Update pyproject.toml

Update docs for load balance, logs, and Distributed Value
Order collector outputs by enabled worker list
2026-02-28 04:53:20 +00:00 · 2026-02-28 04:53:20 +00:00 · 2026-02-28 04:53:20 +00:00 · 2026-02-28 04:53:20 +00:00 · 2026-02-28 04:53:20 +00:00 · 2026-02-28 04:53:20 +00:00
121 changed files with 20836 additions and 10301 deletions
@@ -7,14 +7,18 @@ on:
    paths:
      - "pyproject.toml"

+permissions:
+  issues: write
+
 jobs:
  publish-node:
    name: Publish Custom Node to registry
    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'robertvoy' }}
    steps:
      - name: Check out code
        uses: actions/checkout@v4
      - name: Publish Custom Node
-        uses: Comfy-Org/publish-node-action@main
+        uses: Comfy-Org/publish-node-action@v1
        with:
          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
@@ -0,0 +1,9 @@
+bin/
+logs/
+gpu_config.json
+__pycache__/
+**/__pycache__/
+*.py[cod]
+node_modules/
+npm-debug.log*
+AGENTS.md
@@ -0,0 +1 @@
+20
@@ -1,36 +1,60 @@
 <div align="center">
-<img width="320" src="https://github.com/user-attachments/assets/537d38cc-2e34-4527-bca7-0d79f4120cce" />
+<img width="250" src="https://github.com/user-attachments/assets/533bb98d-0c4a-499f-9bca-5c937e361087" />
 <br><br>
 <a href="https://www.youtube.com/watch?v=p6eE3IlAbOs"><img src="https://img.shields.io/badge/Video_Tutorial-grey?style=flat&logo=youtube&logoColor=white" alt="Video Tutorial"></a>
 <a href="/docs/worker-setup-guides.md"><img src="https://img.shields.io/badge/Setup_Guides-grey?style=flat&logo=gitbook&logoColor=white" alt="Setup Guides"></a>
 <a href="/workflows"><img src="https://img.shields.io/badge/Workflows-grey?style=flat&logo=json&logoColor=white" alt="Workflows"></a>
 <a href="https://buymeacoffee.com/robertvoy"><img src="https://img.shields.io/badge/Donation-grey?style=flat&logo=buymeacoffee&logoColor=white" alt="Donation"></a>
-<a href="https://x.com/robertvoy3D"><img src="https://img.shields.io/twitter/follow/robertvoy3D" alt="Twitter"></a>
+<a href="https://x.com/rbw_ai"><img src="https://img.shields.io/twitter/follow/rbw_ai" alt="Twitter"></a>
 <br><br>
 </div>

-> **A powerful extension for ComfyUI that enables distributed and parallel processing across multiple GPUs and machines. Generate more images and videos and accelerate your upscaling workflows by leveraging all available GPU resources in your network.**
+> **A powerful extension for ComfyUI that enables distributed and parallel processing across multiple GPUs and machines. Generate more images and videos and accelerate your upscaling workflows by leveraging all available GPU resources in your network and cloud.**

 ![Clipboard Image (7)](https://github.com/user-attachments/assets/66aaadef-f195-48a1-a368-17dd0dae477d)

 ---

-## Key Features
+## Key Features

 #### Parallel Workflow Processing
- Run workflows on multiple GPUs simultaneously with varied seeds
+- Run your workflow on multiple GPUs simultaneously with varied seeds, collect results on the master
 - Scale output with more workers
 - Supports images and videos

 #### Distributed Upscaling
 - Accelerate Ultimate SD Upscale by distributing tiles across GPUs
 - Intelligent distribution
- Handles single images and batches
+- Handles single images and videos

-#### Ease of Use
- Auto-setup local workers; easily add remote/cloud ones
- Convert any workflow to distributed with 2 nodes
- JSON configuration with UI controls
+#### Ease of Use
+- Auto-setup local workers; easily add remote/cloud ones
+- Convert any workflow to distributed with 2 nodes
+- JSON configuration with UI controls
+
+---
+
+## Current Architecture
+
+- Workflow-level load balancing is controlled by **Distributed Collector** via the `load_balance` toggle.
+- There is **no Distributed Queue node** anymore.
+- With `load_balance=true`, orchestration selects one least-busy execution participant:
+  - If master participation is enabled, master is included as a candidate.
+  - If master is in orchestrator-only mode, only workers are considered.
+
+---
+
+## Worker Types
+
+<img width="200" align="right" alt="ComfyUI_temp_khvcc_00034_@0 25x" src="https://github.com/user-attachments/assets/651e4912-7c23-4e32-bd88-250f5175e129" />
+
+ComfyUI Distributed supports three types of workers:
+
+- **Local Workers** - Additional GPUs on the same machine (auto-configured on first launch)
+- **Remote Workers** - GPUs on other computers in your network
+- **Cloud Workers** - GPUs hosted on a cloud service like Runpod, accessible via secure tunnels
+
+> For detailed setup instructions, see the [setup guide](/docs/worker-setup-guides.md)

 ---

@@ -38,7 +62,8 @@

 - ComfyUI
 > Note: Desktop app not currently supported
- Multiple GPUs
+- Multiple NVIDIA GPUs
+> No additional GPUs? Use [Cloud Workers](https://github.com/robertvoy/ComfyUI-Distributed/blob/main/docs/worker-setup-guides.md#cloud-workers)
 - That's it

 ---
@@ -50,26 +75,36 @@
   git clone https://github.com/robertvoy/ComfyUI-Distributed.git
   ```
   
-2. **Restart ComfyUI** - If you'll be using remote/cloud workers, add `--enable-cors-header` to your launch arguments on the master
+2. **Restart ComfyUI**
+   - If you'll be using remote/cloud workers, add `--enable-cors-header` to your launch arguments on the master

-3. Read the [setup guide](https://github.com/robertvoy/ComfyUI-Distributed/blob/feature/cloud-workers/docs/worker-setup-guides.md) for adding workers
+3. Read the [setup guide](/docs/worker-setup-guides.md) for adding workers
+
+---
+
+## Official Sponsor
+
+[<img width="1500" height="339" src="https://github.com/user-attachments/assets/c5f75e1f-3e19-4c57-b05d-151311cd1cf0" />](https://get.runpod.io/0bw29uf3ug0p)
+
+Join Runpod with [this link](https://get.runpod.io/0bw29uf3ug0p) and unlock a special bonus.

 ---

 ## Workflow Examples

-### Basic Parallel Generation
-Generate multiple images in the time it takes to generate one. Each worker uses a different seed.
+### Basic Parallel Generation
+Generate multiple images in the time it takes to generate one. Each worker uses a different seed.

 ![Clipboard Image (6)](https://github.com/user-attachments/assets/9598c94c-d9b4-4ccf-ab16-a21398220aeb)

 > [Download workflow](/workflows/distributed-txt2img.json)

 1. Open your ComfyUI workflow
-2. Add **Distributed Seed** → connect to sampler's seed
-3. Add **Distributed Collector** → after VAE Decode
-4. Enable workers in the UI
-5. Run the workflow!
+2. Add **Distributed Seed** → connect to sampler's seed
+3. Add **Distributed Collector** → after VAE Decode
+4. Optional: enable `load_balance` on Distributed Collector to run on one least-busy participant
+5. Enable workers in the UI
+6. Run the workflow!

 ### Parallel WAN Generation
 Generate multiple videos in the time it takes to generate one. Each worker uses a different seed.
@@ -87,12 +122,12 @@ Generate multiple videos in the time it takes to generate one. Each worker uses
 7. Enable workers in the UI
 8. Run the workflow!

-### Distributed Upscaling
-Accelerate the process by distributing tiles across multiple workers, with speed scaling as you add more GPUs.
+### Distributed Image Upscaling
+Accelerate Ultimate SD Upscaler by distributing tiles across multiple workers, with speed scaling as you add more GPUs.

 ![Clipboard Image (3)](https://github.com/user-attachments/assets/ffb57a0d-7b75-4497-96d2-875d60865a1a)

-> [Download workflow](/workflows/distributed-txt2img.json)
+> [Download workflow](/workflows/distributed-upscale.json)

 1. Load your image
 2. Upscale with ESRGAN or similar
@@ -100,6 +135,61 @@ Accelerate the process by distributing tiles across multiple workers, with speed
 4. Configure tile settings
 5. Enable workers for faster processing

+### Distributed Video Upscaling
+Accelerate Ultimate SD Upscaler by distributing video tiles across multiple workers, with speed scaling as you add more GPUs.
+
+![Video Upscaler workflow](https://github.com/user-attachments/assets/3c3d61b1-0b5f-422e-8c58-7c1555fed765)
+
+> [Download workflow](/workflows/distributed-upscale-video.json)
+
+1. Load your video
+2. Optional: upscale with ESRGAN or similar
+3. Connect to **Ultimate SD Upscale Distributed**
+4. Configure tile settings
+5. Use RES4LYF (bong/res2) to get better results
+6. Enable workers for faster processing
+
+> You can run this workflow entirely on Runpod with minimal setup. [Check out the guide here.](https://github.com/robertvoy/ComfyUI-Distributed/blob/main/docs/video-upscaler-runpod-preset.md)
+
+---
+
+## Developer API
+
+Control your distributed cluster programmatically without opening the browser.
+
+* **Endpoint:** `POST /distributed/queue`
+* **Functionality:** Accepts a standard ComfyUI workflow JSON, automatically distributes it to available workers, and returns the execution ID.
+* **Documentation:** [See API Examples & Scripts](https://github.com/robertvoy/ComfyUI-Distributed/blob/main/docs/comfyui-distributed-api.md)
+
+> **⚠️ Security Warning:** Do not expose your ComfyUI port to the public internet. If you need remote access, run ComfyUI behind a secure proxy (like Cloudflare or a VPN).
+
+---
+
+## Distributed Value
+
+Use **Distributed Value** when you want per-worker overrides (for example, different prompts/models/settings per worker).
+
+- Output type adapts to the connected input where possible (`STRING`, `INT`, `FLOAT`, `COMBO`).
+- The node shows only currently enabled workers.
+- If worker enablement changes, worker fields update automatically.
+- When disconnected, it resets to default string mode and clears per-worker overrides.
+- On execution, master uses `default_value`; workers use their mapped override with typed coercion fallback to default.
