app.py

import subprocess
import shlex
subprocess.run(
    shlex.split(
        "pip install ./gradio_magicquill-0.0.1-py3-none-any.whl"
    )
)
import gradio as gr
from gradio_magicquill import MagicQuill
import random
import torch
import numpy as np
from PIL import Image, ImageOps
import base64
import io
from fastapi import FastAPI, Request
import uvicorn
from MagicQuill import folder_paths
from MagicQuill.scribble_color_edit import ScribbleColorEditModel
from gradio_client import Client, handle_file
from huggingface_hub import snapshot_download
import tempfile
import cv2
import os
import requests

snapshot_download(repo_id="LiuZichen/MagicQuill-models", repo_type="model", local_dir="models")
# HF_TOKEN = os.environ.get("HF_TOKEN")
# The client has been made public. Welcome to duplicate our repo.
client = Client("LiuZichen/DrawNGuess")
scribbleColorEditModel = ScribbleColorEditModel()

def tensor_to_numpy(tensor):
    if isinstance(tensor, torch.Tensor):
        return (tensor.detach().cpu().numpy() * 255).astype(np.uint8)
    return tensor

def tensor_to_base64(tensor):
    tensor = tensor.squeeze(0) * 255.
    pil_image = Image.fromarray(tensor.cpu().byte().numpy())
    buffered = io.BytesIO()
    pil_image.save(buffered, format="PNG")
    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
    
    return img_str

def read_base64_image(base64_image):
    if base64_image.startswith("data:image/png;base64,"):
        base64_image = base64_image.split(",")[1]
    elif base64_image.startswith("data:image/jpeg;base64,"):
        base64_image = base64_image.split(",")[1]
    elif base64_image.startswith("data:image/webp;base64,"):
        base64_image = base64_image.split(",")[1]
    else:
        raise ValueError("Unsupported image format.")
    image_data = base64.b64decode(base64_image)
    image = Image.open(io.BytesIO(image_data))
    image = ImageOps.exif_transpose(image)
    return image

def create_alpha_mask(base64_image):
    """Create an alpha mask from the alpha channel of an image."""
    image = read_base64_image(base64_image)
    mask = torch.zeros((1, image.height, image.width), dtype=torch.float32, device="cpu")
    if 'A' in image.getbands():
        alpha_channel = np.array(image.getchannel('A')).astype(np.float32) / 255.0
        mask[0] = 1.0 - torch.from_numpy(alpha_channel)
    return mask

def load_and_preprocess_image(base64_image, convert_to='RGB', has_alpha=False):
    """Load and preprocess a base64 image."""
    image = read_base64_image(base64_image)
    image = image.convert(convert_to)
    image_array = np.array(image).astype(np.float32) / 255.0
    image_tensor = torch.from_numpy(image_array)[None,]
    return image_tensor

def load_and_resize_image(base64_image, convert_to='RGB', max_size=512):
    """Load and preprocess a base64 image, resize if necessary."""
    image = read_base64_image(base64_image)
    image = image.convert(convert_to)
    width, height = image.size
    # if min(width, height) > max_size:
    scaling_factor = max_size / min(width, height)
    new_size = (int(width * scaling_factor), int(height * scaling_factor))
    image = image.resize(new_size, Image.LANCZOS)
    image_array = np.array(image).astype(np.float32) / 255.0
    image_tensor = torch.from_numpy(image_array)[None,]
    return image_tensor

def prepare_images_and_masks(total_mask, original_image, add_color_image, add_edge_image, remove_edge_image):
    total_mask = create_alpha_mask(total_mask)
    original_image_tensor = load_and_preprocess_image(original_image)
    if add_color_image:
        add_color_image_tensor = load_and_preprocess_image(add_color_image)
    else:
        add_color_image_tensor = original_image_tensor
    
    add_edge_mask = create_alpha_mask(add_edge_image) if add_edge_image else torch.zeros_like(total_mask)
    remove_edge_mask = create_alpha_mask(remove_edge_image) if remove_edge_image else torch.zeros_like(total_mask)
    return add_color_image_tensor, original_image_tensor, total_mask, add_edge_mask, remove_edge_mask

def guess_prompt_handler(original_image, add_color_image, add_edge_image):
    original_image_tensor = load_and_preprocess_image(original_image)
    
    if add_color_image:
        add_color_image_tensor = load_and_preprocess_image(add_color_image)
    else:
        add_color_image_tensor = original_image_tensor

