Update app.py

app.py

@@ -18,62 +18,62 @@ TRELLIS_SPACE = "microsoft/TRELLIS.2"
 TEMP_DIR = os.path.join(tempfile.gettempdir(), "trellis_3d")
 os.makedirs(TEMP_DIR, exist_ok=True)
 
+FALLBACK_3D_SPACES = [
+    "TencentARC/InstantMesh",
+    "sudo-ai/zero123plus",
+]
 
-def text_to_image(prompt, seed=0):
-    try:
-        from gradio_client import Client
 
-        logger.info(f"Generating image from text: {prompt}")
-
-        spaces = [
-            {
-                "space": "stabilityai/stable-diffusion-3.5-large-turbo",
-                "args": [prompt, "", seed, True, 1024, 1024, 4, 0],
-                "endpoint": "/infer"
-            },
-            {
-                "space": "black-forest-labs/FLUX.1-schnell",
-                "args": [prompt, seed, True, 1024, 1024, 4],
-                "endpoint": "/infer"
-            },
-        ]
-
-        for space_config in spaces:
-            try:
-                logger.info(f"Trying: {space_config['space']}")
-                img_client = Client(space_config["space"])
-                result = img_client.predict(
-                    *space_config["args"],
-                    api_name=space_config["endpoint"]
-                )
-
-                img_path = None
-                if isinstance(result, str) and os.path.exists(result):
-                    img_path = result
-                elif isinstance(result, tuple) and len(result) > 0:
-                    first = result[0]
-                    if isinstance(first, str) and os.path.exists(first):
-                        img_path = first
-                    elif isinstance(first, dict) and "path" in first:
-                        img_path = first["path"]
-                elif isinstance(result, dict) and "path" in result:
-                    img_path = result["path"]
-                elif hasattr(result, "path"):
-                    img_path = result.path
-
-                if img_path and os.path.exists(img_path):
-                    logger.info(f"Image generated: {img_path}")
-                    return img_path
-
-            except Exception as e:
-                logger.warning(f"{space_config['space']} failed: {e}")
-                continue
+def text_to_image(prompt, seed=0):
+    from gradio_client import Client
+
+    logger.info(f"Generating image from text: {prompt}")
+
+    spaces = [
+        {
+            "space": "stabilityai/stable-diffusion-3.5-large-turbo",
+            "args": [prompt, "", seed, True, 1024, 1024, 4, 0],
+            "endpoint": "/infer"
+        },
+        {
+            "space": "black-forest-labs/FLUX.1-schnell",
+            "args": [prompt, seed, True, 1024, 1024, 4],
+            "endpoint": "/infer"
+        },
+    ]
+
+    for space_config in spaces:
+        try:
+            logger.info(f"Trying image gen: {space_config['space']}")
+            img_client = Client(space_config["space"])
+            result = img_client.predict(
+                *space_config["args"],
+                api_name=space_config["endpoint"]
+            )
 
-        return None
+            img_path = None
+            if isinstance(result, str) and os.path.exists(result):
+                img_path = result
+            elif isinstance(result, tuple) and len(result) > 0:
+                first = result[0]
+                if isinstance(first, str) and os.path.exists(first):
+                    img_path = first
+                elif isinstance(first, dict) and "path" in first:
+                    img_path = first["path"]
+            elif isinstance(result, dict) and "path" in result:
+                img_path = result["path"]
+            elif hasattr(result, "path"):
+                img_path = result.path
+
+            if img_path and os.path.exists(img_path):
+                logger.info(f"Image generated: {img_path}")
+                return img_path
+
+        except Exception as e:
+            logger.warning(f"{space_config['space']} failed: {e}")
+            continue
 
-    except Exception as e:
-        logger.error(f"Text to image failed: {e}")
-        return None
+    return None
 
 
 def generate_3d_from_image(
@@ -101,124 +101,172 @@ def generate_3d_from_image(
     logger.info("Starting 3D generation pipeline")
     logger.info(f"Image: {image_path}")
 
-    client = Client(TRELLIS_SPACE)
-    logger.info("Connected to TRELLIS.2")
+    max_retries = 5
+    retry_delays = [10, 30, 60, 90, 120]
 
