app(7).py

import os
import requests
import subprocess
import gradio as gr

# Token Hugging Face từ biến môi trường
hf_token = os.getenv("HF_TOKEN")

# URLs cần tải
app_url = "https://huggingface.co/datasets/ArrcttacsrjksX/Deffusion/resolve/main/RunModelAppp/App/sdRundeffusiononhuggingfacemaster-ac54e00"
model_url = "https://huggingface.co/datasets/ArrcttacsrjksX/Deffusion/resolve/main/Model/realisticVisionV60B1_v51HyperVAE.safetensors"

# Đường dẫn lưu file
app_path = "sdRundeffusiononhuggingfacemaster-ac54e00"
model_path = "realisticVisionV60B1_v51HyperVAE.safetensors"

# Hàm tải file từ Hugging Face
def download_file(url, output_path, token):
    headers = {"Authorization": f"Bearer {token}"}
    response = requests.get(url, headers=headers, stream=True)
    response.raise_for_status()  # Kiểm tra lỗi
    with open(output_path, "wb") as f:
        for chunk in response.iter_content(chunk_size=8192):
            f.write(chunk)
    print(f"Downloaded: {output_path}")

# Tải các file nếu chưa tồn tại
if not os.path.exists(app_path):
    download_file(app_url, app_path, hf_token)
    subprocess.run(["chmod", "+x", app_path])  # Thay đổi quyền thực thi

if not os.path.exists(model_path):
    download_file(model_url, model_path, hf_token)

# Hàm xử lý chạy ứng dụng
def run_command(prompt, mode, height, width, steps, seed, init_image=None, threads=-1, weight_type="f32", negative_prompt="", cfg_scale=7.0, strength=0.75, style_ratio=0.2, control_strength=0.9, sampling_method="euler_a", batch_count=1, schedule="discrete", clip_skip=-1, vae_tiling=False, vae_on_cpu=False, clip_on_cpu=False, control_net_cpu=False, canny=False, color=False, verbose=False, rng="cuda"):
    try:
        # Lưu ảnh đầu vào nếu được cung cấp
        init_image_path = None
        if init_image is not None:
            init_image_path = "input_image.png"
            init_image.save(init_image_path)

        # Tạo lệnh chạy
        command = [
            f"./{app_path}",
            "-M", mode,
            "-m", model_path,
            "-p", prompt,
            "-H", str(height),
            "-W", str(width),
            "--steps", str(steps),
            "-s", str(seed),
            "-t", str(threads),
            "--type", weight_type,
            "--cfg-scale", str(cfg_scale),
            "--strength", str(strength),
            "--style-ratio", str(style_ratio),
            "--control-strength", str(control_strength),
            "--sampling-method", sampling_method,
            "--batch-count", str(batch_count),
            "--schedule", schedule,
            "--clip-skip", str(clip_skip),
            "--vae-tiling" if vae_tiling else None,
            "--vae-on-cpu" if vae_on_cpu else None,
            "--clip-on-cpu" if clip_on_cpu else None,
            "--control-net-cpu" if control_net_cpu else None,
            "--canny" if canny else None,
            "--color" if color else None,
            "-v" if verbose else None,
            "--rng", rng
        ]
        
        # Loại bỏ các giá trị None trong danh sách lệnh
        command = [arg for arg in command if arg is not None]
        
        # Thêm ảnh đầu vào nếu có
        if mode == "img2img" and init_image_path:
            command.extend(["-i", init_image_path])

        # Chạy lệnh và hiển thị log theo thời gian thực
        process = subprocess.Popen(
            command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True
        )
        logs = []
        for line in process.stdout:
            logs.append(line.strip())  # Lưu log vào danh sách
            print(line, end="")  # In log ra màn hình
        
        process.wait()  # Đợi tiến trình hoàn thành

        # Kiểm tra kết quả và trả về
        if process.returncode == 0:
            output_path = "./output.png"  # Đường dẫn ảnh đầu ra mặc định
            return output_path if os.path.exists(output_path) else None, "\n".join(logs)
        else:
            error_log = process.stderr.read()  # Đọc lỗi
            logs.append(error_log)
            return None, "\n".join(logs)
    except Exception as e:
        return None, str(e)

