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- # Generative Models by Stability AI
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-
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- ![sample1](assets/000.jpg)
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-
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- ## News
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-
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- **November 21, 2023**
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-
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- - We are releasing Stable Video Diffusion, an image-to-video model, for research purposes:
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- - [SVD](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid): This model was trained to generate 14
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- frames at resolution 576x1024 given a context frame of the same size.
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- We use the standard image encoder from SD 2.1, but replace the decoder with a temporally-aware `deflickering decoder`.
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- - [SVD-XT](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt): Same architecture as `SVD` but finetuned
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- for 25 frame generation.
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- - We provide a streamlit demo `scripts/demo/video_sampling.py` and a standalone python script `scripts/sampling/simple_video_sample.py` for inference of both models.
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- - Alongside the model, we will release a technical report shortly. Stay tuned.
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-
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- ![tile](assets/tile.gif)
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-
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- **July 26, 2023**
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-
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- - We are releasing two new open models with a
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- permissive [`CreativeML Open RAIL++-M` license](model_licenses/LICENSE-SDXL1.0) (see [Inference](#inference) for file
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- hashes):
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- - [SDXL-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0): An improved version
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- over `SDXL-base-0.9`.
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- - [SDXL-refiner-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0): An improved version
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- over `SDXL-refiner-0.9`.
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-
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- ![sample2](assets/001_with_eval.png)
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-
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- **July 4, 2023**
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-
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- - A technical report on SDXL is now available [here](https://arxiv.org/abs/2307.01952).
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-
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- **June 22, 2023**
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-
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- - We are releasing two new diffusion models for research purposes:
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- - `SDXL-base-0.9`: The base model was trained on a variety of aspect ratios on images with resolution 1024^2. The
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- base model uses [OpenCLIP-ViT/G](https://github.com/mlfoundations/open_clip)
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- and [CLIP-ViT/L](https://github.com/openai/CLIP/tree/main) for text encoding whereas the refiner model only uses
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- the OpenCLIP model.
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- - `SDXL-refiner-0.9`: The refiner has been trained to denoise small noise levels of high quality data and as such is
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- 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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-
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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),
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- 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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-
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- ## The codebase
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-
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- ### General Philosophy
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-
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- Modularity is king. This repo implements a config-driven approach where we build and combine submodules by
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- calling `instantiate_from_config()` on objects defined in yaml configs. See `configs/` for many examples.
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-
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- ### Changelog from the old `ldm` codebase
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-
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- For training, we use [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/), but it should be easy to use other
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- training wrappers around the base modules. The core diffusion model class (formerly `LatentDiffusion`,
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- now `DiffusionEngine`) has been cleaned up:
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-
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- - No more extensive subclassing! We now handle all types of conditioning inputs (vectors, sequences and spatial
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- conditionings, and all combinations thereof) in a single class: `GeneralConditioner`,
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- 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
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- 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);
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- see `sgm/modules/diffusionmodules/denoiser.py`.
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- * The following features are now independent: weighting of the diffusion loss
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- function (`sgm/modules/diffusionmodules/denoiser_weighting.py`), preconditioning of the
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- network (`sgm/modules/diffusionmodules/denoiser_scaling.py`), and sampling of noise levels during
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- training (`sgm/modules/diffusionmodules/sigma_sampling.py`).
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- - Autoencoding models have also been cleaned up.
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-
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- ## Installation:
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-
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- <a name="installation"></a>
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-
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- #### 1. Clone the repo
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-
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- ```shell
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- git clone [email protected]:Stability-AI/generative-models.git
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- cd generative-models
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- ```
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-
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- #### 2. Setting up the virtualenv
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-
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- This is assuming you have navigated to the `generative-models` root after cloning it.
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-
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- **NOTE:** This is tested under `python3.10`. For other python versions, you might encounter version conflicts.
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-
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- **PyTorch 2.0**
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-
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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 -r requirements/pt2.txt
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- ```
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-
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- #### 3. Install `sgm`
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-
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- ```shell
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- pip3 install .
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- ```
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-
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- #### 4. Install `sdata` for training
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-
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- ```shell
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- pip3 install -e git+https://github.com/Stability-AI/datapipelines.git@main#egg=sdata
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- ```
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-
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- ## Packaging
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-
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- This repository uses PEP 517 compliant packaging using [Hatch](https://hatch.pypa.io/latest/).
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-
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- To build a distributable wheel, install `hatch` and run `hatch build`
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- (specifying `-t wheel` will skip building a sdist, which is not necessary).