    width, height = original_image_tensor.shape[1], original_image_tensor.shape[2]
    add_edge_mask = create_alpha_mask(add_edge_image) if add_edge_image else torch.zeros((1, height, width), dtype=torch.float32, device="cpu")
    
    original_image_numpy = tensor_to_numpy(original_image_tensor.squeeze(0))
    add_color_image_numpy = tensor_to_numpy(add_color_image_tensor.squeeze(0))
    add_edge_mask_numpy = tensor_to_numpy(add_edge_mask.squeeze(0).unsqueeze(-1))
    original_image_numpy = cv2.cvtColor(original_image_numpy, cv2.COLOR_RGB2BGR)
    add_color_image_numpy = cv2.cvtColor(add_color_image_numpy, cv2.COLOR_RGB2BGR)

    original_image_file = tempfile.NamedTemporaryFile(delete=False, suffix=".png", mode='w+b')
    add_color_image_file = tempfile.NamedTemporaryFile(delete=False, suffix=".png", mode='w+b')
    add_edge_mask_file = tempfile.NamedTemporaryFile(delete=False, suffix=".png", mode='w+b')
    
    cv2.imwrite(original_image_file.name, original_image_numpy)
    cv2.imwrite(add_color_image_file.name, add_color_image_numpy)
    cv2.imwrite(add_edge_mask_file.name, add_edge_mask_numpy)

    original_image_file.close()
    add_color_image_file.close()
    add_edge_mask_file.close()
    
    res = client.predict(
        handle_file(original_image_file.name),
        handle_file(add_color_image_file.name),
        handle_file(add_edge_mask_file.name)
    )
    
    if original_image_file and os.path.exists(original_image_file.name):
        os.remove(original_image_file.name)
    if add_color_image_file and os.path.exists(add_color_image_file.name):
        os.remove(add_color_image_file.name)
    if add_edge_mask_file and os.path.exists(add_edge_mask_file.name):
        os.remove(add_edge_mask_file.name)

    return res

def generate(ckpt_name, total_mask, original_image, add_color_image, add_edge_image, remove_edge_image, positive_prompt, negative_prompt, grow_size, stroke_as_edge, fine_edge, edge_strength, color_strength, inpaint_strength, seed, steps, cfg, sampler_name, scheduler):
    add_color_image, original_image, total_mask, add_edge_mask, remove_edge_mask = prepare_images_and_masks(total_mask, original_image, add_color_image, add_edge_image, remove_edge_image)
    progress = None
    if fine_edge == 'disable':
        if torch.sum(remove_edge_mask).item() > 0 and torch.sum(add_edge_mask).item() == 0:
            if positive_prompt == "":
                positive_prompt = "empty scene"
            edge_strength /= 3.

    latent_samples, final_image, lineart_output, color_output = scribbleColorEditModel.process(
        ckpt_name,
        original_image, 
        add_color_image, 
        positive_prompt, 
        negative_prompt, 
        total_mask, 
        add_edge_mask, 
        remove_edge_mask, 
        grow_size, 
        stroke_as_edge, 
        fine_edge, 
        edge_strength, 
        color_strength, 
        inpaint_strength, 
        seed,
        steps,
        cfg,
        sampler_name,
        scheduler,
        progress
    )

    final_image_base64 = tensor_to_base64(final_image)
    return final_image_base64

def generate_image_handler(x, ckpt_name, negative_prompt, fine_edge, grow_size, edge_strength, color_strength, inpaint_strength, seed, steps, cfg, sampler_name, scheduler):
    if seed == -1:
        seed = random.randint(0, 2**32 - 1)
    ms_data = x['from_frontend']
    positive_prompt = x['from_backend']['prompt']
    stroke_as_edge = "enable"
    res = generate(ckpt_name, ms_data['total_mask'], ms_data['original_image'], ms_data['add_color_image'], ms_data['add_edge_image'], ms_data['remove_edge_image'], positive_prompt, negative_prompt, grow_size, stroke_as_edge, fine_edge, edge_strength, color_strength, inpaint_strength, seed, steps, cfg, sampler_name, scheduler)
    x["from_backend"]["generated_image"] = res
    return x

css = '''
.row {
    width: 90%;
    margin: auto;
}
'''

head = """
<meta http-equiv="Content-Security-Policy" content="frame-ancestors 'none'">
"""

