Commit
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527bf99
1
Parent(s):
d64b071
Update app.py
Browse files
app.py
CHANGED
@@ -2,39 +2,11 @@ import gradio as gr
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import numpy as np
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import cv2
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from PIL import Image
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MAX_COLORS = 12
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im = image.getcolors(maxcolors=1024*1024)
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sorted_colors = sorted(im, key=lambda x: x[0], reverse=True)
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freqs = [c[0] for c in sorted_colors]
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mean_freq = sum(freqs) / len(freqs)
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high_freq_colors = [c for c in sorted_colors if c[0] > max(2, mean_freq/3)] # Ignore colors that occur very few times (less than 2) or less than half the average frequency
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return high_freq_colors
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def color_quantization(image, n_colors):
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# Get color histogram
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hist, _ = np.histogramdd(image.reshape(-1, 3), bins=(256, 256, 256), range=((0, 256), (0, 256), (0, 256)))
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# Get most frequent colors
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colors = np.argwhere(hist > 0)
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colors = colors[np.argsort(hist[colors[:, 0], colors[:, 1], colors[:, 2]])[::-1]]
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colors = colors[:n_colors]
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# Replace each pixel with the closest color
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dists = np.sum((image.reshape(-1, 1, 3) - colors.reshape(1, -1, 3))**2, axis=2)
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labels = np.argmin(dists, axis=1)
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return colors[labels].reshape((image.shape[0], image.shape[1], 3)).astype(np.uint8)
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def create_binary_matrix(img_arr, target_color):
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print(target_color)
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# Create mask of pixels with target color
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mask = np.all(img_arr == target_color, axis=-1)
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# Convert mask to binary matrix
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binary_matrix = mask.astype(int)
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return binary_matrix
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def process_sketch(image, binary_matrixes):
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high_freq_colors = get_high_freq_colors(image)
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@@ -43,13 +15,12 @@ def process_sketch(image, binary_matrixes):
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im2arr = color_quantization(im2arr, n_colors=how_many_colors)
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colors_fixed = []
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for color in high_freq_colors
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r = color[1]
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g
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colors_fixed.append(gr.update(value=f'<div class="color-bg-item" style="background-color: rgb({r},{g},{b})"></div>'))
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visibilities = []
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colors = []
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for n in range(MAX_COLORS):
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@@ -62,8 +33,15 @@ def process_sketch(image, binary_matrixes):
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def process_generation(binary_matrixes, master_prompt, *prompts):
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clipped_prompts = prompts[:len(binary_matrixes)]
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css = '''
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#color-bg{display:flex;justify-content: center;align-items: center;}
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@@ -72,15 +50,11 @@ css = '''
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'''
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def update_css(aspect):
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if(aspect=='Square'):
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height = 512
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elif(aspect == 'Horizontal'):
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height = 512
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elif(aspect=='Vertical'):
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height = 768
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return gr.update(value=f"<style>#main-image{{width: {width}px}} .fixed-height{{height: {height}px !important}}</style>")
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with gr.Blocks(css=css) as demo:
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binary_matrixes = gr.State([])
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@@ -89,11 +63,13 @@ with gr.Blocks(css=css) as demo:
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''')
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with gr.Row():
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with gr.Box(elem_id="main-image"):
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with gr.Row():
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prompts = []
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colors = []
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@@ -111,9 +87,8 @@ with gr.Blocks(css=css) as demo:
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gr.Markdown('''
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![Examples](https://multidiffusion.github.io/pics/tight.jpg)
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''')
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css_height = gr.HTML("<style>#main-image{width: 512px} .fixed-height{height: 512px !important}</style>")
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aspect.change(update_css, inputs=aspect, outputs=css_height)
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button_run.click(process_sketch, inputs=[image, binary_matrixes], outputs=[post_sketch, binary_matrixes, *color_row, *colors])
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final_run_btn.click(process_generation, inputs=[binary_matrixes, general_prompt, *prompts], outputs=out_image)
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demo.launch()
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import numpy as np
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import cv2
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from PIL import Image
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from region_control import MultiDiffusion, get_views, preprocess_mask
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from sketch_helper import get_high_freq_colors, color_quantization, create_binary_matrix
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MAX_COLORS = 12
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sd = MultiDiffusion("cuda", "2.1")
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def process_sketch(image, binary_matrixes):
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high_freq_colors = get_high_freq_colors(image)
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im2arr = color_quantization(im2arr, n_colors=how_many_colors)
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colors_fixed = []
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for color in high_freq_colors:
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r, g, b = color[1]
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if any(c != 255 for c in (r, g, b)):
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binary_matrix = create_binary_matrix(im2arr, (r,g,b))
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binary_matrixes.append(binary_matrix)
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colors_fixed.append(gr.update(value=f'<div class="color-bg-item" style="background-color: rgb({r},{g},{b})"></div>'))
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visibilities = []
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colors = []
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for n in range(MAX_COLORS):
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def process_generation(binary_matrixes, master_prompt, *prompts):
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clipped_prompts = prompts[:len(binary_matrixes)]
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prompts = [master_prompt] + list(clipped_prompts)
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neg_prompts = [""] * len(prompts)
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fg_masks = torch.cat([preprocess_mask(mask_path, 512 // 8, 512 // 8, "cuda") for mask_path in binary_matrixes])
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bg_mask = 1 - torch.sum(fg_masks, dim=0, keepdim=True)
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bg_mask[bg_mask < 0] = 0
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masks = torch.cat([bg_mask, fg_masks])
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print(masks.size())
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image = sd.generate(masks, prompts, neg_prompts, 512, 512, 50, bootstrapping=20)
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return(image)
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css = '''
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#color-bg{display:flex;justify-content: center;align-items: center;}
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'''
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def update_css(aspect):
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if(aspect=='Square'):
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return [gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)]
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elif(aspect == 'Horizontal'):
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return [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)]
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elif(aspect=='Vertical'):
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return [gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)]
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with gr.Blocks(css=css) as demo:
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binary_matrixes = gr.State([])
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''')
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with gr.Row():
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with gr.Box(elem_id="main-image"):
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#with gr.Row():
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image = gr.Image(interactive=True, tool="color-sketch", source="canvas", type="pil", shape=(512,512), brush_radius=45)
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#image_horizontal = gr.Image(interactive=True, tool="color-sketch", source="canvas", type="pil", shape=(768,512), visible=False, brush_radius=45)
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#image_vertical = gr.Image(interactive=True, tool="color-sketch", source="canvas", type="pil", shape=(512, 768), visible=False, brush_radius=45)
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#with gr.Row():
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# aspect = gr.Radio(["Square", "Horizontal", "Vertical"], value="Square", label="Aspect Ratio")
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button_run = gr.Button("I've finished my sketch",elem_id="main_button", interactive=True)
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prompts = []
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colors = []
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gr.Markdown('''
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![Examples](https://multidiffusion.github.io/pics/tight.jpg)
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''')
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#css_height = gr.HTML("<style>#main-image{width: 512px} .fixed-height{height: 512px !important}</style>")
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#aspect.change(update_css, inputs=aspect, outputs=[image, image_horizontal, image_vertical])
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button_run.click(process_sketch, inputs=[image, binary_matrixes], outputs=[post_sketch, binary_matrixes, *color_row, *colors])
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final_run_btn.click(process_generation, inputs=[binary_matrixes, general_prompt, *prompts], outputs=out_image)
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demo.launch(debug=True)
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