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# Generative Models by Stability AI |
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![sample1](assets/000.jpg) |
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## News |
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**July 4, 2023** |
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- A technical report on SDXL is now available [here](assets/sdxl_report.pdf). |
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**June 22, 2023** |
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- We are releasing two new diffusion models for research purposes: |
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- `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](https://github.com/mlfoundations/open_clip) and [CLIP-ViT/L](https://github.com/openai/CLIP/tree/main) for text encoding whereas the refiner model only uses the OpenCLIP model. |
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- `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. |
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If you would like to access these models for your research, please apply using one of the following links: |
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[SDXL-0.9-Base model](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9), and [SDXL-0.9-Refiner](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9). |
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This means that you can apply for any of the two links - and if you are granted - you can access both. |
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Please log in to your Hugging Face Account with your organization email to request access. |
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**We plan to do a full release soon (July).** |
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## The codebase |
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### General Philosophy |
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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. |
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### Changelog from the old `ldm` codebase |
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For training, we use [pytorch-lightning](https://www.pytorchlightning.ai/index.html), 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: |
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- 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`, see `sgm/modules/encoders/modules.py`. |
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- We separate guiders (such as classifier-free guidance, see `sgm/modules/diffusionmodules/guiders.py`) from the |
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samplers (`sgm/modules/diffusionmodules/sampling.py`), and the samplers are independent of the model. |
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- We adopt the ["denoiser framework"](https://arxiv.org/abs/2206.00364) for both training and inference (most notable change is probably now the option to train continuous time models): |
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* Discrete times models (denoisers) are simply a special case of continuous time models (denoisers); see `sgm/modules/diffusionmodules/denoiser.py`. |
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* 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`). |
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- Autoencoding models have also been cleaned up. |
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## Installation: |
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<a name="installation"></a> |
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#### 1. Clone the repo |
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```shell |
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git clone git@github.com:Stability-AI/generative-models.git |
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cd generative-models |
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``` |
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#### 2. Setting up the virtualenv |
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This is assuming you have navigated to the `generative-models` root after cloning it. |
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**NOTE:** This is tested under `python3.8` and `python3.10`. For other python versions, you might encounter version conflicts. |
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**PyTorch 1.13** |
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```shell |
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# install required packages from pypi |
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python3 -m venv .pt1 |
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source .pt1/bin/activate |
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pip3 install wheel |
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pip3 install -r requirements_pt13.txt |
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``` |
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**PyTorch 2.0** |
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```shell |
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# install required packages from pypi |
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python3 -m venv .pt2 |
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source .pt2/bin/activate |
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pip3 install wheel |
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pip3 install -r requirements_pt2.txt |
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``` |
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## Inference: |
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We provide a [streamlit](https://streamlit.io/) demo for text-to-image and image-to-image sampling in `scripts/demo/sampling.py`. The following models are currently supported: |
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- [SD-XL 0.9-base](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9) |
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- [SD-XL 0.9-refiner](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9) |
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- [SD 2.1-512](https://huggingface.co/stabilityai/stable-diffusion-2-1-base/blob/main/v2-1_512-ema-pruned.safetensors) |
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- [SD 2.1-768](https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-ema-pruned.safetensors) |
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**Weights for SDXL**: |
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If you would like to access these models for your research, please apply using one of the following links: |
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[SDXL-0.9-Base model](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9), and [SDXL-0.9-Refiner](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9). |
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This means that you can apply for any of the two links - and if you are granted - you can access both. |
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Please log in to your Hugging Face Account with your organization email to request access. |
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After obtaining the weights, place them into `checkpoints/`. |
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Next, start the demo using |
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``` |
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streamlit run scripts/demo/sampling.py --server.port <your_port> |
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``` |
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### Invisible Watermark Detection |
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Images generated with our code use the |
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[invisible-watermark](https://github.com/ShieldMnt/invisible-watermark/) |
