Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| import torch | |
| from diffusers import ( | |
| DiffusionPipeline, | |
| StableDiffusionPipeline, | |
| StableDiffusionXLPipeline, | |
| EulerDiscreteScheduler, | |
| UNet2DConditionModel, | |
| StableDiffusion3Pipeline | |
| ) | |
| from transformers import BlipProcessor, BlipForConditionalGeneration | |
| from pathlib import Path | |
| from safetensors.torch import load_file | |
| from huggingface_hub import hf_hub_download | |
| from PIL import Image | |
| import matplotlib.pyplot as plt | |
| from matplotlib.colors import hex2color | |
| import stone | |
| import os | |
| import spaces | |
| access_token = os.getenv("AccessTokenSD3") | |
| from huggingface_hub import login | |
| login(token = access_token) | |
| # Define model initialization functions | |
| def load_model(model_name): | |
| if model_name == "sinteticoXL": | |
| pipeline = StableDiffusionXLPipeline.from_single_file( | |
| "https://huggingface.co/lucianosb/sinteticoXL-models/blob/main/sinteticoXL_v1dot2.safetensors", | |
| torch_dtype=torch.float16, | |
| variant="fp16", | |
| use_safetensors=True, | |
| ).to("cuda") | |
| elif model_name == "sinteticoXL_Prude": | |
| pipeline = StableDiffusionXLPipeline.from_single_file( | |
| "https://huggingface.co/lucianosb/sinteticoXL-models/blob/main/sinteticoXL_prude_v1dot2.safetensors", | |
| torch_dtype=torch.float16, | |
| variant="fp16", | |
| use_safetensors=True, | |
| ).to("cuda") | |
| else: | |
| raise ValueError("Unknown model name") | |
| return pipeline | |
| # Initialize the default model | |
| default_model = "sinteticoXL" | |
| pipeline_text2image = load_model(default_model) | |
| def getimgen(prompt, model_name): | |
| if model_name == "sinteticoXL": | |
| return pipeline_text2image(prompt=prompt, guidance_scale=6.0, num_inference_steps=20).images[0] | |
| elif model_name == "sinteticoXL_Prude": | |
| return pipeline_text2image(prompt=prompt, guidance_scale=6.0, num_inference_steps=20).images[0] | |
| 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") | |
| 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"] | |
| words = sorted(genlist, key=lambda x: order.index(x)) | |
| colors = {"Man": "lightgreen", "Woman": "darkgreen", "Unsure": "lightgrey"} | |
| word_colors = [colors[word] for word in words] | |
| fig, axes = plt.subplots(2, 5, figsize=(5,5)) | |
| 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): | |
| hex_codes = [code for code in hex_codes if code is not None] | |
| rgb_values = [hex2color(hex_code) for hex_code in hex_codes] | |
| luminance_values = [0.299 * r + 0.587 * g + 0.114 * b for r, g, b in rgb_values] | |
| sorted_hex_codes = [code for _, code in sorted(zip(luminance_values, hex_codes), reverse=True)] | |
| fig, axes = plt.subplots(2, 5, figsize=(5,5)) | |
| plt.subplots_adjust(hspace=0.1, wspace=0.1) | |
| for i, ax in enumerate(axes.flat): | |
| ax.set_axis_off() | |
| if i < len(sorted_hex_codes): | |
| ax.add_patch(plt.Rectangle((0, 0), 1, 1, color=sorted_hex_codes[i])) | |
| return fig | |
| def generate_images_plots(prompt, model_name): | |
| global pipeline_text2image | |
| pipeline_text2image = load_model(model_name) | |
| foldername = "temp" | |
| Path(foldername).mkdir(parents=True, exist_ok=True) | |
| images = [getimgen(prompt, model_name) for _ in range(10)] | |
| 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)) | |
| return images, skintoneplot(skintones), genderplot(genders) | |
| with gr.Blocks(title="Skin Tone and Gender bias in Text-to-Image Generation Models") as demo: | |
| gr.Markdown("# Skin Tone and Gender bias in Text to Image Models") | |
| gr.Markdown(''' | |
| In this demo, we explore the potential biases in text-to-image models by generating multiple images based on user prompts and analyzing the gender and skin tone of the generated subjects. Here's how the analysis works: | |
| 1. **Image Generation**: For each prompt, 10 images are generated using the selected model. | |
| 2. **Gender Detection**: The [BLIP caption generator](https://huggingface.co/Salesforce/blip-image-captioning-large) is used to elicit gender markers by identifying words like "man," "boy," "woman," and "girl" in the captions. | |
| 3. **Skin Tone Classification**: The [skin-tone-classifier library](https://github.com/ChenglongMa/SkinToneClassifier) is used to extract the skin tones of the generated subjects. | |
| #### Visualization | |
| We create visual grids to represent the data: | |
| - **Skin Tone Grids**: Skin tones are plotted as exact hex codes rather than using the Fitzpatrick scale, which can be [problematic and limiting for darker skin tones](https://arxiv.org/pdf/2309.05148). | |
| - **Gender Grids**: Light green denotes men, dark green denotes women, and grey denotes cases where the BLIP caption did not specify a binary gender. | |
| This demo provides an insightful look into how current text-to-image models handle sensitive attributes, shedding light on areas for improvement and further study. | |
| [Here is an article](https://medium.com/@evijit/analysis-of-ai-generated-images-of-indian-people-for-colorism-and-sexism-b80ff946759f) showing how this space can be used to perform such analyses, using colorism and sexism in India as an example. | |
| ''') | |
| model_dropdown = gr.Dropdown( | |
| label="Choose a model", | |
| choices=[ | |
| "sinteticoXL", | |
| "sinteticoXL_Prude" | |
| ], | |
| value=default_model | |
| ) | |
| prompt = gr.Textbox(label="Enter the Prompt", value = "photo of a beautiful Brazilian woman, high quality, good lighting") | |
| 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, model_dropdown], outputs=[gallery, skinplot, genplot]) | |
| demo.launch(debug=True) |