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High-Resolution Image Synthesis with Latent Diffusion Models
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# Latent Diffusion Models [arXiv](https://arxiv.org/abs/2112.10752) | [BibTeX](#bibtex) <p align="center"> <img src=assets/results.gif /> </p> [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)<br/> [Robin Rombach](https://github.com/rromb)\*, [Andreas Blattmann](https://github.com/ablattmann)\*, [Dominik Lorenz](https://github.com/qp-qp)\, [Patrick Esser](https://github.com/pesser), [Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/> \* equal contribution <p align="center"> <img src=assets/modelfigure.png /> </p> ## News ### July 2022 - Inference code and model weights to run our [retrieval-augmented diffusion models](https://arxiv.org/abs/2204.11824) are now available. See [this section](#retrieval-augmented-diffusion-models). ### April 2022 - 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). - 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) - More pre-trained LDMs are available: - A 1.45B [model](#text-to-image) trained on the [LAION-400M](https://arxiv.org/abs/2111.02114) database. - 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]. ## 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 ``` # Pretrained Models A general list of all available checkpoints is available in via our [model zoo](#model-zoo). If you use any of these models in your work, we are always happy to receive a [citation](#bibtex). ## Retrieval Augmented Diffusion Models  We include inference code to run our retrieval-augmented diffusion models (RDMs) as described in [https://arxiv.org/abs/2204.11824](https://arxiv.org/abs/2204.11824). To get started, install the additionally required python packages into your `ldm` environment ```shell script pip install transformers==4.19.2 scann kornia==0.6.4 torchmetrics==0.6.0 pip install git+https://github.com/arogozhnikov/einops.git ``` and download the trained weights (preliminary ceckpoints): ```bash mkdir -p models/rdm/rdm768x768/ wget -O models/rdm/rdm768x768/model.ckpt https://ommer-lab.com/files/rdm/model.ckpt ``` As these models are conditioned on a set of CLIP image embeddings, our RDMs support different inference modes, which are described in the following. #### RDM with text-prompt only (no explicit retrieval needed) Since CLIP offers a shared image/text feature space, and RDMs learn to cover a neighborhood of a given example during training, we can directly take a CLIP text embedding of a given prompt and condition on it. Run this mode via ``` python scripts/knn2img.py --prompt "a happy bear reading a newspaper, oil on canvas" ``` #### RDM with text-to-image retrieval To be able to run a RDM conditioned on a text-prompt and additionally images retrieved from this prompt, you will also need to download the corresponding retrieval database. We provide two distinct databases extracted from the [Openimages-](https://storage.googleapis.com/openimages/web/index.html) and [ArtBench-](https://github.com/liaopeiyuan/artbench) datasets. Interchanging the databases results in different capabilities
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5a07491b097dd435, name:latent diffusion, desc:latent diffusion
