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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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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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how_many_colors = len(high_freq_colors) |
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im2arr = np.array(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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visibilities.append(gr.update(visible=False)) |
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colors.append(gr.update(value=f'<div class="color-bg-item" style="background-color: black"></div>')) |
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for n in range(how_many_colors-1): |
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visibilities[n] = gr.update(visible=True) |
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colors[n] = colors_fixed[n] |
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return [gr.update(visible=True), binary_matrixes, *visibilities, *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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.color-bg-item{width: 100%; height: 32px} |
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#main_button{width:100%} |
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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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gr.Markdown('''## Control your Stable Diffusion generation with Sketches |
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This Space demonstrates MultiDiffusion region-based generation using Stable Diffusion model. To get started, draw your masks and type your prompts. More details in the [project page](https://multidiffusion.github.io). |
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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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image = gr.Image(interactive=True, tool="color-sketch", source="canvas", type="pil", shape=(512,512), brush_radius=45) |
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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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color_row = [None] * MAX_COLORS |
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with gr.Column(visible=False) as post_sketch: |
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general_prompt = gr.Textbox(label="General Prompt") |
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for n in range(MAX_COLORS): |
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with gr.Row(visible=False) as color_row[n]: |
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with gr.Box(elem_id="color-bg"): |
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colors.append(gr.HTML('<div class="color-bg-item" style="background-color: black"></div>')) |
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prompts.append(gr.Textbox(label="Prompt for this color")) |
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final_run_btn = gr.Button("Generate!") |
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out_image = gr.Image(label="Result") |
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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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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) |