Spaces:
Runtime error
Runtime error
add layout controlnet
Browse files- annotator/dsine_hub.py +37 -0
- annotator/midas.py +34 -0
- annotator/upernet.py +190 -0
- annotator/util.py +38 -0
- app.py +147 -4
annotator/dsine_hub.py
ADDED
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import torch
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import numpy as np
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from PIL import Image
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class NormalDetector:
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def __init__(self):
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self.model_path = "hugoycj/DSINE-hub"
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self.dsine = torch.hub.load(self.model_path, "DSINE", trust_repo=True)
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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@torch.no_grad()
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def __call__(self, image):
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self.dsine.model.to(self.device)
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self.dsine.model.pixel_coords = self.dsine.model.pixel_coords.to(self.device)
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H, W, C = image.shape
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normal = self.dsine.infer_pil(image)[0] # Output shape: (H, W, 3)
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normal = (normal + 1.0) / 2.0 # Convert values to the range [0, 1]
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normal = (normal * 255).cpu().numpy().astype(np.uint8).transpose(1, 2, 0)
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normal_img = Image.fromarray(normal).resize((W, H))
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self.dsine.model.to("cpu")
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self.dsine.model.pixel_coords = self.dsine.model.pixel_coords.to("cpu")
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return normal_img
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if __name__ == "__main__":
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from diffusers.utils import load_image
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image = load_image(
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"https://qhstaticssl.kujiale.com/image/jpeg/1716177580588/9AAA49344B9CE33512C4EBD0A287495F.jpg"
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)
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image = np.asarray(image)
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normal_detector = NormalDetector()
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normal_image = normal_detector(image)
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normal_image.save("normal_image.jpg")
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annotator/midas.py
ADDED
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import torch
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import numpy as np
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from PIL import Image
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from transformers import DPTFeatureExtractor
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from transformers import DPTForDepthEstimation
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class DepthDetector:
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def __init__(self, model_path=None):
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if model_path is not None:
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self.model_path = model_path
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else:
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self.model_path = "Intel/dpt-hybrid-midas"
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self.model = DPTForDepthEstimation.from_pretrained(self.model_path)
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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self.feature_extractor = DPTFeatureExtractor.from_pretrained(self.model_path)
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@torch.no_grad()
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def __call__(self, image):
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self.model.to(self.device)
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H, W, C = image.shape
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inputs = self.feature_extractor(images=image, return_tensors="pt")
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inputs["pixel_values"] = inputs["pixel_values"].to(self.device)
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outputs = self.model(**inputs)
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predicted_depth = outputs.predicted_depth
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outputs = predicted_depth.squeeze().cpu().numpy()
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if len(outputs.shape) == 2:
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output = outputs[np.newaxis, :, :]
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else:
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output = outputs
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formatted = (output * 255 / np.max(output)).astype("uint8")
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depth_image = Image.fromarray(formatted[0, ...]).resize((W, H))
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self.model.to("cpu")
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return depth_image
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annotator/upernet.py
ADDED
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import torch
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import numpy as np
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from PIL import Image
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from transformers import AutoImageProcessor
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from transformers import UperNetForSemanticSegmentation
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class SegmDetector:
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def __init__(self, model_path=None):
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if model_path is not None:
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self.model_path = model_path
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else:
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self.model_path = "openmmlab/upernet-convnext-small"
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self.model = UperNetForSemanticSegmentation.from_pretrained(self.model_path)
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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self.feature_extractor = AutoImageProcessor.from_pretrained(self.model_path)