-    # Step 1: Start Session
-    logger.info("Step 1/5: Starting session...")
-    try:
-        client.predict(api_name="/start_session")
-        logger.info("Session started")
-    except Exception as e:
-        logger.warning(f"Start session note: {e}")
+    for attempt in range(max_retries):
+        try:
+            if attempt > 0:
+                wait_time = retry_delays[min(attempt, len(retry_delays) - 1)]
+                logger.info(
+                    f"Retry {attempt + 1}/{max_retries} "
+                    f"after {wait_time}s wait..."
+                )
+                time.sleep(wait_time)
 
-    # Step 2: Preprocess Image
-    logger.info("Step 2/5: Preprocessing image...")
-    processed_path = image_path
-    try:
-        preprocessed = client.predict(
-            handle_file(image_path),
-            api_name="/preprocess_image"
-        )
-        logger.info(f"Preprocessed result: {preprocessed}")
+            client = Client(TRELLIS_SPACE)
+            logger.info("Connected to TRELLIS.2")
 
-        if preprocessed:
-            if isinstance(preprocessed, dict) and "path" in preprocessed:
-                processed_path = preprocessed["path"]
-            elif isinstance(preprocessed, str) and os.path.exists(preprocessed):
-                processed_path = preprocessed
-            elif hasattr(preprocessed, "path"):
-                processed_path = preprocessed.path
+            logger.info("Step 1/5: Starting session...")
+            try:
+                client.predict(api_name="/start_session")
+                logger.info("Session started")
+            except Exception as e:
+                logger.warning(f"Start session note: {e}")
 
-        logger.info(f"Using image: {processed_path}")
+            logger.info("Step 2/5: Preprocessing image...")
+            processed_path = image_path
+            try:
+                preprocessed = client.predict(
+                    handle_file(image_path),
+                    api_name="/preprocess_image"
+                )
+                logger.info(f"Preprocessed: {preprocessed}")
 
-    except Exception as e:
-        logger.warning(f"Preprocess note: {e}")
+                if preprocessed:
+                    if isinstance(preprocessed, dict) and "path" in preprocessed:
+                        processed_path = preprocessed["path"]
+                    elif isinstance(preprocessed, str) and os.path.exists(preprocessed):
+                        processed_path = preprocessed
+                    elif hasattr(preprocessed, "path"):
+                        processed_path = preprocessed.path
 
-    # Step 3: Get Seed
-    logger.info("Step 3/5: Getting seed...")
-    actual_seed = seed
-    try:
-        actual_seed = client.predict(
-            randomize_seed,
-            seed,
-            api_name="/get_seed"
-        )
-        logger.info(f"Seed: {actual_seed}")
-    except Exception as e:
-        logger.warning(f"Get seed note: {e}")
-
-    # Step 4: Image to 3D
-    logger.info("Step 4/5: Generating 3D model (1-3 minutes)...")
-
-    if isinstance(processed_path, str):
-        image_input = handle_file(processed_path)
-    else:
-        image_input = processed_path
-
-    preview_result = client.predict(
-        image_input,
-        actual_seed,
-        resolution,
-        ss_guidance_strength,
-        ss_guidance_rescale,
-        ss_sampling_steps,
-        ss_rescale_t,
-        shape_slat_guidance_strength,
-        shape_slat_guidance_rescale,
-        shape_slat_sampling_steps,
-        shape_slat_rescale_t,
-        tex_slat_guidance_strength,
-        tex_slat_guidance_rescale,
-        tex_slat_sampling_steps,
-        tex_slat_rescale_t,
-        api_name="/image_to_3d"
-    )
+            except Exception as e:
+                logger.warning(f"Preprocess note: {e}")
 
