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import gradio as gr | |
import torch | |
from diffusers import DiffusionPipeline, StableDiffusionPipeline, StableDiffusionXLPipeline, EulerDiscreteScheduler, UNet2DConditionModel | |
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 | |
# 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") | |
else: | |
raise ValueError("Unknown model name") | |
return pipeline | |
# Initialize the default model | |
default_model = "stabilityai/sdxl-turbo" | |
pipeline_text2image = load_model(default_model) | |
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] | |
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 Models") as demo: | |
gr.Markdown("# Skin Tone and Gender bias in Text to Image Models") | |
model_dropdown = gr.Dropdown( | |
label="Choose a model", | |
choices=[ | |
"stabilityai/sdxl-turbo", | |
"runwayml/stable-diffusion-v1-5", | |
"ByteDance/SDXL-Lightning", | |
"segmind/SSD-1B" | |
], | |
value=default_model | |
) | |
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, model_dropdown], outputs=[gallery, skinplot, genplot]) | |
demo.launch(debug=True) |