call facia graphs, use clip prompt graph gui base

app.py

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+#initial fork from https://huggingface.co/spaces/Manjushri/SD-2.1-CPU/raw/main/app.py, https://huggingface.co/spaces/bhautikj/sd_clip_bias
+
+import gradio as gr
+import torch
+import numpy as np
+
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+from PIL import Image
+import collections
+import numpy as np
+import pandas as pd
+import io
+from saac.prompt_generation.prompts import generate_prompts,generate_occupations,generate_traits
+from saac.prompt_generation.prompt_utils import score_prompt
+from saac.evaluation.eval_utils import generate_countplot, lumia_violinplot, process_analysis, generate_histplot
+from datasets import load_dataset
+from diffusers import DiffusionPipeline, PNDMScheduler
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+STABLE_MODELS = ["runwayml/stable-diffusion-v1-5", "Midjourney"]
+
+# TODO Make model selectable input
+scheduler = PNDMScheduler.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="scheduler", prediction_type="v_prediction")
+pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", scheduler=scheduler)
+pipe = pipe.to(device)
+
+tokenizer = pipe.tokenizer
+text_encoder = pipe.text_encoder
+
+#
+# AUTHORS NOTE:
+# Being absolutely explicit: the genders and ethnicities listed here
+# are reductive. This is done not to erase but make analysis here
+# simpler, tractable and easier to understand
+#
+GENDERS = ["male", "female"]
+ETHNICITIES = ["black", "white", "asian"]
+LOOKS = list(generate_traits()['tag'])#["beautiful", "stunning", "handsome", "ugly", "plain", "repulsive", "arrogant", "trustworthy"]
+JOBS = list(generate_occupations()['tag'])#["CEO", "doctor", "nurse", "cashier", "janitor", "engineer", "pilot", "dentist", "leader"]
+RENDERPREFIX = "a high quality photo of a"
+
+def echoToken(token):
+    res = getMostSimilar(tokenizer, text_encoder, token)
+    return ",".join(res)
+
+def getEmbeddingForToken(tokenizer, token):
+    token_ids = tokenizer.encode(token)[1:-1]
+    if len(token_ids) != 1:
+        print(len(token_ids))
+        raise
+    token_id = token_ids[0]
+    return token_id, text_encoder.get_input_embeddings().weight.data[token_id].unsqueeze(0)
+
+def getMostSimilar(tokenizer, text_encoder, token, numResults=50):
+    internal_embs = text_encoder.text_model.embeddings.token_embedding.weight
+    tID, tok = getEmbeddingForToken(tokenizer, token)
+
+    cos = torch.nn.CosineSimilarity(dim=1, eps=1e-6)
+    scores = cos(internal_embs.to("cpu").to(torch.float32), tok.to("cpu").to(torch.float32))
+    sorted_scores, sorted_ids = torch.sort(scores, descending=True)
+    best_ids = sorted_ids[0:numResults].detach().numpy()
+    best_scores = sorted_scores[0:numResults].detach().numpy()
+
+    res = []
+    for best_id, best_score in zip(best_ids, best_scores):
+        #res.append((tokenizer.decode(best_id), best_score))
+        res.append("[" + tokenizer.decode(best_id) + "," + str(best_score) + "]")
+    return res[1:]
+
+def computeTermSimilarity(tokenizer, text_encoder, termA, termB):
+    inputs = tokenizer([termA, termB], padding=True, return_tensors="pt").to("cpu")
+    outputs = text_encoder(**inputs)
+    cos = torch.nn.CosineSimilarity(dim=-1, eps=1e-6)
+    val = cos(outputs.pooler_output[0], outputs.pooler_output[1]).item()                   
+    return float(val)
+
+def computeJob(tokenizer, text_encoder, job):
+    res = {}
+    neutralPrompt = " ".join([RENDERPREFIX, job])
+    titleText = neutralPrompt
+    for gender in GENDERS:
+        for ethnicity in ETHNICITIES:
+            prompt = " ".join([RENDERPREFIX, ethnicity, gender, job])
+            val = computeTermSimilarity(tokenizer, text_encoder, prompt, neutralPrompt)
+            res[prompt] = val
+            
+    return titleText, sorted(res.items(), reverse=True)
+
+def computeLook(tokenizer, text_encoder, look):
+    res = {}
+    titleText = " ".join([RENDERPREFIX, 
+                          look,
+                          "[",
+                          "|".join(GENDERS),
+                          "]"])
+
+    for gender in GENDERS:
+        neutralPromptGender = " ".join([RENDERPREFIX, look, gender])
+        for ethnicity in ETHNICITIES:
+            prompt = " ".join([RENDERPREFIX, look, ethnicity, gender])
+            val = computeTermSimilarity(tokenizer, text_encoder, prompt, neutralPromptGender)
+            res[prompt] = val
+    
+    return titleText, sorted(res.items(), reverse=True)
+
+# via https://stackoverflow.com/questions/57316491/how-to-convert-matplotlib-figure-to-pil-image-object-without-saving-image
+def fig2img(fig):
+    """Convert a Matplotlib figure to a PIL Image and return it"""
+    buf = io.BytesIO()
+    fig.savefig(buf)
+    buf.seek(0)
+    img = Image.open(buf)
+    return img
+
+def computePlot(title, results, scaleXAxis=True):
+    x = list(map(lambda x:x[0], results))
+    y = list(map(lambda x:x[1], results))
+    
+    fig, ax = plt.subplots(1, 1, figsize=(10, 5))
+    y_pos = np.arange(len(x))
+
+    hbars = ax.barh(y_pos, y, left=0, align='center')
+    ax.set_yticks(y_pos, labels=x)
+    ax.invert_yaxis()  # labels read top-to-bottom
+    ax.set_xlabel('Cosine similarity - take care to note compressed X-axis')
+    ax.set_title('Similarity to "' + title + '"')
+
+    # Label with specially formatted floats
+    ax.bar_label(hbars, fmt='%.3f')
+    minR = np.min(y)
+    maxR = np.max(y)
+    diffR = maxR-minR
+
+    if scaleXAxis:
+        ax.set_xlim(left=minR-0.1*diffR, right=maxR+0.1*diffR)
+    else:
+        ax.set_xlim(left=0.0, right=1.0)
+    plt.tight_layout()
+    plt.close()
+    return fig2img(fig)
+
+def computeJobBias(job):
+    title, results = computeJob(tokenizer, text_encoder, job)
+    return computePlot(title, results)
+
+def computeLookBias(look):
