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Running
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A10G
| import streamlit as st | |
| from streamlit_drawable_canvas import st_canvas | |
| from PIL import Image | |
| from typing import Union | |
| import random | |
| import numpy as np | |
| import os | |
| import time | |
| from models import make_image_controlnet, make_inpainting | |
| from segmentation import segment_image | |
| from config import HEIGHT, WIDTH, POS_PROMPT, NEG_PROMPT, COLOR_MAPPING, map_colors, map_colors_rgb | |
| from palette import COLOR_MAPPING_CATEGORY | |
| from preprocessing import preprocess_seg_mask, get_image, get_mask | |
| from explanation import make_inpainting_explanation, make_regeneration_explanation, make_segmentation_explanation | |
| # wide layout | |
| st.set_page_config(layout="wide") | |
| def on_upload() -> None: | |
| """Upload image to the canvas.""" | |
| if 'input_image' in st.session_state and st.session_state['input_image'] is not None: | |
| image = Image.open(st.session_state['input_image']).convert('RGB') | |
| st.session_state['initial_image'] = image | |
| if 'seg' in st.session_state: | |
| del st.session_state['seg'] | |
| if 'unique_colors' in st.session_state: | |
| del st.session_state['unique_colors'] | |
| if 'output_image' in st.session_state: | |
| del st.session_state['output_image'] | |
| def check_reset_state() -> bool: | |
| """Check whether the UI elements need to be reset | |
| Returns: | |
| bool: True if the UI elements need to be reset, False otherwise | |
| """ | |
| if ('reset_canvas' in st.session_state and st.session_state['reset_canvas']): | |
| st.session_state['reset_canvas'] = False | |
| return True | |
| st.session_state['reset_canvas'] = False | |
| return False | |
| def move_image(source: Union[str, Image.Image], | |
| dest: str, | |
| rerun: bool = True, | |
| remove_state: bool = True) -> None: | |
| """Move image from source to destination. | |
| Args: | |
| source (Union[str, Image.Image]): source image | |
| dest (str): destination image location | |
| rerun (bool, optional): rerun streamlit. Defaults to True. | |
| remove_state (bool, optional): remove the canvas state. Defaults to True. | |
| """ | |
| source_image = source if isinstance(source, Image.Image) else st.session_state[source] | |
| if remove_state: | |
| st.session_state['reset_canvas'] = True | |
| if 'seg' in st.session_state: | |
| del st.session_state['seg'] | |
| if 'unique_colors' in st.session_state: | |
| del st.session_state['unique_colors'] | |
| st.session_state[dest] = source_image | |
| if rerun: | |
| st.experimental_rerun() | |
| def on_change_radio() -> None: | |
| """Reset the UI elements when the radio button is changed.""" | |
| st.session_state['reset_canvas'] = True | |
| def make_canvas_dict(canvas_color, brush, paint_mode, _reset_state): | |
| canvas_dict = dict( | |
| fill_color=canvas_color, | |
| stroke_color=canvas_color, | |
| background_color="#FFFFFF", | |
| background_image=st.session_state['initial_image'] if 'initial_image' in st.session_state else None, | |
| stroke_width=brush, | |
| initial_drawing={'version': '4.4.0', 'objects': []} if _reset_state else None, | |
| update_streamlit=True, | |
| height=512, | |
| width=512, | |
| drawing_mode=paint_mode, | |
| key="canvas", | |
| ) | |
| return canvas_dict | |
| def make_prompt_row(): | |
| col_0_0, col_0_1 = st.columns(2) | |
| with col_0_0: | |
| st.text_input(label="Positive prompt", value="a photograph of a room, interior design, 4k, high resolution", key='positive_prompt') | |
| with col_0_1: | |
| st.text_input(label="Negative prompt", value="lowres, watermark, banner, logo, watermark, contactinfo, text, deformed, blurry, blur, out of focus, out of frame, surreal, ugly", key='negative_prompt') | |
| def make_sidebar(): | |
| with st.sidebar: | |
| input_image = st.file_uploader("", type=["png", "jpg"], key='input_image', on_change=on_upload) | |
| generation_mode = st.selectbox("Generation mode", ["Re-generate objects", | |
| "Segmentation conditioning", | |
| "Inpainting"], on_change=on_change_radio) | |
| if generation_mode == "Segmentation conditioning": | |
| paint_mode = st.sidebar.selectbox("Painting mode", ("freedraw", "polygon")) | |
| if paint_mode == "freedraw": | |
| brush = st.slider("Stroke width", 5, 140, 100, key='slider_seg') | |
| else: | |
| brush = 5 | |
| category_chooser = st.sidebar.selectbox("Filter on category", list( | |
| COLOR_MAPPING_CATEGORY.keys()), index=0, key='category_chooser') | |
| chosen_colors = list(COLOR_MAPPING_CATEGORY[category_chooser].keys()) | |
| color_chooser = st.sidebar.selectbox( | |