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self.palette = [
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[120, 120, 120],
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[180, 120, 120],
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[6, 230, 230],
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[80, 50, 50],
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[4, 200, 3],
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[120, 120, 80],
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[140, 140, 140],
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[204, 5, 255],
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[230, 230, 230],
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[4, 250, 7],
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[224, 5, 255],
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[235, 255, 7],
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[150, 5, 61],
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[120, 120, 70],
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[8, 255, 51],
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[255, 6, 82],
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[143, 255, 140],
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[204, 255, 4],
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[255, 51, 7],
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[204, 70, 3],
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[0, 102, 200],
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[61, 230, 250],
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[255, 6, 51],
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[11, 102, 255],
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[255, 7, 71],
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[255, 9, 224],
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[9, 7, 230],
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[220, 220, 220],
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[255, 9, 92],
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[112, 9, 255],
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[8, 255, 214],
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[7, 255, 224],
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[255, 184, 6],
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[10, 255, 71],
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[255, 41, 10],
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[7, 255, 255],
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[224, 255, 8],
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[102, 8, 255],
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[255, 61, 6],
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[255, 194, 7],
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[255, 122, 8],
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[0, 255, 20],
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[255, 8, 41],
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[255, 5, 153],
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[6, 51, 255],
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[235, 12, 255],
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[160, 150, 20],
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[0, 163, 255],
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[140, 140, 140],
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[250, 10, 15],
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[20, 255, 0],
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[31, 255, 0],
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[255, 31, 0],
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[255, 224, 0],
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[153, 255, 0],
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[0, 0, 255],
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[255, 71, 0],
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[0, 235, 255],
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[0, 173, 255],
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[31, 0, 255],
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[11, 200, 200],
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[255, 82, 0],
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[0, 255, 245],
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[0, 61, 255],
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[0, 255, 112],
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[0, 255, 133],
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[255, 0, 0],
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[255, 163, 0],
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[255, 102, 0],
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[194, 255, 0],
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[0, 143, 255],
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[51, 255, 0],
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[0, 82, 255],
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[0, 255, 41],
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[0, 255, 173],
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[10, 0, 255],
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[173, 255, 0],
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[0, 255, 153],
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[255, 92, 0],
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[255, 0, 255],
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[255, 0, 245],
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[255, 0, 102],
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[255, 173, 0],
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[255, 0, 20],
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[255, 184, 184],
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[0, 31, 255],
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[0, 255, 61],
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[0, 71, 255],
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[255, 0, 204],
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[0, 255, 194],
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[0, 255, 82],
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[0, 10, 255],
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[0, 112, 255],
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[51, 0, 255],
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[0, 194, 255],
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[0, 122, 255],
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[0, 255, 163],
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[255, 153, 0],
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[0, 255, 10],
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[255, 112, 0],
