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import gradio as gr |
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import cv2 |
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import numpy as np |
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from annotator.util import resize_image, HWC3 |
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DESCRIPTION = '# ControlNet v1.1 Annotators (that runs on cpu only)' |
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DESCRIPTION += '\n<p>This app generates Control Image for Core ML Stable Diffusion apps such as Mochi Diffusion.</p>' |
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DESCRIPTION += '\n<p>HEIC image is not converted. Please use PNG or JPG image.</p>' |
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model_canny = None |
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def canny(img, res, l, h): |
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img = resize_image(HWC3(img), res) |
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global model_canny |
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if model_canny is None: |
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from annotator.canny import CannyDetector |
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model_canny = CannyDetector() |
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result = model_canny(img, l, h) |
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return [result] |
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model_hed = None |
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def hed(img, res): |
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img = resize_image(HWC3(img), res) |
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global model_hed |
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if model_hed is None: |
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from annotator.hed import HEDdetector |
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model_hed = HEDdetector() |
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result = model_hed(img) |
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return [result] |
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model_pidi = None |
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def pidi(img, res): |
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img = resize_image(HWC3(img), res) |
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global model_pidi |
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if model_pidi is None: |
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from annotator.pidinet import PidiNetDetector |
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model_pidi = PidiNetDetector() |
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result = model_pidi(img) |
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return [result] |
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model_mlsd = None |
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def mlsd(img, res, thr_v, thr_d): |
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img = resize_image(HWC3(img), res) |
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global model_mlsd |
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if model_mlsd is None: |
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from annotator.mlsd import MLSDdetector |
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model_mlsd = MLSDdetector() |
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result = model_mlsd(img, thr_v, thr_d) |
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return [result] |
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model_midas = None |
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def midas(img, res): |
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img = resize_image(HWC3(img), res) |
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global model_midas |
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if model_midas is None: |
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from annotator.midas import MidasDetector |
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model_midas = MidasDetector() |
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result = model_midas(img) |
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return [result] |
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model_zoe = None |
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def zoe(img, res): |
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img = resize_image(HWC3(img), res) |
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global model_zoe |
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if model_zoe is None: |
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from annotator.zoe import ZoeDetector |
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model_zoe = ZoeDetector() |
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result = model_zoe(img) |
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return [result] |
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model_normalbae = None |
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def normalbae(img, res): |
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img = resize_image(HWC3(img), res) |
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global model_normalbae |
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if model_normalbae is None: |
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from annotator.normalbae import NormalBaeDetector |
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model_normalbae = NormalBaeDetector() |
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result = model_normalbae(img) |
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return [result] |
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model_dwpose = None |
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def dwpose(img, res): |
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img = resize_image(HWC3(img), res) |
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global model_dwpose |
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if model_dwpose is None: |
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from annotator.dwpose import DWposeDetector |
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model_dwpose = DWposeDetector() |
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result = model_dwpose(img) |
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return [result] |
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model_openpose = None |
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def openpose(img, res, hand_and_face): |
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img = resize_image(HWC3(img), res) |
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global model_openpose |
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if model_openpose is None: |
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from annotator.openpose import OpenposeDetector |
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model_openpose = OpenposeDetector() |
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result = model_openpose(img, hand_and_face) |
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return [result] |
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model_uniformer = None |
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model_lineart_anime = None |
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def lineart_anime(img, res, invert=True): |
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img = resize_image(HWC3(img), res) |
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global model_lineart_anime |
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if model_lineart_anime is None: |
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from annotator.lineart_anime import LineartAnimeDetector |
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model_lineart_anime = LineartAnimeDetector() |
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if (invert): |
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result = cv2.bitwise_not(model_lineart_anime(img)) |
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else: |
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result = model_lineart_anime(img) |
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return [result] |
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model_lineart = None |
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def lineart(img, res, coarse=False, invert=True): |
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img = resize_image(HWC3(img), res) |
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global model_lineart |
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if model_lineart is None: |
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from annotator.lineart import LineartDetector |
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model_lineart = LineartDetector() |
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if (invert): |
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result = cv2.bitwise_not(model_lineart(img, coarse)) |
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else: |
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result = model_lineart(img, coarse) |
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return [result] |
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model_oneformer_coco = None |
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def oneformer_coco(img, res): |
