW6cx-J?usp=sharing
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## Comments
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- Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)
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and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch).
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Thanks for open-sourcing!
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- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories).
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## BibTeX
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```
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@misc{rombach2021highresolution,
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title={High-Resolution Image Synthesis with Latent Diffusion Models},
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author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
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year={2021},
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eprint={2112.10752},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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- Get model from huggingface hub [lambdalabs/stable-diffusion-image-conditioned](https://huggingface.co/lambdalabs/stable-diffusion-image-conditioned/blob/main/sd-clip-vit-l14-img-embed_ema_only.ckpt)
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- Put model in `models/ldm/stable-diffusion-v1/sd-clip-vit-l14-img-embed_ema_only.ckpt`
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- Run `scripts/image_variations.py` or `scripts/gradio_variations.py`
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Trained by [Justin Pinkney](https://www.justinpinkney.com) ([@Buntworthy](https://twitter.com/Buntworthy)) at [Lambda](https://lambdalabs.com/)
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Binary file not shown.
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model:
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base_learning_rate: 1.0e-04
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target: ldm.models.diffusion.ddpm.LatentDiffusion
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params:
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linear_start: 0.00085
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linear_end: 0.0120
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num_timesteps_cond: 1
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log_every_t: 200
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timesteps: 1000
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first_stage_key: "jpg"
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cond_stage_key: "jpg"
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image_size: 64
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channels: 4
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cond_stage_trainable: false # Note: different from the one we trained before
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conditioning_key: crossattn
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monitor: val/loss_simple_ema
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scale_factor: 0.18215
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scheduler_config: # 10000 warmup steps
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target: ldm.lr_scheduler.LambdaLinearScheduler
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params:
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warm_up_steps: [ 1000 ]
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cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
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f_start: [ 1.e-6 ]
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f_max: [ 1. ]
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f_min: [ 1. ]
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unet_config:
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target: ldm.modules.diffusionmodules.openaimodel.UNetModel
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params:
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image_size: 32 # unused
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in_channels: 4
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out_channels: 4
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model_channels: 320
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attention_resolutions: [ 4, 2, 1 ]
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num_res_blocks: 2
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channel_mult: [ 1, 2, 4, 4 ]
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num_heads: 8
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use_spatial_transformer: True
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transformer_depth: 1
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context_dim: 768