+
+---
+
+## Nodes
+
+| Node | Description |
+|------|-------------|
+| **Distributed Seed** | Generates unique seeds for each worker |
+| **Distributed Collector** | Collects results (image/video frames and optionally audio) from workers back to the master; `load_balance` can route the run to one least-busy participant |
+| **Distributed Value** | Outputs per-worker override values with fallback to default |
+| **Ultimate SD Upscale Distributed** | Distributes upscale tiles across workers |
+| **Image Batch Divider** | Splits image batches for multi-GPU output |
+| **Audio Batch Divider** | Splits audio batches for multi-GPU output |
+| **Distributed Model Name** | Passes model paths to workers, enabling workflows to use models not present on the master in orchestrator-only mode |
+| **Distributed Empty Image** | Produces an empty IMAGE batch used when the master delegates all work |
+
 ---

 ## FAQ
@@ -121,7 +211,7 @@ Currently, it is not compatible with the ComfyUI desktop app.

 <details>
 <summary>Can I combine my RTX 5090 with a GTX 980 to get faster results?</summary>
-Yes, you can combine different GPUs, but performance is optimized when using similar GPUs. A significant performance imbalance between GPUs may cause bottlenecks. For upscaling, setting `static_distribution` to `false` allows the faster GPU to handle more processing, which can mitigate some bottlenecks. Note that this setting only applies to upscaling tasks.
+Yes, you can combine different GPUs, but performance is optimized when using similar GPUs. A significant performance imbalance between GPUs may cause bottlenecks.
 </details>

 <details>
@@ -129,6 +219,11 @@ Yes, you can combine different GPUs, but performance is optimized when using sim
 Yes, it is compatible with cloud providers. Refer to the setup guides for detailed instructions.
 </details>

+<details>
+<summary>Can I use my main machine just to coordinate workers without rendering?</summary>
+Yes. Open the Distributed panel and uncheck the master toggle to run in orchestrator-only mode. The master will distribute work to workers but won't render locally. If all workers become unavailable, the master automatically re-enables to ensure your workflow still runs.
+</details>
+
 <details>
 <summary>Can I make this work with my Docker setup?</summary>
 Yes, it is compatible with Docker setups, but you will need to configure your Docker environment yourself. Unfortunately, assistance with Docker configuration is not provided.
@@ -143,4 +238,14 @@ This software is provided "as is" without any warranties, express or implied, in
 ---

 ## Support the Project
-If my custom nodes have added value to your workflow, consider fueling future development with a coffee! Your support helps keep this project thriving. Buy me a coffee at: https://buymeacoffee.com/robertvoy
+
+<img width="200" align="right" src="https://github.com/user-attachments/assets/84291921-c44e-4556-94f2-a3b16500f4f9" />
+
+If my custom nodes have added value to your workflow, consider fueling future development with a coffee!
+
+Your support helps keep this project thriving.
+
+Buy me a coffee at: https://buymeacoffee.com/robertvoy
+
+
+
@@ -1,39 +1,3 @@
-import os
-import sys
-
-# Add the directory to Python path
-sys.path.append(os.path.dirname(__file__))
-
-# Patch ComfyUI execution validation for DistributedVideoCollector
-try:
-    import execution
-    from .distributed import ImageBatchDivider
-    
-    # Store original validate function if it exists
-    if hasattr(execution, 'validate_outputs'):
-        original_validate_outputs = execution.validate_outputs
-        
-        def patched_validate_outputs(executor, node_id, result, node_class):
-            if node_class == ImageBatchDivider:
-                return  # Skip validation for our dynamic output node
-            return original_validate_outputs(executor, node_id, result, node_class)
-        
-        execution.validate_outputs = patched_validate_outputs
-    else:
-        # Fallback: patch the main execute method if validate_outputs doesn't exist
-        if hasattr(execution.PromptExecutor, 'execute'):
-            original_execute = execution.PromptExecutor.execute
-            
-            def patched_execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]):
-                # This is a more complex patch - for now just call original
-                # The ByPassTypeTuple should handle most validation issues
-                return original_execute(self, prompt, prompt_id, extra_data, execute_outputs)
-            
-            execution.PromptExecutor.execute = patched_execute
-            
-except ImportError:
-    pass  # ComfyUI execution module not available during import
-
 # Import everything needed from the main module
 from .distributed import (
    NODE_CLASS_MAPPINGS as DISTRIBUTED_CLASS_MAPPINGS, 
@@ -45,7 +9,7 @@ from .utils.config import ensure_config_exists, CONFIG_FILE
 from .utils.logging import debug_log

 # Import distributed upscale nodes
-from .distributed_upscale import (
+from .nodes.distributed_upscale import (
    NODE_CLASS_MAPPINGS as UPSCALE_CLASS_MAPPINGS,
    NODE_DISPLAY_NAME_MAPPINGS as UPSCALE_DISPLAY_NAME_MAPPINGS
 )
@@ -62,4 +26,4 @@ __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

 debug_log("Loaded Distributed nodes.")
 debug_log(f"Config file: {CONFIG_FILE}")
-debug_log(f"Available nodes: {list(NODE_CLASS_MAPPINGS.keys())}")
+debug_log(f"Available nodes: {list(NODE_CLASS_MAPPINGS.keys())}")
@@ -0,0 +1,5 @@
+from . import config_routes  # noqa: F401
+from . import tunnel_routes  # noqa: F401
+from . import worker_routes  # noqa: F401
+from . import job_routes  # noqa: F401
+from . import usdu_routes  # noqa: F401
@@ -0,0 +1,277 @@
+import json
+from contextlib import asynccontextmanager
+
+from aiohttp import web
+import server
+
+try:
+    from ..utils.config import config_transaction, load_config, save_config
+except ImportError:
+    from ..utils.config import load_config
+
+    try:
+        from ..utils.config import save_config
+    except ImportError:
+        def save_config(_config):
+            return True
+
+    @asynccontextmanager
+    async def config_transaction():
+        config = load_config()
+        original_snapshot = json.dumps(config, sort_keys=True)
+        yield config
+        if json.dumps(config, sort_keys=True) != original_snapshot:
+            save_config(config)
+from ..utils.logging import debug_log, log
+from ..utils.network import handle_api_error, normalize_host
+
+
+def _positive_int(value):
+    return value > 0
+
+
+CONFIG_SCHEMA = {
+    "workers": (list, None),
+    "master": (dict, None),
+    "settings": (dict, None),
+    "tunnel": (dict, None),
+    "managed_processes": (dict, None),
+    "worker_timeout_seconds": (int, _positive_int),
+    "debug": (bool, None),
+    "auto_launch_workers": (bool, None),
+    "stop_workers_on_master_exit": (bool, None),
+    "master_delegate_only": (bool, None),
+    "websocket_orchestration": (bool, None),
+    "has_auto_populated_workers": (bool, None),
+}
+
+_SETTINGS_FIELDS = {
+    "worker_timeout_seconds",
+    "debug",
+    "auto_launch_workers",
+    "stop_workers_on_master_exit",
+    "master_delegate_only",
+    "websocket_orchestration",
+    "has_auto_populated_workers",
+}
+
+_WORKER_FIELDS = [
+    ("enabled", None, False),
+    ("name", None, False),
+    ("port", None, False),
+    ("host", normalize_host, True),
+    ("cuda_device", None, True),
+    ("extra_args", None, True),
+    ("type", None, False),
+]
+
+_MASTER_FIELDS = [
+    ("name", None, False),
+    ("host", normalize_host, True),
+    ("port", None, False),
+    ("cuda_device", None, False),
+    ("extra_args", None, False),
+]
+
+
+def _apply_field_patch(target: dict, data: dict, field_rules: list) -> None:
+    """Apply a partial update to a target dict based on field rules."""
+    for key, normalizer, remove_on_none in field_rules:
+        if key not in data:
+            continue
+        value = data[key]
+        if value is None and remove_on_none:
+            target.pop(key, None)
+        else:
+            target[key] = normalizer(value) if (normalizer and value is not None) else value
+
+
+@server.PromptServer.instance.routes.get("/distributed/config")
+async def get_config_endpoint(request):
+    config = load_config()
+    return web.json_response(config)
+
+
+@server.PromptServer.instance.routes.post("/distributed/config")
+async def update_config_endpoint(request):
+    """Bulk config update with schema validation."""
+    try:
+        data = await request.json()
+    except Exception as e:
+        return await handle_api_error(request, f"Invalid JSON payload: {e}", 400)
+
+    if not isinstance(data, dict):
+        return await handle_api_error(request, "Config payload must be an object", 400)
+
+    validated_settings = {}
+    validated_root = {}
+    errors = []
+
+    for key, value in data.items():
+        if key not in CONFIG_SCHEMA:
+            errors.append(f"Unknown field: {key}")
+            continue
+
+        expected_type, validator = CONFIG_SCHEMA[key]
+        if not isinstance(value, expected_type):
+            errors.append(f"{key}: expected {expected_type.__name__}")
+            continue
+
+        if validator and not validator(value):
+            errors.append(f"{key}: value {value!r} failed validation")
+            continue
+
+        if key in _SETTINGS_FIELDS:
+            validated_settings[key] = value
+        else:
+            validated_root[key] = value
+
+    if errors:
+        return web.json_response({
+            "status": "error",
+            "error": errors,
+            "message": "; ".join(errors),
+        }, status=400)
+
+    try:
+        async with config_transaction() as config:
+            settings = config.setdefault("settings", {})
+            settings.update(validated_settings)
+            for key, value in validated_root.items():
+                config[key] = value
+            return web.json_response({"status": "success", "config": config})
+    except Exception as e:
+        return await handle_api_error(request, e)
+
+
+@server.PromptServer.instance.routes.get("/distributed/queue_status/{job_id}")
+async def queue_status_endpoint(request):
+    """Check if a job queue is initialized."""
