from modules import scripts_postprocessing, ui_components, errors import gradio as gr from modules.textual_inversion import autocrop class ScriptPostprocessingFocalCrop(scripts_postprocessing.ScriptPostprocessing): name = "Auto focal point crop" order = 4000 def ui(self): with ui_components.InputAccordion(False, label="Auto focal point crop") as enable: face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_focal_crop_face_weight") entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_focal_crop_entropy_weight") edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_focal_crop_edges_weight") debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug") return { "enable": enable, "face_weight": face_weight, "entropy_weight": entropy_weight, "edges_weight": edges_weight, "debug": debug, } def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, face_weight, entropy_weight, edges_weight, debug): if not enable: return if not pp.shared.target_width or not pp.shared.target_height: return dnn_model_path = None try: dnn_model_path = autocrop.download_and_cache_models() except Exception: errors.report("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", exc_info=True) autocrop_settings = autocrop.Settings( crop_width=pp.shared.target_width, crop_height=pp.shared.target_height, face_points_weight=face_weight, entropy_points_weight=entropy_weight, corner_points_weight=edges_weight, annotate_image=debug, dnn_model_path=dnn_model_path, ) result, *others = autocrop.crop_image(pp.image, autocrop_settings) pp.image = result pp.extra_images = [pp.create_copy(x, nametags=["focal-crop-debug"], disable_processing=True) for x in others]