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-
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- ```
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- pip install hatch
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- hatch build -t wheel
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- ```
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-
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- You will find the built package in `dist/`. You can install the wheel with `pip install dist/*.whl`.
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-
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- Note that the package does **not** currently specify dependencies; you will need to install the required packages,
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- depending on your use case and PyTorch version, manually.
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-
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- ## Inference
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-
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- We provide a [streamlit](https://streamlit.io/) demo for text-to-image and image-to-image sampling
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- in `scripts/demo/sampling.py`.
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- We provide file hashes for the complete file as well as for only the saved tensors in the file (
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- see [Model Spec](https://github.com/Stability-AI/ModelSpec) for a script to evaluate that).
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- The following models are currently supported:
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-
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- - [SDXL-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
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- ```
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- File Hash (sha256): 31e35c80fc4829d14f90153f4c74cd59c90b779f6afe05a74cd6120b893f7e5b
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- Tensordata Hash (sha256): 0xd7a9105a900fd52748f20725fe52fe52b507fd36bee4fc107b1550a26e6ee1d7
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- ```
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- - [SDXL-refiner-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0)
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- ```
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- File Hash (sha256): 7440042bbdc8a24813002c09b6b69b64dc90fded4472613437b7f55f9b7d9c5f
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- Tensordata Hash (sha256): 0x1a77d21bebc4b4de78c474a90cb74dc0d2217caf4061971dbfa75ad406b75d81
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- ```
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- - [SDXL-base-0.9](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9)
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- - [SDXL-refiner-0.9](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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-
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- **Weights for SDXL**:
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-
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- **SDXL-1.0:**
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- The weights of SDXL-1.0 are available (subject to
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- a [`CreativeML Open RAIL++-M` license](model_licenses/LICENSE-SDXL1.0)) here:
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-
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- - base model: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/
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- - refiner model: https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/
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-
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- **SDXL-0.9:**
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- The weights of SDXL-0.9 are available and subject to a [research license](model_licenses/LICENSE-SDXL0.9).
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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-base-0.9 model](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9),
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- and [SDXL-refiner-0.9](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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-
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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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- ```
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- streamlit run scripts/demo/sampling.py --server.port <your_port>
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- ```
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-
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- ### Invisible Watermark Detection
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-
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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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-
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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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-
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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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-
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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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-
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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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-
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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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-
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- ## Training:
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-
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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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- ```
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- python main.py --base configs/<config1.yaml> configs/<config2.yaml>
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- ```
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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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-
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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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-
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- **NOTE 1:** Using the non-toy-dataset
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- configs `configs/example_training/imagenet-f8_cond.yaml`, `configs/example_training/txt2img-clipl.yaml`
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- and `configs/example_training/txt2img-clipl-legacy-ucg-training.yaml` for training will require edits depending on the
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- used dataset (which is expected to stored in tar-file in
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- the [webdataset-format](https://github.com/webdataset/webdataset)). To find the parts which have to be adapted, search
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- for comments containing `USER:` in the respective config.
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-
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- **NOTE 2:** This repository supports both `pytorch1.13` and `pytorch2`for training generative models. However for
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- autoencoder training as e.g. in `configs/example_training/autoencoder/kl-f4/imagenet-attnfree-logvar.yaml`,
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- only `pytorch1.13` is supported.
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-
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- **NOTE 3:** Training latent generative models (as e.g. in `configs/example_training/imagenet-f8_cond.yaml`) requires
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- retrieving the checkpoint from [Hugging Face](https://huggingface.co/stabilityai/sdxl-vae/tree/main) and replacing
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- the `CKPT_PATH` placeholder in [this line](configs/example_training/imagenet-f8_cond.yaml#81). The same is to be done
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- for the provided text-to-image configs.
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-
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- ### Building New Diffusion Models
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-
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- #### Conditioner
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-
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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
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- 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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-
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- #### Network
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-
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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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-
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- #### Loss
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-
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- The loss is configured through `loss_config`. For standard diffusion model training, you will have to
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- set `sigma_sampler_config`.
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-
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- #### Sampler config
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-
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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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-
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- ### Dataset Handling
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-
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- For large scale training we recommend using the data pipelines from
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- our [data pipelines](https://github.com/Stability-AI/datapipelines) project. The project is contained in the requirement
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- 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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-
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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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-
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- where we expect images in -1...1, channel-first format.
 
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+ ---
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+ title: Stable Video Diffusion
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+ emoji: 📺
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+ colorFrom: purple
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+ colorTo: purple
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+ sdk: gradio
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+ sdk_version: 4.4.0
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+ app_file: app.py
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+ pinned: false
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+ license: other
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+ ---