+    try:
+        job_id = request.match_info['job_id']
+        
+        # Import to ensure initialization
+        from ..upscale.job_store import ensure_tile_jobs_initialized
+        prompt_server = ensure_tile_jobs_initialized()
+        
+        async with prompt_server.distributed_tile_jobs_lock:
+            exists = job_id in prompt_server.distributed_pending_tile_jobs
+        
+        debug_log(f"Queue status check for job {job_id}: {'exists' if exists else 'not found'}")
+        return web.json_response({"exists": exists, "job_id": job_id})
+    except Exception as e:
+        return await handle_api_error(request, e, 500)
+
+@server.PromptServer.instance.routes.post("/distributed/config/update_worker")
+async def update_worker_endpoint(request):
+    try:
+        data = await request.json()
+        worker_id = data.get("worker_id")
+        
+        if worker_id is None:
+            return await handle_api_error(request, "Missing worker_id", 400)
+
+        async with config_transaction() as config:
+            worker_found = False
+            workers = config.setdefault("workers", [])
+
+            for worker in workers:
+                if worker["id"] == worker_id:
+                    _apply_field_patch(worker, data, _WORKER_FIELDS)
+                    worker_found = True
+                    break
+
+            if not worker_found:
+                # If worker not found and all required fields are provided, create new worker
+                if all(key in data for key in ["name", "port", "cuda_device"]):
+                    new_worker = {
+                        "id": worker_id,
+                        "name": data["name"],
+                        "host": normalize_host(data.get("host", "localhost")),
+                        "port": data["port"],
+                        "cuda_device": data["cuda_device"],
+                        "enabled": data.get("enabled", False),
+                        "extra_args": data.get("extra_args", ""),
+                        "type": data.get("type", "local")
+                    }
+                    workers.append(new_worker)
+                else:
+                    return await handle_api_error(
+                        request,
+                        f"Worker {worker_id} not found and missing required fields for creation",
+                        404,
+                    )
+
+            return web.json_response({"status": "success"})
+    except Exception as e:
+        return await handle_api_error(request, e, 400)
+
+@server.PromptServer.instance.routes.post("/distributed/config/delete_worker")
+async def delete_worker_endpoint(request):
+    try:
+        data = await request.json()
+        worker_id = data.get("worker_id")
+        
+        if worker_id is None:
+            return await handle_api_error(request, "Missing worker_id", 400)
+            
+        async with config_transaction() as config:
+            workers = config.get("workers", [])
+
+            # Find and remove the worker
+            worker_index = -1
+            for i, worker in enumerate(workers):
+                if worker["id"] == worker_id:
+                    worker_index = i
+                    break
+
+            if worker_index == -1:
+                return await handle_api_error(request, f"Worker {worker_id} not found", 404)
+
+            # Remove the worker
+            removed_worker = workers.pop(worker_index)
+
+            return web.json_response({
+                "status": "success",
+                "message": f"Worker {removed_worker.get('name', worker_id)} deleted"
+            })
+    except Exception as e:
+        return await handle_api_error(request, e, 400)
+
+@server.PromptServer.instance.routes.post("/distributed/config/update_setting")
+async def update_setting_endpoint(request):
+    """Updates a specific key in the settings object."""
+    try:
+        data = await request.json()
+        key = data.get("key")
+        value = data.get("value")
+
+        if not key or value is None:
+            return await handle_api_error(request, "Missing 'key' or 'value' in request", 400)
+        if key not in _SETTINGS_FIELDS:
+            return await handle_api_error(request, f"Unknown setting: {key}", 400)
+
+        async with config_transaction() as config:
+            if 'settings' not in config:
+                config['settings'] = {}
+
+            config['settings'][key] = value
+
+            return web.json_response({"status": "success", "message": f"Setting '{key}' updated."})
+    except Exception as e:
+        return await handle_api_error(request, e, 400)
+
+@server.PromptServer.instance.routes.post("/distributed/config/update_master")
+async def update_master_endpoint(request):
+    """Updates master configuration."""
+    try:
+        data = await request.json()
+        
+        async with config_transaction() as config:
+            if 'master' not in config:
+                config['master'] = {}
+            _apply_field_patch(config['master'], data, _MASTER_FIELDS)
+
+            return web.json_response({"status": "success", "message": "Master configuration updated."})
+    except Exception as e:
+        return await handle_api_error(request, e, 400)
@@ -0,0 +1,341 @@
+import json
+import asyncio
+import io
+import os
+import base64
+import binascii
+import time
+
+from aiohttp import web
+import server
+import torch
+from PIL import Image
+
+from ..utils.logging import debug_log
+from ..utils.image import pil_to_tensor, ensure_contiguous
+from ..utils.network import handle_api_error
+from ..utils.constants import JOB_INIT_GRACE_PERIOD, MEMORY_CLEAR_DELAY
+try:
+    from .queue_orchestration import ensure_distributed_state, orchestrate_distributed_execution
+except ImportError:
+    from .queue_orchestration import orchestrate_distributed_execution
+
+    def ensure_distributed_state():
+        return None
+from .queue_request import parse_queue_request_payload
+
+prompt_server = server.PromptServer.instance
+
+# Canonical worker result envelope accepted by POST /distributed/job_complete:
+# { "job_id": str, "worker_id": str, "batch_idx": int, "image": <base64 PNG>, "is_last": bool }
+
+
+def _decode_image_sync(image_path):
+    """Decode image/video file and compute hash in a threadpool worker."""
+    import base64
+    import hashlib
+    import folder_paths
+
+    full_path = folder_paths.get_annotated_filepath(image_path)
+    if not os.path.exists(full_path):
+        raise FileNotFoundError(image_path)
+
+    hash_md5 = hashlib.md5()
+    with open(full_path, 'rb') as f:
+        for chunk in iter(lambda: f.read(4096), b""):
+            hash_md5.update(chunk)
+    file_hash = hash_md5.hexdigest()
+
+    video_extensions = {'.mp4', '.avi', '.mov', '.mkv', '.webm'}
+    file_ext = os.path.splitext(full_path)[1].lower()
+
+    if file_ext in video_extensions:
+        with open(full_path, 'rb') as f:
+            file_data = f.read()
+        mime_types = {
+            '.mp4': 'video/mp4',
+            '.avi': 'video/x-msvideo',
+            '.mov': 'video/quicktime',
+            '.mkv': 'video/x-matroska',
+            '.webm': 'video/webm'
+        }
+        mime_type = mime_types.get(file_ext, 'video/mp4')
+        image_data = f"data:{mime_type};base64,{base64.b64encode(file_data).decode('utf-8')}"
+    else:
+        with Image.open(full_path) as img:
+            if img.mode not in ('RGB', 'RGBA'):
+                img = img.convert('RGB')
+            buffer = io.BytesIO()
+            img.save(buffer, format='PNG', compress_level=1)
+            image_data = f"data:image/png;base64,{base64.b64encode(buffer.getvalue()).decode('utf-8')}"
+
+    return {
+        "status": "success",
+        "image_data": image_data,
+        "hash": file_hash,
+    }
+
+
+def _check_file_sync(filename, expected_hash):
+    """Check file presence and hash in a threadpool worker."""
+    import hashlib
+    import folder_paths
+
+    full_path = folder_paths.get_annotated_filepath(filename)
+    if not os.path.exists(full_path):
+        return {
+            "status": "success",
+            "exists": False,
+        }
+
+    hash_md5 = hashlib.md5()
+    with open(full_path, 'rb') as f:
+        for chunk in iter(lambda: f.read(4096), b""):
+            hash_md5.update(chunk)
+    file_hash = hash_md5.hexdigest()
+
+    return {
+        "status": "success",
+        "exists": True,
+        "hash_matches": file_hash == expected_hash,
+    }
+
+
+def _decode_canonical_png_tensor(image_payload):
+    """Decode canonical base64 PNG payload into a contiguous IMAGE tensor."""
+    if not isinstance(image_payload, str) or not image_payload.strip():
+        raise ValueError("Field 'image' must be a non-empty base64 PNG string.")
+
+    encoded = image_payload.strip()
+    if encoded.startswith("data:"):
+        header, sep, data_part = encoded.partition(",")
+        if not sep:
+            raise ValueError("Field 'image' data URL is malformed.")
+        if not header.lower().startswith("data:image/png;base64"):
+            raise ValueError("Field 'image' must be a PNG data URL when using data:* format.")
+        encoded = data_part
+
+    try:
+        png_bytes = base64.b64decode(encoded, validate=True)
+    except (binascii.Error, ValueError) as exc:
+        raise ValueError("Field 'image' is not valid base64 PNG data.") from exc
+
+    if not png_bytes:
+        raise ValueError("Field 'image' decoded to empty PNG data.")
+
+    try:
+        with Image.open(io.BytesIO(png_bytes)) as img:
+            img = img.convert("RGB")
+            tensor = pil_to_tensor(img)
+        return ensure_contiguous(tensor)
+    except Exception as exc:
+        raise ValueError(f"Failed to decode PNG image payload: {exc}") from exc
+
+
+def _decode_audio_payload(audio_payload):
+    """Decode canonical audio payload into an AUDIO dict."""
+    from ..utils.audio_payload import decode_audio_payload
+
+    return decode_audio_payload(audio_payload)
+
+
+@server.PromptServer.instance.routes.post("/distributed/prepare_job")
+async def prepare_job_endpoint(request):
+    try:
+        data = await request.json()
+        multi_job_id = data.get('multi_job_id')
+        if not multi_job_id:
+            return await handle_api_error(request, "Missing multi_job_id", 400)
+
+        ensure_distributed_state()
+        async with prompt_server.distributed_jobs_lock:
+            if multi_job_id not in prompt_server.distributed_pending_jobs:
+                prompt_server.distributed_pending_jobs[multi_job_id] = asyncio.Queue()
+        
+        debug_log(f"Prepared queue for job {multi_job_id}")
+        return web.json_response({"status": "success"})
+    except Exception as e:
+        return await handle_api_error(request, e)
+
+@server.PromptServer.instance.routes.post("/distributed/clear_memory")
+async def clear_memory_endpoint(request):
+    debug_log("Received request to clear VRAM.")
+    try:
+        # Use ComfyUI's prompt server queue system like the /free endpoint does
+        if hasattr(server.PromptServer.instance, 'prompt_queue'):
+            server.PromptServer.instance.prompt_queue.set_flag("unload_models", True)
+            server.PromptServer.instance.prompt_queue.set_flag("free_memory", True)
+            debug_log("Set queue flags for memory clearing.")
+        
+        # Wait a bit for the queue to process
+        await asyncio.sleep(MEMORY_CLEAR_DELAY)
+        
+        # Also do direct cleanup as backup, but with error handling
+        import gc
+        import comfy.model_management as mm
+        
+        try:
+            mm.unload_all_models()
+        except AttributeError as e:
+            debug_log(f"Warning during model unload: {e}")
+        
+        try:
+            mm.soft_empty_cache()
+        except Exception as e:
+            debug_log(f"Warning during cache clear: {e}")
+        
+        for _ in range(3):
+            gc.collect()
+        
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            torch.cuda.ipc_collect()
+        
+        debug_log("VRAM cleared successfully.")
+        return web.json_response({"status": "success", "message": "GPU memory cleared."})
+    except Exception as e:
+        # Even if there's an error, try to do basic cleanup
+        import gc
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        debug_log(f"Partial VRAM clear completed with warning: {e}")
+        return web.json_response({"status": "success", "message": "GPU memory cleared (with warnings)"})
+
+
+@server.PromptServer.instance.routes.post("/distributed/queue")
+async def distributed_queue_endpoint(request):
+    """Queue a distributed workflow, mirroring the UI orchestration pipeline."""