|
||||
use_checkpoint: True
|
||||
legacy: False
|
||||
|
||||
first_stage_config:
|
||||
target: ldm.models.autoencoder.AutoencoderKL
|
||||
params:
|
||||
embed_dim: 4
|
||||
monitor: val/rec_loss
|
||||
ddconfig:
|
||||
double_z: true
|
||||
z_channels: 4
|
||||
resolution: 256
|
||||
in_channels: 3
|
||||
out_ch: 3
|
||||
ch: 128
|
||||
ch_mult:
|
||||
- 1
|
||||
- 2
|
||||
- 4
|
||||
- 4
|
||||
num_res_blocks: 2
|
||||
attn_resolutions: []
|
||||
dropout: 0.0
|
||||
lossconfig:
|
||||
target: torch.nn.Identity
|
||||
|
||||
cond_stage_config:
|
||||
target: ldm.modules.encoders.modules.FrozenCLIPImageEmbedder
|
||||
|
||||
|
||||
data:
|
||||
target: ldm.data.laion.WebDataModuleFromConfig
|
||||
params:
|
||||
tar_base: "/mnt/data_rome/laion/improved_aesthetics_6plus/ims"
|
||||
batch_size: 6
|
||||
num_workers: 4
|
||||
multinode: True
|
||||
min_size: 256
|
||||
train:
|
||||
shards: '{00000..01209}.tar'
|
||||
shuffle: 10000
|
||||
image_key: jpg
|
||||
image_transforms:
|
||||
- target: torchvision.transforms.Resize
|
||||
params:
|
||||
size: 512
|
||||
interpolation: 3
|
||||
- target: torchvision.transforms.RandomCrop
|
||||
params:
|
||||
size: 512
|
||||
|
||||
# NOTE use enough shards to avoid empty validation loops in workers
|
||||
validation:
|
||||
shards: '{00000..00008}.tar -'
|
||||
shuffle: 0
|
||||
image_key: jpg
|
||||
image_transforms:
|
||||
- target: torchvision.transforms.Resize
|
||||
params:
|
||||
size: 512
|
||||
interpolation: 3
|
||||
- target: torchvision.transforms.CenterCrop
|
||||
params:
|
||||
size: 512
|
||||
|
||||
|
||||
lightning:
|
||||
find_unused_parameters: false
|
||||
modelcheckpoint:
|
||||
params:
|
||||
every_n_train_steps: 5000
|
||||
callbacks:
|
||||
image_logger:
|
||||
target: main.ImageLogger
|
||||
params:
|
||||
batch_frequency: 1000
|
||||
max_images: 8
|
||||
increase_log_steps: False
|
||||
log_first_step: True
|
||||
log_images_kwargs:
|
||||
use_ema_scope: False
|
||||
inpaint: False
|
||||
plot_progressive_rows: False
|
||||
plot_diffusion_rows: False
|
||||
N: 8
|
||||
unconditional_guidance_scale: 3.0
|
||||
unconditional_guidance_label: [""]
|
||||
|
||||
trainer:
|
||||
benchmark: True
|
||||
val_check_interval: 5000000 # really sorry
|
||||
num_sanity_val_steps: 0
|
||||
accumulate_grad_batches: 1
|
||||
|
|
@ -523,7 +523,7 @@ class LatentDiffusion(DDPM):
|
|||
self.instantiate_cond_stage(cond_stage_config)
|
||||
self.cond_stage_forward = cond_stage_forward
|
||||
self.clip_denoised = False
|
||||
self.bbox_tokenizer = None
|
||||
self.bbox_tokenizer = None
|
||||
|
||||
self.restarted_from_ckpt = False
|
||||
if ckpt_path is not None:
|
||||
|
|
@ -904,7 +904,7 @@ class LatentDiffusion(DDPM):
|
|||
|
||||
if hasattr(self, "split_input_params"):
|
||||
assert len(cond) == 1 # todo can only deal with one conditioning atm
|
||||
assert not return_ids
|
||||
assert not return_ids
|
||||
ks = self.split_input_params["ks"] # eg. (128, 128)
|
||||
stride = self.split_input_params["stride"] # eg. (64, 64)
|
||||
|
||||
|
|
@ -1343,7 +1343,9 @@ class LatentDiffusion(DDPM):
|
|||
log["samples_x0_quantized"] = x_samples
|
||||
|
||||
if unconditional_guidance_scale > 1.0:
|
||||
uc = self.get_unconditional_conditioning(N, unconditional_guidance_label)
|
||||
# uc = self.get_unconditional_conditioning(N, unconditional_guidance_label)
|
||||
# FIXME
|
||||
uc = torch.zeros_like(c)
|
||||
with ema_scope("Sampling with classifier-free guidance"):
|
||||
samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
|
||||
ddim_steps=ddim_steps, eta=ddim_eta,
|
||||
|
|
|
|||
|
|
@ -2,9 +2,11 @@ import torch
|
|||
import torch.nn as nn
|
||||
import numpy as np
|
||||
from functools import partial
|
||||
import kornia
|
||||
|
||||
from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
|
||||
from ldm.util import default
|
||||
import clip
|
||||
|
||||
|
||||
class AbstractEncoder(nn.Module):
|
||||
|
|
@ -170,6 +172,42 @@ class FrozenCLIPEmbedder(AbstractEncoder):
|
|||
def encode(self, text):
|
||||
return self(text)
|
||||
|
||||
class FrozenCLIPImageEmbedder(AbstractEncoder):
|
||||
"""
|
||||
Uses the CLIP image encoder.
|
||||
"""
|
||||
def __init__(
|
||||
self,
|
||||
model='ViT-L/14',
|
||||
jit=False,
|
||||
device='cuda' if torch.cuda.is_available() else 'cpu',
|
||||
antialias=False,
|
||||
):
|
||||
super().__init__()
|
||||
self.model, _ = clip.load(name=model, device=device, jit=jit)
|
||||
self.device = device
|
||||
|
||||
self.antialias = antialias
|
||||
|
||||
self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
|
||||
self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
|
||||
|
||||
def preprocess(self, x):
|
||||
# Expects inputs in the range -1, 1
|
||||
x = kornia.geometry.resize(x, (224, 224),
|
||||
interpolation='bicubic',align_corners=True,
|
||||
antialias=self.antialias)
|
||||
x = (x + 1.) / 2.
