added files

app.py

@@ -0,0 +1,161 @@
+import gradio as gr
+import torch
+from diffusers import AutoPipelineForText2Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+from pathlib import Path
+import stone
+import requests
+import io
+import os
+from PIL import Image
+import spaces
+
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.colors import hex2color
+
+
+pipeline_text2image = None
+
+@spaces.GPU
+def loadpipeline():
+    global pipeline_text2image
+    pipeline_text2image = AutoPipelineForText2Image.from_pretrained(
+        "stabilityai/sdxl-turbo",
+        torch_dtype=torch.float16,
+        variant="fp16",
+    )
+    pipeline_text2image = pipeline_text2image.to("cuda")
+
+loadpipeline()
+
+@spaces.GPU
+def getimgen(prompt):
+
+    return pipeline_text2image(
+        prompt=prompt,
+        guidance_scale=0.0,
+        num_inference_steps=2
+    ).images[0]
+
+blip_processor = None
+
+@spaces.GPU
+def loadblip():
+    global blip_processor
+    global blip_model
+    blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+    blip_model = BlipForConditionalGeneration.from_pretrained(
+       "Salesforce/blip-image-captioning-large",
+       torch_dtype=torch.float16
+    ).to("cuda")
+
+loadblip()
+
+@spaces.GPU
+def blip_caption_image(image, prefix):
+    inputs = blip_processor(image, prefix, return_tensors="pt").to("cuda", torch.float16)
+    out = blip_model.generate(**inputs)
+    return blip_processor.decode(out[0], skip_special_tokens=True)
+
+def genderfromcaption(caption):
+    cc = caption.split()
+    if "man" in cc or "boy" in cc:
+        return "Man"
+    elif "woman" in cc or "girl" in cc:
+        return "Woman"
+    return "Unsure"
+
+def genderplot(genlist):    
+    order = ["Man", "Woman", "Unsure"]
+
+    # Sort the list based on the order of keys
+    words = sorted(genlist, key=lambda x: order.index(x))
+
+    # Define colors for each category
+    colors = {"Man": "lightgreen", "Woman": "darkgreen", "Unsure": "lightgrey"}
+
+    # Map each word to its corresponding color
+    word_colors = [colors[word] for word in words]
+
+    # Plot the colors in a grid with reduced spacing
+    fig, axes = plt.subplots(2, 5, figsize=(5,5))
+
+    # Adjust spacing between subplots
+    plt.subplots_adjust(hspace=0.1, wspace=0.1)
+
+    for i, ax in enumerate(axes.flat):
+        ax.set_axis_off()
+        ax.add_patch(plt.Rectangle((0, 0), 1, 1, color=word_colors[i]))
+
+    return fig
+
+def skintoneplot(hex_codes):
+    # Convert hex codes to RGB values
+    rgb_values = [hex2color(hex_code) for hex_code in hex_codes]
+
+    # Calculate luminance for each color
+    luminance_values = [0.299 * r + 0.587 * g + 0.114 * b for r, g, b in rgb_values]
+
+    # Sort hex codes based on luminance in descending order (dark to light)
+    sorted_hex_codes = [code for _, code in sorted(zip(luminance_values, hex_codes), reverse=True)]
+
+    # Plot the colors in a grid with reduced spacing
+    fig, axes = plt.subplots(2, 5, figsize=(5,5))
+
+    # Adjust spacing between subplots
+    plt.subplots_adjust(hspace=0.1, wspace=0.1)
+
+    for i, ax in enumerate(axes.flat):
+        ax.set_axis_off()
+        ax.add_patch(plt.Rectangle((0, 0), 1, 1, color=sorted_hex_codes[i]))
+
+    return fig
+
+@spaces.GPU
+def generate_images_plots(prompt):
+    foldername = "temp"
+    # Generate 10 images
+    images = [getimgen(prompt) for _ in range(10)]
+
+    Path(foldername).mkdir(parents=True, exist_ok=True)
+
+    genders = []
+    skintones = []
+
+    for image, i in zip(images, range(10)):
+        prompt_prefix = "photo of a "
+        caption = blip_caption_image(image, prefix=prompt_prefix)
+        image.save(f"{foldername}/image_{i}.png")
+        try:
+            skintoneres = stone.process(f"{foldername}/image_{i}.png", return_report_image=False)
+            tone = skintoneres['faces'][0]['dominant_colors'][0]['color']
+            skintones.append(tone)
+        except:
+            skintones.append(None)
+
+        genders.append(genderfromcaption(caption))
+
+    print(genders, skintones)
+
+    return images, skintoneplot(skintones), genderplot(genders)
+
+
+with gr.Blocks(title = "Skin Tone and Gender bias in SDXL Demo - Inference API") as demo:
+
+    gr.Markdown("# Skin Tone and Gender bias in SDXL Demo")
+
+    prompt = gr.Textbox(label="Enter the Prompt")
+    gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery", 
+                         columns=[5], rows=[2], object_fit="contain", height="auto")
+    btn = gr.Button("Generate images", scale=0)
+    with gr.Row(equal_height=True):
+        skinplot = gr.Plot(label="Skin Tone")
+        genplot = gr.Plot(label="Gender")
+
+
+    btn.click(generate_images_plots, inputs = prompt, outputs = [gallery, skinplot, genplot])
+
+
+
+demo.launch(debug=True)

css.py

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+custom_css = """
+/* Full width space */
+a {  
+  text-decoration: underline;
+  # text-decoration-style: dotted;
+}
+
+h1, h2, h3, h4, h5, h6 {
+    margin: 0;
+}
+
+.tag {
+padding: .1em .3em;
+background-color: lightgrey;
+border-radius: 12px;
+}
+"""

requirements.txt

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+gradio
+torch
+diffusers
+transformers
+spaces
+skin-tone-classifier
+matplotlib
+pillow
+numpy