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img = resize_image(HWC3(img), res) |
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global model_oneformer_coco |
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if model_oneformer_coco is None: |
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from annotator.oneformer import OneformerCOCODetector |
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model_oneformer_coco = OneformerCOCODetector() |
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result = model_oneformer_coco(img) |
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return [result] |
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model_oneformer_ade20k = None |
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def oneformer_ade20k(img, res): |
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img = resize_image(HWC3(img), res) |
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global model_oneformer_ade20k |
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if model_oneformer_ade20k is None: |
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from annotator.oneformer import OneformerADE20kDetector |
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model_oneformer_ade20k = OneformerADE20kDetector() |
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result = model_oneformer_ade20k(img) |
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return [result] |
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model_content_shuffler = None |
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def content_shuffler(img, res): |
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img = resize_image(HWC3(img), res) |
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global model_content_shuffler |
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if model_content_shuffler is None: |
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from annotator.shuffle import ContentShuffleDetector |
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model_content_shuffler = ContentShuffleDetector() |
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result = model_content_shuffler(img) |
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return [result] |
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model_color_shuffler = None |
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def color_shuffler(img, res): |
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img = resize_image(HWC3(img), res) |
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global model_color_shuffler |
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if model_color_shuffler is None: |
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from annotator.shuffle import ColorShuffleDetector |
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model_color_shuffler = ColorShuffleDetector() |
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result = model_color_shuffler(img) |
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return [result] |
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model_inpaint = None |
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def inpaint(image, invert): |
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color = HWC3(image["background"]) |
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if(invert): |
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alpha = image["layers"][0][:, :, 3:] |
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else: |
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alpha = 255 - image["layers"][0][:, :, 3:] |
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result = np.concatenate([color, alpha], axis=2) |
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return [result] |
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block = gr.Blocks().queue() |
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with block: |
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gr.Markdown(DESCRIPTION) |
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with gr.Row(): |
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gr.Markdown("## Canny Edge") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(label="Input Image", type="numpy", height=512) |
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low_threshold = gr.Slider(label="low_threshold", minimum=1, maximum=255, value=100, step=1) |
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high_threshold = gr.Slider(label="high_threshold", minimum=1, maximum=255, value=200, step=1) |
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resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64) |
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run_button = gr.Button("Run") |
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with gr.Column(): |
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gallery = gr.Gallery(label="Generated images", show_label=False, height="auto") |
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run_button.click(fn=canny, inputs=[input_image, resolution, low_threshold, high_threshold], outputs=[gallery]) |
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gr.Markdown("<hr>") |
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with gr.Row(): |
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gr.Markdown("## HED Edge "SoftEdge"") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(label="Input Image", type="numpy", height=512) |
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resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64) |
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run_button = gr.Button("Run") |
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with gr.Column(): |
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gallery = gr.Gallery(label="Generated images", show_label=False, height="auto") |
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run_button.click(fn=hed, inputs=[input_image, resolution], outputs=[gallery]) |
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gr.Markdown("<hr>") |
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with gr.Row(): |
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gr.Markdown("## Pidi Edge "SoftEdge"") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(label="Input Image", type="numpy", height=512) |
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resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64) |
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run_button = gr.Button("Run") |
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with gr.Column(): |
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gallery = gr.Gallery(label="Generated images", show_label=False, height="auto") |
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run_button.click(fn=pidi, inputs=[input_image, resolution], outputs=[gallery]) |
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gr.Markdown("<hr>") |
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with gr.Row(): |
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gr.Markdown("## MLSD Edge") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(label="Input Image", type="numpy", height=512) |
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value_threshold = gr.Slider(label="value_threshold", minimum=0.01, maximum=2.0, value=0.1, step=0.01) |
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distance_threshold = gr.Slider(label="distance_threshold", minimum=0.01, maximum=20.0, value=0.1, step=0.01) |
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resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=384, step=64) |
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run_button = gr.Button("Run") |
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with gr.Column(): |
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gallery = gr.Gallery(label="Generated images", show_label=False, height="auto") |
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run_button.click(fn=mlsd, inputs=[input_image, resolution, value_threshold, distance_threshold], outputs=[gallery]) |
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gr.Markdown("<hr>") |
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with gr.Row(): |
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gr.Markdown("## MIDAS Depth") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(label="Input Image", type="numpy", height=512) |
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resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=384, step=64) |
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run_button = gr.Button("Run") |
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with gr.Column(): |
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gallery = gr.Gallery(label="Generated images", show_label=False, height="auto") |
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run_button.click(fn=midas, inputs=[input_image, resolution], outputs=[gallery]) |
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gr.Markdown("<hr>") |
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with gr.Row(): |
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gr.Markdown("## Zoe Depth") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(label="Input Image", type="numpy", height=512) |
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resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64) |
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run_button = gr.Button("Run") |
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with gr.Column(): |
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gallery = gr.Gallery(label="Generated images", show_label=False, height="auto") |
