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MIT License
Copyright (c) 2022 Stability AI
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
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SOFTWARE.
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Copyright (c) 2022 Stability AI and contributors
CreativeML Open RAIL++-M License
dated November 24, 2022
Section I: PREAMBLE
Multimodal generative models are being widely adopted and used, and have the potential to transform the way artists, among other individuals, conceive and benefit from AI or ML technologies as a tool for content creation.
Notwithstanding the current and potential benefits that these artifacts can bring to society at large, there are also concerns about potential misuses of them, either due to their technical limitations or ethical considerations.
In short, this license strives for both the open and responsible downstream use of the accompanying model. When it comes to the open character, we took inspiration from open source permissive licenses regarding the grant of IP rights. Referring to the downstream responsible use, we added use-based restrictions not permitting the use of the Model in very specific scenarios, in order for the licensor to be able to enforce the license in case potential misuses of the Model may occur. At the same time, we strive to promote open and responsible research on generative models for art and content generation.
Even though downstream derivative versions of the model could be released under different licensing terms, the latter will always have to include - at minimum - the same use-based restrictions as the ones in the original license (this license). We believe in the intersection between open and responsible AI development; thus, this License aims to strike a balance between both in order to enable responsible open-science in the field of AI.
This License governs the use of the model (and its derivatives) and is informed by the model card associated with the model.
NOW THEREFORE, You and Licensor agree as follows:
1. Definitions
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5. Use-based restrictions. The restrictions set forth in Attachment A are considered Use-based restrictions. Therefore You cannot use the Model and the Derivatives of the Model for the specified restricted uses. You may use the Model subject to this License, including only for lawful purposes and in accordance with the License. Use may include creating any content with, finetuning, updating, running, training, evaluating and/or reparametrizing the Model. You shall require all of Your users who use the Model or a Derivative of the Model to comply with the terms of this paragraph (paragraph 5).
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Section IV: OTHER PROVISIONS
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END OF TERMS AND CONDITIONS
Attachment A
Use Restrictions
You agree not to use the Model or Derivatives of the Model:
- In any way that violates any applicable national, federal, state, local or international law or regulation;
- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
- To generate or disseminate personal identifiable information that can be used to harm an individual;
- To defame, disparage or otherwise harass others;
- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories;
- To provide medical advice and medical results interpretation;
- To generate or disseminate information for the purpose to be used for administration of justice, law enforcement, immigration or asylum processes, such as predicting an individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships between assertions made in documents, indiscriminate and arbitrarily-targeted use).
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# 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_, [Hugging Face](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).
- A public demo of SD-unCLIP is already available at [clipdrop.co/stable-diffusion-reimagine](https://clipdrop.co/stable-diffusion-reimagine)
**December 7, 2022**
*Version 2.1*
- New stable diffusion model (_Stable Diffusion 2.1-v_, [Hugging Face](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 <thescript.py>`
**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/)<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/>
_[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 <path/to/768model.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 <path/to/model.ckpt/> --config <path/to/config.yaml/>
```
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 <number of an instance> --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt <path/to/768model.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 <number of an instance> --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt <path/to/model.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 <number of an instance> --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt <path/to/768model.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 <number of an instance> --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt <path/to/model.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 <path-to-ckpt>
```
or
```
streamlit run scripts/streamlit/depth2img.py configs/stable-diffusion/v2-midas-inference.yaml <path-to-ckpt>
```
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.
<p align="center">
<b> depth2image </b><br/>
<img src=assets/stable-samples/depth2img/d2i.gif>
</p>
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 <path-to-img.jpg> --strength 0.8 --ckpt <path/to/model.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 <path-to-checkpoint>
```
or
```
streamlit run scripts/streamlit/superresolution.py -- configs/stable-diffusion/x4-upscaling.yaml <path-to-checkpoint>
```
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 <path-to-checkpoint>
```
or
```
streamlit run scripts/streamlit/inpainting.py -- configs/stable-diffusion/v2-inpainting-inference.yaml <path-to-checkpoint>
```
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}
}
```
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