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 == "stabilityai/sdxl-turbo": pipeline = DiffusionPipeline.from_pretrained( model_name, torch_dtype=torch.float16, variant="fp16" ).to("cuda") elif model_name == "runwayml/stable-diffusion-v1-5": pipeline = StableDiffusionPipeline.from_pretrained( model_name, torch_dtype=torch.float16 ).to("cuda") elif model_name == "ByteDance/SDXL-Lightning": base = "stabilityai/stable-diffusion-xl-base-1.0" ckpt = "sdxl_lightning_4step_unet.safetensors" unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16) unet.load_state_dict(load_file(hf_hub_download(model_name, ckpt), device="cuda")) pipeline = StableDiffusionXLPipeline.from_pretrained( base, unet=unet, torch_dtype=torch.float16, variant="fp16" ).to("cuda") pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config, timestep_spacing="trailing") elif model_name == "segmind/SSD-1B": pipeline = StableDiffusionXLPipeline.from_pretrained( model_name, torch_dtype=torch.float16, use_safetensors=True, variant="fp16" ).to("cuda") elif model_name == "stabilityai/stable-diffusion-3-medium-diffusers": pipeline = StableDiffusion3Pipeline.from_pretrained( model_name, torch_dtype=torch.float16 ).to("cuda") elif model_name == "stabilityai/stable-diffusion-2": scheduler = EulerDiscreteScheduler.from_pretrained(model_name, subfolder="scheduler") pipeline = StableDiffusionPipeline.from_pretrained( model_name, scheduler=scheduler, torch_dtype=torch.float16 ).to("cuda") else: raise ValueError("Unknown model name") return pipeline # Initialize the default model default_model = "stabilityai/stable-diffusion-3-medium-diffusers" pipeline_text2image = load_model(default_model) @spaces.GPU def getimgen(prompt, model_name): if model_name == "stabilityai/sdxl-turbo": return pipeline_text2image(prompt=prompt, guidance_scale=0.0, num_inference_steps=2).images[0] elif model_name == "runwayml/stable-diffusion-v1-5": return pipeline_text2image(prompt).images[0] elif model_name == "ByteDance/SDXL-Lightning": return pipeline_text2image(prompt, num_inference_steps=4, guidance_scale=0).images[0] elif model_name == "segmind/SSD-1B": neg_prompt = "ugly, blurry, poor quality" return pipeline_text2image(prompt=prompt, negative_prompt=neg_prompt).images[0] elif model_name == "stabilityai/stable-diffusion-3-medium-diffusers": return pipeline_text2image(prompt=prompt, negative_prompt="", num_inference_steps=28, guidance_scale=7.0).images[0] elif model_name == "stabilityai/stable-diffusion-2": return pipeline_text2image(prompt=prompt).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") @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"] 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 @spaces.GPU(duration=200) 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 detect gender 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=[ "stabilityai/stable-diffusion-3-medium-diffusers", "stabilityai/sdxl-turbo", "ByteDance/SDXL-Lightning", "stabilityai/stable-diffusion-2", "runwayml/stable-diffusion-v1-5", "segmind/SSD-1B" ], value=default_model ) prompt = gr.Textbox(label="Enter the Prompt", value = "photo of a doctor in india, detailed, 8k, sharp, 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)