# Giao diện Gradio
def toggle_image_input(mode):
    """Hiển thị hoặc ẩn ô Drop Image dựa trên mode."""
    return gr.update(visible=(mode == "img2img"))

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        # 🌟 **Stable Diffusion Interface**
        Generate stunning images from text or modify existing images with AI-powered tools.
        """
    )
    
    # Thiết lập giao diện
    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(
                label="🎨 Prompt", placeholder="Enter your creative idea here...", lines=2
            )
            mode = gr.Radio(
                choices=["txt2img", "img2img"], value="txt2img", label="Mode", interactive=True
            )
            init_image = gr.Image(
                label="Drop Image (for img2img mode)", type="pil", visible=False
            )
            mode.change(toggle_image_input, inputs=mode, outputs=init_image)
            negative_prompt = gr.Textbox(
                label="Negative Prompt", placeholder="Anything to avoid in the image", lines=2
            )
            threads = gr.Slider(-1, 64, value=-1, step=1, label="Threads", interactive=True)
            weight_type = gr.Dropdown(choices=["f32", "f16", "q4_0", "q4_1", "q5_0", "q5_1", "q8_0", "q2_k", "q3_k", "q4_k"], value="f32", label="Weight Type")
            cfg_scale = gr.Slider(0, 20, value=7.0, step=0.1, label="CFG Scale", interactive=True)
            strength = gr.Slider(0, 1, value=0.75, step=0.01, label="Strength", interactive=True)
            style_ratio = gr.Slider(0, 1, value=0.2, step=0.01, label="Style Ratio", interactive=True)
            control_strength = gr.Slider(0, 1, value=0.9, step=0.01, label="Control Strength", interactive=True)
            sampling_method = gr.Dropdown(choices=["euler", "euler_a", "heun", "dpm2", "dpm++2s_a", "dpm++2m", "dpm++2mv2", "ipndm", "ipndm_v", "lcm"], value="euler_a", label="Sampling Method")
            batch_count = gr.Slider(1, 10, value=1, step=1, label="Batch Count", interactive=True)
            schedule = gr.Dropdown(choices=["discrete", "karras", "exponential", "ays", "gits"], value="discrete", label="Denoiser Schedule")
            clip_skip = gr.Slider(-1, 2, value=-1, step=1, label="Clip Skip", interactive=True)
            vae_tiling = gr.Checkbox(label="VAE Tiling", interactive=True)
            vae_on_cpu = gr.Checkbox(label="VAE on CPU", interactive=True)
            clip_on_cpu = gr.Checkbox(label="CLIP on CPU", interactive=True)
            control_net_cpu = gr.Checkbox(label="Control Net on CPU", interactive=True)
            canny = gr.Checkbox(label="Apply Canny Preprocessor", interactive=True)
            color = gr.Checkbox(label="Color Logs", interactive=True)
            verbose = gr.Checkbox(label="Verbose Output", interactive=True)
            rng = gr.Radio(choices=["std_default", "cuda"], value="cuda", label="Random Number Generator", interactive=True)

        with gr.Column():
            height = gr.Slider(
                128, 1024, value=512, step=64, label="Image Height (px)", interactive=True
            )
            width = gr.Slider(
                128, 1024, value=512, step=64, label="Image Width (px)", interactive=True
            )
            steps = gr.Slider(
                1, 100, value=20, step=1, label="Sampling Steps", interactive=True
            )
            seed = gr.Slider(
                1, 10000, value=42, step=1, label="Seed", interactive=True
            )

            generate_btn = gr.Button("Run")
            output_image = gr.Image(label="Generated Image")
            logs_output = gr.Textbox(label="Logs", interactive=False, lines=15)

    generate_btn.click(
        run_command,
        inputs=[
            prompt, mode, height, width, steps, seed, init_image, threads, weight_type, negative_prompt,
            cfg_scale, strength, style_ratio, control_strength, sampling_method, batch_count, schedule,
            clip_skip, vae_tiling, vae_on_cpu, clip_on_cpu, control_net_cpu, canny, color, verbose, rng
        ],
        outputs=[output_image, logs_output],
    )

demo.launch()