Stable Diffusion 2

Model/Pipeline/Scheduler description

Perhaps you already have something in the works for this, but I couldn’t see an existing issue for this.

Stable Diffusion 2 just got released on a separate repo. There are a few variations including the base, depth2img, and inpainting models. Though useable in the stable diffusion repo now, it would be really awesome to have support in diffusers to allow for ease of us in other applications!

Open source status

• The model implementation is available
• The model weights are available (Only relevant if addition is not a scheduler).

Provide useful links for the implementation

Model code and inference scripts can be found here

Model weights seem already available on 🤗 Hub at stabilityai/stable-diffusion-2-***

[vvvm23]

From @pcuenca on the HF discord:

We are busy preparing a new release of diffusers to fully support Stable Diffusion 2. We are still ironing things out, but the basics already work from the main branch in github. Here’s how to do it:

• Install diffusers from github alongside its dependencies:

pip install --upgrade git+https://github.com/huggingface/diffusers.git transformers accelerate scipy

• Use the code in this script to run your predictions:

from diffusers import DiffusionPipeline, EulerDiscreteScheduler
import torch

repo_id = "stabilityai/stable-diffusion-2"
device = "cuda"

scheduler = EulerDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler", prediction_type="v_prediction")
pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16", scheduler=scheduler)
pipe = pipe.to(device)

prompt = "High quality photo of an astronaut riding a horse in space"
image = pipe(prompt, width=768, height=768, guidance_scale=9).images[0]
image.save("astronaut.png")

[Marcophono2]

And available in 0.9.0! Damn fast job, guys!