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| # Stable Diffusion | |
| *Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous work:* | |
| [**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/> | |
| which is available on [GitHub](https://github.com/CompVis/latent-diffusion). | |
|  | |
| [Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion | |
| model. | |
| Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database. | |
| Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487), | |
| this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. | |
| With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM. | |
| See [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion). | |
| ## 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 | |
| ``` | |
| You can also update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running | |
| ``` | |
| conda install pytorch torchvision -c pytorch | |
| pip install transformers==4.19.2 | |
| pip install -e . | |
| ``` | |
| ## Stable Diffusion v1 | |
| Stable Diffusion v1 refers to a specific configuration of the model | |
| architecture that uses a downsampling-factor 8 autoencoder with an 860M UNet | |
| and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and | |
| then finetuned on 512x512 images. | |
| *Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are present | |
| in its training data. | |
| Details on the training procedure and data, as well as the intended use of the model can be found in the corresponding [model card](https://huggingface.co/CompVis/stable-diffusion). | |
| Research into the safe deployment of general text-to-image models is an ongoing effort. To prevent misuse and harm, we currently provide access to the checkpoints only for [academic research purposes upon request](TODO). | |
| **This is an experiment in safe and community-driven publication of a capable and general text-to-image model. We are working on a public release with a more permissive license that also incorporates ethical considerations.*** | |
| [Request access to Stable Diffusion v1 checkpoints for academic research](TODO) | |
| ### Weights | |
| We currently provide three checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`, | |
| which were trained as follows, | |
| - `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). | |
| 194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). | |
| - `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`. | |
| 515k steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, | |
| filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). | |
| - `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-improved-aesthetics" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). | |
| Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, | |
| 5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling | |
| steps show the relative improvements of the checkpoints: | |
|  | |
| ### Text-to-Image with Stable Diffusion | |
|  | |
|  | |
| Stable Diffusion is a latent diffusion model conditioned on the (non-pooled) text embeddings of a CLIP ViT-L/14 text encoder. | |
| After [obtaining the weights](#weights), link them | |
| ``` | |
| mkdir -p models/ldm/stable-diffusion-v1/ | |
| ln -s <path/to/model.ckpt> models/ldm/stable-diffusion-v1/model.ckpt | |
| ``` | |
| and sample with | |
| ``` | |
| python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms | |
| ``` | |
| By default, this uses a guidance scale of `--scale 7.5`, [Katherine Crowson's implementation](https://github.com/CompVis/latent-diffusion/pull/51) of the [PLMS](https://arxiv.org/abs/2202.09778) sampler, | |
| and renders images of size 512x512 (which it was trained on) in 50 steps. All supported arguments are listed below (type `python scripts/txt2img.py --help`). | |
| ```commandline | |
| usage: txt2img.py [-h] [--prompt [PROMPT]] [--outdir [OUTDIR]] [--skip_grid] [--skip_save] [--ddim_steps DDIM_STEPS] [--plms] [--laion400m] [--fixed_code] [--ddim_eta DDIM_ETA] [--n_iter N_ITER] [--H H] [--W W] [--C C] [--f F] [--n_samples N_SAMPLES] [--n_rows N_ROWS] | |
| [--scale SCALE] [--from-file FROM_FILE] [--config CONFIG] [--ckpt CKPT] [--seed SEED] [--precision {full,autocast}] | |
| optional arguments: | |
| -h, --help show this help message and exit | |
| --prompt [PROMPT] the prompt to render | |
| --outdir [OUTDIR] dir to write results to | |
| --skip_grid do not save a grid, only individual samples. Helpful when evaluating lots of samples | |
| --skip_save do not save individual samples. For speed measurements. | |
| --ddim_steps DDIM_STEPS | |
| number of ddim sampling steps | |
| --plms use plms sampling | |
| --laion400m uses the LAION400M model | |
| --fixed_code if enabled, uses the same starting code across samples | |
| --ddim_eta DDIM_ETA ddim eta (eta=0.0 corresponds to deterministic sampling | |
| --n_iter N_ITER sample this often | |
| --H H image height, in pixel space | |
| --W W image width, in pixel space | |
| --C C latent channels | |
| --f F downsampling factor | |
| --n_samples N_SAMPLES | |
| how many samples to produce for each given prompt. A.k.a. batch size | |
| --n_rows N_ROWS rows in the grid (default: n_samples) | |
| --scale SCALE unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty)) | |
| --from-file FROM_FILE | |
| if specified, load prompts from this file | |
| --config CONFIG path to config which constructs model | |
| --ckpt CKPT path to checkpoint of model | |
| --seed SEED the seed (for reproducible sampling) | |
| --precision {full,autocast} | |
| evaluate at this precision | |
| ``` | |
| Note: The inference config for all v1 versions is designed to be used with EMA-only checkpoints. | |
| For this reason `use_ema=False` is set in the configuration, otherwise the code will try to switch from | |
| non-EMA to EMA weights. If you want to examine the effect of EMA vs no EMA, we provide "full" checkpoints | |
| which contain both types of weights. For these, `use_ema=False` will load and use the non-EMA weights. | |
| ### Image Modification with Stable Diffusion | |
| By using a diffusion-denoising mechanism as first proposed by [SDEdit](https://arxiv.org/abs/2108.01073), the model can be used for different | |
| tasks such as text-guided image-to-image translation and upscaling. Similar to the txt2img sampling script, | |
| we provide a script to perform image modification with Stable Diffusion. | |
| The following describes an example where a rough sketch made in [Pinta](https://www.pinta-project.com/) is converted into a detailed artwork. | |
| ``` | |
| python scripts/img2img.py --prompt "A fantasy landscape, trending on artstation" --init-img <path-to-img.jpg> --strength 0.8 | |
| ``` | |
| Here, strength is a value between 0.0 and 1.0, that controls the amount of noise that is added to the input image. | |
| Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input. See the following example. | |
| **Input** | |
|  | |
| **Outputs** | |
|  | |
|  | |
| This procedure can, for example, also be used to upscale samples from the base model. | |
| ## Comments | |
| - Our codebase for the diffusion models builds heavily on [OpenAI's ADM 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). | |
| ## BibTeX | |
| ``` | |
| @misc{rombach2021highresolution, | |
| title={High-Resolution Image Synthesis with Latent Diffusion Models}, | |
| author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer}, | |
| year={2021}, | |
| eprint={2112.10752}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV} | |
| } | |
| ``` | |