|
||||
# renormalize according to clip
|
||||
x = kornia.enhance.normalize(x, self.mean, self.std)
|
||||
return x
|
||||
|
||||
def forward(self, x):
|
||||
# x is assumed to be in range [-1,1]
|
||||
return self.model.encode_image(self.preprocess(x)).float()
|
||||
|
||||
def encode(self, im):
|
||||
return self(im).unsqueeze(1)
|
||||
|
||||
class SpatialRescaler(nn.Module):
|
||||
def __init__(self,
|
||||
|
|
|
|||
25
main.py
25
main.py
|
|
@ -21,7 +21,7 @@ from ldm.data.base import Txt2ImgIterableBaseDataset
|
|||
from ldm.util import instantiate_from_config
|
||||
|
||||
|
||||
MULTINODE_HACKS = True
|
||||
MULTINODE_HACKS = False
|
||||
|
||||
|
||||
def get_parser(**parser_kwargs):
|
||||
|
|
@ -36,6 +36,13 @@ def get_parser(**parser_kwargs):
|
|||
raise argparse.ArgumentTypeError("Boolean value expected.")
|
||||
|
||||
parser = argparse.ArgumentParser(**parser_kwargs)
|
||||
parser.add_argument(
|
||||
"--finetune_from",
|
||||
type=str,
|
||||
nargs="?",
|
||||
default="",
|
||||
help="path to checkpoint to load model state from"
|
||||
)
|
||||
parser.add_argument(
|
||||
"-n",
|
||||
"--name",
|
||||
|
|
@ -644,6 +651,20 @@ if __name__ == "__main__":
|
|||
# model
|
||||
model = instantiate_from_config(config.model)
|
||||
|
||||
if not opt.finetune_from == "":
|
||||
print(f"Attempting to load state from {opt.finetune_from}")
|
||||
old_state = torch.load(opt.finetune_from, map_location="cpu")
|
||||
if "state_dict" in old_state:
|
||||
print(f"Found nested key 'state_dict' in checkpoint, loading this instead")
|
||||
old_state = old_state["state_dict"]
|
||||
m, u = model.load_state_dict(old_state, strict=False)
|
||||
if len(m) > 0:
|
||||
print("missing keys:")
|
||||
print(m)
|
||||
if len(u) > 0:
|
||||
print("unexpected keys:")
|
||||
print(u)
|
||||
|
||||
# trainer and callbacks
|
||||
trainer_kwargs = dict()
|
||||
|
||||
|
|
@ -666,7 +687,7 @@ if __name__ == "__main__":
|
|||
}
|
||||
},
|
||||
}
|
||||
default_logger_cfg = default_logger_cfgs["testtube"]
|
||||
default_logger_cfg = default_logger_cfgs["wandb"]
|
||||
if "logger" in lightning_config:
|
||||
logger_cfg = lightning_config.logger
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -1,17 +1,20 @@
|
|||
albumentations==0.4.3
|
||||
opencv-python
|
||||
opencv-python==4.5.5.64
|
||||
pudb==2019.2
|
||||
imageio==2.9.0
|
||||
imageio-ffmpeg==0.4.2
|
||||
pytorch-lightning==1.4.2
|
||||
torchmetrics==0.6
|
||||
omegaconf==2.1.1
|
||||
test-tube>=0.7.5
|
||||
streamlit>=0.73.1
|
||||
einops==0.3.0
|
||||
torch-fidelity==0.3.0
|
||||
transformers==4.19.2
|
||||
transformers
|
||||
kornia==0.6
|
||||
webdataset==0.2.5
|
||||
torchmetrics==0.6.0
|
||||
fire==0.4.0
|
||||
gradio==3.2
|
||||
-e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
|
||||
-e git+https://github.com/openai/CLIP.git@main#egg=clip
|
||||
-e .
