Update app.py

app.py

@@ -28,12 +28,15 @@ def download_file(url, output_path, token):
 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):
+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
@@ -51,8 +54,30 @@ def run_command(prompt, mode, height, width, steps, seed, init_image=None):
             "-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])
@@ -65,9 +90,8 @@ def run_command(prompt, mode, height, width, steps, seed, init_image=None):
         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
 
+        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
@@ -91,7 +115,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         Generate stunning images from text or modify existing images with AI-powered tools.
         """
     )
-    
+
     # Thiết lập giao diện
     with gr.Row():
         with gr.Column():
@@ -119,11 +143,42 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             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)
@@ -131,8 +186,12 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     # Kết nối nút Run với hàm xử lý
     run_button.click(
         run_command,
-        inputs=[prompt, mode, height, width, steps, seed, init_image],
+        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()
+demo.launch()