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Model hashes #70

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Jul 26, 2023
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Model hashes #70

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39 changes: 24 additions & 15 deletions README.md
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Expand Up @@ -5,7 +5,7 @@
## News

**July 26, 2023**
- We are releasing two new open models with a permissive [`CreativeML Open RAIL++-M` license](model_licenses/LICENSE-SDXL1.0):
- We are releasing two new open models with a permissive [`CreativeML Open RAIL++-M` license](model_licenses/LICENSE-SDXL1.0) (see [Inference](#inference) for file hashes):
- [SDXL-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0): An improved version over `SDXL-base-0.9`.
- [SDXL-refiner-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0): An improved version over `SDXL-refiner-0.9`.

Expand All @@ -22,11 +22,11 @@
- `SDXL-base-0.9`: 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.
- `SDXL-refiner-0.9`: 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](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).
This means that you can apply for any of the two links - and if you are granted - you can access both.
If you would like to access these models for your research, please apply using one of the following links:
[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).
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 Hugging Face Account with your organization email to request access.
**We plan to do a full release soon (July).**
**We plan to do a full release soon (July).**

## The codebase

Expand Down Expand Up @@ -58,12 +58,12 @@ cd generative-models

#### 2. Setting up the virtualenv

This is assuming you have navigated to the `generative-models` root after cloning it.
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**
**PyTorch 1.13**

```shell
# install required packages from pypi
Expand All @@ -72,7 +72,7 @@ source .pt13/bin/activate
pip3 install -r requirements/pt13.txt
```

**PyTorch 2.0**
**PyTorch 2.0**


```shell
Expand Down Expand Up @@ -112,13 +112,22 @@ You will find the built package in `dist/`. You can install the wheel with `pip
Note that the package does **not** currently specify dependencies; you will need to install the required packages,
depending on your use case and PyTorch version, manually.

## Inference:
## Inference

We provide a [streamlit](https://streamlit.io/) demo for text-to-image and image-to-image sampling in `scripts/demo/sampling.py`.
We provide a [streamlit](https://streamlit.io/) demo for text-to-image and image-to-image sampling in `scripts/demo/sampling.py`.
We provide file hashes for the complete file as well as for only the saved tensors in the file (see [Model Spec](https://github.com/Stability-AI/ModelSpec) for a script to evaluate that).
The following models are currently supported:

- [SDXL-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
```
File Hash (sha256): 31e35c80fc4829d14f90153f4c74cd59c90b779f6afe05a74cd6120b893f7e5b
Tensordata Hash (sha256): 0xd7a9105a900fd52748f20725fe52fe52b507fd36bee4fc107b1550a26e6ee1d7
```
- [SDXL-refiner-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0)
```
File Hash (sha256): 7440042bbdc8a24813002c09b6b69b64dc90fded4472613437b7f55f9b7d9c5f
Tensordata Hash (sha256): 0x1a77d21bebc4b4de78c474a90cb74dc0d2217caf4061971dbfa75ad406b75d81
```
- [SDXL-base-0.9](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9)
- [SDXL-refiner-0.9](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9)
- [SD-2.1-512](https://huggingface.co/stabilityai/stable-diffusion-2-1-base/blob/main/v2-1_512-ema-pruned.safetensors)
Expand All @@ -134,13 +143,13 @@ The weights of SDXL-1.0 are available (subject to a [`CreativeML Open RAIL++-M`

**SDXL-0.9:**
The weights of SDXL-0.9 are available and subject to a [research license](model_licenses/LICENSE-SDXL0.9).
If you would like to access these models for your research, please apply using one of the following links:
[SDXL-base-0.9 model](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9), and [SDXL-refiner-0.9](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9).
This means that you can apply for any of the two links - and if you are granted - you can access both.
If you would like to access these models for your research, please apply using one of the following links:
[SDXL-base-0.9 model](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9), and [SDXL-refiner-0.9](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9).
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 Hugging Face Account with your organization email to request access.


After obtaining the weights, place them into `checkpoints/`.
After obtaining the weights, place them into `checkpoints/`.
Next, start the demo using

```
Expand Down Expand Up @@ -194,7 +203,7 @@ 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 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.
**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.

**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.

Expand Down
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