File size: 19,732 Bytes
3d4d894
3e8c07c
 
 
3d4d894
 
 
 
 
 
 
 
dd0ab9f
 
f8c7d9d
3d4d894
 
07cf2eb
3d4d894
 
 
 
 
 
 
71af695
 
 
 
3790166
 
71af695
b054e3b
 
 
 
 
 
 
3d4d894
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
71af695
 
 
 
3d4d894
 
9ff56a4
3d4d894
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ef697d2
3d4d894
7ea369b
3d4d894
 
 
 
c53fc43
 
9bfe550
3d4d894
 
c53fc43
9bfe550
 
 
 
 
 
3d4d894
 
 
 
 
 
 
 
9bfe550
c53fc43
9bfe550
 
 
 
3d4d894
9bfe550
 
 
 
 
 
3d4d894
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6a46ded
3d4d894
 
 
 
 
 
 
 
 
c53fc43
71af695
 
 
3d4d894
71af695
 
 
 
 
 
 
 
 
 
 
 
 
 
c882d5b
71af695
 
 
 
 
c53fc43
3d4d894
 
 
2ea3bd8
0cae6b6
 
6e3a1b8
0cae6b6
5a67d9b
 
2379311
5a67d9b
 
 
 
e97c302
 
 
 
 
 
 
5a67d9b
13e5061
2379311
8604dfb
0d84e52
9bfe550
5a67d9b
3d4d894
 
 
f6b9c19
 
 
 
 
 
 
3d4d894
 
 
 
d0613b1
3d4d894
 
 
cbc9e9a
3d4d894
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c882d5b
3d4d894
 
b999def
3d4d894
 
 
 
 
 
 
 
 
 
 
6a46ded
3d4d894
a972e32
 
 
c53fc43
d24f993
 
 
 
a972e32
3dbec71
5d49ed1
 
 
 
 
 
5981c2d
e6abd00
 
 
5d49ed1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e6abd00
 
 
 
 
1c669b7
5d49ed1
1af0054
a27c870
161c8b5
1af0054
 
5981c2d
32be5ef
161c8b5
 
 
 
 
 
 
5981c2d
 
f42f3a8
5981c2d
161c8b5
 
 
 
32be5ef
 
 
 
b1dbf88
 
 
 
 
79067b4
 
3b9bd52
1fa1dda
3b9bd52
cde7bf0
b054e3b
3d4d894
 
 
 
 
07cf2eb
 
c53fc43
07cf2eb
c53fc43
07cf2eb
 
3d4d894
 
 
 
 
 
 
 
 
 
 
 
 
5981c2d
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
import streamlit as st
# wide layout
st.set_page_config(layout="wide")

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


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 make_image_row(image_0, image_1):
    col_0, col_1 = st.columns(2)
    with col_0:
        st.image(image_0, use_column_width=True)
    with col_1:
        st.image(image_1, use_column_width=True)


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
    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", ["Regenerate",
                                                           "Segmentation",
                                                           "Inpainting"], on_change=on_change_radio)


        if generation_mode == "Segmentation":
            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 == "Regenerate":
            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":
        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 == "Regenerate":
        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 ('initial_image' in st.session_state and st.session_state['initial_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 and re-generating objects, which "
                 "use our custom trained controlnet model. The main idea is that you can iterate over the "
                 "generated images, because you will rarely get something perfect in one step (although it's possible). "
                 "We added functionality to load in the generated image into the input, so you can keep "
                 "changing the image until you are satisfied."
                 )
        with st.expander("Useful information", expanded=True):
            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("For the gathering and inference of the metadata we used the Fondant framework (https://github.com/ml6team/fondant) provided by ML6 (https://www.ml6.eu/), which is an open source "
                     "data centric framework for data preparation. The pipeline used for training this controlnet will soon be available as an "
                     "example pipeline within Fondant and can be easily adapted for building your own dataset."
                     )
            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("### Trivia")
            st.write("The first time someone uses the demo after startup, the models still need to be loaded into memory. "
                    "After this initial load, the model is cached as a resource and can be used for all the users. "
                    "To avoid simultaneous requests, we have implemented a queueing mechanism that ensures that only one "
                    "user accesses the model at a time (similar to the Gradio framework).\n"
                    )
            st.write("To enable the features in the demo, we calculate the underlying segmentation maps and categories that "
                    "are present in the image. This allows us to hide some of the manual work for the user, and "
                    "by doing this, the users don't need to make a segmentation map in an external tool. Everything needed can be done within this demo."
                    )
            
            # st.write("### News: Fondant - an open source data-centric framework for Foundation model finetuning")
            # st.write("The ML6 team  is proud to announce that we are open sourcing our Fondant framework, which is a "
            #         "data-centric framework that allows you to prepare large scale multimodal datasets with ease. We have implemented the components "
            #         "that we used to train this controlnet model in Fondant as an example pipeline, and we are excited to see what you can do with it! In the future we will add a whole library of plug-and-play data preparation components, such as different ML models and filtering steps, in addition to dataset scraping components that connect to LAION5B."
            #         )
            # st.write("The framework is built on top of kubeflow pipelines and abstracts all the complexity of efficient storing and moving of large datasets, so you can focus on implemented just that piece of code that you need without worrying about the rest. We also build it to run on each Cloud provider or VM. You can find the code on our github page: https://github.com/ml6team/fondant.")

        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 by clicking on the 'use example X' buttons")
        
        st.session_state['example_image_0'] = Image.open("content/example_0.png")
        st.session_state['example_image_1'] = Image.open("content/example_1.jpg")
        st.session_state['example_image_2'] = Image.open("content/example_2.jpg")
        st.session_state['example_image_3'] = Image.open("content/example_3.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', 'initial_image', remove_state=True, rerun=True)

            st.image(st.session_state['example_image_2'], caption="Example image 3", use_column_width=True)
            if st.button("Use example 3"):
                move_image('example_image_2', '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', 'initial_image', remove_state=True, rerun=True)

            st.image(st.session_state['example_image_3'], caption="Example image 4", use_column_width=True)
            if st.button("Use example 4"):
                move_image('example_image_3', 'initial_image', remove_state=True, rerun=True)

        st.write("## Generated examples")
        make_image_row(Image.open("content/output_1.png"), Image.open("content/regen_example.png"))
        make_image_row(Image.open("content/keep background 2.png"), Image.open("content/output_0.png"))
        make_image_row(Image.open("content/segmentation window.png"), Image.open("content/output_3.png"))
        
        st.write("## Example video")
        st.write("### Video 1")
        st.video(open('content/controlnet_sprint_demo.mp4', 'rb').read())
        st.write("### Video 2")
        st.video(open('content/controlnet_demo_video_2.mp4', 'rb').read())

    else:
        make_prompt_row()

        _reset_state = check_reset_state()

        if generation_mode == "Inpainting":
            make_inpainting_explanation()
        elif generation_mode == "Segmentation":
            make_segmentation_explanation()
        elif generation_mode == "Regenerate":
            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()