app.py

import random

import gradio as gr
import torch
from diffusers.utils import load_image
from PIL import Image
import numpy as np
import base64
from io import BytesIO

from mediapipe_face_common import generate_annotation

from diffusers import (
    ControlNetModel,
    StableDiffusionControlNetPipeline,
)


# Download the SD 1.5 model from HF
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
controlnet = ControlNetModel.from_pretrained(
    "CrucibleAI/ControlNetMediaPipeFace", torch_dtype=torch.float16, variant="fp16")
model = StableDiffusionControlNetPipeline.from_pretrained(
    "stabilityai/stable-diffusion-2-1-base", controlnet=controlnet, torch_dtype=torch.float16
)
model = model.to(device)
model.enable_model_cpu_offload()


canvas_html = "<face-canvas id='canvas-root' data-mode='crucibleAI' style='display:flex;max-width: 500px;margin: 0 auto;'></face-canvas>"
load_js = """
async () => {
const url = "https://huggingface.co/datasets/radames/gradio-components/raw/main/face-canvas.js"
fetch(url)
  .then(res => res.text())
  .then(text => {
    const script = document.createElement('script');
    script.type = "module"
    script.src = URL.createObjectURL(new Blob([text], { type: 'application/javascript' }));
    document.head.appendChild(script);
  });
}
"""
get_js_image = """
async (input_image, prompt, a_prompt, n_prompt, max_faces, min_confidence, num_samples, ddim_steps, guess_mode, strength, scale, seed, eta, image_file_live_opt, live_conditioning) => {
  const canvasEl = document.getElementById("canvas-root");
  const imageData = canvasEl? canvasEl._data : null;
  return [input_image, prompt, a_prompt, n_prompt, max_faces, min_confidence, num_samples, ddim_steps, guess_mode, strength, scale, seed, eta, image_file_live_opt, imageData];
}
"""


def pad_image(input_image):
    pad_w, pad_h = np.max(((2, 2), np.ceil(
        np.array(input_image.size) / 64).astype(int)), axis=0) * 64 - input_image.size
    im_padded = Image.fromarray(
        np.pad(np.array(input_image), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
    w, h = im_padded.size
    if w == h:
        return im_padded
    elif w > h:
        new_image = Image.new(im_padded.mode, (w, w), (0, 0, 0))
        new_image.paste(im_padded, (0, (w - h) // 2))
        return new_image
    else:
        new_image = Image.new(im_padded.mode, (h, h), (0, 0, 0))
        new_image.paste(im_padded, ((h - w) // 2, 0))
        return new_image


def process(input_image: Image.Image, prompt, a_prompt, n_prompt, max_faces: int, min_confidence: float, num_samples, ddim_steps, guess_mode, strength: float, scale, seed: int, eta, image_file_live_opt="file", live_conditioning=None):
    if input_image is None and 'image' not in live_conditioning:
        raise gr.Error("Please provide an image")
    try:
        if image_file_live_opt == 'file':
            # Resize before annotation so that we can keep our line-widths consistent with the training data.
            input_image = pad_image(input_image.convert('RGB')).resize((512, 512))
            empty = generate_annotation(np.array(input_image), max_faces, min_confidence)
            visualization = Image.fromarray(empty)
        elif image_file_live_opt == 'webcam':
            base64_img = live_conditioning['image']
            image_data = base64.b64decode(base64_img.split(',')[1])
            visualization = Image.open(BytesIO(image_data)).convert('RGB').resize((512, 512))
        if seed == -1:
            seed = random.randint(0, 2147483647)
        generator = torch.Generator(device).manual_seed(seed)

        output = model(prompt=prompt + ' ' + a_prompt,
                       negative_prompt=n_prompt,
                       image=visualization,
                       generator=generator,
                       num_images_per_prompt=num_samples,
                       num_inference_steps=ddim_steps,
                       controlnet_conditioning_scale=float(strength),
                       guidance_scale=scale,
                       eta=eta,
                       )
        results = [visualization] + output.images

        return results
    except Exception as e:
        raise gr.Error(str(e))

