|
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.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 Diffusion v.2](https://huggingface.co/stabilityai/stable-diffusion-2) and [DALLE-2](https://openai.com/dall-e-2/) represent different professions and adjectives") |
|
|
|
|
|
|
|
|
|
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) |
|
|
|
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) |
|
|
|
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.") |
|
|
|
|
|
|
|
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]) |
|
|
|
|
|
|
|
demo.launch() |
|
|
