app.py

import json
import gradio as gr
import os
from PIL import Image
import plotly.graph_objects as go
import plotly.express as px
import operator

TITLE = "Identity Representation in Diffusion Models"

_INTRO = """
# Identity Representation in Diffusion Models

Explore the data generated from [DiffusionBiasExplorer](https://huggingface.co/spaces/tti-bias/diffusion-bias-explorer)!
This demo showcases patterns in images generated by Stable Diffusion and Dalle-2 systems.
Specifically, images obtained from prompt inputs that span various gender- and ethnicity-related terms are clustered to show how those shape visual representations (more details below).
We encourage users to take advantage of this app to explore those trends, for example through the lens of the following questions:
- Find the cluster that has the most prompts denoting a gender or ethnicity that you identify with. Do you think the generated images look like you?
- Find two clusters that have a similar distribution of gender terms but different distributions of ethnicity terms. Do you see any meaningful differences in how gender is visually represented?
- Do you find that some ethnicity terms lead to more stereotypical visual representations than others?
- Do you find that some gender terms lead to more stereotypical visual representations than others?

These questions only scratch the surface of what we can learn from demos like this one,
let us know what you find [in the discussions tab](https://huggingface.co/spaces/tti-bias/DiffusionFaceClustering/discussions),
or if you think of other relevant questions!
"""

_CONTEXT = """
##### How do diffusion-based models represent gender and ethnicity?

In order to evaluate the *social biases* that Text-to-Image (TTI) systems may reproduce or exacerbate,
we need to first understand how the visual representations they generate relate to notions of gender and ethnicity.
These two aspects of a person's identity, however, ar known as **socialy constructed characteristics**:
that is to say, gender and ethnicity only exist in interactions between people, they do not have an independent existence based solely on physical (or visual) attributes.
This means that while we can characterize trends in how the models associate visual features with specific *identity terms in the generation prompts*,
we should not assign a specific gender or ethnicity to a synthetic figure generated by an ML model.

In this app, we instead take a 2-step clustering-based approach. First, we generate 680 images for each model by varying mentions of terms that denote gender or ethnicity in the prompts.
Then, we use a [VQA-based model](https://huggingface.co/Salesforce/blip-vqa-base) to cluster these images at different granularities (12, 24, or 48 clusters).
Exploring these clusters allows us to examine trends in the models' associations between visual features and textual representation of social attributes.

**Note:** this demo was developed with a limited set of gender- and ethnicity-related terms that are more relevant to the US context as a first approach,
so users may not always find themselves represented.
"""

clusters_12 = json.load(open("clusters/id_all_blip_clusters_12.json"))
clusters_24 = json.load(open("clusters/id_all_blip_clusters_24.json"))
clusters_48 = json.load(open("clusters/id_all_blip_clusters_48.json"))

clusters_by_size = {
    12: clusters_12,
    24: clusters_24,
    48: clusters_48,
}


def to_string(label):
    if label == "SD_2":
        label = "Stable Diffusion 2.0"
    elif label == "SD_14":
        label = "Stable Diffusion 1.4"
    elif label == "DallE":
        label = "Dall-E 2"
    elif label == "non-binary":
        label = "non-binary person"
    elif label == "person":
        label = "<i>unmarked</i> (person)"
    elif label == "":
        label = "<i>unmarked</i> ()"
    elif label == "gender":
        label = "gender term"
    return label


