jaidaken
f09734b0ee
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Includes 30 custom nodes committed directly, 7 Civitai-exclusive loras stored via Git LFS, and a setup script that installs all dependencies and downloads HuggingFace-hosted models on vast.ai. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
import impact.additional_dependencies
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import numpy as np
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from impact import utils
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import logging
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impact.additional_dependencies.ensure_onnx_package()
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try:
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import onnxruntime
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def onnx_inference(image, onnx_model):
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# prepare image
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pil = utils.tensor2pil(image)
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image = np.ascontiguousarray(pil)
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image = image[:, :, ::-1] # to BGR image
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image = image.astype(np.float32)
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image -= [103.939, 116.779, 123.68] # 'caffe' mode image preprocessing
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# do detection
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onnx_model = onnxruntime.InferenceSession(onnx_model, providers=["CPUExecutionProvider"])
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outputs = onnx_model.run(
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[s_i.name for s_i in onnx_model.get_outputs()],
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{onnx_model.get_inputs()[0].name: np.expand_dims(image, axis=0)},
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)
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labels = [op for op in outputs if op.dtype == "int32"][0]
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scores = [op for op in outputs if isinstance(op[0][0], np.float32)][0]
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boxes = [op for op in outputs if isinstance(op[0][0], np.ndarray)][0]
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# filter-out useless item
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idx = np.where(labels[0] == -1)[0][0]
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labels = labels[0][:idx]
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scores = scores[0][:idx]
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boxes = boxes[0][:idx].astype(np.uint32)
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return labels, scores, boxes
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except Exception:
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logging.error("[Impact Pack] ComfyUI-Impact-Pack: 'onnxruntime' package doesn't support 'python 3.11', yet.\t{e}")
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