+    try:
+        raw_payload = await request.json()
+    except Exception as exc:
+        return await handle_api_error(request, f"Invalid JSON payload: {exc}", 400)
+
+    try:
+        payload = parse_queue_request_payload(raw_payload)
+    except ValueError as exc:
+        return await handle_api_error(request, exc, 400)
+
+    try:
+        prompt_id, worker_count = await orchestrate_distributed_execution(
+            payload.prompt,
+            payload.workflow_meta,
+            payload.client_id,
+            enabled_worker_ids=payload.enabled_worker_ids,
+            delegate_master=payload.delegate_master,
+            trace_execution_id=payload.trace_execution_id,
+        )
+        return web.json_response({
+            "prompt_id": prompt_id,
+            "worker_count": worker_count,
+            "auto_prepare_supported": True,
+        })
+    except Exception as exc:
+        return await handle_api_error(request, exc, 500)
+
+@server.PromptServer.instance.routes.post("/distributed/load_image")
+async def load_image_endpoint(request):
+    """Load an image or video file and return it as base64 data with hash."""
+    try:
+        data = await request.json()
+        image_path = data.get("image_path")
+        
+        if not image_path:
+            return await handle_api_error(request, "Missing image_path", 400)
+        loop = asyncio.get_running_loop()
+        payload = await loop.run_in_executor(None, _decode_image_sync, image_path)
+        return web.json_response(payload)
+    except FileNotFoundError:
+        return await handle_api_error(request, f"File not found: {image_path}", 404)
+    except Exception as e:
+        return await handle_api_error(request, e, 500)
+
+@server.PromptServer.instance.routes.post("/distributed/check_file")
+async def check_file_endpoint(request):
+    """Check if a file exists and matches the given hash."""
+    try:
+        data = await request.json()
+        filename = data.get("filename")
+        expected_hash = data.get("hash")
+        
+        if not filename or not expected_hash:
+            return await handle_api_error(request, "Missing filename or hash", 400)
+        loop = asyncio.get_running_loop()
+        payload = await loop.run_in_executor(None, _check_file_sync, filename, expected_hash)
+        return web.json_response(payload)
+        
+    except Exception as e:
+        return await handle_api_error(request, e, 500)
+
+
+@server.PromptServer.instance.routes.post("/distributed/job_complete")
+async def job_complete_endpoint(request):
+    try:
+        data = await request.json()
+    except Exception as exc:
+        return await handle_api_error(request, f"Invalid JSON payload: {exc}", 400)
+
+    if not isinstance(data, dict):
+        return await handle_api_error(request, "Expected a JSON object body", 400)
+
+    try:
+        job_id = data.get("job_id")
+        worker_id = data.get("worker_id")
+        batch_idx = data.get("batch_idx")
+        image_payload = data.get("image")
+        audio_payload = data.get("audio")
+        is_last = data.get("is_last")
+
+        errors = []
+        if not isinstance(job_id, str) or not job_id.strip():
+            errors.append("job_id: expected non-empty string")
+        if not isinstance(worker_id, str) or not worker_id.strip():
+            errors.append("worker_id: expected non-empty string")
+        if not isinstance(batch_idx, int) or batch_idx < 0:
+            errors.append("batch_idx: expected non-negative integer")
+        if not isinstance(image_payload, str) or not image_payload.strip():
+            errors.append("image: expected non-empty base64 PNG string")
+        if audio_payload is not None and not isinstance(audio_payload, dict):
+            errors.append("audio: expected object when provided")
+        if not isinstance(is_last, bool):
+            errors.append("is_last: expected boolean")
+        if errors:
+            return await handle_api_error(request, errors, 400)
+
+        tensor = _decode_canonical_png_tensor(image_payload)
+        decoded_audio = _decode_audio_payload(audio_payload) if audio_payload is not None else None
+        multi_job_id = job_id.strip()
+        worker_id = worker_id.strip()
+
+        pending = None
+        queue_size = 0
+        deadline = time.monotonic() + float(JOB_INIT_GRACE_PERIOD)
+        while pending is None:
+            async with prompt_server.distributed_jobs_lock:
+                pending = prompt_server.distributed_pending_jobs.get(multi_job_id)
+                if pending is not None:
+                    await pending.put(
+                        {
+                            "tensor": tensor,
+                            "worker_id": worker_id,
+                            "image_index": int(batch_idx),
+                            "is_last": is_last,
+                            "audio": decoded_audio,
+                        }
+                    )
+                    queue_size = pending.qsize()
+                    break
+
+            if time.monotonic() > deadline:
+                return await handle_api_error(request, "job not initialized", 404)
+            await asyncio.sleep(0.05)
+
+        debug_log(
+            f"job_complete received canonical envelope - job_id: {multi_job_id}, "
+            f"worker: {worker_id}, batch_idx: {batch_idx}, is_last: {is_last}, "
+            f"queue_size: {queue_size}"
+        )
+
+        return web.json_response({"status": "success"})
+    except Exception as e:
+        return await handle_api_error(request, e)
@@ -0,0 +1,4 @@
+# Orchestration helpers split out from queue_orchestration.py:
+# - prompt_transform.py: graph pruning + hidden input overrides
+# - media_sync.py: remote media/path normalization
+# - dispatch.py: worker probe + prompt dispatch
@@ -0,0 +1,268 @@
+import asyncio
+import json
+import uuid
+
+import aiohttp
+
+from ...utils.logging import debug_log, log
+from ...utils.network import build_worker_url, get_client_session, probe_worker
+try:
+    from ...utils.trace_logger import trace_debug, trace_info
+except ImportError:
+    def trace_debug(*_args, **_kwargs):
+        return None
+
+    def trace_info(*_args, **_kwargs):
+        return None
+
+try:
+    from ..schemas import parse_positive_int
+except ImportError:
+    def parse_positive_int(value, default):
+        try:
+            parsed = int(value)
+            return parsed if parsed > 0 else default
+        except (TypeError, ValueError):
+            return default
+
+_least_busy_rr_index = 0
+
+
+async def worker_is_active(worker):
+    """Ping worker's /prompt endpoint to confirm it's reachable."""
+    url = build_worker_url(worker)
+    return await probe_worker(url, timeout=3.0) is not None
+
+
+async def worker_ws_is_active(worker):
+    """Ping worker's websocket endpoint to confirm it's reachable."""
+    session = await get_client_session()
+    url = build_worker_url(worker, "/distributed/worker_ws")
+    try:
+        ws = await session.ws_connect(url, heartbeat=20, timeout=3)
+        await ws.close()
+        return True
+    except asyncio.TimeoutError:
+        debug_log(f"[Distributed] Worker WS probe timed out: {url}")
+        return False
+    except aiohttp.ClientConnectorError:
+        debug_log(f"[Distributed] Worker WS unreachable: {url}")
+        return False
+    except Exception as e:
+        debug_log(f"[Distributed] Worker WS probe unexpected error: {e}")
+        return False
+
+
+async def _probe_worker_active(worker, use_websocket, semaphore):
+    async with semaphore:
+        is_active = await (worker_ws_is_active(worker) if use_websocket else worker_is_active(worker))
+        return worker, is_active
+
+
+async def _dispatch_via_websocket(worker_url, payload, client_id, timeout=60.0):
+    """Open a fresh worker websocket, dispatch one prompt, wait for ack, then close."""
+    request_id = uuid.uuid4().hex
+    ws_payload = {
+        "type": "dispatch_prompt",
+        "request_id": request_id,
+        "prompt": payload.get("prompt"),
+        "workflow": payload.get("workflow"),
+        "client_id": client_id,
+    }
+    ws_url = worker_url.replace("http://", "ws://").replace("https://", "wss://")
+    ws_url = f"{ws_url}/distributed/worker_ws"
+    session = await get_client_session()
+
+    async with session.ws_connect(ws_url, heartbeat=20, timeout=timeout) as ws:
+        await ws.send_json(ws_payload)
+        async for msg in ws:
+            if msg.type == aiohttp.WSMsgType.TEXT:
+                data = json.loads(msg.data or "{}")
+                if data.get("type") == "dispatch_ack" and data.get("request_id") == request_id:
+                    if data.get("ok"):
+                        return
+                    error_text = data.get("error") or "Worker rejected websocket dispatch."
+                    validation_error = data.get("validation_error")
+                    node_errors = data.get("node_errors")
+                    if validation_error:
+                        error_text = f"{error_text} | validation_error={validation_error}"
+                    if node_errors:
+                        error_text = f"{error_text} | node_errors={node_errors}"
+                    raise RuntimeError(error_text)
+            elif msg.type in (aiohttp.WSMsgType.ERROR, aiohttp.WSMsgType.CLOSED):
+                raise RuntimeError(f"Worker websocket closed unexpectedly: {msg.type}")
+
+    raise RuntimeError("Worker websocket closed before dispatch_ack was received.")
+
+
+async def dispatch_worker_prompt(
+    worker,
+    prompt_obj,
+    workflow_meta,
+    client_id=None,
+    use_websocket=False,
+    trace_execution_id=None,
+):
+    """Send the prepared prompt to a worker ComfyUI instance."""
+    worker_url = build_worker_url(worker)
+    url = build_worker_url(worker, "/prompt")
+    payload = {"prompt": prompt_obj}
+    extra_data = {}
+    if workflow_meta:
+        extra_data.setdefault("extra_pnginfo", {})["workflow"] = workflow_meta
+    if extra_data:
+        payload["extra_data"] = extra_data
+
+    if use_websocket:
+        try:
+            await _dispatch_via_websocket(
+                worker_url,
+                {
+                    "prompt": prompt_obj,
+                    "workflow": workflow_meta,
+                },
+                client_id,
+            )
+            return
+        except Exception as exc:
+            worker_id = worker.get("id")
+            if trace_execution_id:
+                trace_info(trace_execution_id, f"Websocket dispatch failed for worker {worker_id}: {exc}")
+            else:
+                log(f"[Distributed] Websocket dispatch failed for worker {worker_id}: {exc}")
+            raise
+
+    session = await get_client_session()
+    async with session.post(
+        url,
+        json=payload,
+        timeout=aiohttp.ClientTimeout(total=60),
+    ) as resp:
+        resp.raise_for_status()
+
+
+async def select_active_workers(
+    workers,
+    use_websocket,
+    delegate_master,
+    trace_execution_id=None,
+    probe_concurrency=8,
+):
+    """Probe workers and return (active_workers, updated_delegate_master)."""