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[143, 255, 0],
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[82, 0, 255],
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[163, 255, 0],
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[255, 235, 0],
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[8, 184, 170],
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[133, 0, 255],
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[0, 255, 92],
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[184, 0, 255],
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[255, 0, 31],
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[0, 184, 255],
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[0, 214, 255],
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[255, 0, 112],
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[92, 255, 0],
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[0, 224, 255],
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[112, 224, 255],
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[70, 184, 160],
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[163, 0, 255],
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[153, 0, 255],
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[71, 255, 0],
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[255, 0, 163],
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[255, 204, 0],
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[255, 0, 143],
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[0, 255, 235],
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[133, 255, 0],
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[255, 0, 235],
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[245, 0, 255],
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[255, 0, 122],
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[255, 245, 0],
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[10, 190, 212],
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[214, 255, 0],
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[0, 204, 255],
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[20, 0, 255],
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[255, 255, 0],
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[0, 153, 255],
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[0, 41, 255],
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[0, 255, 204],
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[41, 0, 255],
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[41, 255, 0],
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[173, 0, 255],
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[0, 245, 255],
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[71, 0, 255],
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[122, 0, 255],
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[0, 255, 184],
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[0, 92, 255],
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[184, 255, 0],
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[0, 133, 255],
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[255, 214, 0],
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[25, 194, 194],
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[102, 255, 0],
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[92, 0, 255],
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]
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@torch.no_grad()
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def __call__(self, image):
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self.model.to(self.device)
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H, W, C = image.shape
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pixel_values = self.feature_extractor(
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images=image, return_tensors="pt"
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).pixel_values
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pixel_values = pixel_values.to(self.device)
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outputs = self.model(pixel_values)
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segm_image = self.feature_extractor.post_process_semantic_segmentation(outputs)
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segm_image = segm_image[0].cpu()
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color_seg = np.zeros(
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(segm_image.shape[0], segm_image.shape[1], 3), dtype=np.uint8
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)
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for label, color in enumerate(self.palette):
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color_seg[segm_image == label, :] = color
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color_seg = color_seg.astype(np.uint8)
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segm_image = Image.fromarray(color_seg).resize((W, H))
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self.model.to("cpu")
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return segm_image
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annotator/util.py
ADDED
@@ -0,0 +1,38 @@
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import numpy as np
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import cv2
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def HWC3(x):
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assert x.dtype == np.uint8
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if x.ndim == 2:
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x = x[:, :, None]
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assert x.ndim == 3
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H, W, C = x.shape
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assert C == 1 or C == 3 or C == 4
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if C == 3:
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return x
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if C == 1:
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return np.concatenate([x, x, x], axis=2)
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if C == 4:
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color = x[:, :, 0:3].astype(np.float32)
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alpha = x[:, :, 3:4].astype(np.float32) / 255.0
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19 |
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y = color * alpha + 255.0 * (1.0 - alpha)
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y = y.clip(0, 255).astype(np.uint8)
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return y
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def resize_image(input_image, resolution):
