Diffusers Generation Text Box

🚀 Stable Diffusion v1 - 4 Model Card

Stable Diffusion is a latent text - to - image diffusion model. It can generate photo - realistic images based on any text input. For more details about how it works, refer to 🤗's Stable Diffusion with 🧨Diffusers blog.

The Stable - Diffusion - v1 - 4 checkpoint was initialized with the weights of the [Stable - Diffusion - v1 - 2](https:/steps/huggingface.co/CompVis/stable - diffusion - v1 - 2) checkpoint. Then it was fine - tuned for 225k steps at a resolution of 512x512 on "laion - aesthetics v2 5+". A 10% drop of the text - conditioning was applied to improve classifier - free guidance sampling.

These weights are designed for use with the 🧨 Diffusers library. If you need weights for the CompVis Stable Diffusion codebase, [click here](https://huggingface.co/CompVis/stable - diffusion - v - 1 - 4 - original).

✨ Features

• Capable of generating photo - realistic images from text input.
• Fine - tuned to improve classifier - free guidance sampling.

📦 Installation

We recommend using 🤗's Diffusers library to run Stable Diffusion.

PyTorch

pip install --upgrade diffusers transformers scipy

💻 Usage Examples

Basic Usage - PyTorch

Running the pipeline with the default PNDM scheduler:

import torch
from diffusers import StableDiffusionPipeline

model_id = "CompVis/stable-diffusion-v1-4"
device = "cuda"

pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to(device)

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

image.save("astronaut_rides_horse.png")

Advanced Usage - PyTorch

Note: If you are limited by GPU memory and have less than 4GB of GPU RAM available, please make sure to load the StableDiffusionPipeline in float16 precision instead of the default float32 precision as done above. You can do so by telling diffusers to expect the weights to be in float16 precision:

import torch

pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to(device)
pipe.enable_attention_slicing()

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

image.save("astronaut_rides_horse.png")

To swap out the noise scheduler, pass it to from_pretrained:

from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler

model_id = "CompVis/stable-diffusion-v1-4"

# Use the Euler scheduler here instead
scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, 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")

Basic Usage - JAX/Flax

To use StableDiffusion on TPUs and GPUs for faster inference you can leverage JAX/Flax. Running the pipeline with default PNDMScheduler

import jax
import numpy as np
from flax.jax_utils import replicate
from flax.training.common_utils import shard

from diffusers import FlaxStableDiffusionPipeline

pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4", revision="flax", dtype=jax.numpy.bfloat16
)

prompt = "a photo of an astronaut riding a horse on mars"

prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 50

num_samples = jax.device_count()
prompt = num_samples * [prompt]
prompt_ids = pipeline.prepare_inputs(prompt)

# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, num_samples)
prompt_ids = shard(prompt_ids)

images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))

Advanced Usage - JAX/Flax

Note: If you are limited by TPU memory, please make sure to load the FlaxStableDiffusionPipeline in bfloat16 precision instead of the default float32 precision as done above. You can do so by telling diffusers to load the weights from "bf16" branch.

import jax
import numpy as np
from flax.jax_utils import replicate
from flax.training.common_utils import shard

from diffusers import FlaxStableDiffusionPipeline

pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4", revision="bf16", dtype=jax.numpy.bfloat16
)

prompt = "a photo of an astronaut riding a horse on mars"

prng_seed = jax.random.PRNGKey(0)
num_inference_steps = 50

num_samples = jax.device_count()
prompt = num_samples * [prompt]
prompt_ids = pipeline.prepare_inputs(prompt)

# shard inputs and rng
params = replicate(params)
prng_seed = jax.random.split(prng_seed, num_samples)
prompt_ids = shard(prompt_ids)

images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:])))

📚 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 intended for research purposes only. Possible research areas and tasks include:

• Safe deployment of models which have 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.

Excluded uses are described below.

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

⚠️ Important Note

This section is taken from the [DALLE - MINI model card](https://huggingface.co/dalle - mini/dalle - mini), but applies in the same way to Stable Diffusion v1.

The 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.

Out - of - Scope Use

The 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.

Misuse and Malicious Use

Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:

• Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
• Intentionally promoting or propagating discriminatory content or harmful stereotypes.
• Impersonating individuals without their consent.
• Sexual content without consent of the people who might see it.
• Mis - and disinformation
• Representations of egregious violence and gore
• Sharing of copyrighted or licensed material in violation of its terms of use.
• Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.

Limitations and Bias

Limitations

• The model does not achieve perfect photorealism.
• The model cannot render legible text.
• 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”.
• Faces and people in general may not be generated properly.
• The model was trained mainly with English captions and will not work as well in other languages.
• The autoencoding part of the model is lossy.
• The model was trained on a large - scale dataset [LAION - 5B](https://laion.ai/blog/laion - 5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations.
• No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip - retrieval/](https://rom1504.github.io/clip - retrieval/) to possibly assist in the detection of memorized images.

Bias

While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION - 2B(en)](https://laion.ai/blog/laion - 5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non - English prompts is significantly worse than with English - language prompts.

Safety Module

The intended use of this model is with the Safety Checker in Diffusers. This checker works by checking model outputs against known hard - coded NSFW concepts. The concepts are intentionally hidden to reduce the likelihood of reverse - engineering this filter. Specifically, the checker compares the class probability of harmful concepts in the embedding space of the CLIPTextModel after generation of the images. The concepts are passed into the model with the generated image and compared to a hand - engineered weight for each NSFW concept.

Training

Training Data The model developers used the following dataset for training the model:

• LAION - 2B (en) and subsets thereof (see next section)

Training Procedure Stable Diffusion v1 - 4 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, images are encoded...

📄 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. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license.
3. You may re - distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL - M to all your users (please read the license entirely and carefully).

Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable - diffusion - license