File size: 7,390 Bytes
f0ed665
 
 
 
8440562
 
f0ed665
 
b2250a6
 
f0ed665
b2250a6
8440562
 
f0ed665
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b2250a6
f0ed665
 
 
 
 
 
 
 
 
 
8440562
f0ed665
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b2250a6
f0ed665
 
 
 
 
 
8440562
f0ed665
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b2250a6
f0ed665
 
 
 
 
 
b2250a6
8440562
f0ed665
 
 
 
 
 
 
 
 
 
 
 
b2250a6
8440562
f0ed665
 
8440562
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
#!/usr/bin/env python3
import gradio as gr
from clip_interrogator import Config, Interrogator

# MODELS = ['ViT-L (best for Stable Diffusion 1.*)', 'ViT-H (best for Stable Diffusion 2.*)']
# MODELS = ['ViT-L (best for Stable Diffusion 1.*)',]

# load BLIP and ViT-L https://huggingface.co/openai/clip-vit-large-patch14
from PIL import Image
from clip_interrogator import Config, Interrogator

ci = Interrogator(Config(clip_model_name="ViT-L-14/openai"))

def image_analysis(image):
    image = image.convert('RGB')
    image_features = ci.image_to_features(image)

    top_mediums = ci.mediums.rank(image_features, 5)
    top_artists = ci.artists.rank(image_features, 5)
    top_movements = ci.movements.rank(image_features, 5)
    top_trendings = ci.trendings.rank(image_features, 5)
    top_flavors = ci.flavors.rank(image_features, 5)

    medium_ranks = {medium: sim for medium, sim in zip(top_mediums, ci.similarities(image_features, top_mediums))}
    artist_ranks = {artist: sim for artist, sim in zip(top_artists, ci.similarities(image_features, top_artists))}
    movement_ranks = {movement: sim for movement, sim in zip(top_movements, ci.similarities(image_features, top_movements))}
    trending_ranks = {trending: sim for trending, sim in zip(top_trendings, ci.similarities(image_features, top_trendings))}
    flavor_ranks = {flavor: sim for flavor, sim in zip(top_flavors, ci.similarities(image_features, top_flavors))}
    
    return medium_ranks, artist_ranks, movement_ranks, trending_ranks, flavor_ranks


def image_to_prompt(image, mode):
    image = image.convert('RGB')
    if mode == 'best':
        prompt = ci.interrogate(image)
    elif mode == 'classic':
        prompt = ci.interrogate_classic(image)
    elif mode == 'fast':
        prompt = ci.interrogate_fast(image)
    elif mode == 'negative':
        prompt = ci.interrogate_negative(image)

    return prompt


TITLE = """
    <div style="text-align: center; max-width: 650px; margin: 0 auto;">
        <div
        style="
            display: inline-flex;
            align-items: center;
            gap: 0.8rem;
            font-size: 1.75rem;
        "
        >
        <h1 style="font-weight: 900; margin-bottom: 7px;">
            CLIP Interrogator
        </h1>
        </div>
        <p style="margin-bottom: 10px; font-size: 94%">
        Want to figure out what a good prompt might be to create new images like an existing one?<br>The CLIP Interrogator is here to get you answers!
        </p>
        <p>You can skip the queue by duplicating this space and upgrading to gpu in settings: <a style='display:inline-block' href='https://huggingface.co/spaces/pharma/CLIP-Interrogator?duplicate=true'><img src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14' alt='Duplicate Space'></a></p>
    </div>
"""

ARTICLE = """
<div style="text-align: center; max-width: 650px; margin: 0 auto;">
    <p>
    Example art by <a href="https://pixabay.com/illustrations/watercolour-painting-art-effect-4799014/">Layers</a> 
    and <a href="https://pixabay.com/illustrations/animal-painting-cat-feline-pet-7154059/">Lin Tong</a> 
    from pixabay.com
    </p>

    <p>
    Server busy? You can also run on <a href="https://colab.research.google.com/github/pharmapsychotic/clip-interrogator/blob/main/clip_interrogator.ipynb">Google Colab</a>
    </p>

    <p>
    Has this been helpful to you? Follow me on twitter 
    <a href="https://twitter.com/pharmapsychotic">@pharmapsychotic</a><br>
    and check out more tools at my
    <a href="https://pharmapsychotic.com/tools.html">Ai generative art tools list</a>
    </p>
</div>
"""

CSS = """
    #col-container {margin-left: auto; margin-right: auto;}
    a {text-decoration-line: underline; font-weight: 600;}
    .animate-spin {
        animation: spin 1s linear infinite;
    }
    @keyframes spin {
        from { transform: rotate(0deg); }
        to { transform: rotate(360deg); }
    }
    #share-btn-container {
        display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
    }
    #share-btn {
        all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
    }
    #share-btn * {
        all: unset;
    }
    #share-btn-container div:nth-child(-n+2){
        width: auto !important;
        min-height: 0px !important;
    }
    #share-btn-container .wrap {
        display: none !important;
    }
"""

def analyze_tab():
    with gr.Column():
        with gr.Row():
            image = gr.Image(type='pil', label="Image")
            model = gr.Dropdown(MODELS, value=MODELS[0], label='CLIP Model')
        with gr.Row():
            medium = gr.Label(label="Medium", num_top_classes=5)
            artist = gr.Label(label="Artist", num_top_classes=5)        
            movement = gr.Label(label="Movement", num_top_classes=5)
            trending = gr.Label(label="Trending", num_top_classes=5)
            flavor = gr.Label(label="Flavor", num_top_classes=5)

    button = gr.Button("Analyze")
    button.click(image_analysis, inputs=[image, model], outputs=[medium, artist, movement, trending, flavor])

    examples=[['example01.jpg', MODELS[0]], ['example02.jpg', MODELS[0]]]
    ex = gr.Examples(
        examples=examples, 
        fn=image_analysis, 
        inputs=[input_image], 
        outputs=[medium, artist, movement, trending, flavor], 
        cache_examples=True, 
        run_on_click=True
    )
    ex.dataset.headers = [""]


with gr.Blocks(css=CSS) as block:
    with gr.Column(elem_id="col-container"):
        gr.HTML(TITLE)

        with gr.Tab("Prompt"):
            with gr.Row():
                input_image = gr.Image(type='pil', elem_id="input-img")
                with gr.Column():
                    input_mode = gr.Radio(['best', 'fast', 'classic', 'negative'], value='best', label='Mode')
            submit_btn = gr.Button("Submit")
            output_text = gr.Textbox(label="Output", elem_id="output-txt")

            examples=[['example01.jpg', MODELS[0], 'best'], ['example02.jpg', MODELS[0], 'best']]
            ex = gr.Examples(
                examples=examples, 
                fn=image_to_prompt, 
                inputs=[input_image, input_mode], 
                outputs=[output_text], 
                cache_examples=True, 
                run_on_click=True
            )
            ex.dataset.headers = [""]

        with gr.Tab("Analyze"):
            analyze_tab()
        
        gr.HTML(ARTICLE)

    submit_btn.click(
        fn=image_to_prompt, 
        inputs=[input_image, input_mode], 
        outputs=[output_text]
    )

block.queue(max_size=64).launch(show_api=False)