+    probe_limit = parse_positive_int(probe_concurrency, 8)
+    probe_semaphore = asyncio.Semaphore(probe_limit)
+
+    if trace_execution_id and workers:
+        trace_debug(
+            trace_execution_id,
+            f"Probing {len(workers)} workers with probe_concurrency={probe_limit}",
+        )
+
+    probe_results = await asyncio.gather(
+        *[
+            _probe_worker_active(worker, use_websocket, probe_semaphore)
+            for worker in workers
+        ]
+    )
+
+    active_workers = []
+    for worker, is_active in probe_results:
+        if is_active:
+            active_workers.append(worker)
+        else:
+            if trace_execution_id:
+                trace_info(trace_execution_id, f"Worker {worker['name']} ({worker['id']}) is offline, skipping.")
+            else:
+                log(f"[Distributed] Worker {worker['name']} ({worker['id']}) is offline, skipping.")
+
+    if trace_execution_id and workers:
+        trace_debug(
+            trace_execution_id,
+            f"Worker probe complete: active={len(active_workers)}/{len(workers)}",
+        )
+
+    if not active_workers and delegate_master:
+        if trace_execution_id:
+            trace_debug(trace_execution_id, "All workers offline while delegate-only requested; enabling master participation.")
+        else:
+            debug_log("All workers offline while delegate-only requested; enabling master participation.")
+        delegate_master = False
+
+    return active_workers, delegate_master
+
+
+def _extract_queue_remaining(payload):
+    if not isinstance(payload, dict):
+        return 0
+    try:
+        queue_remaining = int(payload.get("exec_info", {}).get("queue_remaining", 0))
+    except (TypeError, ValueError):
+        queue_remaining = 0
+    return max(queue_remaining, 0)
+
+
+async def _probe_worker_queue(worker, semaphore, probe_timeout):
+    async with semaphore:
+        worker_url = build_worker_url(worker)
+        payload = await probe_worker(worker_url, timeout=probe_timeout)
+        if payload is None:
+            return None
+        return {
+            "worker": worker,
+            "queue_remaining": _extract_queue_remaining(payload),
+        }
+
+
+def _select_idle_round_robin(statuses):
+    global _least_busy_rr_index
+    if not statuses:
+        return None
+    index = _least_busy_rr_index % len(statuses)
+    _least_busy_rr_index += 1
+    return statuses[index]
+
+
+async def select_least_busy_worker(
+    workers,
+    trace_execution_id=None,
+    probe_concurrency=8,
+    probe_timeout=3.0,
+):
+    """Select one worker by queue depth, round-robin among idle workers."""
+    if not workers:
+        return None
+
+    probe_limit = parse_positive_int(probe_concurrency, 8)
+    probe_semaphore = asyncio.Semaphore(probe_limit)
+    statuses = await asyncio.gather(
+        *[
+            _probe_worker_queue(worker, probe_semaphore, probe_timeout)
+            for worker in workers
+        ]
+    )
+    statuses = [status for status in statuses if status is not None]
+    if not statuses:
+        if trace_execution_id:
+            trace_info(trace_execution_id, "Least-busy selection failed: no worker queue probes succeeded.")
+        else:
+            log("[Distributed] Least-busy selection failed: no worker queue probes succeeded.")
+        return None
+
+    idle_statuses = [status for status in statuses if status["queue_remaining"] == 0]
+    if idle_statuses:
+        selected = _select_idle_round_robin(idle_statuses)
+    else:
+        selected = min(statuses, key=lambda status: status["queue_remaining"])
+
+    worker = selected["worker"]
+    queue_remaining = selected["queue_remaining"]
+    if trace_execution_id:
+        trace_debug(
+            trace_execution_id,
+            f"Least-busy worker selected: {worker.get('name')} ({worker.get('id')}), queue_remaining={queue_remaining}",
+        )
+    else:
+        debug_log(
+            f"Least-busy worker selected: {worker.get('name')} ({worker.get('id')}), queue_remaining={queue_remaining}"
+        )
+    return worker
@@ -0,0 +1,256 @@
+import asyncio
+import hashlib
+import mimetypes
+import os
+import re
+
+import aiohttp
+
+from ...utils.logging import debug_log, log
+from ...utils.network import build_worker_url, get_client_session
+from ...utils.trace_logger import trace_debug, trace_info
+
+
+LIKELY_FILENAME_RE = re.compile(
+    r"\.(ckpt|safetensors|pt|pth|bin|yaml|json|png|jpg|jpeg|webp|gif|bmp|mp4|avi|mov|mkv|webm|"
+    r"wav|mp3|flac|m4a|aac|ogg|opus|aiff|aif|wma|latent|txt|vae|lora|embedding)"
+    r"(\s*\[\w+\])?$",
+    re.IGNORECASE,
+)
+MEDIA_FILE_RE = re.compile(
+    r"\.(png|jpg|jpeg|webp|gif|bmp|mp4|avi|mov|mkv|webm|wav|mp3|flac|m4a|aac|ogg|opus|aiff|aif|wma)(\s*\[\w+\])?$",
+    re.IGNORECASE,
+)
+
+
+def _normalize_media_reference(value):
+    """Normalize one media string value to a path-like reference or None."""
+    if not isinstance(value, str):
+        return None
+    cleaned = re.sub(r"\s*\[\w+\]$", "", value).strip().replace("\\", "/")
+    if MEDIA_FILE_RE.search(cleaned):
+        return cleaned
+    return None
+
+
+def convert_paths_for_platform(obj, target_separator):
+    """Recursively normalize likely file paths for the worker platform separator."""
+    if target_separator not in ("/", "\\"):
+        return obj
+
+    def _convert(value):
+        if isinstance(value, str):
+            if ("/" in value or "\\" in value) and LIKELY_FILENAME_RE.search(value):
+                trimmed = value.strip()
+                has_drive = bool(re.match(r"^[A-Za-z]:(\\\\|/)", trimmed))
+                is_absolute = trimmed.startswith("/") or trimmed.startswith("\\\\")
+                has_protocol = bool(re.match(r"^\w+://", trimmed))
+
+                # URLs are not local paths and should never be separator-normalized.
+                if has_protocol:
+                    return trimmed
+
+                # Keep relative media-style paths in forward-slash form (Comfy-style annotated paths).
+                if not has_drive and not is_absolute and not has_protocol and MEDIA_FILE_RE.search(trimmed):
+                    return re.sub(r"[\\]+", "/", trimmed)
+
+                if target_separator == "\\":
+                    return re.sub(r"[\\/]+", r"\\", trimmed)
+                return re.sub(r"[\\/]+", "/", trimmed)
+            return value
+        if isinstance(value, list):
+            return [_convert(item) for item in value]
+        if isinstance(value, dict):
+            return {key: _convert(item) for key, item in value.items()}
+        return value
+
+    return _convert(obj)
+
+
+def _find_media_references(prompt_obj):
+    """Find media file references in image/video/audio/file inputs used by worker prompts."""
+    media_refs = set()
+    for node in prompt_obj.values():
+        if not isinstance(node, dict):
+            continue
+        inputs = node.get("inputs", {})
+        for key in ("image", "video", "audio", "file"):
+            cleaned = _normalize_media_reference(inputs.get(key))
+            if cleaned:
+                media_refs.add(cleaned)
+    return sorted(media_refs)
+
+
+def _rewrite_prompt_media_inputs(prompt_obj, worker_media_paths):
+    """Rewrite media string inputs to worker-local uploaded paths."""
+    if not isinstance(worker_media_paths, dict) or not worker_media_paths:
+        return
+
+    for node in prompt_obj.values():
+        if not isinstance(node, dict):
+            continue
+        inputs = node.get("inputs", {})
+        if not isinstance(inputs, dict):
+            continue
+        for key in ("image", "video", "audio", "file"):
+            value = inputs.get(key)
+            cleaned = _normalize_media_reference(value)
+            if not cleaned:
+                continue
+            worker_path = worker_media_paths.get(cleaned)
+            if worker_path:
+                inputs[key] = worker_path
+
+
+def _load_media_file_sync(filename):
+    """Load local media bytes and hash for worker upload sync."""
+    import folder_paths
+
+    full_path = folder_paths.get_annotated_filepath(filename)
+    if not os.path.exists(full_path):
+        raise FileNotFoundError(filename)
+
+    with open(full_path, "rb") as f:
+        file_bytes = f.read()
+
+    file_hash = hashlib.md5(file_bytes).hexdigest()
+    mime_type = mimetypes.guess_type(full_path)[0]
+    if not mime_type:
+        ext = os.path.splitext(full_path)[1].lower()
+        if ext in {".mp4", ".avi", ".mov", ".mkv", ".webm"}:
+            mime_type = "video/mp4"
+        else:
+            mime_type = "image/png"
+    return file_bytes, file_hash, mime_type
+
+
+async def fetch_worker_path_separator(worker, trace_execution_id=None):
+    """Best-effort fetch of a worker's path separator from /distributed/system_info."""
+    url = build_worker_url(worker, "/distributed/system_info")
+    session = await get_client_session()
+    try:
+        async with session.get(url, timeout=aiohttp.ClientTimeout(total=5)) as resp:
+            if resp.status != 200:
+                return None
+            payload = await resp.json()
+            separator = ((payload or {}).get("platform") or {}).get("path_separator")
+            return separator if separator in ("/", "\\") else None
+    except Exception as exc:
+        if trace_execution_id:
+            trace_debug(trace_execution_id, f"Failed to fetch worker system info ({worker.get('id')}): {exc}")
+        else:
+            debug_log(f"[Distributed] Failed to fetch worker system info ({worker.get('id')}): {exc}")
+        return None
+
+
+async def _upload_media_to_worker(worker, filename, file_bytes, file_hash, mime_type, trace_execution_id=None):
+    """Upload one media file to worker iff missing or hash-mismatched."""
+    session = await get_client_session()
+    normalized = filename.replace("\\", "/")
+
+    check_url = build_worker_url(worker, "/distributed/check_file")
+    try:
+        async with session.post(
+            check_url,
+            json={"filename": normalized, "hash": file_hash},
+            timeout=aiohttp.ClientTimeout(total=6),
+        ) as resp:
+            if resp.status == 200:
+                payload = await resp.json()
+                if payload.get("exists") and payload.get("hash_matches"):
+                    return False, normalized
+    except Exception as exc:
+        if trace_execution_id:
+            trace_debug(trace_execution_id, f"Media check failed for '{normalized}' on worker {worker.get('id')}: {exc}")
+        else:
+            debug_log(f"[Distributed] Media check failed for '{normalized}' on worker {worker.get('id')}: {exc}")
+
+    parts = normalized.split("/")
+    clean_name = parts[-1]
+    subfolder = "/".join(parts[:-1])
+
+    form = aiohttp.FormData()
+    form.add_field("image", file_bytes, filename=clean_name, content_type=mime_type)
+    form.add_field("type", "input")
+    form.add_field("subfolder", subfolder)
+    form.add_field("overwrite", "true")
+
+    upload_url = build_worker_url(worker, "/upload/image")
+    async with session.post(
+        upload_url,
+        data=form,
+        timeout=aiohttp.ClientTimeout(total=30),
+    ) as resp:
+        resp.raise_for_status()
+        try:
+            payload = await resp.json()
+        except Exception:
+            payload = {}
+
+    name = str((payload or {}).get("name") or clean_name).strip()
+    subfolder = str((payload or {}).get("subfolder") or "").strip().replace("\\", "/").strip("/")
+    worker_path = f"{subfolder}/{name}" if subfolder else name
+    return True, worker_path
+
+
+async def sync_worker_media(worker, prompt_obj, trace_execution_id=None):
+    """Sync referenced media files from master to a remote worker before dispatch."""