# switch between file upload and webcam


def toggle(choice):
    if choice == "file":
        return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
    elif choice == "webcam":
        return gr.update(visible=False, value=None), gr.update(visible=True, value=canvas_html)


block = gr.Blocks().queue()
with block:
    # hidden JSON component to store live conditioning
    live_conditioning = gr.JSON(value={}, visible=False)
    with gr.Row():
        gr.Markdown("## Control Stable Diffusion with a Facial Pose")
    with gr.Row():
        with gr.Column():
            image_file_live_opt = gr.Radio(["file", "webcam"], value="file",
                                           label="How would you like to upload your image?")
            input_image = gr.Image(source="upload", visible=True, type="pil")
            canvas = gr.HTML(None, elem_id="canvas_html", visible=False)

            image_file_live_opt.change(fn=toggle,
                                       inputs=[image_file_live_opt],
                                       outputs=[input_image, canvas],
                                       queue=False)

            prompt = gr.Textbox(label="Prompt")
            run_button = gr.Button(label="Run")
            with gr.Accordion("Advanced options", open=False):
                num_samples = gr.Slider(
                    label="Images", minimum=1, maximum=4, value=1, step=1)
                max_faces = gr.Slider(
                    label="Max Faces", minimum=1, maximum=10, value=5, step=1)
                min_confidence = gr.Slider(
                    label="Min Confidence", minimum=0.01, maximum=1.0, value=0.5, step=0.01)
                strength = gr.Slider(
                    label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
                guess_mode = gr.Checkbox(label='Guess Mode', value=False)
                ddim_steps = gr.Slider(
                    label="Steps", minimum=1, maximum=100, value=20, step=1)
                scale = gr.Slider(label="Guidance Scale",
                                  minimum=0.1, maximum=30.0, value=9.0, step=0.1)
                seed = gr.Slider(label="Seed", minimum=-1,
                                 maximum=2147483647, step=1, randomize=True)
                eta = gr.Number(label="eta (DDIM)", value=0.0)
                a_prompt = gr.Textbox(
                    label="Added Prompt", value='best quality, extremely detailed')
                n_prompt = gr.Textbox(label="Negative Prompt",
                                      value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality')
        with gr.Column():
            result_gallery = gr.Gallery(
                label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
    ips = [input_image, prompt, a_prompt, n_prompt, max_faces, min_confidence,
           num_samples, ddim_steps, guess_mode, strength, scale, seed, eta]
    run_button.click(fn=process, inputs=ips + [image_file_live_opt, live_conditioning],
                     outputs=[result_gallery],
                     _js=get_js_image)

    # load js for live conditioning
    block.load(None, None, None, _js=load_js)
    gr.Examples(fn=process,
                examples=[
                    ["./examples/two2.jpeg",
                        "Highly detailed photograph of two clowns",
                        "best quality, extremely detailed",
                        "cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
                        10, 0.4, 3, 20, False, 1.0, 9.0, -1, 0.0],
                    ["./examples/two.jpeg",
                        "a photo of two silly men",
                        "best quality, extremely detailed",
                        "cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
                        10, 0.4, 3, 20, False, 1.0, 9.0, -1, 0.0],
                    ["./examples/pedro-512.jpg",
                        "Highly detailed photograph of young woman smiling, with palm trees in the background",
                        "best quality, extremely detailed",
                        "cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
                        10, 0.4, 3, 20, False, 1.0, 9.0, -1, 0.0],
                    ["./examples/image1.jpg",
                        "Highly detailed photograph of a scary clown",
                        "best quality, extremely detailed",
                        "cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
                        10, 0.4, 3, 20, False, 1.0, 9.0, -1, 0.0],
                    ["./examples/image0.jpg",
                        "Highly detailed photograph of Madonna",
                        "best quality, extremely detailed",
                        "cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
                        10, 0.4, 3, 20, False, 1.0, 9.0, -1, 0.0],
                ],
                inputs=ips,
                outputs=[result_gallery],
                cache_examples=True)

block.launch(server_name='0.0.0.0')