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import os
import torch
from PIL import Image
from diffusers import AutoencoderKL
torch_device = (
"cuda"
if torch.cuda.is_available()
else "mps"
if torch.backends.mps.is_available()
else "cpu"
)
if "mps" == torch_device:
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="vae").to(torch_device)
def pil_to_latent(input_im):
# Single image -> single latent in a batch (so size 1, 4, 64, 64)
with torch.no_grad():
latent = vae.encode(tfms.ToTensor()(input_im).unsqueeze(0).to(torch_device)*2-1) # Note scaling
return 0.18215 * latent.latent_dist.sample()
def latents_to_pil(latents, torch_device=torch_device):
# bath of latents -> list of images
latents = (1 / 0.18215) * latents
with torch.no_grad():
image = vae.decode(latents.to(torch_device)).sample
image = (image / 2 + 0.5).clamp(0, 1)
image = image.detach().cpu().permute(0, 2, 3, 1).numpy()
images = (image * 255).round().astype("uint8")
pil_images = [Image.fromarray(image) for image in images]
return pil_images
def load_embedding_bin(path):
return torch.load(path)
# Prep Scheduler
def set_timesteps(scheduler, num_inference_steps):
scheduler.set_timesteps(num_inference_steps)
scheduler.timesteps = scheduler.timesteps.to(torch.float32) # minor fix to ensure MPS compatibility, fixed in diffusers PR 3925
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