| "Choose a color", chosen_colors, index=0, format_func=map_colors, key='color_chooser' | |
| ) | |
| elif generation_mode == "Re-generate objects": | |
| color_chooser = "rgba(0, 0, 0, 0.0)" | |
| paint_mode = 'freedraw' | |
| brush = 0 | |
| else: | |
| paint_mode = st.sidebar.selectbox("Painting mode", ("freedraw", "polygon")) | |
| if paint_mode == "freedraw": | |
| brush = st.slider("Stroke width", 5, 140, 100, key='slider_seg') | |
| else: | |
| brush = 5 | |
| color_chooser = "#000000" | |
| return input_image, generation_mode, brush, color_chooser, paint_mode | |
| def make_output_image(): | |
| if 'output_image' in st.session_state: | |
| output_image = st.session_state['output_image'] | |
| if isinstance(output_image, np.ndarray): | |
| output_image = Image.fromarray(output_image) | |
| if isinstance(output_image, Image.Image): | |
| output_image = output_image.resize((512, 512)) | |
| else: | |
| output_image = Image.new('RGB', (512, 512), (255, 255, 255)) | |
| st.write("#### Output image") | |
| st.image(output_image, width=512) | |
| if st.button("Move to input image"): | |
| move_image('output_image', 'initial_image', remove_state=True, rerun=True) | |
| def make_editing_canvas(canvas_color, brush, _reset_state, generation_mode, paint_mode): | |
| st.write("#### Input image") | |
| canvas_dict = make_canvas_dict( | |
| canvas_color=canvas_color, | |
| paint_mode=paint_mode, | |
| brush=brush, | |
| _reset_state=_reset_state | |
| ) | |
| if generation_mode == "Segmentation conditioning": | |
| canvas = st_canvas( | |
| **canvas_dict, | |
| ) | |
| if st.button("generate image", key='generate_button'): | |
| image = get_image() | |
| print("Preparing image segmentation") | |
| real_seg = segment_image(Image.fromarray(image)) | |
| mask, seg = preprocess_seg_mask(canvas, real_seg) | |
| with st.spinner(text="Generating image"): | |
| print("Making image") | |
| result_image = make_image_controlnet(image=image, | |
| mask_image=mask, | |
| controlnet_conditioning_image=seg, | |
| positive_prompt=st.session_state['positive_prompt'], | |
| negative_prompt=st.session_state['negative_prompt'], | |
| seed=random.randint(0, 100000) # nosec | |
| ) | |
| if isinstance(result_image, np.ndarray): | |
| result_image = Image.fromarray(result_image) | |
| st.session_state['output_image'] = result_image | |
| elif generation_mode == "Re-generate objects": | |
| canvas = st_canvas( | |
| **canvas_dict, | |
| ) | |
| if 'seg' not in st.session_state: | |
| with st.spinner(text="Preparing image segmentation"): | |
| image = get_image() | |
| real_seg = np.array(segment_image(Image.fromarray(image))) | |
| st.session_state['seg'] = real_seg | |
| if 'unique_colors' not in st.session_state: | |
| real_seg = st.session_state['seg'] | |
| unique_colors = np.unique(real_seg.reshape(-1, real_seg.shape[2]), axis=0) | |
| unique_colors = [tuple(color) for color in unique_colors] | |
| st.session_state['unique_colors'] = unique_colors | |
| with st.expander("Explanation", expanded=True): | |
| st.write("This mode allows you to choose which objects you want to re-generate in the image. " | |
| "Use the selection dropdown to add or remove objects. If you are ready, press the generate button" | |
| " to generate the image, which can take up to 30 seconds. If you want to improve the generated image, click" | |
| " the 'move image to input' button." | |
| ) | |
| chosen_colors = st.multiselect( | |
| label="Choose which concepts you want to regenerate in the image", | |
| options=st.session_state['unique_colors'], | |
| key='chosen_colors', | |
| default=st.session_state['unique_colors'], | |
| format_func=map_colors_rgb, | |
| ) | |
| if st.button("generate image", key='generate_button'): | |
| image = get_image() | |
| print(chosen_colors) | |
| segmentation = st.session_state['seg'] | |
| mask = np.zeros_like(segmentation) | |
| for color in chosen_colors: | |
| # if the color is in the segmentation, set mask to 1 | |
| mask[np.where((segmentation == color).all(axis=2))] = 1 | |
| with st.spinner(text="Generating image"): | |
| result_image = make_image_controlnet(image=image, | |
| mask_image=mask, | |
| controlnet_conditioning_image=segmentation, | |
| positive_prompt=st.session_state['positive_prompt'], | |
| negative_prompt=st.session_state['negative_prompt'], | |
| seed=random.randint(0, 100000) # nosec | |
| ) | |
| if isinstance(result_image, np.ndarray): | |
| result_image = Image.fromarray(result_image) | |
| st.session_state['output_image'] = result_image | |
| elif generation_mode == "Inpainting": | |
| image = get_image() | |
| canvas = st_canvas( | |
| **canvas_dict, | |
| ) | |