-    logger.info(f"3D result type: {type(preview_result)}")
-    logger.info(f"3D result: {str(preview_result)[:500]}")
+            logger.info("Step 3/5: Getting seed...")
+            actual_seed = seed
+            try:
+                actual_seed = client.predict(
+                    randomize_seed,
+                    seed,
+                    api_name="/get_seed"
+                )
+                logger.info(f"Seed: {actual_seed}")
+            except Exception as e:
+                logger.warning(f"Seed note: {e}")
+
+            logger.info("Step 4/5: Generating 3D (1-3 min)...")
+
+            if isinstance(processed_path, str):
+                image_input = handle_file(processed_path)
+            else:
+                image_input = processed_path
+
+            preview_result = client.predict(
+                image_input,
+                actual_seed,
+                resolution,
+                ss_guidance_strength,
+                ss_guidance_rescale,
+                ss_sampling_steps,
+                ss_rescale_t,
+                shape_slat_guidance_strength,
+                shape_slat_guidance_rescale,
+                shape_slat_sampling_steps,
+                shape_slat_rescale_t,
+                tex_slat_guidance_strength,
+                tex_slat_guidance_rescale,
+                tex_slat_sampling_steps,
+                tex_slat_rescale_t,
+                api_name="/image_to_3d"
+            )
 
-    # Step 5: Extract GLB
-    logger.info("Step 5/5: Extracting GLB file...")
+            logger.info(f"3D result: {str(preview_result)[:300]}")
 
-    glb_result = client.predict(
-        decimation_target,
-        texture_size,
-        api_name="/extract_glb"
-    )
+            logger.info("Step 5/5: Extracting GLB...")
 
-    logger.info(f"GLB result type: {type(glb_result)}")
-    logger.info(f"GLB result: {glb_result}")
+            glb_result = client.predict(
+                decimation_target,
+                texture_size,
+                api_name="/extract_glb"
+            )
+
+            logger.info(f"GLB result: {glb_result}")
+
+            glb_path = extract_glb_path(glb_result)
+
+            if glb_path and os.path.exists(glb_path):
+                file_id = uuid.uuid4().hex[:8]
+                output_path = os.path.join(TEMP_DIR, f"model_{file_id}.glb")
+                shutil.copy2(glb_path, output_path)
+                logger.info(f"GLB saved: {output_path}")
+                return output_path
+
+            logger.error(f"No GLB from: {glb_result}")
+            return None
+
+        except Exception as e:
+            error_msg = str(e)
+            logger.error(f"Attempt {attempt + 1} failed: {error_msg}")
+
+            is_quota_error = any(
+                phrase in error_msg.lower()
+                for phrase in [
+                    "gpu quota",
+                    "exceeded",
+                    "queue",
+                    "too many",
+                    "rate limit",
+                    "capacity",
+                    "busy"
+                ]
+            )
 
-    glb_path = None
+            if is_quota_error and attempt < max_retries - 1:
+                logger.info("GPU quota issue. Will retry...")
+                continue
+            elif is_quota_error:
+                raise Exception(
+                    "GPU quota exceeded on TRELLIS. "
+                    "The free GPU is busy right now. "
+                    "Please try again in 2-5 minutes. "
+                    "Tip: Use resolution 512 for faster processing."
+                )
+            else:
+                raise
+
+    return None
+
+
+def extract_glb_path(glb_result):
+    if glb_result is None:
+        return None
 
     if isinstance(glb_result, tuple):
         for item in glb_result:
-            if isinstance(item, str) and os.path.exists(item):
-                glb_path = item
-                break
-            elif isinstance(item, dict) and "path" in item:
-                if os.path.exists(item["path"]):
-                    glb_path = item["path"]
-                    break
-            elif hasattr(item, "path") and os.path.exists(item.path):
-                glb_path = item.path
-                break
-    elif isinstance(glb_result, str) and os.path.exists(glb_result):
-        glb_path = glb_result
-    elif isinstance(glb_result, dict) and "path" in glb_result:
-        glb_path = glb_result["path"]
-    elif hasattr(glb_result, "path"):
-        glb_path = glb_result.path
-
-    if glb_path and os.path.exists(glb_path):
-        file_id = uuid.uuid4().hex[:8]
-        output_path = os.path.join(TEMP_DIR, f"model_{file_id}.glb")
-        shutil.copy2(glb_path, output_path)
-        logger.info(f"GLB saved to: {output_path}")
-        return output_path
-
-    logger.error(f"Could not extract GLB from: {glb_result}")
+            path = extract_single_path(item)
+            if path:
+                return path
+
+    return extract_single_path(glb_result)
+
+
+def extract_single_path(item):
+    if item is None:
+        return None
+
+    if isinstance(item, str) and os.path.exists(item):
+        return item
+
+    if isinstance(item, dict) and "path" in item:
+        if os.path.exists(item["path"]):
+            return item["path"]
+
+    if hasattr(item, "path"):
+        if isinstance(item.path, str) and os.path.exists(item.path):
+            return item.path
+
     return None
 
