WIBE-HuggingFace/stable-diffusion-2-1-base

1---2license: openrail++3tags:4- stable-diffusion5- text-to-image6---7 8# Stable Diffusion v2-base Model Card9This model card focuses on the model associated with the Stable Diffusion v2-base model, available [here](https://github.com/Stability-AI/stablediffusion).10 11The model is trained from scratch 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`. Then it is further trained for 850k steps at resolution `512x512` on the same dataset on images with resolution `>= 512x512`.12 13![image](https://github.com/Stability-AI/stablediffusion/blob/main/assets/stable-samples/txt2img/merged-0003.png?raw=true)14 15- Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `512-base-ema.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-2-base/resolve/main/512-base-ema.ckpt).16- Use it with 🧨 [`diffusers`](https://huggingface.co/stabilityai/stable-diffusion-2-base#examples)17 18## Model Details19- **Developed by:** Robin Rombach, Patrick Esser20- **Model type:** Diffusion-based text-to-image generation model21- **Language(s):** English22- **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL)23- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)).24- **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/).25- **Cite as:**26 27      @InProceedings{Rombach_2022_CVPR,28          author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},29          title     = {High-Resolution Image Synthesis With Latent Diffusion Models},30          booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},31          month     = {June},32          year      = {2022},33          pages     = {10684-10695}34      }35      36 37## Examples38 39Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner.40 41```bash42pip install diffusers transformers accelerate scipy safetensors43```44Running the pipeline (if you don't swap the scheduler it will run with the default PNDM/PLMS scheduler, in this example we are swapping it to EulerDiscreteScheduler):45 46```python47from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler48import torch49 50model_id = "stabilityai/stable-diffusion-2-base"51 52# Use the Euler scheduler here instead53scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")54pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)55pipe = pipe.to("cuda")56 57prompt = "a photo of an astronaut riding a horse on mars"58image = pipe(prompt).images[0]  59    60image.save("astronaut_rides_horse.png")61```62 63**Notes**:64- Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance)65- If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed)66 67# Uses68 69## Direct Use 70The model is intended for research purposes only. Possible research areas and tasks include71 72- Safe deployment of models which have the potential to generate harmful content.73- Probing and understanding the limitations and biases of generative models.74- Generation of artworks and use in design and other artistic processes.75- Applications in educational or creative tools.76- Research on generative models.77 78Excluded uses are described below.79 80 ### Misuse, Malicious Use, and Out-of-Scope Use81_Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_.82 83The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.84 85#### Out-of-Scope Use86The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.87 88#### Misuse and Malicious Use89Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:90 91- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.92- Intentionally promoting or propagating discriminatory content or harmful stereotypes.93- Impersonating individuals without their consent.94- Sexual content without consent of the people who might see it.95- Mis- and disinformation96- Representations of egregious violence and gore97- Sharing of copyrighted or licensed material in violation of its terms of use.98- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.99 100## Limitations and Bias101 102### Limitations103 104- The model does not achieve perfect photorealism105- The model cannot render legible text106- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”107- Faces and people in general may not be generated properly.108- The model was trained mainly with English captions and will not work as well in other languages.109- The autoencoding part of the model is lossy110- The model was trained on a subset of the large-scale dataset111  [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section).112 113### Bias114While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. 115Stable Diffusion vw was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), 116which consists of images that are limited to English descriptions. 117Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. 118This affects the overall output of the model, as white and western cultures are often set as the default. Further, the 119ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.120Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.121 122 123## Training124 125**Training Data**126The model developers used the following dataset for training the model:127 128- LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic.129 130**Training Procedure**131Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, 132 133- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4134- Text prompts are encoded through the OpenCLIP-ViT/H text-encoder.135- The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.136- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512.137 138We currently provide the following checkpoints:139 140- `512-base-ema.ckpt`: 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`.141  850k steps at resolution `512x512` on the same dataset with resolution `>= 512x512`.142- `768-v-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on a `768x768` subset of our dataset.143- `512-depth-ema.ckpt`: Resumed from `512-base-ema.ckpt` and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by [MiDaS](https://github.com/isl-org/MiDaS) (`dpt_hybrid`) which is used as an additional conditioning.144The additional input channels of the U-Net which process this extra information were zero-initialized.145- `512-inpainting-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning.146The additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the [1.5-inpainting checkpoint](https://github.com/saic-mdal/lama).147- `x4-upscaling-ema.ckpt`: Trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752).148In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml). 149 150- **Hardware:** 32 x 8 x A100 GPUs151- **Optimizer:** AdamW152- **Gradient Accumulations**: 1153- **Batch:** 32 x 8 x 2 x 4 = 2048154- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant155 156## Evaluation Results 157Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,1585.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints:159 160![pareto](model-variants.jpg) 161 162Evaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution.  Not optimized for FID scores.163 164## Environmental Impact165 166**Stable Diffusion v1** **Estimated Emissions**167Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.168 169- **Hardware Type:** A100 PCIe 40GB170- **Hours used:** 200000171- **Cloud Provider:** AWS172- **Compute Region:** US-east173- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq.174 175## Citation176    @InProceedings{Rombach_2022_CVPR,177        author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},178        title     = {High-Resolution Image Synthesis With Latent Diffusion Models},179        booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},180        month     = {June},181        year      = {2022},182        pages     = {10684-10695}183    }184 185*This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*