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import gradio as gr
import random, os, shutil
from PIL import Image
import pandas as pd
import tempfile
def open_sd_ims(adj, group, seed):
if group != '':
if adj != '':
prompt=adj+'_'+group.replace(' ','_')
if os.path.isdir(prompt) == False:
shutil.unpack_archive('zipped_images/stablediffusion/'+ prompt.replace(' ', '_') +'.zip', prompt, 'zip')
else:
prompt=group
if os.path.isdir(prompt) == False:
shutil.unpack_archive('zipped_images/stablediffusion/'+ prompt.replace(' ', '_') +'.zip', prompt, 'zip')
imnames= os.listdir(prompt+'/Seed_'+ str(seed)+'/')
images = [(Image.open(prompt+'/Seed_'+ str(seed)+'/'+name)) for name in imnames]
return images[:9]
def open_ims(model, adj, group):
seed = 48040
with tempfile.TemporaryDirectory() as tmpdirname:
print('created temporary directory', tmpdirname)
if model == "Dall-E 2":
if group != '':
if adj != '':
prompt=adj+'_'+group.replace(' ','_')
if os.path.isdir(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt) == False:
shutil.unpack_archive('zipped_images/'+ model.replace(' ','').lower()+ '/'+ prompt.replace(' ', '_') +'.zip', tmpdirname+ '/'+ model.replace(' ','').lower()+ '/'+ prompt, 'zip')
else:
prompt=group
if os.path.isdir(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt) == False:
shutil.unpack_archive('zipped_images/' + model.replace(' ','').lower() + '/'+ prompt.replace(' ', '_') +'.zip', tmpdirname + '/' + model.replace(' ','').lower()+ '/' + prompt, 'zip')
imnames= os.listdir(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt+'/')
images = [(Image.open(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt+'/'+name)).convert("RGB") for name in imnames]
return images[:9]
else:
if group != '':
if adj != '':
prompt=adj+'_'+group.replace(' ','_')
if os.path.isdir(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt) == False:
shutil.unpack_archive('zipped_images/'+ model.replace(' ','').lower()+ '/'+ prompt.replace(' ', '_') +'.zip', tmpdirname + '/' +model.replace(' ','').lower()+ '/'+ prompt, 'zip')
else:
prompt=group
if os.path.isdir(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt) == False:
shutil.unpack_archive('zipped_images/' + model.replace(' ','').lower() + '/'+ prompt.replace(' ', '_') +'.zip', tmpdirname + '/' + model.replace(' ','').lower()+'/'+ prompt, 'zip')
imnames= os.listdir(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt+'/'+'Seed_'+ str(seed)+'/')
images = [(Image.open(tmpdirname + '/' + model.replace(' ','').lower()+ '/'+ prompt +'/'+'Seed_'+ str(seed)+'/'+name)) for name in imnames]
return images[:9]
vowels = ["a","e","i","o","u"]
prompts = pd.read_csv('promptsadjectives.csv')
seeds = [46267, 48040, 51237, 54325, 60884, 64830, 67031, 72935, 92118, 93109]
m_adjectives = prompts['Masc-adj'].tolist()[:10]
f_adjectives = prompts['Fem-adj'].tolist()[:10]
adjectives = sorted(m_adjectives+f_adjectives)
#adjectives = ['attractive','strong']
adjectives.insert(0, '')
professions = sorted([p.lower() for p in prompts['Occupation-Noun'].tolist()])
models = ["Stable Diffusion 1.4", "Dall-E 2","Stable Diffusion 2"]
with gr.Blocks() as demo:
gr.Markdown("# Diffusion Bias Explorer")
gr.Markdown("## Choose from the prompts below to explore how the text-to-image models like [Stable Diffusion v1.4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original), [Stable Diffion v.2](https://huggingface.co/stabilityai/stable-diffusion-2) and [DALLE-2](https://openai.com/dall-e-2/) represent different professions and adjectives")
gr.HTML("""<span style="color:red">⚠️ <b>DISCLAIMER: the images displayed by this tool were generated by text-to-image models and may depict offensive stereotypes or contain explicit content.</b></span>""")
# gr.Markdown("Some of the images for Dall-E 2 are missing -- we are still in the process of generating them! If you get an 'error', please pick another prompt.")
# seed_choice = gr.State(0)
# seed_choice = 93109
# print("Seed choice is: " + str(seed_choice))
with gr.Row():
with gr.Column():
model1 = gr.Dropdown(models, label = "Choose a model to compare results", value = models[0], interactive=True)
adj1 = gr.Dropdown(adjectives, label = "Choose a first adjective (or leave this blank!)", interactive=True)
choice1 = gr.Dropdown(professions, label = "Choose a first group", interactive=True)
# seed1= gr.Dropdown(seeds, label = "Choose a random seed to compare results", value = seeds[1], interactive=True)
images1 = gr.Gallery(label="Images").style(grid=[3], height="auto")
with gr.Column():
model2 = gr.Dropdown(models, label = "Choose a model to compare results", value = models[0], interactive=True)
adj2 = gr.Dropdown(adjectives, label = "Choose a second adjective (or leave this blank!)", interactive=True)
choice2 = gr.Dropdown(professions, label = "Choose a second group", interactive=True)
# seed2= gr.Dropdown(seeds, label = "Choose a random seed to compare results", value= seeds[1], interactive=True)
images2 = gr.Gallery(label="Images").style(grid=[3], height="auto")
gr.Markdown("### [Research](http://gender-decoder.katmatfield.com/static/documents/Gaucher-Friesen-Kay-JPSP-Gendered-Wording-in-Job-ads.pdf) has shown that \
certain words are considered more masculine- or feminine-coded based on how appealing job descriptions containing these words \
seemed to male and female research participants and to what extent the participants felt that they 'belonged' in that occupation.")
#demo.load(random_image, None, [images])
choice1.change(open_ims, [model1, adj1,choice1], [images1])
choice2.change(open_ims, [model2, adj2,choice2], [images2])
adj1.change(open_ims, [model1, adj1, choice1], [images1])
adj2.change(open_ims, [model2, adj2, choice2], [images2])
# seed1.change(open_ims, [adj1,choice1,seed1], [images1])
# seed2.change(open_ims, [adj2,choice2,seed2], [images2])
demo.launch()
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