 
@@ -258,16 +306,15 @@ def handle_image_to_3d(
         elif hasattr(image, "save"):
             image.save(img_path, "PNG")
         else:
-            raise gr.Error("Invalid image")
+            raise gr.Error("Invalid image format")
     except gr.Error:
         raise
     except Exception as e:
         raise gr.Error(f"Image error: {e}")
 
-    progress(0.1, desc="Connecting to TRELLIS...")
-
     try:
-        progress(0.15, desc="Generating 3D model... (1-3 min)")
+        progress(0.1, desc="Connecting to TRELLIS (free GPU)...")
+        progress(0.15, desc="Generating 3D... This takes 1-3 min. If GPU is busy it will auto-retry...")
 
         glb_path = generate_3d_from_image(
             image_path=img_path,
@@ -290,11 +337,14 @@ def handle_image_to_3d(
             texture_size=texture_size
         )
 
-        progress(0.95, desc="Almost done...")
         duration = time.time() - start_time
 
         if glb_path and os.path.exists(glb_path):
-            status = f"Generated in {duration:.1f}s"
+            file_size = os.path.getsize(glb_path) / (1024 * 1024)
+            status = (
+                f"Done! Generated in {duration:.1f}s | "
+                f"File size: {file_size:.1f} MB"
+            )
             progress(1.0, desc="Done!")
             return glb_path, glb_path, status
         else:
@@ -303,7 +353,7 @@ def handle_image_to_3d(
     except gr.Error:
         raise
     except Exception as e:
-        logger.error(f"Generation error: {e}", exc_info=True)
+        logger.error(f"Error: {e}", exc_info=True)
         raise gr.Error(f"Generation failed: {str(e)}")
 
 
@@ -333,17 +383,19 @@ def handle_text_to_3d(
 
     start_time = time.time()
 
-    progress(0.05, desc="Generating image from text...")
+    progress(0.05, desc="Step 1: Generating image from text...")
 
     img_path = text_to_image(prompt, seed)
 
     if img_path is None:
         raise gr.Error(
             "Could not generate image from text. "
-            "Try using the Image to 3D tab instead."
+            "The image AI spaces might be busy. "
+            "Try the Image to 3D tab instead - "
+            "generate an image with any AI tool and upload it."
         )
 
-    progress(0.3, desc="Image ready! Now generating 3D...")
+    progress(0.3, desc="Step 2: Image ready! Generating 3D model...")
 
     try:
         glb_path = generate_3d_from_image(
@@ -367,11 +419,15 @@ def handle_text_to_3d(
             texture_size=texture_size
         )
 
-        progress(0.95, desc="Almost done...")
         duration = time.time() - start_time
 
         if glb_path and os.path.exists(glb_path):
-            status = f"Generated in {duration:.1f}s from prompt: {prompt}"
+            file_size = os.path.getsize(glb_path) / (1024 * 1024)
+            status = (
+                f"Done! Generated in {duration:.1f}s | "
+                f"Prompt: {prompt} | "
+                f"File: {file_size:.1f} MB"
+            )
             progress(1.0, desc="Done!")
             return img_path, glb_path, glb_path, status
         else:
@@ -388,19 +444,29 @@ def check_status():
     try:
         from gradio_client import Client
 