+    title, results = computeLook(tokenizer, text_encoder, look)
+    return computePlot(title, results)
+def trait_graph(trait,hist=True):
+    tda_res, occ_result = process_analysis()
+    fig = None
+    if not hist:
+        fig = generate_countplot(tda_res, 'tda_sentiment_val', 'gender_detected_val',
+                       title='Gender Count by Trait Sentiment',
+                       xlabel='Trait Sentiment',
+                       ylabel='Count',
+                       legend_title='Gender')
+    else:
+        df = tda_res
+        df['tda_sentiment_val'] = pd.Categorical(df['tda_sentiment_val'],
+                                             ['very negative', 'negative', 'neutral', 'positive', 'very positive'])
+        fig = generate_histplot(tda_res, 'tda_sentiment_val', 'gender_detected_val',
+                      title='Gender Distribution by Trait Sentiment',
+                      xlabel='Trait Sentiment',
+                      ylabel='Count', )
+
+    fig2 = lumia_violinplot(df = tda_res,
+    x_col = 'tda_compound',
+    rgb_col = 'skincolor',
+    n_bins = 21,
+    widths_val = 0.05,
+    points_val = 100,
+    x_label = 'TDA Sentiment',
+    y_label = 'Skincolor Intensity',
+    title = 'Skin Color Intensity, Binned by TDA Sentiment',)
+    return fig2img(fig),fig2img(fig2)
+def occ_graph(occ):
+    tda_res, occ_result = process_analysis()
+    fig = generate_histplot(occ_result, 'a_median', 'gender_detected_val',
+                   title='Gender Distribution by Median Annual Salary',
+                   xlabel= 'Median Annual Salary',
+                   ylabel= 'Count',)
+    fig2 = lumia_violinplot(df=occ_result, x_col='a_median',
+                     rgb_col='skincolor',
+                     n_bins=21,
+                     widths_val=7500.0,
+                     points_val=100,
+                     x_label='Median Salary',
+                     y_label='Skincolor Intensity',
+                     title='Skin Color Intensity, Binned by Median Salary')
+    return fig2img(fig),fig2img(fig2)
+
+if __name__=='__main__':
+    disclaimerString = ""
+
+    jobInterface = gr.Interface(fn=occ_graph,
+                                 inputs=[gr.Dropdown(JOBS, label="occupation")],
+                                 outputs=['image','image'],
+                                 description="Referencing a specific profession comes loaded with associations of gender and ethnicity."
+                                             " Text to image models provide an opportunity to explicitly specify an underrepresented group, but first we must understand our default behavior.",
+                                title="How occupation affects txt2img gender and skin color representation",
+                                 article = "To view how mentioning a particular occupation affects the gender and skin colors in faces of text to image generators, select a job."
+                                           " Promotional materials, advertising, and even criminal sketches which do not explicitly specify a gender or ethnicity term will tend towards the displayed distributions.")
+
+    affectInterface = gr.Interface(fn=trait_graph,
+                                   inputs=[gr.Dropdown(LOOKS, label="trait")],
+                                   outputs=['image','image'],
+                                   description="Certain adjectives can reinforce harmful stereotypes associated with gender roles and ethnic backgrounds."
+                                               "Text to image models provide an opportunity to understand how prompting a particular human expression could be triggering,"
+                                               " or why an uncommon combination might provide important examples to minorities without default representation.",
+                                   title="How word sentiment affects txt2img gender and skin color representation",
+                                   article = "To view how characterizing a person with a positive, negative, or neutral term influences the gender and skin color composition of AI-generated faces, select a direction.")
+
+    jobInterfaceManual = gr.Interface(fn=score_prompt,
+                                      inputs=[gr.inputs.Textbox()],
+                                      outputs='text',
+                                      description="Analyze prompt",
+                                      title="Understand which prompts require further engineering to represent equally genders and skin colors",
+                                      article = "Try modifying a trait or occupational prompt to produce a result in the minority representation!")
+
+
+    toolInterface = gr.Interface(fn=lambda t: trait_graph(t,hist=False),inputs=[gr.Dropdown(STABLE_MODELS,label="text-to-image model")],outputs='image',
+                                title="How different models fare in gender and skin color representation across a variety of prompts",
+                                 description="The training set, vocabulary, pre and post processing of generative AI tools doesn't treat everyone equally. "
+                                             "Within a 95% margin of statistical error, the following tests expose bias in gender and skin color.",
+                                 article="To learn more about this process, <a href=\"http://github.com/TRSS-Research/SAAC.git\"/> Visit the repo</a>"
+                                 )
+
+    gr.TabbedInterface(
+        [jobInterface, affectInterface, jobInterfaceManual,toolInterface],
+        ["Occupational Bias", "Adjectival Bias", "Prompt analysis",'FACIA model auditing'],
+        title = "Text-to-Image Bias Explorer"
+    ).launch(share=True)

requirements.txt

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+git+https://github.com/TRSS-Research/SAAC.git
+gradio