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library to embed an invisible watermark into the model output. We also provide |
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a script to easily detect that watermark. Please note that this watermark is |
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not the same as in previous Stable Diffusion 1.x/2.x versions. |
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To run the script you need to either have a working installation as above or |
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try an _experimental_ import using only a minimal amount of packages: |
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```bash |
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python -m venv .detect |
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source .detect/bin/activate |
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pip install "numpy>=1.17" "PyWavelets>=1.1.1" "opencv-python>=4.1.0.25" |
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pip install --no-deps invisible-watermark |
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``` |
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To run the script you need to have a working installation as above. The script |
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is then useable in the following ways (don't forget to activate your |
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virtual environment beforehand, e.g. `source .pt1/bin/activate`): |
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```bash |
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# test a single file |
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python scripts/demo/detect.py <your filename here> |
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# test multiple files at once |
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python scripts/demo/detect.py <filename 1> <filename 2> ... <filename n> |
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# test all files in a specific folder |
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python scripts/demo/detect.py <your folder name here>/* |
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``` |
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## Training: |
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We are providing example training configs in `configs/example_training`. To launch a training, run |
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``` |
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python main.py --base configs/<config1.yaml> configs/<config2.yaml> |
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``` |
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where configs are merged from left to right (later configs overwrite the same values). |
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This can be used to combine model, training and data configs. However, all of them can also be |
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defined in a single config. For example, to run a class-conditional pixel-based diffusion model training on MNIST, |
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run |
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```bash |
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python main.py --base configs/example_training/toy/mnist_cond.yaml |
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``` |
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**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 depending on the used dataset (which is expected to stored in tar-file in the [webdataset-format](https://github.com/webdataset/webdataset)). To find the parts which have to be adapted, search for comments containing `USER:` in the respective config. |
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**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. |
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**NOTE 3:** Training latent generative models (as e.g. in `configs/example_training/imagenet-f8_cond.yaml`) requires retrieving the checkpoint from [Hugging Face](https://huggingface.co/stabilityai/sdxl-vae/tree/main) and replacing the `CKPT_PATH` placeholder in [this line](configs/example_training/imagenet-f8_cond.yaml#81). The same is to be done for the provided text-to-image configs. |
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### Building New Diffusion Models |
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#### Conditioner |
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The `GeneralConditioner` is configured through the `conditioner_config`. Its only attribute is `emb_models`, a list of |
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different embedders (all inherited from `AbstractEmbModel`) that are used to condition the generative model. |
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All embedders should define whether or not they are trainable (`is_trainable`, default `False`), a classifier-free |
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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. |
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When computing conditionings, the embedder will get `batch[input_key]` as input. |
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We currently support two to four dimensional conditionings and conditionings of different embedders are concatenated |
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appropriately. |
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Note that the order of the embedders in the `conditioner_config` is important. |
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#### Network |
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The neural network is set through the `network_config`. This used to be called `unet_config`, which is not general |
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enough as we plan to experiment with transformer-based diffusion backbones. |
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#### Loss |
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The loss is configured through `loss_config`. For standard diffusion model training, you will have to set `sigma_sampler_config`. |
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#### Sampler config |
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As discussed above, the sampler is independent of the model. In the `sampler_config`, we set the type of numerical |
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solver, number of steps, type of discretization, as well as, for example, guidance wrappers for classifier-free |
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guidance. |
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### Dataset Handling |
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For large scale training we recommend using the data pipelines from our [data pipelines](https://github.com/Stability-AI/datapipelines) project. The project is contained in the requirement and automatically included when following the steps from the [Installation section](#installation). |
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Small map-style datasets should be defined here in the repository (e.g., MNIST, CIFAR-10, ...), and return a dict of |
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data keys/values, |
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e.g., |
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```python |
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example = {"jpg": x, # this is a tensor -1...1 chw |
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"txt": "a beautiful image"} |
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``` |
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where we expect images in -1...1, channel-first format. |
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