+        start = time.time()
         client = Client(TRELLIS_SPACE)
+        connect_time = time.time() - start
         api_str = client.view_api(return_format="str")
 
         return (
             "## Connected to TRELLIS.2\n\n"
             f"**Space:** `{TRELLIS_SPACE}`\n\n"
+            f"**Connection time:** {connect_time:.1f}s\n\n"
             "**Status:** Online and Ready\n\n"
-            "### Pipeline Steps:\n"
-            "1. `/start_session` - Initialize\n"
-            "2. `/preprocess_image` - Clean image\n"
-            "3. `/get_seed` - Prepare seed\n"
-            "4. `/image_to_3d` - Generate 3D (15 params)\n"
-            "5. `/extract_glb` - Download GLB file\n\n"
+            "### How it works (all FREE):\n\n"
+            "| Step | Endpoint | What it does |\n"
+            "|------|----------|-------------|\n"
+            "| 1 | /start_session | Initialize |\n"
+            "| 2 | /preprocess_image | Clean image |\n"
+            "| 3 | /get_seed | Prepare seed |\n"
+            "| 4 | /image_to_3d | Generate 3D |\n"
+            "| 5 | /extract_glb | Get GLB file |\n\n"
+            "### Tips for free GPU:\n"
+            "- Use resolution **512** for fastest results\n"
+            "- If GPU quota error, wait 2-5 min and retry\n"
+            "- Auto-retry is built in (up to 5 attempts)\n"
+            "- Off-peak hours (night/early morning) work best\n\n"
             "### Raw API:\n"
             f"```\n{api_str}\n```"
         )
@@ -409,10 +475,11 @@ def check_status():
         return (
             "## Connection Failed\n\n"
             f"**Error:** `{str(e)}`\n\n"
-            "The TRELLIS space might be:\n"
-            "- Sleeping (wait 2-3 min, it auto-wakes)\n"
+            "**Possible reasons:**\n"
+            "- Space is sleeping (wait 2-3 min)\n"
             "- At capacity (try again later)\n"
-            "- Under maintenance"
+            "- Under maintenance\n\n"
+            "Click Check Connection again to retry."
         )
 
 
@@ -428,10 +495,6 @@ def cleanup():
         return f"Error: {e}"
 
 
-# ============================================
-# GRADIO UI
-# ============================================
-
 css = """
 .gradio-container {
     max-width: 1200px !important;
@@ -464,6 +527,26 @@ css = """
     -webkit-text-fill-color: transparent;
     font-size: 2.2em !important;
 }
+
+.tip-box {
+    background: #e8f5e9;
+    border: 1px solid #4caf50;
+    border-radius: 8px;
+    padding: 10px 15px;
+    margin: 8px 0;
+    color: #2e7d32;
+    font-size: 14px;
+}
+
+.warn-box {
+    background: #fff3e0;
+    border: 1px solid #ff9800;
+    border-radius: 8px;
+    padding: 10px 15px;
+    margin: 8px 0;
+    color: #e65100;
+    font-size: 14px;
+}
 """
 
 with gr.Blocks(
@@ -481,17 +564,23 @@ with gr.Blocks(
         '<div class="header-area">'
         "<h1>TRELLIS 3D Generator</h1>"
         '<p style="color: #888; font-size: 15px;">'
-        "Generate 3D models from images or text - Powered by Microsoft TRELLIS 2"
+        "Generate 3D models from images or text | 100% Free | Powered by Microsoft TRELLIS 2"
         "</p>"
         "</div>"
     )
 
     with gr.Tabs():
 
-        # ==========================================
-        # TAB 1: IMAGE TO 3D
-        # ==========================================
         with gr.Tab("Image to 3D"):
+
+            gr.HTML(
+                '<div class="tip-box">'
+                "<strong>Tip:</strong> Use resolution 512 for fastest results. "
+                "If you get a GPU quota error, the app will auto-retry up to 5 times. "
+                "Best results during off-peak hours."
+                "</div>"
+            )
+
             with gr.Row():
 
                 with gr.Column(scale=2):
@@ -524,57 +613,69 @@ with gr.Blocks(
 
                     img_resolution = gr.Radio(
                         choices=["512", "1024", "1536"],
-                        value="1024",
-                        label="Resolution"
+                        value="512",
+                        label="Resolution (512 = fastest, less GPU usage)"
                     )
 