with gr.Blocks(css=css, head=head) as demo:
    with gr.Row(elem_classes="row"):
        text = gr.Markdown(
        """
        # Welcome to MagicQuill! The paper has been accepted to CVPR 2025.
        Click the [link](https://magicquill.art) to view our demo and tutorial. Give us a [GitHub star](https://github.com/magic-quill/magicquill) if you are interested. 
        """)
    with gr.Row(elem_classes="row"):
        ms = MagicQuill()
    with gr.Row(elem_classes="row"):
        with gr.Column():
            btn = gr.Button("Run", variant="primary")
        with gr.Column():
            with gr.Accordion("parameters", open=False):
                ckpt_name = gr.Dropdown(
                    label="Base Model Name",
                    choices=folder_paths.get_filename_list("checkpoints"),
                    value='SD1.5/realisticVisionV60B1_v51VAE.safetensors',
                    interactive=True
                )
                negative_prompt = gr.Textbox(
                    label="Negative Prompt",
                    value="",
                    interactive=True
                )
                # stroke_as_edge = gr.Radio(
                #     label="Stroke as Edge",
                #     choices=['enable', 'disable'],
                #     value='enable',
                #     interactive=True
                # )
                fine_edge = gr.Radio(
                    label="Fine Edge",
                    choices=['enable', 'disable'],
                    value='disable',
                    interactive=True
                )
                grow_size = gr.Slider(
                    label="Grow Size",
                    minimum=0,
                    maximum=100,
                    value=15,
                    step=1,
                    interactive=True
                )
                edge_strength = gr.Slider(
                    label="Edge Strength",
                    minimum=0.0,
                    maximum=5.0,
                    value=0.55,
                    step=0.01,
                    interactive=True
                )
                color_strength = gr.Slider(
                    label="Color Strength",
                    minimum=0.0,
                    maximum=5.0,
                    value=0.55,
                    step=0.01,
                    interactive=True
                )
                inpaint_strength = gr.Slider(
                    label="Inpaint Strength",
                    minimum=0.0,
                    maximum=5.0,
                    value=1.0,
                    step=0.01,
                    interactive=True
                )
                seed = gr.Number(
                    label="Seed",
                    value=-1,
                    precision=0,
                    interactive=True
                )
                steps = gr.Slider(
                    label="Steps",
                    minimum=1,
                    maximum=50,
                    value=20,
                    step=1,
                    interactive=True
                )
                cfg = gr.Slider(
                    label="CFG",
                    minimum=0.0,
                    maximum=20.0,
                    value=5.0,
                    step=0.1,
                    interactive=True
                )
                sampler_name = gr.Dropdown(
                    label="Sampler Name",
                    choices=["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu", "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", "ddim", "uni_pc", "uni_pc_bh2"],
                    value='euler_ancestral',
                    interactive=True
                )
                scheduler = gr.Dropdown(
                    label="Scheduler",
                    choices=["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"],
                    value='karras',
                    interactive=True
                )
        btn.click(generate_image_handler, inputs=[ms, ckpt_name, negative_prompt, fine_edge, grow_size, edge_strength, color_strength, inpaint_strength, seed, steps, cfg, sampler_name, scheduler], outputs=ms, concurrency_limit=1)
    
    with gr.Row(elem_classes="row"):
        text = gr.Markdown(
        """
        Note: This demo is governed by the license of CC BY-NC 4.0. We strongly advise users not to knowingly generate or allow others to knowingly generate harmful content, including hate speech, violence, pornography, deception, etc. (注：本演示受CC BY-NC的许可协议限制。我们强烈建议，用户不应传播及不应允许他人传播以下内容，包括但不限于仇恨言论、暴力、色情、欺诈相关的有害信息。)
        """)
    demo.queue(max_size=20, status_update_rate=0.1)

app = FastAPI()

@app.post("/magic_quill/guess_prompt")
async def guess_prompt(request: Request):
    data = await request.json()
    res = guess_prompt_handler(data['original_image'], data['add_color_image'], data['add_edge_image'])
    return res

@app.post("/magic_quill/process_background_img")
async def process_background_img(request: Request):
    img = await request.json()
    resized_img_tensor = load_and_resize_image(img)
    resized_img_base64 = "data:image/png;base64," + tensor_to_base64(resized_img_tensor)
    return resized_img_base64

app = gr.mount_gradio_app(app, demo, "/")

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)
    # demo.launch()