mirror of
https://github.com/Stability-AI/stablediffusion.git
synced 2024-12-22 15:44:58 +00:00
327 lines
18 KiB
Markdown
327 lines
18 KiB
Markdown
# Stable Diffusion Version 2
|
|
|
|
|
|
|
|
|
|
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
|
|
|
|
**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 <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.
|
|
|
|
|
|
- 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:
|
|
|
|
|
|
|
|
|
|
|
|
### Text-to-Image
|
|
|
|
|
|
|
|
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: [](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.
|
|
|
|
### Stable unCLIP
|
|
_++++++ NOTE: preliminary checkpoints for internal testing ++++++_
|
|
|
|
[unCLIP](https://openai.com/dall-e-2/) is the approach behind OpenAI's [DALL·E 2](https://openai.com/dall-e-2/),
|
|
trained to invert CLIP image embeddings.
|
|
We finetuned SD 2.1 to accept a CLIP ViT-L/14 image embedding in addition to the text encodings.
|
|
This means that the model can be used to produce image variations, but can also be combined with a text-to-image
|
|
embedding prior to yield a full text-to-image model at 768x768 resolution.
|
|
We provide two models, trained on OpenAI CLIP-L and OpenCLIP-H image embeddings, respectively, available
|
|
_[TODO: +++prelim private upload on HF+++]_ from [https://huggingface.co/stabilityai/stable-unclip-preview](https://huggingface.co/stabilityai/stable-unclip-preview).
|
|
To use them, download from Hugging Face, and put and the weights into the `checkpoints` folder.
|
|
#### Image Variations
|
|
|
|
|
|
|
|
_++TODO: Input images from the DIV2K dataset. check license++_
|
|
|
|
Run
|
|
|
|
```
|
|
streamlit run scripts/streamlit/stableunclip.py
|
|
```
|
|
to launch a streamlit script than can be used to make image variations with both models (CLIP-L and OpenCLIP-H).
|
|
These models can process a `noise_level`, which specifies an amount of Gaussian noise added to the CLIP embeddings.
|
|
This can be used to increase output variance as in the following examples.
|
|
|
|
**noise_level = 0**
|
|
|
|
|
|
**noise_level = 500**
|
|
|
|
|
|
**noise_level = 800**
|
|
|
|
|
|
|
|
|
|
|
|
### Stable Diffusion Meets Karlo
|
|
|
|
|
|
Recently, [KakaoBrain](https://kakaobrain.com/) openly released [Karlo](https://github.com/kakaobrain/karlo), a pretrained, large-scale replication of [unCLIP](https://arxiv.org/abs/2204.06125).
|
|
We introduce _Stable Karlo_, a combination of the Karlo CLIP image embedding prior, and Stable Diffusion v2.1-768.
|
|
|
|
To run the model, first download the KARLO checkpoints
|
|
```shell
|
|
mkdir -p checkpoints/karlo_models
|
|
cd checkpoints/karlo_models
|
|
wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/096db1af569b284eb76b3881534822d9/ViT-L-14.pt
|
|
wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/0b62380a75e56f073e2844ab5199153d/ViT-L-14_stats.th
|
|
wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/85626483eaca9f581e2a78d31ff905ca/prior-ckpt-step%3D01000000-of-01000000.ckpt
|
|
cd ../../
|
|
```
|
|
and the finetuned SD2.1 unCLIP-L checkpoint _[TODO: +++prelim private upload on HF+++]_ from [https://huggingface.co/stabilityai/stable-unclip-preview](https://huggingface.co/stabilityai/stable-unclip-preview), and put the ckpt into the `checkpoints folder`
|
|
|
|
Then, run
|
|
|
|
```
|
|
streamlit run scripts/streamlit/stableunclip.py
|
|
```
|
|
and pick the `use_karlo` option in the GUI.
|
|
The script optionally supports sampling from the full Karlo model. To use it, download the 64x64 decoder and 64->256 upscaler
|
|
via
|
|
```shell
|
|
cd checkpoints/karlo_models
|
|
wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/efdf6206d8ed593961593dc029a8affa/decoder-ckpt-step%3D01000000-of-01000000.ckpt
|
|
wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/4226b831ae0279020d134281f3c31590/improved-sr-ckpt-step%3D1.2M.ckpt
|
|
cd ../../
|
|
```
|
|
|
|
### Image Modification with Stable Diffusion
|
|
|
|
|
|
#### 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.
|
|
|
|
|
|
|
|
#### 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
|
|
|
|
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
|
|
|
|
|
|
|
|
[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}
|
|
}
|
|
```
|
|
|
|
|