+    media_refs = _find_media_references(prompt_obj)
+    if not media_refs:
+        return
+
+    loop = asyncio.get_running_loop()
+    uploaded = 0
+    skipped = 0
+    missing = 0
+    worker_media_paths = {}
+    for filename in media_refs:
+        try:
+            file_bytes, file_hash, mime_type = await loop.run_in_executor(
+                None, _load_media_file_sync, filename
+            )
+        except FileNotFoundError:
+            missing += 1
+            if trace_execution_id:
+                trace_info(trace_execution_id, f"Media file '{filename}' not found on master; worker may fail to load it.")
+            else:
+                log(f"[Distributed] Media file '{filename}' not found on master; worker may fail to load it.")
+            continue
+        except Exception as exc:
+            if trace_execution_id:
+                trace_info(trace_execution_id, f"Failed to load media '{filename}' for worker sync: {exc}")
+            else:
+                log(f"[Distributed] Failed to load media '{filename}' for worker sync: {exc}")
+            continue
+
+        try:
+            changed, worker_path = await _upload_media_to_worker(
+                worker,
+                filename,
+                file_bytes,
+                file_hash,
+                mime_type,
+                trace_execution_id=trace_execution_id,
+            )
+            if worker_path:
+                worker_media_paths[filename] = worker_path
+            if changed:
+                uploaded += 1
+            else:
+                skipped += 1
+        except Exception as exc:
+            if trace_execution_id:
+                trace_info(trace_execution_id, f"Failed to upload media '{filename}' to worker {worker.get('id')}: {exc}")
+            else:
+                log(f"[Distributed] Failed to upload media '{filename}' to worker {worker.get('id')}: {exc}")
+
+    _rewrite_prompt_media_inputs(prompt_obj, worker_media_paths)
+
+    summary = (
+        f"Media sync for worker {worker.get('id')}: "
+        f"uploaded={uploaded}, skipped={skipped}, missing={missing}, referenced={len(media_refs)}"
+    )
+    if trace_execution_id:
+        trace_debug(trace_execution_id, summary)
+    else:
+        debug_log(f"[Distributed] {summary}")
@@ -0,0 +1,352 @@
+import json
+from collections import deque
+
+from ...utils.logging import debug_log
+
+
+class PromptIndex:
+    """Cache prompt metadata for faster worker/master prompt preparation."""
+
+    def __init__(self, prompt_obj):
+        self._prompt_json = json.dumps(prompt_obj)
+        self.nodes_by_class = {}
+        self.class_by_node = {}
+        self.inputs_by_node = {}
+        for node_id, node in _iter_prompt_nodes(prompt_obj):
+            class_type = node.get("class_type")
+            node_id_str = str(node_id)
+            if class_type:
+                self.nodes_by_class.setdefault(class_type, []).append(node_id_str)
+            self.class_by_node[node_id_str] = class_type
+            self.inputs_by_node[node_id_str] = node.get("inputs", {})
+        self._upstream_cache = {}
+
+    def copy_prompt(self):
+        return json.loads(self._prompt_json)
+
+    def nodes_for_class(self, class_name):
+        return self.nodes_by_class.get(class_name, [])
+
+    def has_upstream(self, start_node_id, target_class):
+        cache_key = (str(start_node_id), target_class)
+        if cache_key in self._upstream_cache:
+            return self._upstream_cache[cache_key]
+
+        visited = set()
+        stack = [str(start_node_id)]
+        while stack:
+            node_id = stack.pop()
+            if node_id in visited:
+                continue
+            visited.add(node_id)
+            inputs = self.inputs_by_node.get(node_id, {})
+            for value in inputs.values():
+                if isinstance(value, list) and len(value) == 2:
+                    upstream_id = str(value[0])
+                    if self.class_by_node.get(upstream_id) == target_class:
+                        self._upstream_cache[cache_key] = True
+                        return True
+                    if upstream_id in self.inputs_by_node:
+                        stack.append(upstream_id)
+
+        self._upstream_cache[cache_key] = False
+        return False
+
+
+def _iter_prompt_nodes(prompt_obj):
+    for node_id, node in prompt_obj.items():
+        if isinstance(node, dict):
+            yield str(node_id), node
+
+
+def find_nodes_by_class(prompt_obj, class_name):
+    nodes = []
+    for node_id, node in _iter_prompt_nodes(prompt_obj):
+        if node.get("class_type") == class_name:
+            nodes.append(node_id)
+    return nodes
+
+
+def _find_downstream_nodes(prompt_obj, start_ids):
+    """Return all nodes reachable downstream from the provided IDs."""
+    adjacency = {}
+    for node_id, node in _iter_prompt_nodes(prompt_obj):
+        inputs = node.get("inputs", {})
+        for value in inputs.values():
+            if isinstance(value, list) and len(value) == 2:
+                source_id = str(value[0])
+                adjacency.setdefault(source_id, set()).add(str(node_id))
+
+    connected = set(start_ids)
+    queue = deque(start_ids)
+    while queue:
+        current = queue.popleft()
+        for dependent in adjacency.get(current, ()):  # pragma: no branch - simple iteration
+            if dependent not in connected:
+                connected.add(dependent)
+                queue.append(dependent)
+    return connected
+
+
+def _create_numeric_id_generator(prompt_obj):
+    """Return a closure that yields new numeric string IDs."""
+    max_id = 0
+    for node_id in prompt_obj.keys():
+        try:
+            numeric = int(node_id)
+        except (TypeError, ValueError):
+            continue
+        max_id = max(max_id, numeric)
+
+    counter = max_id
+
+    def _next_id():
+        nonlocal counter
+        counter += 1
+        return str(counter)
+
+    return _next_id
+
+
+def _find_upstream_nodes(prompt_obj, start_ids):
+    """Return all nodes reachable upstream from start_ids, including start nodes."""
+    connected = set(str(node_id) for node_id in start_ids)
+    queue = deque(connected)
+    while queue:
+        node_id = queue.popleft()
+        node = prompt_obj.get(node_id) or {}
+        inputs = node.get("inputs", {})
+        for value in inputs.values():
+            if isinstance(value, list) and len(value) == 2:
+                source_id = str(value[0])
+                if source_id in prompt_obj and source_id not in connected:
+                    connected.add(source_id)
+                    queue.append(source_id)
+    return connected
+
+
+def prune_prompt_for_worker(prompt_obj):
+    """Prune worker prompt to distributed nodes and their upstream dependencies."""
+    collector_ids = find_nodes_by_class(prompt_obj, "DistributedCollector")
+    upscale_ids = find_nodes_by_class(prompt_obj, "UltimateSDUpscaleDistributed")
+    distributed_ids = collector_ids + upscale_ids
+    if not distributed_ids:
+        return prompt_obj
+
+    connected = _find_upstream_nodes(prompt_obj, distributed_ids)
+    pruned_prompt = {}
+    for node_id in connected:
+        node = prompt_obj.get(node_id)
+        if node is not None:
+            pruned_prompt[node_id] = json.loads(json.dumps(node))
+
+    # Generate IDs from the original prompt so we never reuse IDs from pruned downstream nodes.
+    next_id = _create_numeric_id_generator(prompt_obj)
+    for dist_id in distributed_ids:
+        if dist_id not in pruned_prompt:
+            continue
+        downstream = _find_downstream_nodes(prompt_obj, [dist_id])
+        has_removed_downstream = any(node_id != dist_id for node_id in downstream)
+        if has_removed_downstream:
+            preview_id = next_id()
+            pruned_prompt[preview_id] = {
+                "inputs": {
+                    "images": [dist_id, 0],
+                },
+                "class_type": "PreviewImage",
+                "_meta": {
+                    "title": "Preview Image (auto-added)",
+                },
+            }
+
+    return pruned_prompt
+
+
+def prepare_delegate_master_prompt(prompt_obj, collector_ids):
+    """Prune master prompt so it only executes post-collector nodes in delegate mode."""
+    downstream = _find_downstream_nodes(prompt_obj, collector_ids)
+    nodes_to_keep = set(collector_ids)
+    nodes_to_keep.update(downstream)
+
+    pruned_prompt = {}
+    for node_id in nodes_to_keep:
+        node = prompt_obj.get(node_id)
+        if node is not None:
+            pruned_prompt[node_id] = json.loads(json.dumps(node))
+
+    pruned_ids = set(pruned_prompt.keys())
+    for node_id, node in pruned_prompt.items():
+        inputs = node.get("inputs")
+        if not inputs:
+            continue
+        for input_name, input_value in list(inputs.items()):
+            if isinstance(input_value, list) and len(input_value) == 2:
+                source_id = str(input_value[0])
+                if source_id not in pruned_ids:
+                    inputs.pop(input_name, None)
+                    debug_log(
+                        f"Removed upstream reference '{input_name}' from node {node_id} for delegate-only master prompt."
+                    )
+
+    # Generate IDs from the original prompt to avoid ID collisions with pruned nodes.
+    next_id = _create_numeric_id_generator(prompt_obj)
+    for collector_id in collector_ids:
+        collector_entry = pruned_prompt.get(collector_id)
+        if not collector_entry:
+            continue
+        placeholder_id = next_id()
+        pruned_prompt[placeholder_id] = {
+            "class_type": "DistributedEmptyImage",
+            "inputs": {
+                "height": 64,
+                "width": 64,
+                "channels": 3,
+            },
+            "_meta": {
+                "title": "Distributed Empty Image (auto-added)",
+            },
+        }
+        collector_entry.setdefault("inputs", {})["images"] = [placeholder_id, 0]
+        debug_log(
+            f"Inserted placeholder node {placeholder_id} for collector {collector_id} in delegate-only master prompt."