|
||||
|
|
|
|||
|
|
@ -0,0 +1,112 @@
|
|||
from contextlib import nullcontext
|
||||
from functools import partial
|
||||
|
||||
import fire
|
||||
import gradio as gr
|
||||
import numpy as np
|
||||
import torch
|
||||
from einops import rearrange
|
||||
from ldm.models.diffusion.ddim import DDIMSampler
|
||||
from ldm.models.diffusion.plms import PLMSSampler
|
||||
from omegaconf import OmegaConf
|
||||
from PIL import Image
|
||||
from torch import autocast
|
||||
from torchvision import transforms
|
||||
|
||||
from scripts.image_variations import load_model_from_config
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def sample_model(input_im, model, sampler, precision, h, w, ddim_steps, n_samples, scale, ddim_eta):
|
||||
precision_scope = autocast if precision=="autocast" else nullcontext
|
||||
with precision_scope("cuda"):
|
||||
with model.ema_scope():
|
||||
c = model.get_learned_conditioning(input_im).tile(n_samples,1,1)
|
||||
|
||||
if scale != 1.0:
|
||||
uc = torch.zeros_like(c)
|
||||
else:
|
||||
uc = None
|
||||
|
||||
shape = [4, h // 8, w // 8]
|
||||
samples_ddim, _ = sampler.sample(S=ddim_steps,
|
||||
conditioning=c,
|
||||
batch_size=n_samples,
|
||||
shape=shape,
|
||||
verbose=False,
|
||||
unconditional_guidance_scale=scale,
|
||||
unconditional_conditioning=uc,
|
||||
eta=ddim_eta,
|
||||
x_T=None)
|
||||
|
||||
x_samples_ddim = model.decode_first_stage(samples_ddim)
|
||||
return torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0).cpu()
|
||||
|
||||
|
||||
def main(
|
||||
model,
|
||||
device,
|
||||
input_im,
|
||||
scale=3.0,
|
||||
n_samples=4,
|
||||
plms=True,
|
||||
ddim_steps=50,
|
||||
ddim_eta=1.0,
|
||||
precision="fp32",
|
||||
h=512,
|
||||
w=512,
|
||||
):
|
||||
|
||||
input_im = transforms.ToTensor()(input_im).unsqueeze(0).to(device)
|
||||
input_im = input_im*2-1
|
||||
|
||||
if plms:
|
||||
sampler = PLMSSampler(model)
|
||||
ddim_eta = 0.0
|
||||
else:
|
||||
sampler = DDIMSampler(model)
|
||||
|
||||
x_samples_ddim = sample_model(input_im, model, sampler, precision, h, w, ddim_steps, n_samples, scale, ddim_eta)
|
||||
output_ims = []
|
||||
for x_sample in x_samples_ddim:
|
||||
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
|
||||
output_ims.append(Image.fromarray(x_sample.astype(np.uint8)))
|
||||
return output_ims
|
||||
|
||||
|
||||
def run_demo(
|
||||
device_idx=0,
|
||||
ckpt="models/ldm/stable-diffusion-v1/sd-clip-vit-l14-img-embed_ema_only.ckpt",
|
||||
config="configs/stable-diffusion/sd-image-condition-finetune.yaml",
|
||||
):
|
||||
|
||||
device = f"cuda:{device_idx}"
|
||||
config = OmegaConf.load(config)
|
||||
model = load_model_from_config(config, ckpt, device=device)
|
||||
|
||||
inputs = [
|
||||
gr.Image(),
|
||||
gr.Slider(0, 25, value=3, step=1, label="cfg scale"),
|
||||
gr.Slider(1, 4, value=1, step=1, label="Number images"),
|
||||
gr.Checkbox(True, label="plms"),
|
||||
gr.Slider(5, 250, value=25, step=5, label="steps"),
|
||||
]
|
||||
output = gr.Gallery(label="Generated variations")
|
||||
output.style(height="auto", grid=2)
|
||||
|
||||
fn_with_model = partial(main, model, device)
|
||||
fn_with_model.__name__ = "fn_with_model"
|
||||
|
||||
demo = gr.Interface(
|
||||
fn=fn_with_model,
|
||||
title="Stable Diffusion Image Variations",
|
||||
description="Generate variations on an input image using a fine-tuned version of Stable Diffision",
|
||||
article="TODO",
|
||||
inputs=inputs,
|
||||
outputs=output,
|
||||
)
|
||||
# demo.queue()
|
||||
demo.launch(share=False, server_name="0.0.0.0")
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(run_demo)
|
||||
|
|
@ -0,0 +1,122 @@
|
|||
from io import BytesIO
|
||||
import os
|
||||
from contextlib import nullcontext
|
||||
|
||||
import fire
|
||||
import numpy as np
|
||||
import torch
|
||||
from einops import rearrange
