62 lines
4.2 KiB
Python
62 lines
4.2 KiB
Python
import gradio as gr
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from scripts.tools import processing, model_list
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from modules import script_callbacks
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import modules.shared as shared
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def on_ui_tabs():
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with gr.Blocks(analytics_enabled=False) as PBRemTools:
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input_tab_state = gr.State(value=0)
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with gr.Row():
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with gr.Column():
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with gr.Tabs():
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with gr.TabItem(label="Single") as input_tab_single:
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single_image = gr.Image(type="pil")
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with gr.TabItem(label="Batch") as input_tab_batch:
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batch_image = gr.File(label="Batch Images", file_count="multiple", interactive=True, type="file")
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with gr.TabItem(label="Batch from Dir") as input_tab_dir:
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input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
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output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
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output_mask_dir = gr.Textbox(label="Output Mask directory", **shared.hide_dirs)
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show_result = gr.Checkbox(label="Show result images", value=True)
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with gr.Accordion("Mask Setting", open=True):
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with gr.Tab("Segment Anything & CLIP"):
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sa_enabled = gr.Checkbox(label="enabled", show_label=True)
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model_name = gr.Dropdown(label="Model", elem_id="sam_model", choices=model_list,
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value=model_list[0] if len(model_list) > 0 else None)
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seg_query = gr.Textbox(label = "segmentation prompt", show_label=True)
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predicted_iou_threshold = gr.Slider(0, 1, value=0.9, step=0.01, label="predicted_iou_threshold", show_label=True)
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stability_score_threshold = gr.Slider(0, 1, value=0.9, step=0.01, label="stability_score_threshold", show_label=True)
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clip_threshold = gr.Slider(0, 1, value=0.1, step=0.01, label="clip_threshold", show_label=True)
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with gr.Accordion("Post Processing", open=True):
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with gr.Tab("tile division BG Removers"):
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td_abg_enabled = gr.Checkbox(label="enabled", show_label=True)
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h_split = gr.Slider(1, 2048, value=256, step=4, label="horizontal split num", show_label=True)
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v_split = gr.Slider(1, 2048, value=256, step=4, label="vertical split num", show_label=True)
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n_cluster = gr.Slider(1, 1000, value=500, step=10, label="cluster num", show_label=True)
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alpha = gr.Slider(1, 255, value=50, step=1, label="alpha threshold", show_label=True)
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th_rate = gr.Slider(0, 1, value=0.1, step=0.01, label="mask content ratio", show_label=True)
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with gr.Tab("cascadePSP"):
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cascadePSP_enabled = gr.Checkbox(label="enabled", show_label=True)
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fast = gr.Checkbox(label="fast", show_label=True)
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psp_L = gr.Slider(1, 2048, value=900, step=1, label="Memory usage", show_label=True)
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submit = gr.Button(value="Submit")
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with gr.Row():
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with gr.Column():
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gallery = gr.Gallery(label="outputs", show_label=True, elem_id="gallery", columns=2, object_fit="contain")
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# 0: single 1: batch 2: batch dir
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input_tab_single.select(fn=lambda: 0, inputs=[], outputs=[input_tab_state])
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input_tab_batch.select(fn=lambda: 1, inputs=[], outputs=[input_tab_state])
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input_tab_dir.select(fn=lambda: 2, inputs=[], outputs=[input_tab_state])
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submit.click(
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processing,
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inputs=[single_image, batch_image, input_dir, output_dir, output_mask_dir, show_result, input_tab_state, td_abg_enabled, h_split, v_split, n_cluster, alpha, th_rate, cascadePSP_enabled, fast, psp_L, sa_enabled, seg_query, model_name, predicted_iou_threshold, stability_score_threshold, clip_threshold],
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outputs=gallery
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)
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return [(PBRemTools, "PBRemTools", "pbremtools")]
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script_callbacks.on_ui_tabs(on_ui_tabs)
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