+        )
+
+    return pruned_prompt
+
+
+def generate_job_id_map(prompt_index, prefix):
+    """Create stable per-node job IDs for distributed nodes."""
+    job_map = {}
+    distributed_nodes = prompt_index.nodes_for_class("DistributedCollector") + prompt_index.nodes_for_class(
+        "UltimateSDUpscaleDistributed"
+    )
+    for node_id in distributed_nodes:
+        job_map[node_id] = f"{prefix}_{node_id}"
+    return job_map
+
+
+def _override_seed_nodes(prompt_copy, prompt_index, is_master, participant_id, worker_index_map):
+    """Configure DistributedSeed nodes for master or worker role."""
+    for node_id in prompt_index.nodes_for_class("DistributedSeed"):
+        node = prompt_copy.get(node_id)
+        if not isinstance(node, dict):
+            continue
+        inputs = node.setdefault("inputs", {})
+        inputs["is_worker"] = not is_master
+        if is_master:
+            inputs["worker_id"] = ""
+        else:
+            inputs["worker_id"] = f"worker_{worker_index_map.get(participant_id, 0)}"
+
+
+def _override_collector_nodes(
+    prompt_copy,
+    prompt_index,
+    is_master,
+    participant_id,
+    job_id_map,
+    master_url,
+    enabled_json,
+    delegate_master,
+):
+    """Configure DistributedCollector nodes for master or worker role."""
+    for node_id in prompt_index.nodes_for_class("DistributedCollector"):
+        node = prompt_copy.get(node_id)
+        if not isinstance(node, dict):
+            continue
+
+        if prompt_index.has_upstream(node_id, "UltimateSDUpscaleDistributed"):
+            node.setdefault("inputs", {})["pass_through"] = True
+            continue
+
+        inputs = node.setdefault("inputs", {})
+        inputs["multi_job_id"] = job_id_map.get(node_id, node_id)
+        inputs["is_worker"] = not is_master
+        inputs["enabled_worker_ids"] = enabled_json
+        if is_master:
+            inputs["delegate_only"] = bool(delegate_master)
+            inputs.pop("master_url", None)
+            inputs.pop("worker_id", None)
+        else:
+            inputs["master_url"] = master_url
+            inputs["worker_id"] = participant_id
+            inputs["delegate_only"] = False
+
+
+def _override_upscale_nodes(
+    prompt_copy,
+    prompt_index,
+    is_master,
+    participant_id,
+    job_id_map,
+    master_url,
+    enabled_json,
+):
+    """Configure UltimateSDUpscaleDistributed nodes for master or worker role."""
+    for node_id in prompt_index.nodes_for_class("UltimateSDUpscaleDistributed"):
+        node = prompt_copy.get(node_id)
+        if not isinstance(node, dict):
+            continue
+        inputs = node.setdefault("inputs", {})
+        inputs["multi_job_id"] = job_id_map.get(node_id, node_id)
+        inputs["is_worker"] = not is_master
+        inputs["enabled_worker_ids"] = enabled_json
+        if is_master:
+            inputs.pop("master_url", None)
+            inputs.pop("worker_id", None)
+        else:
+            inputs["master_url"] = master_url
+            inputs["worker_id"] = participant_id
+
+
+def _override_value_nodes(prompt_copy, prompt_index, is_master, participant_id, worker_index_map):
+    """Configure DistributedValue nodes for master or worker role."""
+    for node_id in prompt_index.nodes_for_class("DistributedValue"):
+        node = prompt_copy.get(node_id)
+        if not isinstance(node, dict):
+            continue
+        inputs = node.setdefault("inputs", {})
+        inputs["is_worker"] = not is_master
+        if is_master:
+            inputs["worker_id"] = ""
+        else:
+            inputs["worker_id"] = f"worker_{worker_index_map.get(participant_id, 0)}"
+
+
+def apply_participant_overrides(
+    prompt_copy,
+    participant_id,
+    enabled_worker_ids,
+    job_id_map,
+    master_url,
+    delegate_master,
+    prompt_index,
+):
+    """Return a prompt copy with hidden inputs configured for master/worker."""
+    is_master = participant_id == "master"
+    worker_index_map = {wid: idx for idx, wid in enumerate(enabled_worker_ids)}
+    enabled_json = json.dumps(enabled_worker_ids)
+
+    _override_seed_nodes(prompt_copy, prompt_index, is_master, participant_id, worker_index_map)
+    _override_value_nodes(prompt_copy, prompt_index, is_master, participant_id, worker_index_map)
+    _override_collector_nodes(
+        prompt_copy,
+        prompt_index,
+        is_master,
+        participant_id,
+        job_id_map,
+        master_url,
+        enabled_json,
+        delegate_master,
+    )
+    _override_upscale_nodes(
+        prompt_copy,
+        prompt_index,
+        is_master,
+        participant_id,
+        job_id_map,
+        master_url,
+        enabled_json,
+    )
+
+    return prompt_copy
@@ -0,0 +1,393 @@
+import asyncio
+import time
+import uuid
+
+import server
+
+from ..utils.async_helpers import queue_prompt_payload
+from ..utils.config import load_config
+from ..utils.constants import (
+    ORCHESTRATION_MEDIA_SYNC_CONCURRENCY,
+    ORCHESTRATION_MEDIA_SYNC_TIMEOUT,
+    ORCHESTRATION_WORKER_PROBE_CONCURRENCY,
+    ORCHESTRATION_WORKER_PREP_CONCURRENCY,
+)
+from ..utils.logging import debug_log, log
+from ..utils.network import build_master_url
+from ..utils.trace_logger import trace_debug
+from .schemas import parse_positive_float, parse_positive_int
+from .orchestration.dispatch import (
+    dispatch_worker_prompt,
+    select_active_workers,
+    select_least_busy_worker,
+)
+from .orchestration.media_sync import convert_paths_for_platform, fetch_worker_path_separator, sync_worker_media
+from .orchestration.prompt_transform import (
+    PromptIndex,
+    apply_participant_overrides,
+    find_nodes_by_class,
+    generate_job_id_map,
+    prepare_delegate_master_prompt,
+    prune_prompt_for_worker,
+)
+
+
+prompt_server = server.PromptServer.instance
+
+
+def _generate_execution_trace_id():
+    return f"exec_{int(time.time() * 1000)}_{uuid.uuid4().hex[:6]}"
+
+
+def ensure_distributed_state(server_instance=None):
+    """Ensure prompt_server has the state used by distributed queue orchestration."""
+    ps = server_instance or prompt_server
+    if not hasattr(ps, "distributed_pending_jobs"):
+        ps.distributed_pending_jobs = {}
+    if not hasattr(ps, "distributed_jobs_lock"):
+        ps.distributed_jobs_lock = asyncio.Lock()
+
+
+# Initialize top-level distributed queue state at module import time.
+ensure_distributed_state()
+
+
+async def _ensure_distributed_queue(job_id):
+    """Ensure a queue exists for the given distributed job ID."""
+    ensure_distributed_state()
+    async with prompt_server.distributed_jobs_lock:
+        if job_id not in prompt_server.distributed_pending_jobs:
+            prompt_server.distributed_pending_jobs[job_id] = asyncio.Queue()
+
+
+def _resolve_enabled_workers(config, requested_ids=None):
+    """Return a list of worker configs that should participate."""
+    workers = []
+    for worker in config.get("workers", []):
+        worker_id = str(worker.get("id") or "").strip()
+        if not worker_id:
+            continue
+
+        if requested_ids is not None:
+            if worker_id not in requested_ids:
+                continue
+        elif not worker.get("enabled", False):
+            continue
+
+        raw_port = worker.get("port", worker.get("listen_port", 8188))
+        try:
+            port = int(raw_port or 8188)
+        except (TypeError, ValueError):
+            log(f"[Distributed] Invalid port '{raw_port}' for worker {worker_id}; defaulting to 8188.")
+            port = 8188
+
+        workers.append(
+            {
+                "id": worker_id,
+                "name": worker.get("name", worker_id),
+                "host": worker.get("host"),
+                "port": port,
+                "type": worker.get("type", "local"),
+            }
+        )
+    return workers
+
+
+def _resolve_orchestration_limits(config):
+    """Resolve bounded concurrency/timeouts for worker preparation pipeline."""
+    settings = (config or {}).get("settings", {}) or {}
+    worker_probe_concurrency = parse_positive_int(
+        settings.get("worker_probe_concurrency"),
+        ORCHESTRATION_WORKER_PROBE_CONCURRENCY,
+    )
+    worker_prep_concurrency = parse_positive_int(
+        settings.get("worker_prep_concurrency"),
+        ORCHESTRATION_WORKER_PREP_CONCURRENCY,
+    )
+    media_sync_concurrency = parse_positive_int(
+        settings.get("media_sync_concurrency"),
+        ORCHESTRATION_MEDIA_SYNC_CONCURRENCY,
+    )
+    media_sync_timeout_seconds = parse_positive_float(
+        settings.get("media_sync_timeout_seconds"),
+        ORCHESTRATION_MEDIA_SYNC_TIMEOUT,
+    )
+    return (
+        worker_probe_concurrency,
+        worker_prep_concurrency,
+        media_sync_concurrency,
+        media_sync_timeout_seconds,
+    )
+
+
+def _is_load_balance_enabled(value):
+    if isinstance(value, bool):
+        return value
+    if isinstance(value, (int, float)):
+        return bool(value)
+    if isinstance(value, str):
+        return value.strip().lower() in {"1", "true", "yes", "on"}
+    return False
+
+
+def _prompt_requests_load_balance(prompt_index):
+    for node_id in prompt_index.nodes_for_class("DistributedCollector"):
+        inputs = prompt_index.inputs_by_node.get(node_id, {})
+        if _is_load_balance_enabled(inputs.get("load_balance", False)):
+            return True
+    return False
+
+
+async def _prepare_worker_payload(
+    worker,
+    prompt_index,
+    enabled_ids,
+    job_id_map,
+    master_url,
+    delegate_master,
+    trace_execution_id,
+    worker_prep_semaphore,
+    media_sync_semaphore,
+    media_sync_timeout_seconds,
+):
+    """Prepare one worker prompt payload with bounded concurrency and media-sync timeout."""