                     with gr.Accordion("Advanced: Sparse Structure", open=False):
                         img_ss_guidance = gr.Slider(
-                            0, 20, 7.5, step=0.1, label="Guidance Strength"
+                            0, 20, 7.5, step=0.1,
+                            label="Guidance Strength"
                         )
                         img_ss_rescale = gr.Slider(
-                            0, 1, 0.7, step=0.05, label="Guidance Rescale"
+                            0, 1, 0.7, step=0.05,
+                            label="Guidance Rescale"
                         )
                         img_ss_steps = gr.Slider(
-                            1, 50, 12, step=1, label="Sampling Steps"
+                            1, 50, 12, step=1,
+                            label="Sampling Steps"
                         )
                         img_ss_rescale_t = gr.Slider(
-                            0, 10, 5.0, step=0.1, label="Rescale T"
+                            0, 10, 5.0, step=0.1,
+                            label="Rescale T"
                         )
 
                     with gr.Accordion("Advanced: Shape Latent", open=False):
                         img_shape_guidance = gr.Slider(
-                            0, 20, 7.5, step=0.1, label="Guidance Strength"
+                            0, 20, 7.5, step=0.1,
+                            label="Guidance Strength"
                         )
                         img_shape_rescale = gr.Slider(
-                            0, 1, 0.5, step=0.05, label="Guidance Rescale"
+                            0, 1, 0.5, step=0.05,
+                            label="Guidance Rescale"
                         )
                         img_shape_steps = gr.Slider(
-                            1, 50, 12, step=1, label="Sampling Steps"
+                            1, 50, 12, step=1,
+                            label="Sampling Steps"
                         )
                         img_shape_rescale_t = gr.Slider(
-                            0, 10, 3.0, step=0.1, label="Rescale T"
+                            0, 10, 3.0, step=0.1,
+                            label="Rescale T"
                         )
 
                     with gr.Accordion("Advanced: Texture Latent", open=False):
                         img_tex_guidance = gr.Slider(
-                            0, 20, 1.0, step=0.1, label="Guidance Strength"
+                            0, 20, 1.0, step=0.1,
+                            label="Guidance Strength"
                         )
                         img_tex_rescale = gr.Slider(
-                            0, 1, 0.0, step=0.05, label="Guidance Rescale"
+                            0, 1, 0.0, step=0.05,
+                            label="Guidance Rescale"
                         )
                         img_tex_steps = gr.Slider(
-                            1, 50, 12, step=1, label="Sampling Steps"
+                            1, 50, 12, step=1,
+                            label="Sampling Steps"
                         )
                         img_tex_rescale_t = gr.Slider(
-                            0, 10, 3.0, step=0.1, label="Rescale T"
+                            0, 10, 3.0, step=0.1,
+                            label="Rescale T"
                         )
 
                     with gr.Accordion("Advanced: Export Settings", open=False):
                         img_decimation = gr.Slider(
                             10000, 1000000, 300000,
                             step=10000,
-                            label="Mesh Faces (Decimation Target)"
+                            label="Mesh Faces"
                         )
                         img_texture_size = gr.Slider(
                             512, 4096, 2048,
@@ -595,7 +696,10 @@ with gr.Blocks(
 
                 with gr.Column(scale=3):
                     gr.Markdown("### 3D Model Result")
-                    img_model = gr.Model3D(label="3D Viewer", height=500)
+                    img_model = gr.Model3D(
+                        label="3D Viewer",
+                        height=500
+                    )
                     img_download = gr.File(label="Download GLB")
                     gr.Markdown(
                         "Drag = Rotate | Scroll = Zoom | Right-drag = Pan"
@@ -627,16 +731,15 @@ with gr.Blocks(
                 show_progress="full"
             )
 
-        # ==========================================
-        # TAB 2: TEXT TO 3D
-        # ==========================================
         with gr.Tab("Text to 3D"):
 