|
||||
from omegaconf import OmegaConf
|
||||
from PIL import Image
|
||||
from torch import autocast
|
||||
from torchvision import transforms
|
||||
import requests
|
||||
|
||||
from ldm.models.diffusion.ddim import DDIMSampler
|
||||
from ldm.models.diffusion.plms import PLMSSampler
|
||||
from ldm.util import instantiate_from_config
|
||||
|
||||
|
||||
def load_model_from_config(config, ckpt, device, verbose=False):
|
||||
print(f"Loading model from {ckpt}")
|
||||
pl_sd = torch.load(ckpt, map_location="cpu")
|
||||
if "global_step" in pl_sd:
|
||||
print(f"Global Step: {pl_sd['global_step']}")
|
||||
sd = pl_sd["state_dict"]
|
||||
model = instantiate_from_config(config.model)
|
||||
m, u = model.load_state_dict(sd, strict=False)
|
||||
if len(m) > 0 and verbose:
|
||||
print("missing keys:")
|
||||
print(m)
|
||||
if len(u) > 0 and verbose:
|
||||
print("unexpected keys:")
|
||||
print(u)
|
||||
|
||||
model.to(device)
|
||||
model.eval()
|
||||
return model
|
||||
|
||||
|
||||
def load_im(im_path):
|
||||
if im_path.startswith("http"):
|
||||
response = requests.get(im_path)
|
||||
response.raise_for_status()
|
||||
im = Image.open(BytesIO(response.content))
|
||||
else:
|
||||
im = Image.open(im_path).convert("RGB")
|
||||
tforms = transforms.Compose([
|
||||
transforms.Resize(224),
|
||||
transforms.CenterCrop((224, 224)),
|
||||
transforms.ToTensor(),
|
||||
])
|
||||
inp = tforms(im).unsqueeze(0)
|
||||
return inp*2-1
|
||||
|
||||
@torch.no_grad()
|
||||
def sample_model(input_im, model, sampler, precision, h, w, ddim_steps, n_samples, scale, ddim_eta):
|
||||
precision_scope = autocast if precision=="autocast" else nullcontext
|
||||
with precision_scope("cuda"):
|
||||
with model.ema_scope():
|
||||
c = model.get_learned_conditioning(input_im).tile(n_samples,1,1)
|
||||
|
||||
if scale != 1.0:
|
||||
uc = torch.zeros_like(c)
|
||||
else:
|
||||
uc = None
|
||||
|
||||
shape = [4, h // 8, w // 8]
|
||||
samples_ddim, _ = sampler.sample(S=ddim_steps,
|
||||
conditioning=c,
|
||||
batch_size=n_samples,
|
||||
shape=shape,
|
||||
verbose=False,
|
||||
unconditional_guidance_scale=scale,
|
||||
unconditional_conditioning=uc,
|
||||
eta=ddim_eta,
|
||||
x_T=None)
|
||||
|
||||
x_samples_ddim = model.decode_first_stage(samples_ddim)
|
||||
return torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
|
||||
|
||||
def main(
|
||||
im_path="data/example_conditioning/superresolution/sample_0.jpg",
|
||||
ckpt="models/ldm/stable-diffusion-v1/sd-clip-vit-l14-img-embed_ema_only.ckpt",
|
||||
config="configs/stable-diffusion/sd-image-condition-finetune.yaml",
|
||||
outpath="im_variations",
|
||||
scale=3.0,
|
||||
h=512,
|
||||
w=512,
|
||||
n_samples=4,
|
||||
precision="fp32",
|
||||
plms=True,
|
||||
ddim_steps=50,
|
||||
ddim_eta=1.0,
|
||||
device_idx=0,
|
||||
):
|
||||
|
||||
device = f"cuda:{device_idx}"
|
||||
input_im = load_im(im_path).to(device)
|
||||
config = OmegaConf.load(config)
|
||||
model = load_model_from_config(config, ckpt, device=device)
|
||||
|
||||
if plms:
|
||||
sampler = PLMSSampler(model)
|
||||
ddim_eta = 0.0
|
||||
else:
|
||||
sampler = DDIMSampler(model)
|
||||
|
||||
os.makedirs(outpath, exist_ok=True)
|
||||
|
||||
sample_path = os.path.join(outpath, "samples")
|
||||
os.makedirs(sample_path, exist_ok=True)
|
||||
base_count = len(os.listdir(sample_path))
|
||||
|
||||
x_samples_ddim = sample_model(input_im, model, sampler, precision, h, w, ddim_steps, n_samples, scale, ddim_eta)
|
||||
for x_sample in x_samples_ddim:
|
||||
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
|
||||
Image.fromarray(x_sample.astype(np.uint8)).save(
|
||||
os.path.join(sample_path, f"{base_count:05}.png"))
|
||||
base_count += 1
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(main)
|
||||
Loading…
Reference in New Issue