Skip to content

v0.9.0: Stable Diffusion 2
Compare
Choose a tag to compare
@anton-l anton-l released this 25 Nov 17:17
· 3552 commits to main since this release
v0.9.0
6b02323

🎨 Stable Diffusion 2 is here!

Installation

pip install diffusers[torch]==0.9 transformers

Stable Diffusion 2.0 is available in several flavors:

Stable Diffusion 2.0-V at 768x768

New stable diffusion model (Stable Diffusion 2.0-v) at 768x768 resolution. Same number of parameters in the U-Net as 1.5, but uses OpenCLIP-ViT/H as the text encoder and is trained from scratch. SD 2.0-v is a so-called v-prediction model.

image

import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler

repo_id = "stabilityai/stable-diffusion-2"
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, guidance_scale=9, num_inference_steps=25).images[0]
image.save("astronaut.png")

Stable Diffusion 2.0-base at 512x512

The above model is finetuned from SD 2.0-base, which was trained as a standard noise-prediction model on 512x512 images and is also made available.

image

import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler

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")

Stable Diffusion 2.0 for Inpanting

This model for text-guided inpanting is finetuned from SD 2.0-base. Follows the mask-generation strategy presented in LAMA which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning.

image

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")

Stable Diffusion X4 Upscaler

The model was trained on crops of size 512x512 and is a text-guided latent upscaling diffusion model. In 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.

image

import requests
from PIL import Image
from io import BytesIO
from diffusers import StableDiffusionUpscalePipeline
import torch

model_id = "stabilityai/stable-diffusion-x4-upscaler"
pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, revision="fp16", torch_dtype=torch.float16)
pipeline = pipeline.to("cuda")

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")

Saving & Loading is fixed for Versatile Diffusion

Previously there was a 🐛 when saving & loading versatile diffusion - this is fixed now so that memory efficient saving & loading works as expected.

📝 Changelog

Contributors

kashif, pcuenca, and 3 other contributors
Assets 2
Loading