-            gr.Markdown(
-                "> **Note:** Text to 3D works in 2 steps. "
-                "First generates an image from your text using Stable Diffusion, "
-                "then converts that image to 3D using TRELLIS. "
-                "This may take 2-5 minutes total."
+            gr.HTML(
+                '<div class="warn-box">'
+                "<strong>Note:</strong> Text to 3D works in 2 steps: "
+                "First generates an image from your text (Stable Diffusion), "
+                "then converts that image to 3D (TRELLIS). "
+                "Takes 2-5 minutes. 100% free."
+                "</div>"
             )
 
             with gr.Row():
@@ -646,7 +749,7 @@ with gr.Blocks(
 
                     txt_prompt = gr.Textbox(
                         label="Text Prompt",
-                        placeholder="A cute cat sitting, white background, single object, 3D render style...",
+                        placeholder="A cute cat sitting, white background, single object, 3D render...",
                         lines=3
                     )
 
@@ -689,7 +792,7 @@ with gr.Blocks(
 
                     gr.Markdown(
                         "**Tip:** Add 'white background, 3D render, single object' "
-                        "to your prompt for better results."
+                        "for better results."
                     )
 
                     with gr.Row():
@@ -707,50 +810,62 @@ with gr.Blocks(
 
                     txt_resolution = gr.Radio(
                         choices=["512", "1024", "1536"],
-                        value="1024",
-                        label="Resolution"
+                        value="512",
+                        label="Resolution (512 = fastest)"
                     )
 
                     with gr.Accordion("Advanced: Sparse Structure", open=False):
                         txt_ss_guidance = gr.Slider(
-                            0, 20, 7.5, step=0.1, label="Guidance Strength"
+                            0, 20, 7.5, step=0.1,
+                            label="Guidance Strength"
                         )
                         txt_ss_rescale = gr.Slider(
-                            0, 1, 0.7, step=0.05, label="Guidance Rescale"
+                            0, 1, 0.7, step=0.05,
+                            label="Guidance Rescale"
                         )
                         txt_ss_steps = gr.Slider(
-                            1, 50, 12, step=1, label="Sampling Steps"
+                            1, 50, 12, step=1,
+                            label="Sampling Steps"
                         )
                         txt_ss_rescale_t = gr.Slider(
-                            0, 10, 5.0, step=0.1, label="Rescale T"
+                            0, 10, 5.0, step=0.1,
+                            label="Rescale T"
                         )
 
                     with gr.Accordion("Advanced: Shape Latent", open=False):
                         txt_shape_guidance = gr.Slider(
-                            0, 20, 7.5, step=0.1, label="Guidance Strength"
+                            0, 20, 7.5, step=0.1,
+                            label="Guidance Strength"
                         )
                         txt_shape_rescale = gr.Slider(
-                            0, 1, 0.5, step=0.05, label="Guidance Rescale"
+                            0, 1, 0.5, step=0.05,
+                            label="Guidance Rescale"
                         )
                         txt_shape_steps = gr.Slider(
-                            1, 50, 12, step=1, label="Sampling Steps"
+                            1, 50, 12, step=1,
+                            label="Sampling Steps"
                         )
                         txt_shape_rescale_t = gr.Slider(
-                            0, 10, 3.0, step=0.1, label="Rescale T"
+                            0, 10, 3.0, step=0.1,
+                            label="Rescale T"
                         )
 
                     with gr.Accordion("Advanced: Texture Latent", open=False):
                         txt_tex_guidance = gr.Slider(
-                            0, 20, 1.0, step=0.1, label="Guidance Strength"
+                            0, 20, 1.0, step=0.1,
+                            label="Guidance Strength"
                         )
                         txt_tex_rescale = gr.Slider(
-                            0, 1, 0.0, step=0.05, label="Guidance Rescale"
+                            0, 1, 0.0, step=0.05,
+                            label="Guidance Rescale"
                         )
                         txt_tex_steps = gr.Slider(
-                            1, 50, 12, step=1, label="Sampling Steps"
+                            1, 50, 12, step=1,
+                            label="Sampling Steps"
                         )
                         txt_tex_rescale_t = gr.Slider(
-                            0, 10, 3.0, step=0.1, label="Rescale T"
+                            0, 10, 3.0, step=0.1,
+                            label="Rescale T"
                         )
 