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run_button.click(fn=zoe, inputs=[input_image, resolution], outputs=[gallery]) |
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gr.Markdown("<hr>") |
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with gr.Row(): |
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gr.Markdown("## Normal Bae") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(label="Input Image", type="numpy", height=512) |
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resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64) |
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run_button = gr.Button("Run") |
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with gr.Column(): |
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gallery = gr.Gallery(label="Generated images", show_label=False, height="auto") |
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run_button.click(fn=normalbae, inputs=[input_image, resolution], outputs=[gallery]) |
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gr.Markdown("<hr>") |
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with gr.Row(): |
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gr.Markdown("## DWPose") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(label="Input Image", type="numpy", height=512) |
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resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64) |
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run_button = gr.Button("Run") |
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with gr.Column(): |
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gallery = gr.Gallery(label="Generated images", show_label=False, height="auto") |
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run_button.click(fn=dwpose, inputs=[input_image, resolution], outputs=[gallery]) |
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gr.Markdown("<hr>") |
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with gr.Row(): |
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gr.Markdown("## Openpose") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(label="Input Image", type="numpy", height=512) |
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hand_and_face = gr.Checkbox(label='Hand and Face', value=False) |
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resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64) |
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run_button = gr.Button("Run") |
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with gr.Column(): |
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gallery = gr.Gallery(label="Generated images", show_label=False, height="auto") |
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run_button.click(fn=openpose, inputs=[input_image, resolution, hand_and_face], outputs=[gallery]) |
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gr.Markdown("<hr>") |
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with gr.Row(): |
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gr.Markdown("## Lineart Anime \n<p>Check Invert to use with Mochi Diffusion.") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(label="Input Image", type="numpy", height=512) |
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invert = gr.Checkbox(label='Invert', value=True) |
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resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64) |
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run_button = gr.Button("Run") |
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with gr.Column(): |
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gallery = gr.Gallery(label="Generated images", show_label=False, height="auto") |
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run_button.click(fn=lineart_anime, inputs=[input_image, resolution, invert], outputs=[gallery]) |
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gr.Markdown("<hr>") |
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with gr.Row(): |
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gr.Markdown("## Lineart \n<p>Check Invert to use with Mochi Diffusion. Inverted image can also be created here for use with ControlNet Scribble.") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(label="Input Image", type="numpy", height=512) |
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coarse = gr.Checkbox(label='Using coarse model', value=False) |
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invert = gr.Checkbox(label='Invert', value=True) |
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resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64) |
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run_button = gr.Button("Run") |
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with gr.Column(): |
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gallery = gr.Gallery(label="Generated images", show_label=False, height="auto") |
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run_button.click(fn=lineart, inputs=[input_image, resolution, coarse, invert], outputs=[gallery]) |
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gr.Markdown("<hr>") |
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with gr.Row(): |
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gr.Markdown("## InPaint") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.ImageMask(sources="upload", type="numpy", height="auto") |
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invert = gr.Checkbox(label='Invert Mask', value=False) |
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run_button = gr.Button("Run") |
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with gr.Column(): |
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gallery = gr.Gallery(label="Generated images", show_label=False, height="auto") |
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run_button.click(fn=inpaint, inputs=[input_image, invert], outputs=[gallery]) |
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gr.Markdown("<hr>") |
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with gr.Row(): |
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gr.Markdown("## Oneformer COCO Segmentation") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(label="Input Image", type="numpy", height=512) |
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resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64) |
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run_button = gr.Button("Run") |
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with gr.Column(): |
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gallery = gr.Gallery(label="Generated images", show_label=False, height="auto") |
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run_button.click(fn=oneformer_coco, inputs=[input_image, resolution], outputs=[gallery]) |
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gr.Markdown("<hr>") |
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with gr.Row(): |
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gr.Markdown("## Oneformer ADE20K Segmentation") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(label="Input Image", type="numpy", height=512) |
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resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=640, step=64) |
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run_button = gr.Button("Run") |
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with gr.Column(): |
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gallery = gr.Gallery(label="Generated images", show_label=False, height="auto") |
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run_button.click(fn=oneformer_ade20k, inputs=[input_image, resolution], outputs=[gallery]) |
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gr.Markdown("<hr>") |
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with gr.Row(): |
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gr.Markdown("## Content Shuffle") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(label="Input Image", type="numpy", height=512) |
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resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64) |
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run_button = gr.Button("Run") |
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with gr.Column(): |
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gallery = gr.Gallery(label="Generated images", show_label=False, height="auto") |
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run_button.click(fn=content_shuffler, inputs=[input_image, resolution], outputs=[gallery]) |
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gr.Markdown("<hr>") |
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with gr.Row(): |
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gr.Markdown("## Color Shuffle") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(label="Input Image", type="numpy", height=512) |
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resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64) |
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run_button = gr.Button("Run") |
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with gr.Column(): |
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gallery = gr.Gallery(label="Generated images", show_label=False, height="auto") |
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run_button.click(fn=color_shuffler, inputs=[input_image, resolution], outputs=[gallery]) |
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block.launch(server_name='0.0.0.0') |
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