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Running
on
Zero
File size: 3,986 Bytes
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# code based in https://github.com/TheMistoAI/ComfyUI-Anyline/blob/main/anyline.py
import os
import cv2
import numpy as np
import torch
from einops import rearrange
from huggingface_hub import hf_hub_download
from PIL import Image
from skimage import morphology
from ..teed.ted import TED
from ..util import HWC3, resize_image, safe_step
class AnylineDetector:
def __init__(self, model):
self.model = model
@classmethod
def from_pretrained(cls, pretrained_model_or_path, filename=None, subfolder=None):
if os.path.isdir(pretrained_model_or_path):
model_path = os.path.join(pretrained_model_or_path, filename)
else:
model_path = hf_hub_download(
pretrained_model_or_path, filename, subfolder=subfolder
)
model = TED()
model.load_state_dict(torch.load(model_path, map_location="cpu"))
return cls(model)
def to(self, device):
self.model.to(device)
return self
def __call__(
self,
input_image,
detect_resolution=1280,
guassian_sigma=2.0,
intensity_threshold=3,
output_type="pil",
):
device = next(iter(self.model.parameters())).device
if not isinstance(input_image, np.ndarray):
input_image = np.array(input_image, dtype=np.uint8)
output_type = output_type or "pil"
else:
output_type = output_type or "np"
original_height, original_width, _ = input_image.shape
input_image = HWC3(input_image)
input_image = resize_image(input_image, detect_resolution)
assert input_image.ndim == 3
height, width, _ = input_image.shape
with torch.no_grad():
image_teed = torch.from_numpy(input_image.copy()).float().to(device)
image_teed = rearrange(image_teed, "h w c -> 1 c h w")
edges = self.model(image_teed)
edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges]
edges = [
cv2.resize(e, (width, height), interpolation=cv2.INTER_LINEAR)
for e in edges
]
edges = np.stack(edges, axis=2)
edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64)))
edge = safe_step(edge, 2)
edge = (edge * 255.0).clip(0, 255).astype(np.uint8)
mteed_result = edge
mteed_result = HWC3(mteed_result)
x = input_image.astype(np.float32)
g = cv2.GaussianBlur(x, (0, 0), guassian_sigma)
intensity = np.min(g - x, axis=2).clip(0, 255)
intensity /= max(16, np.median(intensity[intensity > intensity_threshold]))
intensity *= 127
lineart_result = intensity.clip(0, 255).astype(np.uint8)
lineart_result = HWC3(lineart_result)
lineart_result = self.get_intensity_mask(
lineart_result, lower_bound=0, upper_bound=255
)
cleaned = morphology.remove_small_objects(
lineart_result.astype(bool), min_size=36, connectivity=1
)
lineart_result = lineart_result * cleaned
final_result = self.combine_layers(mteed_result, lineart_result)
final_result = cv2.resize(
final_result,
(original_width, original_height),
interpolation=cv2.INTER_LINEAR,
)
if output_type == "pil":
final_result = Image.fromarray(final_result)
return final_result
def get_intensity_mask(self, image_array, lower_bound, upper_bound):
mask = image_array[:, :, 0]
mask = np.where((mask >= lower_bound) & (mask <= upper_bound), mask, 0)
mask = np.expand_dims(mask, 2).repeat(3, axis=2)
return mask
def combine_layers(self, base_layer, top_layer):
mask = top_layer.astype(bool)
temp = 1 - (1 - top_layer) * (1 - base_layer)
result = base_layer * (~mask) + temp * mask
return result
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