# Stable Diffusion Version 2 ![t2i](assets/stable-samples/txt2img/768/merged-0006.png) ![t2i](assets/stable-samples/txt2img/768/merged-0002.png) ![t2i](assets/stable-samples/txt2img/768/merged-0005.png) This repository contains [Stable Diffusion](https://github.com/CompVis/stable-diffusion) models trained from scratch and will be continuously updated with new checkpoints. The following list provides an overview of all currently available models. More coming soon. ## News **March 24, 2023** *Stable UnCLIP 2.1* - New stable diffusion finetune (_Stable unCLIP 2.1_, [HuggingFace](https://huggingface.co/stabilityai/)) at 768x768 resolution, based on SD2.1-768. This model allows for image variations and mixing operations as described in [*Hierarchical Text-Conditional Image Generation with CLIP Latents*](https://arxiv.org/abs/2204.06125), and, thanks to its modularity, can be combined with other models such as [KARLO](https://github.com/kakaobrain/karlo). Comes in two variants: [*Stable unCLIP-L*](https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/blob/main/sd21-unclip-l.ckpt) and [*Stable unCLIP-H*](https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/blob/main/sd21-unclip-h.ckpt), which are conditioned on CLIP ViT-L and ViT-H image embeddings, respectively. Instructions are available [here](doc/UNCLIP.MD). **December 7, 2022** *Version 2.1* - New stable diffusion model (_Stable Diffusion 2.1-v_, [HuggingFace](https://huggingface.co/stabilityai/stable-diffusion-2-1)) at 768x768 resolution and (_Stable Diffusion 2.1-base_, [HuggingFace](https://huggingface.co/stabilityai/stable-diffusion-2-1-base)) at 512x512 resolution, both based on the same number of parameters and architecture as 2.0 and fine-tuned on 2.0, on a less restrictive NSFW filtering of the [LAION-5B](https://laion.ai/blog/laion-5b/) dataset. Per default, the attention operation of the model is evaluated at full precision when `xformers` is not installed. To enable fp16 (which can cause numerical instabilities with the vanilla attention module on the v2.1 model) , run your script with `ATTN_PRECISION=fp16 python ` **November 24, 2022** *Version 2.0* - New stable diffusion model (_Stable Diffusion 2.0-v_) at 768x768 resolution. Same number of parameters in the U-Net as 1.5, but uses [OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip) as the text encoder and is trained from scratch. _SD 2.0-v_ is a so-called [v-prediction](https://arxiv.org/abs/2202.00512) model. - The above model is finetuned from _SD 2.0-base_, which was trained as a standard noise-prediction model on 512x512 images and is also made available. - Added a [x4 upscaling latent text-guided diffusion model](#image-upscaling-with-stable-diffusion). - New [depth-guided stable diffusion model](#depth-conditional-stable-diffusion), finetuned from _SD 2.0-base_. The model is conditioned on monocular depth estimates inferred via [MiDaS](https://github.com/isl-org/MiDaS) and can be used for structure-preserving img2img and shape-conditional synthesis. ![d2i](assets/stable-samples/depth2img/depth2img01.png) - A [text-guided inpainting model](#image-inpainting-with-stable-diffusion), finetuned from SD _2.0-base_. We follow the [original repository](https://github.com/CompVis/stable-diffusion) and provide basic inference scripts to sample from the models. ________________ *The original Stable Diffusion model was created in a collaboration with [CompVis](https://arxiv.org/abs/2202.00512) and [RunwayML](https://runwayml.com/) and builds upon the work:* [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)
[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)
_[CVPR '22 Oral](https://openaccess.thecvf.com/content/CVPR2022/html/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.html) | [GitHub](https://github.com/CompVis/latent-diffusion) | [arXiv](https://arxiv.org/abs/2112.10752) | [Project page](https://ommer-lab.com/research/latent-diffusion-models/)_ and [many others](#shout-outs). Stable Diffusion is a latent text-to-image diffusion model. ________________________________ ## Requirements You can update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running ``` conda install pytorch==1.12.1 torchvision==0.13.1 -c pytorch pip install transformers==4.19.2 diffusers invisible-watermark pip install -e . ``` #### xformers efficient attention For more efficiency and speed on GPUs, we highly recommended installing the [xformers](https://github.com/facebookresearch/xformers) library. Tested on A100 with CUDA 11.4. Installation needs a somewhat recent version of nvcc and gcc/g++, obtain those, e.g., via ```commandline export CUDA_HOME=/usr/local/cuda-11.4 conda install -c nvidia/label/cuda-11.4.0 cuda-nvcc conda install -c conda-forge gcc conda install -c conda-forge gxx_linux-64==9.5.0 ``` Then, run the following (compiling takes up to 30 min). ```commandline cd .. git clone https://github.com/facebookresearch/xformers.git cd xformers git submodule update --init --recursive pip install -r requirements.txt pip install -e . cd ../stablediffusion ``` Upon successful installation, the code will automatically default to [memory efficient attention](https://github.com/facebookresearch/xformers) for the self- and cross-attention layers in the U-Net and autoencoder. ## General Disclaimer Stable Diffusion models are general text-to-image diffusion models and therefore mirror biases and (mis-)conceptions that are present in their training data. Although efforts were made to reduce the inclusion of explicit pornographic material, **we do not recommend using the provided weights for services or products without additional safety mechanisms and considerations. The weights are research artifacts and should be treated as such.