feat: adaptation of Lineart processor

Adapted from https://github.com/huggingface/controlnet_aux

invokeai/app/invocations/controlnet_image_processors.py

@@ -10,7 +10,6 @@ from controlnet_aux import (
     ContentShuffleDetector,
     LeresDetector,
     LineartAnimeDetector,
-    LineartDetector,
     MediapipeFaceDetector,
     MidasDetector,
     MLSDdetector,
@@ -41,6 +40,7 @@ from invokeai.backend.image_util.canny import get_canny_edges
 from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
 from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector
 from invokeai.backend.image_util.hed import HEDProcessor
+from invokeai.backend.image_util.lineart import LineartProcessor
 
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
 
@@ -243,9 +243,9 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
     image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
     coarse: bool = InputField(default=False, description="Whether to use coarse mode")
 
-    def run_processor(self, image):
-        lineart_processor = LineartDetector.from_pretrained("lllyasviel/Annotators")
-        processed_image = lineart_processor(
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        lineart_processor = LineartProcessor()
+        processed_image = lineart_processor.run(
             image, detect_resolution=self.detect_resolution, image_resolution=self.image_resolution, coarse=self.coarse
         )
         return processed_image

invokeai/backend/image_util/lineart.py

@@ -0,0 +1,158 @@
+"""Adapted from https://github.com/huggingface/controlnet_aux (Apache-2.0 license)."""
+
+import cv2
+import numpy as np
+import torch
+import torch.nn as nn
+from einops import rearrange
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+from invokeai.backend.image_util.util import (
+    fit_image_to_resolution,
+    normalize_image_channel_count,
+    np_to_pil,
+    pil_to_np,
+)
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, in_features):
+        super(ResidualBlock, self).__init__()
+
+        conv_block = [
+            nn.ReflectionPad2d(1),
+            nn.Conv2d(in_features, in_features, 3),
+            nn.InstanceNorm2d(in_features),
+            nn.ReLU(inplace=True),
+            nn.ReflectionPad2d(1),
+            nn.Conv2d(in_features, in_features, 3),
+            nn.InstanceNorm2d(in_features),
+        ]
+
+        self.conv_block = nn.Sequential(*conv_block)
+
+    def forward(self, x):
+        return x + self.conv_block(x)
+
+
+class Generator(nn.Module):
+    def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True):
+        super(Generator, self).__init__()
+
+        # Initial convolution block
+        model0 = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, 64, 7), nn.InstanceNorm2d(64), nn.ReLU(inplace=True)]
+        self.model0 = nn.Sequential(*model0)
+
+        # Downsampling
+        model1 = []
+        in_features = 64
+        out_features = in_features * 2
+        for _ in range(2):
+            model1 += [
+                nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
+                nn.InstanceNorm2d(out_features),
+                nn.ReLU(inplace=True),
+            ]
+            in_features = out_features
+            out_features = in_features * 2
+        self.model1 = nn.Sequential(*model1)
+
+        model2 = []
+        # Residual blocks
+        for _ in range(n_residual_blocks):
+            model2 += [ResidualBlock(in_features)]
+        self.model2 = nn.Sequential(*model2)
+
+        # Upsampling
+        model3 = []
+        out_features = in_features // 2
+        for _ in range(2):
+            model3 += [
+                nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1),
+                nn.InstanceNorm2d(out_features),
+                nn.ReLU(inplace=True),
+            ]
+            in_features = out_features
+            out_features = in_features // 2
+        self.model3 = nn.Sequential(*model3)
+
+        # Output layer
+        model4 = [nn.ReflectionPad2d(3), nn.Conv2d(64, output_nc, 7)]
+        if sigmoid:
+            model4 += [nn.Sigmoid()]
+
+        self.model4 = nn.Sequential(*model4)
+
+    def forward(self, x, cond=None):
+        out = self.model0(x)
+        out = self.model1(out)
+        out = self.model2(out)
+        out = self.model3(out)
+        out = self.model4(out)
+
+        return out
+
+
+class LineartProcessor:
+    """Processor for lineart detection."""
+
+    def __init__(self):
+        model_path = hf_hub_download("lllyasviel/Annotators", "sk_model.pth")
+        self.model = Generator(3, 1, 3)
+        self.model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
+        self.model.eval()
+
+        coarse_model_path = hf_hub_download("lllyasviel/Annotators", "sk_model2.pth")
+        self.model_coarse = Generator(3, 1, 3)
+        self.model_coarse.load_state_dict(torch.load(coarse_model_path, map_location=torch.device("cpu")))
+        self.model_coarse.eval()
+
+    def to(self, device: torch.device):
+        self.model.to(device)
+        self.model_coarse.to(device)
+        return self
+
+    def run(
+        self, input_image: Image.Image, coarse: bool = False, detect_resolution: int = 512, image_resolution: int = 512
+    ) -> Image.Image:
+        """Processes an image to detect lineart.
+
+        Args:
+            input_image: The input image.
+            coarse: Whether to use the coarse model.
+            detect_resolution: The resolution to fit the image to before edge detection.
+            image_resolution: The resolution of the output image.
+
+        Returns:
+            The detected lineart.
+        """
+        device = next(iter(self.model.parameters())).device
+
+        np_image = pil_to_np(input_image)
+        np_image = normalize_image_channel_count(np_image)
+        np_image = fit_image_to_resolution(np_image, detect_resolution)
+
+        model = self.model_coarse if coarse else self.model
+        assert np_image.ndim == 3
+        image = np_image
+        with torch.no_grad():
+            image = torch.from_numpy(image).float().to(device)
+            image = image / 255.0
+            image = rearrange(image, "h w c -> 1 c h w")
+            line = model(image)[0][0]
+
+            line = line.cpu().numpy()
+            line = (line * 255.0).clip(0, 255).astype(np.uint8)
+
+        detected_map = line
+
+        detected_map = normalize_image_channel_count(detected_map)
+
+        img = fit_image_to_resolution(np_image, image_resolution)
+        H, W, C = img.shape
+
+        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
+        detected_map = 255 - detected_map
+
+        return np_to_pil(detected_map)