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import os
import numpy as np
import torch
import torch.nn.functional as F
from torchvision.transforms.functional import normalize
from PIL import Image, ImageOps, ImageSequence
from typing import List
from pathlib import Path
from huggingface_hub import snapshot_download, hf_hub_download
def tensor_to_pil(images: torch.Tensor | List[torch.Tensor]) -> List[Image.Image]:
if not isinstance(images, list):
images = [images]
imgs = []
for image in images:
i = 255.0 * image.cpu().numpy()
img = Image.fromarray(np.clip(np.squeeze(i), 0, 255).astype(np.uint8))
imgs.append(img)
return imgs
def pad_image(input_image):
pad_w, pad_h = (
np.max(((2, 2), np.ceil(np.array(input_image.size) / 64).astype(int)), axis=0)
* 64
- input_image.size
)
im_padded = Image.fromarray(
np.pad(np.array(input_image), ((0, pad_h), (0, pad_w), (0, 0)), mode="edge")
)
w, h = im_padded.size
if w == h:
return im_padded
elif w > h:
new_image = Image.new(im_padded.mode, (w, w), (0, 0, 0))
new_image.paste(im_padded, (0, (w - h) // 2))
return new_image
else:
new_image = Image.new(im_padded.mode, (h, h), (0, 0, 0))
new_image.paste(im_padded, ((h - w) // 2, 0))
return new_image
def pil_to_tensor(image: Image.Image) -> tuple[torch.Tensor, torch.Tensor]:
output_images = []
output_masks = []
for i in ImageSequence.Iterator(image):
i = ImageOps.exif_transpose(i)
if i.mode == "I":
i = i.point(lambda i: i * (1 / 255))
image = i.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
if "A" in i.getbands():
mask = np.array(i.getchannel("A")).astype(np.float32) / 255.0
mask = 1.0 - torch.from_numpy(mask)
else:
mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
output_images.append(image)
output_masks.append(mask.unsqueeze(0))
if len(output_images) > 1:
output_image = torch.cat(output_images, dim=0)
output_mask = torch.cat(output_masks, dim=0)
else:
output_image = output_images[0]
output_mask = output_masks[0]
return (output_image, output_mask)
def preprocess_image(im: np.ndarray, model_input_size: list) -> torch.Tensor:
if len(im.shape) < 3:
im = im[:, :, np.newaxis]
# orig_im_size=im.shape[0:2]
im_tensor = torch.tensor(im, dtype=torch.float32).permute(2, 0, 1)
im_tensor = F.interpolate(
torch.unsqueeze(im_tensor, 0), size=model_input_size, mode="bilinear"
).type(torch.uint8)
image = torch.divide(im_tensor, 255.0)
image = normalize(image, [0.5, 0.5, 0.5], [1.0, 1.0, 1.0])
return image
def postprocess_image(result: torch.Tensor, im_size: list) -> np.ndarray:
result = torch.squeeze(F.interpolate(result, size=im_size, mode="bilinear"), 0)
ma = torch.max(result)
mi = torch.min(result)
result = (result - mi) / (ma - mi)
im_array = (result * 255).permute(1, 2, 0).cpu().data.numpy().astype(np.uint8)
im_array = np.squeeze(im_array)
return im_array
def downloadModels():
MODEL_PATH = hf_hub_download(
repo_id="lllyasviel/fav_models",
subfolder="fav",
filename="juggernautXL_v8Rundiffusion.safetensors",
)
LAYERS_PATH = snapshot_download(
repo_id="LayerDiffusion/layerdiffusion-v1", allow_patterns="*.safetensors"
)
for file in Path(LAYERS_PATH).glob("*.safetensors"):
target_path = Path(f"./ComfyUI/models/layer_model/{file.name}")
if not target_path.exists():
os.symlink(file, target_path)
model_target_path = Path(
"./ComfyUI/models/checkpoints/juggernautXL_v8Rundiffusion.safetensors"
)
if not model_target_path.exists():
os.symlink(MODEL_PATH, model_target_path)
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