def summarize_clusters(clusters_list, max_terms=3):
    for cl_id, cl_dict in enumerate(clusters_list):
        total = len(cl_dict["img_path_list"])
        gdr_list = cl_dict["labels_gender"]
        eth_list = cl_dict["labels_ethnicity"]
        cl_dict["sentence_desc"] = (
            f"Cluster {cl_id} | \t"
            + f"gender terms incl.: {gdr_list[0][0].replace('person', 'unmarked(gender)')}"
            + (
                f" - {gdr_list[1][0].replace('person', 'unmarked(gender)')} | "
                if len(gdr_list) > 1
                else " | "
            )
            + f"ethnicity terms incl.: {'unmarked(ethnicity)' if eth_list[0][0] == '' else eth_list[0][0]}"
            + (
                f" - {'unmarked(ethnicity)' if eth_list[1][0] == '' else eth_list[1][0]}"
                if len(eth_list) > 1
                else ""
            )
        )
        cl_dict["summary_desc"] = (
            f"Cluster {cl_id} has {total} images.\n"
            + f"- The most represented gender terms are {gdr_list[0][0].replace('person', 'unmarked')} ({gdr_list[0][1]})"
            + (
                f" and {gdr_list[1][0].replace('person', 'unmarked')} ({gdr_list[1][1]}).\n"
                if len(gdr_list) > 1
                else ".\n"
            )
            + f"- The most represented ethnicity terms are {'unmarked' if eth_list[0][0] == '' else eth_list[0][0]} ({eth_list[0][1]})"
            + (
                f" and {'unmarked' if eth_list[1][0] == '' else eth_list[1][0]} ({eth_list[1][1]}).\n"
                if len(eth_list) > 1
                else ".\n"
            )
            + "See below for a more detailed description."
        )


for _, clusters_list in clusters_by_size.items():
    summarize_clusters(clusters_list)

dropdown_descs = dict(
    (num_clusters, [cl_dct["sentence_desc"] for cl_dct in clusters_list])
    for num_clusters, clusters_list in clusters_by_size.items()
)


def describe_cluster(cl_dict, block="label", max_items=4):
    labels_values = sorted(cl_dict.items(), key=operator.itemgetter(1))
    labels_values.reverse()
    total = float(sum(cl_dict.values()))
    lv_prcnt = list(
        (item[0], round(item[1] * 100 / total, 0)) for item in labels_values
    )
    top_label = lv_prcnt[0][0]
    description_string = (
        "<span>The most represented %s is <b>%s</b>, making up about <b>%d%%</b> of the cluster.</span>"
        % (to_string(block), to_string(top_label), lv_prcnt[0][1])
    )
    description_string += "<p>This is followed by: "
    for lv in lv_prcnt[1 : min(len(lv_prcnt), 1 + max_items)]:
        description_string += "<BR/><b>%s:</b> %d%%" % (to_string(lv[0]), lv[1])
    if len(lv_prcnt) > max_items + 1:
        description_string += "<BR/><b> - Other terms:</b> %d%%" % (
            sum(lv[1] for lv in lv_prcnt[max_items + 1 :]),
        )
    description_string += "</p>"
    return description_string


def show_cluster(cl_id, num_clusters):
    if not cl_id:
        cl_id = 0
    else:
        cl_id = (
            dropdown_descs[num_clusters].index(cl_id)
            if cl_id in dropdown_descs[num_clusters]
            else 0
        )
    if not num_clusters:
        num_clusters = 12
    cl_dct = clusters_by_size[num_clusters][cl_id]
    images = []
    for i in range(8):
        img_path = "/".join(
            [st.replace("/", "") for st in cl_dct["img_path_list"][i].split("//")][3:]
        )
        im = Image.open(img_path)
        # .resize((256, 256))
        caption = (
            "_".join([img_path.split("/")[0], img_path.split("/")[-1]])
            .replace("Photo_portrait_of_an_", "")
            .replace("Photo_portrait_of_a_", "")
            .replace("SD_v2_random_seeds_identity_", "(SD v.2) ")
            .replace("dataset-identities-dalle2_", "(Dall-E 2) ")
            .replace("SD_v1.4_random_seeds_identity_", "(SD v.1.4) ")
            .replace("_", " ")
        )
        images.append((im, caption))
    model_fig = go.Figure()
    model_fig.add_trace(
        go.Pie(
            labels=list(dict(cl_dct["labels_model"]).keys()),
            values=list(dict(cl_dct["labels_model"]).values()),
        )
    )
    model_description = describe_cluster(dict(cl_dct["labels_model"]), "system")

