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import gradio as gr |
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import random, os |
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from PIL import Image |
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import pandas as pd |
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def open_ims(adj, group): |
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if group != '': |
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if adj != '': |
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if adj[0] in vowels: |
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dirname='images/'+'Photo_portrait_of_an_'+ adj+'_'+ group.replace(' ','_')+'/Seed_'+ str(seed_choice)+'/' |
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else: |
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dirname='images/'+'Photo_portrait_of_a_'+ adj+ '_'+ group.replace(' ','_')+'/Seed_'+ str(seed_choice)+'/' |
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else: |
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if group[0] in vowels: |
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dirname='images/'+'Photo_portrait_of_an_'+ group.replace(' ','_')+'/Seed_'+ str(seed_choice)+'/' |
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else: |
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dirname='images/'+'Photo_portrait_of_a_'+ group.replace(' ','_')+'/Seed_'+ str(seed_choice)+'/' |
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imnames= os.listdir(dirname) |
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images = [(Image.open(dirname+name)) for name in imnames] |
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return images[:9] |
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vowels = ["a","e","i","o","u"] |
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prompts = pd.read_csv('promptsadjectives.csv') |
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seeds = [46267, 48040, 51237, 54325, 60884, 64830, 67031, 72935, 92118, 93109] |
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m_adjectives = prompts['Masc-adj'].tolist()[:10] |
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f_adjectives = prompts['Fem-adj'].tolist()[:10] |
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adjectives = sorted(m_adjectives+f_adjectives) |
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adjectives.insert(0, '') |
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professions = sorted([p.lower() for p in prompts['Occupation-Noun'].tolist()]) |
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with gr.Blocks() as demo: |
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gr.Markdown("# Stable Diffusion Explorer") |
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gr.Markdown("## Choose from the prompts below to explore how the Stable Diffusion model represents different professions and adjectives") |
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seed_choice = gr.State(0) |
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seed_choice = 93109 |
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print("Seed choice is: " + str(seed_choice)) |
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with gr.Row(): |
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with gr.Column(): |
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adj1 = gr.Dropdown(adjectives, label = "Choose a first adjective (or leave this blank!)", interactive=True) |
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choice1 = gr.Dropdown(professions, label = "Choose a first group", interactive=True) |
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images1 = gr.Gallery(label="Images").style(grid=[3], height="auto") |
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with gr.Column(): |
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adj2 = gr.Dropdown(adjectives, label = "Choose a second adjective (or leave this blank!)", interactive=True) |
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choice2 = gr.Dropdown(professions, label = "Choose a second group", interactive=True) |
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images2 = gr.Gallery(label="Images").style(grid=[3], height="auto") |
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gr.Markdown("### [Research](http://gender-decoder.katmatfield.com/static/documents/Gaucher-Friesen-Kay-JPSP-Gendered-Wording-in-Job-ads.pdf) has shown that \ |
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certain words are considered more masculine- or feminine-coded based on how appealing job descriptions containing these words \ |
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seemed to male and female research participants and to what extent the participants felt that they 'belonged' in that occupation.") |
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choice1.change(open_ims, [adj1,choice1], [images1]) |
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choice2.change(open_ims, [adj2,choice2], [images2]) |
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adj1.change(open_ims, [adj1,choice1], [images1]) |
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adj2.change(open_ims, [adj2,choice2], [images2]) |
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demo.launch() |
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