| if st.button("generate images", key='generate_button'): | |
| canvas_mask = canvas.image_data | |
| if not isinstance(canvas_mask, np.ndarray): | |
| canvas_mask = np.array(canvas_mask) | |
| mask = get_mask(canvas_mask) | |
| with st.spinner(text="Generating new images"): | |
| print("Making image") | |
| result_image = make_inpainting(positive_prompt=st.session_state['positive_prompt'], | |
| image=Image.fromarray(image), | |
| mask_image=mask, | |
| negative_prompt=st.session_state['negative_prompt'], | |
| ) | |
| if isinstance(result_image, np.ndarray): | |
| result_image = Image.fromarray(result_image) | |
| st.session_state['output_image'] = result_image | |
| def main(): | |
| # center text | |
| st.write("## Controlnet sprint - interior design", unsafe_allow_html=True) | |
| input_image, generation_mode, brush, color_chooser, paint_mode = make_sidebar() | |
| # check if there is an input_image | |
| if not ('input_image' in st.session_state and st.session_state['input_image'] is not None): | |
| st.success("Upload an image to start") | |
| st.write("Welcome to the interior design controlnet demo! " | |
| "You can start by uploading a picture of your room, after which you will see " | |
| "a good variety of options to edit your current room to generate the room of your dreams! " | |
| "You can choose between inpainting, segmentation conditioning and re-generating objects, which " | |
| "use our custom trained controlnet model." | |
| ) | |
| st.write("### About the dataset") | |
| st.write("To make this demo as good as possible, our team spend a lot of time training a custom model. " | |
| "We used the LAION5B dataset to build our custom dataset, which contains 130k images of 15 types of rooms " | |
| "in almost 30 design styles. After fetching all these images, we started adding metadata such as " | |
| "captions (from the BLIP captioning model) and segmentation maps (from the HuggingFace UperNetForSemanticSegmentation model). " | |
| ) | |
| st.write("### About the model") | |
| st.write( | |
| "These were then used to train the controlnet model to generate quality interior design images by using " | |
| "the segmentation maps and prompts as conditioning information for the model. " | |
| "By training on segmentation maps, the enduser has a very finegrained control over which objects they " | |
| "want to place in their room. " | |
| "The resulting model is then used in a community pipeline that supports image2image and inpainting, " | |
| "so the user can keep elements of their room and change specific parts of the image." | |
| "" | |
| ) | |
| st.write("### Testing images") | |
| st.write("If you don't have any pictures close, you can use one of these images to test the model:") | |
| st.session_state['example_image_0'] = Image.open("content/example_0.png") | |
| st.session_state['example_image_1'] = Image.open("content/example_1.jpg") | |
| col_im_0, col_im_1 = st.columns(2) | |
| with col_im_0: | |
| st.image(st.session_state['example_image_0'], caption="Example image 1", use_column_width=True) | |
| if st.button("Use example 1"): | |
| move_image('example_image_0', 'input_image', remove_state=True, rerun=False) | |
| move_image('example_image_0', 'initial_image', remove_state=True, rerun=True) | |
| with col_im_1: | |
| st.image(st.session_state['example_image_1'], caption="Example image 2", use_column_width=True) | |
| if st.button("Use example 2"): | |
| move_image('example_image_1', 'input_image', remove_state=True, rerun=False) | |
| move_image('example_image_1', 'initial_image', remove_state=True, rerun=True) | |
| st.write("## Generated examples") | |
| col_ex_0, col_ex_1 = st.columns(2) | |
| with col_ex_0: | |
| st.image(Image.open("content/output_1.png"), caption="Generated example 1, regenerating certain objects in the room", use_column_width=True) | |
| with col_ex_1: | |
| st.image(Image.open("content/output_0.png"), caption="Generated example 1, regenerating certain objects in the room", use_column_width=True) | |
| else: | |
| make_prompt_row() | |
| _reset_state = check_reset_state() | |
| if generation_mode == "Inpainting": | |
| make_inpainting_explanation() | |
| elif generation_mode == "Segmentation conditioning": | |
| make_segmentation_explanation() | |
| elif generation_mode == "Re-generate objects": | |
| make_regeneration_explanation() | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| make_editing_canvas(canvas_color=color_chooser, | |
| brush=brush, | |
| _reset_state=_reset_state, | |
| generation_mode=generation_mode, | |
| paint_mode=paint_mode | |
| ) | |
| with col2: | |
| make_output_image() | |
| if __name__ == "__main__": | |
| main() |