    gender_fig = go.Figure()
    gender_fig.add_trace(
        go.Pie(
            labels=list(dict(cl_dct["labels_gender"]).keys()),
            values=list(dict(cl_dct["labels_gender"]).values()),
        )
    )
    gender_description = describe_cluster(dict(cl_dct["labels_gender"]), "gender")

    ethnicity_fig = go.Figure()
    ethnicity_fig.add_trace(
        go.Bar(
            x=list(dict(cl_dct["labels_ethnicity"]).keys()),
            y=list(dict(cl_dct["labels_ethnicity"]).values()),
            marker_color=px.colors.qualitative.G10,
        )
    )
    ethnicity_description = describe_cluster(
        dict(cl_dct["labels_ethnicity"]), "ethnicity"
    )

    return (
        clusters_by_size[num_clusters][cl_id]["summary_desc"],
        gender_fig,
        gender_description,
        model_fig,
        model_description,
        ethnicity_fig,
        ethnicity_description,
        images,
        gr.update(choices=dropdown_descs[num_clusters]),
        #        gr.update(choices=[i for i in range(num_clusters)]),
    )


with gr.Blocks(title=TITLE) as demo:
    gr.Markdown(_INTRO)
    with gr.Accordion(
        "How do diffusion-based models represent gender and ethnicity?", open =False
    ):
        gr.Markdown(_CONTEXT)
    gr.HTML(
        """<span style="color:red" font-size:smaller>⚠️ DISCLAIMER: the images displayed by this tool were generated by text-to-image systems and may depict offensive stereotypes or contain explicit content.</span>"""
    )
    num_clusters = gr.Radio(
        [12, 24, 48],
        value=12,
        label="How many clusters do you want to make from the data?",
    )

    with gr.Row():
        with gr.Column():
            cluster_id = gr.Dropdown(
                choices=dropdown_descs[num_clusters.value],
                value=0,
                label="Select cluster to visualize:",
            )
            a = gr.Text(label="Cluster summary")
        with gr.Column():
            gallery = gr.Gallery(label="Most representative images in cluster").style(
                grid=[2, 4], height="auto"
            )
    with gr.Row():
        with gr.Column():
            c = gr.Plot(label="How many images from each system?")
            c_desc = gr.HTML(label="")
        with gr.Column(scale=1):
            b = gr.Plot(label="Which gender terms are represented?")
            b_desc = gr.HTML(label="")
        with gr.Column(scale=2):
            d = gr.Plot(label="Which ethnicity terms are present?")
            d_desc = gr.HTML(label="")

    gr.Markdown(
        "### Plot Descriptions \n\n"
        + " The **System makeup** plot (*left*) corresponds to the number of images from the cluster that come from each of the TTI systems that we are comparing: Dall-E 2, Stable Diffusion v.1.4. and Stable Diffusion v.2.\n\n"
        + " The **Gender term makeup** plot (*middle*) shows the number of images based on the input prompts that used the phrases man, woman, non-binary person, and person (unmarked) to describe the figure's gender.\n\n"
        + " The **Ethnicity label makeup** plot (*right*) corresponds to the number of images from each of the 18 ethnicity descriptions used in the prompts. A blank value denotes unmarked ethnicity.\n\n"
    )
    demo.load(
        fn=show_cluster,
        inputs=[cluster_id, num_clusters],
        outputs=[a, b, b_desc, c, c_desc, d, d_desc, gallery, cluster_id],
    )
    num_clusters.change(
        fn=show_cluster,
        inputs=[cluster_id, num_clusters],
        outputs=[
            a,
            b,
            b_desc,
            c,
            c_desc,
            d,
            d_desc,
            gallery,
            cluster_id,
        ],
    )
    cluster_id.change(
        fn=show_cluster,
        inputs=[cluster_id, num_clusters],
        outputs=[a, b, b_desc, c, c_desc, d, d_desc, gallery, cluster_id],
    )

if __name__ == "__main__":
    demo.queue().launch(debug=True)