Generative Models by Stability AI
News
June 22, 2023
- We are releasing two new diffusion models for research purposes:
SD-XL 0.9-base
: The base model was trained on a variety of aspect ratios on images with resolution 1024^2. The base model uses OpenCLIP-ViT/G and CLIP-ViT/L for text encoding whereas the refiner model only uses the OpenCLIP model.SD-XL 0.9-refiner
: The refiner has been trained to denoise small noise levels of high quality data and as such is not expected to work as a text-to-image model; instead, it should only be used as an image-to-image model.
If you would like to access these models for your research, please apply using one of the following links: SDXL-0.9-Base model, and SDXL-0.9-Refiner. This means that you can apply for any of the two links - and if you are granted - you can access both. Please log in to your HuggingFace Account with your organization email to request access. We plan to do a full release soon (July).
The codebase
General Philosophy
Modularity is king. This repo implements a config-driven approach where we build and combine submodules by calling instantiate_from_config()
on objects defined in yaml configs. See configs/
for many examples.
ldm
codebase
Changelog from the old For training, we use pytorch-lightning, but it should be easy to use other training wrappers around the base modules. The core diffusion model class (formerly LatentDiffusion
, now DiffusionEngine
) has been cleaned up:
- No more extensive subclassing! We now handle all types of conditioning inputs (vectors, sequences and spatial conditionings, and all combinations thereof) in a single class:
GeneralConditioner
, seesgm/modules/encoders/modules.py
. - We separate guiders (such as classifier-free guidance, see
sgm/modules/diffusionmodules/guiders.py
) from the samplers (sgm/modules/diffusionmodules/sampling.py
), and the samplers are independent of the model. - We adopt the "denoiser framework" for both training and inference (most notable change is probably now the option to train continuous time models):
- Discrete times models (denoisers) are simply a special case of continuous time models (denoisers); see
sgm/modules/diffusionmodules/denoiser.py
. - The following features are now independent: weighting of the diffusion loss function (
sgm/modules/diffusionmodules/denoiser_weighting.py
), preconditioning of the network (sgm/modules/diffusionmodules/denoiser_scaling.py
), and sampling of noise levels during training (sgm/modules/diffusionmodules/sigma_sampling.py
).
- Discrete times models (denoisers) are simply a special case of continuous time models (denoisers); see
- Autoencoding models have also been cleaned up.
Installation:
1. Clone the repo
git clone git@github.com:Stability-AI/generative-models.git
cd generative-models
2. Setting up the virtualenv
This is assuming you have navigated to the generative-models
root after cloning it.
NOTE: This is tested under python3.8
and python3.10
. For other python versions, you might encounter version conflicts.
PyTorch 1.13
# install required packages from pypi
python3 -m venv .pt1
source .pt1/bin/activate
pip3 install wheel
pip3 install -r requirements_pt13.txt
PyTorch 2.0
# install required packages from pypi
python3 -m venv .pt2
source .pt2/bin/activate
pip3 install wheel
pip3 install -r requirements_pt2.txt
Inference:
We provide a streamlit demo for text-to-image and image-to-image sampling in scripts/demo/sampling.py
. The following models are currently supported:
Weights for SDXL: If you would like to access these models for your research, please apply using one of the following links: SDXL-0.9-Base model, and SDXL-0.9-Refiner. This means that you can apply for any of the two links - and if you are granted - you can access both. Please log in to your HuggingFace Account with your organization email to request access.
After obtaining the weights, place them into checkpoints/
.
Next, start the demo using
streamlit run scripts/demo/sampling.py --server.port <your_port>
Invisible Watermark Detection
Images generated with our code use the invisible-watermark library to embed an invisible watermark into the model output. We also provide a script to easily detect that watermark. Please note that this watermark is not the same as in previous Stable Diffusion 1.x/2.x versions.
To run the script you need to either have a working installation as above or try an experimental import using only a minimal amount of packages:
python -m venv .detect
source .detect/bin/activate
pip install "numpy>=1.17" "PyWavelets>=1.1.1" "opencv-python>=4.1.0.25"
pip install --no-deps invisible-watermark
To run the script you need to have a working installation as above. The script
is then useable in the following ways (don't forget to activate your
virtual environment beforehand, e.g. source .pt1/bin/activate
):
# test a single file
python scripts/demo/detect.py <your filename here>
# test multiple files at once
python scripts/demo/detect.py <filename 1> <filename 2> ... <filename n>
# test all files in a specific folder
python scripts/demo/detect.py <your folder name here>/*
Training:
We are providing example training configs in configs/example_training
. To launch a training, run
python main.py --base configs/<config1.yaml> configs/<config2.yaml>
where configs are merged from left to right (later configs overwrite the same values). This can be used to combine model, training and data configs. However, all of them can also be defined in a single config. For example, to run a class-conditional pixel-based diffusion model training on MNIST, run
python main.py --base configs/example_training/toy/mnist_cond.yaml
NOTE 1: Using the non-toy-dataset configs configs/example_training/imagenet-f8_cond.yaml
, configs/example_training/txt2img-clipl.yaml
and configs/example_training/txt2img-clipl-legacy-ucg-training.yaml
for training will require edits depdending on the used dataset (which is expected to stored in tar-file in the webdataset-format). To find the parts which have to be adapted, search for comments containing USER:
in the respective config.
NOTE 2: This repository supports both pytorch1.13
and pytorch2
for training generative models. However for autoencoder training as e.g. in configs/example_training/autoencoder/kl-f4/imagenet-attnfree-logvar.yaml
, only pytorch1.13
is supported.
NOTE 3: Training latent generative models (as e.g. in configs/example_training/imagenet-f8_cond.yaml
) requires retrieving the checkpoint from Hugging Face and replacing the CKPT_PATH
placeholder in this line. The same is to be done for the provided text-to-image configs.
Building New Diffusion Models
Conditioner
The GeneralConditioner
is configured through the conditioner_config
. Its only attribute is emb_models
, a list of
different embedders (all inherited from AbstractEmbModel
) that are used to condition the generative model.
All embedders should define whether or not they are trainable (is_trainable
, default False
), a classifier-free
guidance dropout rate is used (ucg_rate
, default 0
), and an input key (input_key
), for example, txt
for text-conditioning or cls
for class-conditioning.
When computing conditionings, the embedder will get batch[input_key]
as input.
We currently support two to four dimensional conditionings and conditionings of different embedders are concatenated
appropriately.
Note that the order of the embedders in the conditioner_config
is important.
Network
The neural network is set through the network_config
. This used to be called unet_config
, which is not general
enough as we plan to experiment with transformer-based diffusion backbones.
Loss
The loss is configured through loss_config
. For standard diffusion model training, you will have to set sigma_sampler_config
.
Sampler config
As discussed above, the sampler is independent of the model. In the sampler_config
, we set the type of numerical
solver, number of steps, type of discretization, as well as, for example, guidance wrappers for classifier-free
guidance.
Dataset Handling
For large scale training we recommend using the datapipelines from our datapipelines project. The project is contained in the requirement and automatically included when following the steps from the Installation section. Small map-style datasets should be defined here in the repository (e.g., MNIST, CIFAR-10, ...), and return a dict of data keys/values, e.g.,
example = {"jpg": x, # this is a tensor -1...1 chw
"txt": "a beautiful image"}
where we expect images in -1...1, channel-first format.