Stable Diffusion 2 is a text-to-image latent diffusion model built upon the work of the original , and it was led by Robin Rombach and Katherine Crowson from and .
The Stable Diffusion 2.0 release includes robust text-to-image models trained using a brand new text encoder (OpenCLIP), developed by LAION with support from Stability AI, which greatly improves the quality of the generated images compared to earlier V1 releases. The text-to-image models in this release can generate images with default resolutions of both 512x512 pixels and 768x768 pixels. These models are trained on an aesthetic subset of the created by the DeepFloyd team at Stability AI, which is then further filtered to remove adult content using .
For more details about how Stable Diffusion 2 works and how it differs from the original Stable Diffusion, please refer to the official .
The architecture of Stable Diffusion 2 is more or less identical to the original so check out it’s API documentation for how to use Stable Diffusion 2. We recommend using the as it’s currently the fastest scheduler.
Stable Diffusion 2 is available for tasks like text-to-image, inpainting, super-resolution, and depth-to-image:
Task
Repository
text-to-image (512x512)
text-to-image (768x768)
inpainting
super-resolution
depth-to-image
Here are some examples for how to use Stable Diffusion 2 for each task:
Make sure to check out the Stable Diffusion section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
If you’re interested in using one of the official checkpoints for a task, explore the , , and Hub organizations!
Text-to-image
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from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
import torch
repo_id = "stabilityai/stable-diffusion-2-base"
pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")
prompt = "High quality photo of an astronaut riding a horse in space"
image = pipe(prompt, num_inference_steps=25).images[0]
image.save("astronaut.png")
Inpainting
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import PIL
import requests
import torch
from io import BytesIO
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
def download_image(url):
response = requests.get(url)
return PIL.Image.open(BytesIO(response.content)).convert("RGB")
img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
init_image = download_image(img_url).resize((512, 512))
mask_image = download_image(mask_url).resize((512, 512))
repo_id = "stabilityai/stable-diffusion-2-inpainting"
pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")
prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
image = pipe(prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=25).images[0]
image.save("yellow_cat.png")
Super-resolution
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import requests
from PIL import Image
from io import BytesIO
from diffusers import StableDiffusionUpscalePipeline
import torch
# load model and scheduler
model_id = "stabilityai/stable-diffusion-x4-upscaler"
pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipeline = pipeline.to("cuda")
# let's download an image
url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png"
response = requests.get(url)
low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
low_res_img = low_res_img.resize((128, 128))
prompt = "a white cat"
upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]
upscaled_image.save("upsampled_cat.png")
