Upload app (9).py

app (9).py

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+import os
+import requests
+import subprocess
+import gradio as gr
+from datetime import datetime
+from huggingface_hub import HfApi, Repository
+
+# 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/sdmaster-d9b5942LatestJan182025"
+model_url = "https://huggingface.co/datasets/ArrcttacsrjksX/Deffusion/resolve/main/Model/realisticVisionV60B1_v51HyperVAE.safetensors"
+
+# Đường dẫn lưu file
+app_path = "sdmaster-d9b5942LatestJan182025"
+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 lưu kết quả lên Hugging Face Dataset
+def save_to_huggingface(image_path, hf_token):
+    # Tạo tên thư mục và tên file dựa trên ngày giờ hiện tại
+    now = datetime.now()
+    folder_name = f"SetImages+{now.strftime('%d/%m/%Y')}"
+    file_name = f"Image+{now.strftime('%S/%M/%H')}.png"
+    
+    # Khởi tạo API Hugging Face
+    api = HfApi(token=hf_token)
+    
+    # Tải ảnh lên dataset
+    try:
+        api.upload_file(
+            path_or_fileobj=image_path,
+            path_in_repo=f"{folder_name}/{file_name}",
+            repo_id="ArrcttacsrjksX/Deffusion",
+            repo_type="dataset"
+        )
+        print(f"Uploaded: {folder_name}/{file_name}")
+    except Exception as e:
+        print(f"Failed to upload: {e}")
+
+# Hàm xử lý chạy ứng dụng
+def run_command(
+    prompt, mode, height, width, steps, seed, cfg_scale, strength, sampling_method,
+    batch_count, schedule, clip_skip, vae_tiling, vae_on_cpu, clip_on_cpu, diffusion_fa,
+    control_net_cpu, canny, verbose, init_image=None
+):
+    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),
+            "--cfg-scale", str(cfg_scale),
+            "--strength", str(strength),
+            "--sampling-method", sampling_method,
+            "--batch-count", str(batch_count),
+            "--schedule", schedule,
+            "--clip-skip", str(clip_skip),
+        ]
+
+        # Thêm tùy chọn VAE tiling
+        if vae_tiling:
+            command.append("--vae-tiling")
+        if vae_on_cpu:
+            command.append("--vae-on-cpu")
+        if clip_on_cpu:
+            command.append("--clip-on-cpu")
+        if diffusion_fa:
+            command.append("--diffusion-fa")
+        if control_net_cpu:
+            command.append("--control-net-cpu")
+        if canny:
+            command.append("--canny")
+        if verbose:
+            command.append("-v")
+
+        # 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
+            if os.path.exists(output_path):
+                # Lưu ảnh lên Hugging Face Dataset
+                save_to_huggingface(output_path, hf_token)
+                return output_path, "\n".join(logs)
+            else:
+                return 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):
+    """Hi���n 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)
+
+        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="Random Seed (-1 for random)", interactive=True
+            )
+            cfg_scale = gr.Slider(
+                1, 20, value=7, step=0.1, label="CFG Scale", interactive=True
+            )
+            strength = gr.Slider(
+                0, 1, value=0.75, step=0.01, label="Strength (img2img only)", interactive=True
+            )
+
+    with gr.Row():
+        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", interactive=True
+        )
+        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 Sigma Schedule", interactive=True
+        )
+
+    with gr.Row():
+        clip_skip = gr.Slider(
+            -1, 10, value=-1, step=1, label="CLIP Skip Layers", interactive=True
+        )
+        vae_tiling = gr.Checkbox(label="VAE Tiling", value=False)
+        vae_on_cpu = gr.Checkbox(label="VAE on CPU", value=False)
+        clip_on_cpu = gr.Checkbox(label="CLIP on CPU", value=False)
+        diffusion_fa = gr.Checkbox(label="Diffusion Flash Attention", value=False)
+        control_net_cpu = gr.Checkbox(label="ControlNet on CPU", value=False)
+        canny = gr.Checkbox(label="Canny Preprocessor", value=False)
+        verbose = gr.Checkbox(label="Verbose Logging", value=False)
+
+    # Nút chạy và kết quả
+    with gr.Row():
+        run_button = gr.Button("🚀 Run", variant="primary")
+
+    with gr.Row():
+        output_image = gr.File(label="Download Image", interactive=False)
+        log_output = gr.Textbox(label="Logs", interactive=False, lines=10)
+
+    # Kết nối nút Run với hàm xử lý
+    run_button.click(
+        run_command,
+        inputs=[
+            prompt, mode, height, width, steps, seed, cfg_scale, strength, sampling_method,
+            batch_count, schedule, clip_skip, vae_tiling, vae_on_cpu, clip_on_cpu, diffusion_fa,
+            control_net_cpu, canny, verbose, init_image
+        ],
+        outputs=[output_image, log_output],
+    )
+
+demo.launch()