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H, W, C = input_image.shape
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26 |
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H = float(H)
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27 |
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W = float(W)
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28 |
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k = float(resolution) / max(H, W)
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29 |
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H *= k
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W *= k
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31 |
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H = int(np.round(H / 64.0)) * 64
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32 |
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W = int(np.round(W / 64.0)) * 64
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33 |
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img = cv2.resize(
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34 |
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input_image,
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35 |
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(W, H),
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36 |
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interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA,
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37 |
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)
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38 |
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return img
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app.py
CHANGED
@@ -1,7 +1,150 @@
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1 |
import gradio as gr
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|
1 |
+
import torch
|
2 |
+
import spaces
|
3 |
+
import numpy as np
|
4 |
+
from diffusers import (
|
5 |
+
ControlNetModel,
|
6 |
+
StableDiffusionControlNetPipeline,
|
7 |
+
UniPCMultistepScheduler,
|
8 |
+
)
|
9 |
import gradio as gr
|
10 |
|
11 |
+
from annotator.util import resize_image, HWC3
|
12 |
+
from annotator.midas import DepthDetector
|
13 |
+
from annotator.dsine_hub import NormalDetector
|
14 |
+
from annotator.upernet import SegmDetector
|
15 |
|
16 |
+
controlnet_checkpoint = "kujiale-ai/controlnet"
|
17 |
+
# Initialize pipeline
|
18 |
+
controlnet = ControlNetModel.from_pretrained(
|
19 |
+
controlnet_checkpoint,
|
20 |
+
subfolder="control_v1_sd15_layout_fp16",
|
21 |
+
torch_dtype=torch.float16,
|
22 |
+
)
|
23 |
+
pipe = StableDiffusionControlNetPipeline.from_pretrained(
|
24 |
+
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
|
25 |
+
).to("cuda")
|
26 |
+
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
|
27 |
+
|
28 |
+
apply_depth = DepthDetector()
|
29 |
+
apply_normal = NormalDetector()
|
30 |
+
apply_segm = SegmDetector()
|
31 |
+
|
32 |
+
|
33 |
+
@spaces.GPU(duration=10)
|
34 |
+
def generate(
|
35 |
+
input_image,
|
36 |
+
prompt,
|
37 |
+
a_prompt,
|
38 |
+
n_prompt,
|
39 |
+
num_samples,
|
40 |
+
image_resolution,
|
41 |
+
steps,
|
42 |
+
strength,
|
43 |
+
guidance_scale,
|
44 |
+
seed,
|
45 |
+
):
|
46 |
+
color_image = resize_image(HWC3(input_image), image_resolution)
|
47 |
+
# set seed
|
48 |
+
np.random.seed(seed)
|
49 |
+
torch.manual_seed(seed)
|
50 |
+
|
51 |
+
with torch.no_grad():
|
52 |
+
depth_image = apply_depth(color_image)
|
53 |
+
normal_image = apply_normal(color_image)
|
54 |
+
segm_image = apply_segm(color_image)
|
55 |
+
|
56 |
+
# Prepare Layout Control Image
|
57 |
+
depth_image = np.array(depth_image, dtype=np.float32) / 255.0
|
58 |
+
depth_image = torch.from_numpy(depth_image[:, :, None])[None].permute(
|
59 |
+
0, 3, 1, 2
|
60 |
+
)
|
61 |
+
normal_image = np.array(normal_image, dtype=np.float32)
|
62 |
+
normal_image = normal_image / 127.5 - 1.0
|
63 |
+
normal_image = torch.from_numpy(normal_image)[None].permute(0, 3, 1, 2)
|
64 |
+
segm_image = np.array(segm_image, dtype=np.float32) / 255.0
|
65 |
+
segm_image = torch.from_numpy(segm_image)[None].permute(0, 3, 1, 2)
|
66 |
+
control_image = torch.cat([depth_image, normal_image, segm_image], dim=1)
|
67 |
+
|
68 |
+
generator = torch.Generator(device="cuda").manual_seed(seed)
|
69 |
+
images = pipe(
|
70 |
+
prompt + a_prompt,
|
71 |
+
negative_prompt=n_prompt,
|
72 |
+
num_images_per_prompt=num_samples,
|
73 |
+
num_inference_steps=steps,
|
74 |
+
image=control_image,
|
75 |
+
generator=generator,
|
76 |
+
guidance_scale=guidance_scale,
|
77 |
+
controlnet_conditioning_scale=strength,
|
78 |
+
).images
|
79 |
+
return images
|
80 |
+
|
81 |
+
|
82 |
+
block = gr.Blocks().queue()
|
83 |
+
with block:
|
84 |
+
with gr.Row():
|
85 |
+
gr.Markdown("## KuJiaLe Layout ControlNet Demo")
|
86 |
+
with gr.Row():
|
87 |
+
input_image = gr.Image(source="upload", type="numpy", label="input_image")
|
88 |
+
with gr.Row():
|
89 |
+
prompt = gr.Textbox(label="Prompt")
|
90 |
+
with gr.Row():
|
91 |
+
run_button = gr.Button(label="Run")
|
92 |
+
with gr.Row():
|
93 |
+
with gr.Column():
|
94 |
+
with gr.Accordion("Advanced options", open=False):
|
95 |
+
num_samples = gr.Slider(
|
96 |
+
label="Images", minimum=1, maximum=2, value=1, step=1
|
97 |
+
)
|
98 |
+
image_resolution = gr.Slider(
|
99 |
+
label="Image Resolution",
|
100 |
+
minimum=512,
|
101 |
+
maximum=768,
|
102 |
+
value=768,
|
103 |
+
step=64,
|
104 |
+
)
|
105 |
+
strength = gr.Slider(
|
106 |
+
label="Control Strength",
|
107 |
+
minimum=0.0,
|
108 |
+
maximum=2.0,
|
109 |
+
value=1,
|
110 |
+
step=0.1,
|
111 |
+
)
|
112 |
+
steps = gr.Slider(
|
113 |
+
label="Steps", minimum=1, maximum=50, value=25, step=1
|
114 |
+
)
|
115 |
+
guidance_scale = gr.Slider(
|
116 |
+
label="Guidance Scale",
|
117 |
+
minimum=0.1,
|
118 |
+
maximum=20.0,
|
119 |
+
value=7.5,
|
120 |
+
step=0.1,
|
121 |
+
)
|
122 |
+
seed = gr.Slider(
|
123 |
+
label="Seed", minimum=-1, maximum=2147483647, value=1, step=1
|
124 |
+
)
|
125 |
+
a_prompt = gr.Textbox(
|
126 |
+
label="Added Prompt", value="best quality, extremely detailed"
|
127 |
+
)
|
128 |
+
n_prompt = gr.Textbox(
|
129 |
+
label="Negative Prompt",
|
130 |
+
value="longbody, lowres, bad anatomy, human, extra digit, fewer digits, cropped, worst quality, low quality",
|
131 |
+
)
|
132 |
+
|
133 |
+
with gr.Row():
|
134 |
+
image_gallery = gr.Gallery(
|
135 |
+
label="Output", show_label=False, elem_id="gallery"
|
136 |
+
).style(grid=1, height="auto")
|
137 |
+
|
138 |
+
ips = [
|
139 |
+
input_image,
|
140 |
+
prompt,
|
141 |
+
a_prompt,
|
142 |
+
n_prompt,
|
143 |
+
num_samples,
|
144 |
+
image_resolution,
|
145 |
+
steps,
|
146 |
+
strength,
|
147 |
+
guidance_scale,
|
148 |
+
seed,
|
149 |
+
]
|
150 |
+
run_button.click(fn=generate, inputs=ips, outputs=[image_gallery])
|