** 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/stabilityai/stable-diffusion-2). The weights are available via [the StabilityAI organization at Hugging Face](https://huggingface.co/StabilityAI) under the [CreativeML Open RAIL++-M License](LICENSE-MODEL). ## Stable Diffusion v2 Stable Diffusion v2 refers to a specific configuration of the model architecture that uses a downsampling-factor 8 autoencoder with an 865M UNet and OpenCLIP ViT-H/14 text encoder for the diffusion model. The _SD 2-v_ model produces 768x768 px outputs. 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 DDIM sampling steps show the relative improvements of the checkpoints: ![sd evaluation results](assets/model-variants.jpg) ### Text-to-Image ![txt2img-stable2](assets/stable-samples/txt2img/merged-0003.png) ![txt2img-stable2](assets/stable-samples/txt2img/merged-0001.png) Stable Diffusion 2 is a latent diffusion model conditioned on the penultimate text embeddings of a CLIP ViT-H/14 text encoder. We provide a [reference script for sampling](#reference-sampling-script). #### Reference Sampling Script This script incorporates an [invisible watermarking](https://github.com/ShieldMnt/invisible-watermark) of the outputs, to help viewers [identify the images as machine-generated](scripts/tests/test_watermark.py). We provide the configs for the _SD2-v_ (768px) and _SD2-base_ (512px) model. First, download the weights for [_SD2.1-v_](https://huggingface.co/stabilityai/stable-diffusion-2-1) and [_SD2.1-base_](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). To sample from the _SD2.1-v_ model, run the following: ``` python scripts/txt2img.py --prompt "a professional photograph of an astronaut riding a horse" --ckpt --config configs/stable-diffusion/v2-inference-v.yaml --H 768 --W 768 ``` or try out the Web Demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/stabilityai/stable-diffusion). To sample from the base model, use ``` python scripts/txt2img.py --prompt "a professional photograph of an astronaut riding a horse" --ckpt --config ``` By default, this uses the [DDIM sampler](https://arxiv.org/abs/2010.02502), and renders images of size 768x768 (which it was trained on) in 50 steps. Empirically, the v-models can be sampled with higher guidance scales. Note: The inference config for all model 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. #### Enable Intel® Extension for PyTorch* optimizations in Text-to-Image script If you're planning on running Text-to-Image on Intel® CPU, try to sample an image with TorchScript and Intel® Extension for PyTorch* optimizations. Intel® Extension for PyTorch* extends PyTorch by enabling up-to-date features optimizations for an extra performance boost on Intel® hardware. It can optimize memory layout of the operators to Channel Last memory format, which is generally beneficial for Intel CPUs, take advantage of the most advanced instruction set available on a machine, optimize operators and many more. **Prerequisites** Before running the script, make sure you have all needed libraries installed. (the optimization was checked on `Ubuntu 20.04`). Install [jemalloc](https://github.com/jemalloc/jemalloc), [numactl](https://linux.die.net/man/8/numactl), Intel® OpenMP and Intel® Extension for PyTorch*. ```bash apt-get install numactl libjemalloc-dev pip install intel-openmp pip install intel_extension_for_pytorch -f https://software.intel.com/ipex-whl-stable ``` To sample from the _SD2.1-v_ model with TorchScript+IPEX optimizations, run the following. Remember to specify desired number of instances you want to run the program on ([more](https://github.com/intel/intel-extension-for-pytorch/blob/master/intel_extension_for_pytorch/cpu/launch.py#L48)). ``` MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/intel/v2-inference-v-fp32.yaml --H 768 --W 768 --precision full --device cpu --torchscript --ipex ``` To sample from the base model with IPEX optimizations, use ``` MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/intel/v2-inference-fp32.yaml --n_samples 1 --n_iter 4 --precision full --device cpu --torchscript --ipex ``` If you're using a CPU that supports `bfloat16`, consider sample from the model with bfloat16 enabled for a performance boost, like so ```bash # SD2.1-v MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/intel/v2-inference-v-bf16.yaml --H 768 --W 768 --precision full --device cpu --torchscript --ipex --bf16 # SD2.1-base MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt --config configs/stable-diffusion/intel/v2-inference-bf16.yaml --precision full --device cpu --torchscript --ipex --bf16 ``` ### Image Modification with Stable Diffusion ![depth2img-stable2](assets/stable-samples/depth2img/merged-0000.png) #### Depth-Conditional Stable Diffusion To augment the well-established [img2img](https://github.com/CompVis/stable-diffusion#image-modification-with-stable-diffusion) functionality of Stable Diffusion, we provide a _shape-preserving_ stable diffusion model. Note that the original method for image modification introduces significant semantic changes w.r.t. the initial image. If that is not desired, download our [depth-conditional stable diffusion](https://huggingface.co/stabilityai/stable-diffusion-2-depth) model and the `dpt_hybrid` MiDaS [model weights](https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt), place the latter in a folder `midas_models` and sample via ``` python scripts/gradio/depth2img.py configs/stable-diffusion/v2-midas-inference.yaml ``` or ``` streamlit run scripts/streamlit/depth2img.py configs/stable-diffusion/v2-midas-inference.yaml ``` This method can be used on the samples of the base model itself. For example, take [this sample](assets/stable-samples/depth2img/old_man.png) generated by an anonymous discord user. Using the [gradio](https://gradio.app) or [streamlit](https://streamlit.io/) script `depth2img.py`, the MiDaS model first infers a monocular depth estimate given this input, and the diffusion model is then conditioned on the (relative) depth output.

