removing unneccessary

zipped_images/stablediffusion1.4/README.md

@@ -1,13 +0,0 @@
----
-title: StableDiffusionBiasExplorer
-emoji: 📊
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
-license: cc-by-sa-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

zipped_images/stablediffusion1.4/app.py

@@ -1,64 +0,0 @@
-import gradio as gr
-import random, os, shutil
-from PIL import Image
-import pandas as pd
-
-def open_ims(adj, group, seed):
-    if group != '':
-        if adj != '':
-            prompt=adj+'_'+group.replace(' ','_')
-            if os.path.isdir(prompt) == False:
-                shutil.unpack_archive('zipped_images/'+ prompt.replace(' ', '_') +'.zip', prompt, 'zip')
-        else:
-            prompt=group
-            if os.path.isdir(prompt) == False:
-                shutil.unpack_archive('zipped_images/'+ 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]
-
-
-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()])
-
-with gr.Blocks() as demo:
-    gr.Markdown("# Stable Diffusion Explorer")
-    gr.Markdown("## Choose from the prompts below to explore how the [Stable Diffusion v1.4 model](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) represents different professions and adjectives")
-#    seed_choice = gr.State(0)
-#    seed_choice = 93109
-#    print("Seed choice is: " + str(seed_choice))
-    with gr.Row():
-        with gr.Column():
-            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():
-            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, [adj1,choice1,seed1], [images1])
-    choice2.change(open_ims, [adj2,choice2,seed2], [images2])
-    adj1.change(open_ims, [adj1,choice1,seed1], [images1])
-    adj2.change(open_ims, [adj2,choice2,seed2], [images2])
-    seed1.change(open_ims, [adj1,choice1,seed1], [images1])
-    seed2.change(open_ims, [adj2,choice2,seed2], [images2])
-
-demo.launch()

zipped_images/stablediffusion1.4/requirements.txt

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-pillow
-pandas