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| 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/society-ethics/DiffusionBiasExplorer)! | |
| 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/society-ethics/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 developped 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. | |
| If you have suggestions for additional categories you would particularly like to see in the next version, | |
| please tell us about them [in the discussions tab](https://huggingface.co/spaces/society-ethics/DiffusionFaceClustering/discussions)! | |
| """ | |
| 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(os.path.join("identities-images", 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) | |