Stable Diffusion V1 5 Archive

🚀 Stable Diffusion v1-5 Model Card

Stable Diffusion is a latent text-to-image diffusion model that can generate photo-realistic images from any text input. For more details on how Stable Diffusion works, refer to 🤗's Stable Diffusion blog.

🚀 Quick Start

Using the Model

You can use this model with both the 🧨Diffusers library and the Archive of RunwayML GitHub repository.

💻 Usage Examples

Basic Usage

from diffusers import StableDiffusionPipeline
import torch

model_id = "ashllay/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")

prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]  
    
image.save("astronaut_rides_horse.png")

For more detailed instructions, use-cases, and examples in JAX, follow the instructions here.

Downloading Weights and Following Instructions

1. Download the weights:
   • v1-5-pruned-emaonly.ckpt - 4.27GB, ema-only weight. Uses less VRAM, suitable for inference.
   • v1-5-pruned.ckpt - 7.7GB, ema+non-ema weights. Uses more VRAM, suitable for fine-tuning.
2. Follow instructions here.

✨ Features

• Powerful Image Generation: Capable of generating photo-realistic images from text input.
• Multiple Usage Options: Can be used with the 🧨Diffusers library and the Archive of RunwayML GitHub repository.

📚 Documentation

Model Details

@InProceedings{Rombach_2022_CVPR,
    author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj"orn},
    title     = {High-Resolution Image Synthesis With Latent Diffusion Models},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {10684-10695}
}

Uses

Direct Use

The model is for research purposes only. Possible research areas and tasks include:

• Safe deployment of models with the potential to generate harmful content.
• Probing and understanding the limitations and biases of generative models.
• Generation of artworks and use in design and other artistic processes.
• Applications in educational or creative tools.
• Research on generative models.

Misuse, Malicious Use, and Out-of-Scope Use

Note: This section is from the DALLE-MINI model card and applies to Stable Diffusion v1 as well. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating disturbing, distressing, or offensive images, or content that propagates stereotypes.

• Out-of-Scope Use: The model wasn't trained to provide factual or true representations of people or events, so using it for such content is out of its capabilities.
• Misuse and Malicious Use: Using the model to generate cruel content towards individuals is a misuse. This includes generating demeaning representations, promoting discriminatory content, impersonating individuals without consent, creating non-consensual sexual content, spreading mis- and disinformation, showing egregious violence and gore, and sharing copyrighted material in violation of its terms.

Limitations and Bias

Limitations

• The model doesn't achieve perfect photorealism.
• It can't render legible text.
• It performs poorly on tasks involving compositionality, like rendering “A red cube on top of a blue sphere”.
• Faces and people may not be generated properly.
• It was mainly trained with English captions and works less well in other languages.
• The autoencoding part of the model is lossy.
• It was trained on LAION-5B, which contains adult material and isn't suitable for product use without additional safety measures.
• There was no deduplication of the dataset, leading to some memorization of duplicated training images. You can search the training data at https://rom1504.github.io/clip-retrieval/ to detect memorized images.

Bias

Image generation models can reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of LAION-2B(en), mainly with English descriptions. Texts and images from non-English communities are likely underrepresented, affecting the overall output and making the model perform worse with non-English prompts.

Safety Module

The model is intended to be used with the Safety Checker in Diffusers. This checker compares model outputs against hard-coded NSFW concepts in the embedding space of the CLIPTextModel after image generation. The concepts are hidden to prevent reverse-engineering.

Training

Training Data

The model was trained on LAION-2B (en) and its subsets.

Training Procedure

Stable Diffusion v1-5 is a latent diffusion model combining an autoencoder with a diffusion model trained in the autoencoder's latent space. During training:

• Images are encoded into latent representations by an encoder with a relative downsampling factor of 8, mapping H x W x 3 images to H/f x W/f x 4 latents.
• Text prompts are encoded by a ViT-L/14 text-encoder.
• The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
• The loss is a reconstruction objective between the added noise and the UNet's prediction.

There are six Stable Diffusion checkpoints trained as follows:

• stable-diffusion-v1-1: 237,000 steps at 256x256 on laion2B-en, then 194,000 steps at 512x512 on laion-high-resolution.

• stable-diffusion-v1-2: Resumed from stable-diffusion-v1-1, 515,000 steps at 512x512 on "laion-improved-aesthetics".

• stable-diffusion-v1-3: Resumed from stable-diffusion-v1-2, 195,000 steps at 512x512 on "laion-improved-aesthetics" with 10% text-conditioning dropping.

• stable-diffusion-v1-4: Resumed from stable-diffusion-v1-2, 225,000 steps at 512x512 on "laion-aesthetics v2 5+" with 10% text-conditioning dropping.

• stable-diffusion-v1-5: Resumed from stable-diffusion-v1-2, 595,000 steps at 512x512 on "laion-aesthetics v2 5+" with 10% text-conditioning dropping.

• stable-diffusion-inpainting: Resumed from stable-diffusion-v1-5, 440,000 steps of inpainting training at 512x512 on “laion-aesthetics v2 5+” with 10% text-conditioning dropping.

• Hardware: 32 x 8 x A100 GPUs

• Optimizer: AdamW

• Gradient Accumulations: 2

• Batch: 32 x 8 x 2 x 4 = 2048

• Learning rate: Warmed up to 0.0001 for 10,000 steps and then kept constant.

Evaluation Results

Evaluations were conducted with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PNDM/PLMS sampling.

📄 License

This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.

The CreativeML OpenRAIL License specifies:

1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content.
2. CompVis claims no rights on the outputs you generate. You are free to use them but accountable for their use, which must not violate the license provisions.
3. You may re-distribute the weights and use the model commercially or as a service. If you do, include the same use restrictions as in the license and share a copy of the CreativeML OpenRAIL-M with all users.

Please read the full license carefully here.