depth2image

This model is particularly useful for a photorealistic style; see the [examples](assets/stable-samples/depth2img). For a maximum strength of 1.0, the model removes all pixel-based information and only relies on the text prompt and the inferred monocular depth estimate. ![depth2img-stable3](assets/stable-samples/depth2img/merged-0005.png) #### Classic Img2Img For running the "classic" img2img, use ``` python scripts/img2img.py --prompt "A fantasy landscape, trending on artstation" --init-img --strength 0.8 --ckpt ``` and adapt the checkpoint and config paths accordingly. ### Image Upscaling with Stable Diffusion ![upscaling-x4](assets/stable-samples/upscaling/merged-dog.png) After [downloading the weights](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler), run ``` python scripts/gradio/superresolution.py configs/stable-diffusion/x4-upscaling.yaml ``` or ``` streamlit run scripts/streamlit/superresolution.py -- configs/stable-diffusion/x4-upscaling.yaml ``` for a Gradio or Streamlit demo of the text-guided x4 superresolution model. This model can be used both on real inputs and on synthesized examples. For the latter, we recommend setting a higher `noise_level`, e.g. `noise_level=100`. ### Image Inpainting with Stable Diffusion ![inpainting-stable2](assets/stable-inpainting/merged-leopards.png) [Download the SD 2.0-inpainting checkpoint](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) and run ``` python scripts/gradio/inpainting.py configs/stable-diffusion/v2-inpainting-inference.yaml ``` or ``` streamlit run scripts/streamlit/inpainting.py -- configs/stable-diffusion/v2-inpainting-inference.yaml ``` for a Gradio or Streamlit demo of the inpainting model. This scripts adds invisible watermarking to the demo in the [RunwayML](https://github.com/runwayml/stable-diffusion/blob/main/scripts/inpaint_st.py) repository, but both should work interchangeably with the checkpoints/configs. ## Shout-Outs - Thanks to [Hugging Face](https://huggingface.co/) and in particular [Apolinário](https://github.com/apolinario) for support with our model releases! - Stable Diffusion would not be possible without [LAION](https://laion.ai/) and their efforts to create open, large-scale datasets. - The [DeepFloyd team](https://twitter.com/deepfloydai) at Stability AI, for creating the subset of [LAION-5B](https://laion.ai/blog/laion-5b/) dataset used to train the model. - Stable Diffusion 2.0 uses [OpenCLIP](https://laion.ai/blog/large-openclip/), trained by [Romain Beaumont](https://github.com/rom1504). - 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! - [CompVis](https://github.com/CompVis/stable-diffusion) initial stable diffusion release - [Patrick](https://github.com/pesser)'s [implementation](https://github.com/runwayml/stable-diffusion/blob/main/scripts/inpaint_st.py) of the streamlit demo for inpainting. - `img2img` is an application of [SDEdit](https://arxiv.org/abs/2108.01073) by [Chenlin Meng](https://cs.stanford.edu/~chenlin/) from the [Stanford AI Lab](https://cs.stanford.edu/~ermon/website/). - [Kat's implementation]((https://github.com/CompVis/latent-diffusion/pull/51)) of the [PLMS](https://arxiv.org/abs/2202.09778) sampler, and [more](https://github.com/crowsonkb/k-diffusion). - [DPMSolver](https://arxiv.org/abs/2206.00927) [integration](https://github.com/CompVis/stable-diffusion/pull/440) by [Cheng Lu](https://github.com/LuChengTHU). - Facebook's [xformers](https://github.com/facebookresearch/xformers) for efficient attention computation. - [MiDaS](https://github.com/isl-org/MiDaS) for monocular depth estimation. ## License The code in this repository is released under the MIT License. The weights are available via [the StabilityAI organization at Hugging Face](https://huggingface.co/StabilityAI), and released under the [CreativeML Open RAIL++-M License](LICENSE-MODEL) License. ## 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} } ```