imaginAIry/imaginairy/img_utils.py

68 lines
2.3 KiB
Python

from typing import Sequence
import numpy as np
import PIL
import torch
from einops import rearrange, repeat
from PIL import Image
from imaginairy.utils import get_device
def pillow_fit_image_within(image: PIL.Image.Image, max_height=512, max_width=512):
image = image.convert("RGB")
w, h = image.size
resize_ratio = 1
if w > max_width or h > max_height:
resize_ratio = min(max_width / w, max_height / h)
elif w < max_width and h < max_height:
# it's smaller than our target image, enlarge
resize_ratio = max(max_width / w, max_height / h)
if resize_ratio != 1:
w, h = int(w * resize_ratio), int(h * resize_ratio)
w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64
if (w, h) != image.size:
image = image.resize((w, h), resample=Image.Resampling.LANCZOS)
return image
def pillow_img_to_torch_image(img: PIL.Image.Image):
img = img.convert("RGB")
img = np.array(img).astype(np.float32) / 255.0
img = img[None].transpose(0, 3, 1, 2)
img = torch.from_numpy(img)
return 2.0 * img - 1.0
def pillow_img_to_opencv_img(img: PIL.Image.Image):
open_cv_image = np.array(img)
# Convert RGB to BGR
open_cv_image = open_cv_image[:, :, ::-1].copy()
return open_cv_image
def model_latents_to_pillow_imgs(latents: torch.Tensor) -> Sequence[PIL.Image.Image]:
from imaginairy.model_manager import get_current_diffusion_model # noqa
model = get_current_diffusion_model()
latents = model.decode_first_stage(latents)
latents = torch.clamp((latents + 1.0) / 2.0, min=0.0, max=1.0)
imgs = []
for latent in latents:
latent = 255.0 * rearrange(latent.cpu().numpy(), "c h w -> h w c")
img = Image.fromarray(latent.astype(np.uint8))
imgs.append(img)
return imgs
def pillow_img_to_model_latent(model, img, batch_size=1, half=True):
# init_image = pil_img_to_torch(img, half=half).to(device)
init_image = pillow_img_to_torch_image(img).to(get_device())
init_image = repeat(init_image, "1 ... -> b ...", b=batch_size)
if half:
return model.get_first_stage_encoding(
model.encode_first_stage(init_image.half())
)
return model.get_first_stage_encoding(model.encode_first_stage(init_image))