                     with gr.Accordion("Advanced: Export", open=False):
@@ -777,13 +892,12 @@ with gr.Blocks(
                     )
 
                 with gr.Column(scale=3):
-                    gr.Markdown("### Generated Image then 3D Model")
+                    gr.Markdown("### Generated Image to 3D Model")
 
                     txt_gen_image = gr.Image(
                         label="Generated Image (Step 1)",
                         height=200
                     )
-
                     txt_model = gr.Model3D(
                         label="3D Model (Step 2)",
                         height=400
@@ -824,16 +938,15 @@ with gr.Blocks(
                 show_progress="full"
             )
 
-        # ==========================================
-        # TAB 3: STATUS AND API
-        # ==========================================
         with gr.Tab("Status and API"):
+
             with gr.Row():
 
                 with gr.Column():
-                    gr.Markdown("### Connection")
+                    gr.Markdown("### Connection Status")
+
                     status_display = gr.Markdown(
-                        "Click Check Connection to start."
+                        "Click Check Connection to test."
                     )
 
                     with gr.Row():
@@ -861,23 +974,26 @@ with gr.Blocks(
                     gr.Markdown("### How It Works")
                     gr.Markdown(
                         "**Text to 3D Pipeline:**\n\n"
-                        "Text Prompt -> Stable Diffusion (Image) -> TRELLIS (3D) -> GLB File\n\n"
+                        "Text -> Stable Diffusion (free) -> Image -> TRELLIS (free) -> GLB\n\n"
                         "**Image to 3D Pipeline:**\n\n"
-                        "Image Upload -> TRELLIS (3D) -> GLB File\n\n"
-                        "### TRELLIS Pipeline Steps\n\n"
-                        "1. `start_session` - Initialize\n"
-                        "2. `preprocess_image` - Clean image\n"
-                        "3. `get_seed` - Prepare seed\n"
-                        "4. `image_to_3d` - Generate 3D (15 params)\n"
-                        "5. `extract_glb` - Download GLB file\n\n"
-                        "### API Usage (Python)\n\n"
+                        "Image -> TRELLIS (free) -> GLB\n\n"
+                        "---\n\n"
+                        "### Free GPU Tips\n\n"
+                        "- Use resolution **512** for fastest/most reliable results\n"
+                        "- If GPU quota error appears, app retries up to 5 times\n"
+                        "- Best time: late night or early morning (less traffic)\n"
+                        "- Each generation uses about 60-120s of GPU time\n"
+                        "- The free quota resets every few minutes\n\n"
+                        "---\n\n"
+                        "### API Usage\n\n"
                         "```python\n"
-                        "from gradio_client import Client, handle_file\n\n"
-                        "client = Client('YOUR-SPACE-URL')\n\n"
-                        "# Image to 3D\n"
-                        "model, file = client.predict(\n"
+                        "from gradio_client import Client, handle_file\n"
+                        "\n"
+                        "client = Client('YOUR-SPACE-URL')\n"
+                        "\n"
+                        "result = client.predict(\n"
                         "    handle_file('image.png'),\n"
-                        "    0, True, '1024',\n"
+                        "    0, True, '512',\n"
                         "    7.5, 0.7, 12, 5.0,\n"
                         "    7.5, 0.5, 12, 3.0,\n"
                         "    1.0, 0.0, 12, 3.0,\n"
@@ -888,10 +1004,10 @@ with gr.Blocks(
                     )
 
     gr.Markdown(
-        "<div style='text-align:center; padding:15px; opacity:0.5; font-size:12px;'>"
-        "TRELLIS 3D Generator - "
-        "<a href='https://github.com/microsoft/TRELLIS.2'>Microsoft TRELLIS 2</a> - "
-        "Built with Gradio"
+        "<div style='text-align:center; padding:15px; "
+        "opacity:0.5; font-size:12px;'>"
+        "TRELLIS 3D Generator | 100% Free | "
+        "Powered by Microsoft TRELLIS 2 | Built with Gradio"
         "</div>"
     )