| import gradio as gr |
| import requests |
|
|
| url = "https://api.newnative.ai/stable-diffusion?prompt=" |
|
|
| def run(gender, height, weight, age, ethnicity, skin_color, hair_length, hair_color, eye_color, facial_hair, facial_hair_color): |
| bmi = weight/(height/100)**2 |
|
|
| if bmi < 18.5: |
| bodyShape = "slim" |
| elif bmi < 25: |
| bodyShape = "average" |
| elif bmi < 30: |
| bodyShape = "overweight" |
| else: |
| bodyShape = "obese" |
|
|
| prompt = f"""mugshot portrait, {age + " " if age else ""}{bodyShape} {skin_color} {ethnicity} {gender}{" with " + hair_length if hair_length else ""} {hair_color + " hair, " if hair_length and hair_color else ""}{eye_color + " eyes, " if eye_color else ""}{facial_hair_color + " " if facial_hair and facial_hair_color else ""}{facial_hair+ ", " if facial_hair else ""}canon EOS""" |
| |
| print (prompt.lower()) |
| return getImage(prompt.lower()) |
|
|
| def getHeightInFeet(height): |
| feet = height*0.0328084 |
| inches = (feet - int(feet))*0.393701 |
| return str(int(feet)) + "'" + str(int(inches)) + '"' |
|
|
| def getWeightInPounds(weight): |
| return str(int(weight*2.20462)) + " lbs" |
| |
| def getImage(prompt): |
| r = requests.get(url + prompt) |
| data = r.json() |
| return(data["image_url"]) |
|
|
| demo = gr.Interface( |
| fn = run, |
| article = "<p>For better accuracy, please enter all the information about the person you want to generate a portrait for.</p><h3>Biases and content acknowledgment</h3><p>Beware to the fact that this is a pre-trained model that may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on the LAION-5B dataset, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes.</p><p>You can read more in the model card <a href=\"https://huggingface.co/CompVis/stable-diffusion-v1-4\" target=\"_blank\">HERE.</a></p>" |
| , |
| inputs = [ |
| |
| gr.Radio(["Male", "Female"], label="Gender" ), |
| gr.Slider(100,250,value=160, label="Height (cm)"), |
| gr.Slider(40,250,value=50, label="Weight (kg)"), |
| gr.Radio(["Young", "Adult", "Middle-aged", "Old"], label="Age" ), |
| gr.Dropdown(sorted(["South Asian", "North Asian", "White", "African American", "American Indian", "Hispanic", "Latin"]), label="Race"), |
| gr.Dropdown(sorted(["Black", "Brown", "Yellow", "Peach","Tan","Beige", "White", "Grey"]), label="Skin color" ), |
| gr.Radio(sorted(["Short", "Long", "Bald", "Medium"]), label="Hair length" ), |
| gr.Dropdown(sorted(["Black","Dark Brown", "Light Brown", "Blond", "Red", "Grey"]), label="Hair color" ), |
| gr.Dropdown(sorted(["Black", "Dark Brown", "Light Brown", "Green", "Blue", "Hazel", "Amber", "Red", "Pink"]), label="Eye color" ), |
| gr.Radio(sorted(["Mustache", "Beard", "Unibrow"]), label="Facial hair" ), |
| gr.Dropdown(sorted(["Ginger", "Black", "Brown", "Grey", "Yellow", "White"]), label="Facial hair color" ), |
| |
| |
| ], |
| outputs = "image", |
| title = "AI Portrait Generator", |
| description = "Generate a portrait of a person with the given attributes", |
| examples=[ |
| ["Male", 180, 80, "Adult", "White", "White", "Short", "Blond", "Blue", "Mustache", "Ginger"], |
| ["Male", 160, 50, "Young", "Latin", "Brown", "Medium", "Dark Brown", "Dark Brown", "Beard", "Brown"], |
| ["Female", 150, 70, "Old", "South Asian", "White", "Long", "Black", "Light Brown", "Unibrow", "Brown"], |
| ["Female", 170, 60, "Middle-aged", "African american", "Black", "Medium", "Black", "Black", None, None], |
|
|
| |
|
|
| ], |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
|
|