+    async with worker_prep_semaphore:
+        worker_prompt = prompt_index.copy_prompt()
+
+        worker_type = str(worker.get("type") or "local").strip().lower()
+        is_remote_like = bool(worker.get("host")) and worker_type != "local"
+        if is_remote_like:
+            path_separator = await fetch_worker_path_separator(worker, trace_execution_id=trace_execution_id)
+            if path_separator:
+                worker_prompt = convert_paths_for_platform(worker_prompt, path_separator)
+
+        worker_prompt = prune_prompt_for_worker(worker_prompt)
+        worker_prompt = apply_participant_overrides(
+            worker_prompt,
+            worker["id"],
+            enabled_ids,
+            job_id_map,
+            master_url,
+            delegate_master,
+            prompt_index,
+        )
+
+        if is_remote_like:
+            async with media_sync_semaphore:
+                try:
+                    await asyncio.wait_for(
+                        sync_worker_media(worker, worker_prompt, trace_execution_id=trace_execution_id),
+                        timeout=media_sync_timeout_seconds,
+                    )
+                except asyncio.TimeoutError:
+                    trace_debug(
+                        trace_execution_id,
+                        (
+                            f"Media sync timed out after {media_sync_timeout_seconds:.1f}s "
+                            f"for worker {worker.get('name')} ({worker.get('id')}); continuing dispatch."
+                        ),
+                    )
+
+        return worker, worker_prompt
+
+
+async def orchestrate_distributed_execution(
+    prompt_obj,
+    workflow_meta,
+    client_id,
+    enabled_worker_ids=None,
+    delegate_master=None,
+    trace_execution_id=None,
+):
+    """Core orchestration logic for the /distributed/queue endpoint.
+
+    Returns:
+        tuple[str, int]: (prompt_id, worker_count)
+    """
+    ensure_distributed_state()
+    execution_trace_id = trace_execution_id or _generate_execution_trace_id()
+
+    config = load_config()
+    use_websocket = bool(config.get("settings", {}).get("websocket_orchestration", False))
+    master_url = build_master_url(config=config, prompt_server_instance=prompt_server)
+    (
+        worker_probe_concurrency,
+        worker_prep_concurrency,
+        media_sync_concurrency,
+        media_sync_timeout_seconds,
+    ) = _resolve_orchestration_limits(config)
+    requested_ids = enabled_worker_ids if enabled_worker_ids is not None else None
+    workers = _resolve_enabled_workers(config, requested_ids)
+    prompt_index = PromptIndex(prompt_obj)
+    load_balance_requested = _prompt_requests_load_balance(prompt_index)
+    trace_debug(
+        execution_trace_id,
+        (
+            f"Orchestration start: requested_workers={len(workers)}, "
+            f"requested_ids={requested_ids if requested_ids is not None else 'enabled_only'}, "
+            f"websocket={use_websocket}, "
+            f"probe_concurrency={worker_probe_concurrency}, "
+            f"prep_concurrency={worker_prep_concurrency}, "
+            f"media_sync_concurrency={media_sync_concurrency}, "
+            f"media_sync_timeout={media_sync_timeout_seconds:.1f}s, "
+            f"load_balance={load_balance_requested}"
+        ),
+    )
+
+    # Respect master delegate-only configuration
+    if delegate_master is None:
+        delegate_master = bool(config.get("settings", {}).get("master_delegate_only", False))
+
+    if not workers and delegate_master:
+        trace_debug(
+            execution_trace_id,
+            "Delegate-only requested but no workers are enabled. Falling back to master execution.",
+        )
+        delegate_master = False
+
+    active_workers, delegate_master = await select_active_workers(
+        workers,
+        use_websocket,
+        delegate_master,
+        trace_execution_id=execution_trace_id,
+        probe_concurrency=worker_probe_concurrency,
+    )
+
+    if load_balance_requested:
+        candidate_workers = list(active_workers)
+        if not delegate_master:
+            # Include master in load balancing only when master participation is enabled.
+            candidate_workers.append(
+                {
+                    "id": "master",
+                    "name": "Master",
+                    "host": master_url,
+                    "type": "local",
+                }
+            )
+
+        selected_worker = None
+        if candidate_workers:
+            selected_worker = await select_least_busy_worker(
+                candidate_workers,
+                trace_execution_id=execution_trace_id,
+                probe_concurrency=worker_probe_concurrency,
+            )
+        if selected_worker is None and candidate_workers:
+            trace_debug(
+                execution_trace_id,
+                "Load-balance selection probe failed; using first available candidate.",
+            )
+            selected_worker = candidate_workers[0]
+
+        if selected_worker is not None and str(selected_worker.get("id")) == "master":
+            # Master selected as least busy; run master workload only.
+            active_workers = []
+            delegate_master = False
+            trace_debug(
+                execution_trace_id,
+                "Load-balance selected master for execution (workers skipped).",
+            )
+        elif selected_worker is not None:
+            active_workers = [selected_worker]
+            # Worker selected as least busy; keep master orchestrator-only for this run.
+            delegate_master = True
+            trace_debug(
+                execution_trace_id,
+                f"Load-balance selected worker {selected_worker.get('id')} (master set to delegate-only).",
+            )
+        else:
+            trace_debug(
+                execution_trace_id,
+                "Load-balance requested but no execution candidates were available.",
+            )
+            active_workers = []
+            delegate_master = False
+
+    enabled_ids = [worker["id"] for worker in active_workers]
+
+    discovery_prefix = f"exec_{int(time.time() * 1000)}_{uuid.uuid4().hex[:6]}"
+    job_id_map = generate_job_id_map(prompt_index, discovery_prefix)
+
+    if not job_id_map:
+        trace_debug(execution_trace_id, "No distributed nodes detected; queueing prompt on master only.")
+        prompt_id = await queue_prompt_payload(prompt_obj, workflow_meta, client_id)
+        return prompt_id, 0
+
+    for job_id in job_id_map.values():
+        await _ensure_distributed_queue(job_id)
+
+    master_prompt = prompt_index.copy_prompt()
+    master_prompt = apply_participant_overrides(
+        master_prompt,
+        "master",
+        enabled_ids,
+        job_id_map,
+        master_url,
+        delegate_master,
+        prompt_index,
+    )
+
+    if delegate_master:
+        collector_ids = find_nodes_by_class(master_prompt, "DistributedCollector")
+        upscale_nodes = find_nodes_by_class(master_prompt, "UltimateSDUpscaleDistributed")
+        if upscale_nodes:
+            debug_log(
+                "Delegate-only master mode currently does not support UltimateSDUpscaleDistributed nodes; running full prompt on master."
+            )
+        elif not collector_ids:
+            debug_log(
+                "Delegate-only master mode requested but no collectors found in master prompt. Running full prompt on master."
+            )
+        else:
+            master_prompt = prepare_delegate_master_prompt(master_prompt, collector_ids)
+
+    if active_workers:
+        trace_debug(
+            execution_trace_id,
+            "Active distributed workers: "
+            + ", ".join(f"{worker['name']} ({worker['id']})" for worker in active_workers),
+        )
+    worker_payloads = []
+    if active_workers:
+        worker_prep_semaphore = asyncio.Semaphore(worker_prep_concurrency)
+        media_sync_semaphore = asyncio.Semaphore(media_sync_concurrency)
+        worker_payloads = await asyncio.gather(
+            *[
+                _prepare_worker_payload(
+                    worker,
+                    prompt_index,
+                    enabled_ids,
+                    job_id_map,
+                    master_url,
+                    delegate_master,
+                    execution_trace_id,
+                    worker_prep_semaphore,
+                    media_sync_semaphore,
+                    media_sync_timeout_seconds,
+                )
+                for worker in active_workers
+            ]
+        )
+
+    if worker_payloads:
+        await asyncio.gather(
+            *[
+                dispatch_worker_prompt(
+                    worker,
+                    wprompt,
+                    workflow_meta,
+                    client_id,
+                    use_websocket=use_websocket,
+                    trace_execution_id=execution_trace_id,
+                )
+                for worker, wprompt in worker_payloads
+            ]
+        )
+
+    prompt_id = await queue_prompt_payload(master_prompt, workflow_meta, client_id)
+    trace_debug(
+        execution_trace_id,
+        f"Orchestration complete: prompt_id={prompt_id}, dispatched_workers={len(worker_payloads)}, delegate_master={delegate_master}",
+    )
+    return prompt_id, len(worker_payloads)
@@ -0,0 +1,79 @@
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional
+
+
+@dataclass(frozen=True)
+class QueueRequestPayload:
+    prompt: Dict[str, Any]
+    workflow_meta: Any
+    client_id: str
+    delegate_master: Optional[bool]
+    enabled_worker_ids: List[str]
+    auto_prepare: bool
+    trace_execution_id: Optional[str]
+
+
+def parse_queue_request_payload(data: Any) -> QueueRequestPayload:
+    """Parse and validate /distributed/queue payload into a normalized shape."""
+    if not isinstance(data, dict):
+        raise ValueError("Expected a JSON object body")
+
+    auto_prepare_raw = data.get("auto_prepare", True)
+    if not isinstance(auto_prepare_raw, bool):
+        raise ValueError("auto_prepare must be a boolean when provided")
+    auto_prepare = auto_prepare_raw
+
+    prompt = data.get("prompt")
+    # Auto-prepare is always on server-side; keep the field for wire compatibility.
+    if prompt is None:
+        workflow_payload = data.get("workflow")
+        if isinstance(workflow_payload, dict):
+            candidate_prompt = workflow_payload.get("prompt")
+            if isinstance(candidate_prompt, dict):
+                prompt = candidate_prompt
+
+    if not isinstance(prompt, dict):
+        raise ValueError("Field 'prompt' must be an object")
+
+    enabled_ids_raw = data.get("enabled_worker_ids")
+    workers_field = data.get("workers")
+    if enabled_ids_raw is None and workers_field is not None:
+        if not isinstance(workers_field, list):
+            raise ValueError("Field 'workers' must be a list when provided")
+        enabled_ids_raw = []
+        for entry in workers_field:
+            worker_id = entry.get("id") if isinstance(entry, dict) else entry
+            if worker_id is not None:
+                enabled_ids_raw.append(str(worker_id))
+
+    if enabled_ids_raw is None:
+        raise ValueError("enabled_worker_ids required")
+    else:
+        if not isinstance(enabled_ids_raw, list):
+            raise ValueError("enabled_worker_ids must be a list of worker IDs")
+        enabled_ids = [str(worker_id).strip() for worker_id in enabled_ids_raw if str(worker_id).strip()]
+
+    delegate_master = data.get("delegate_master")
+    if delegate_master is not None and not isinstance(delegate_master, bool):
+        raise ValueError("delegate_master must be a boolean when provided")
+
+    client_id = data.get("client_id")
+    if not isinstance(client_id, str) or not client_id.strip():
+        raise ValueError("client_id required")
+    client_id = client_id.strip()
+
+    trace_execution_id = data.get("trace_execution_id")
+    if trace_execution_id is not None:
+        if not isinstance(trace_execution_id, str):
+            raise ValueError("trace_execution_id must be a string when provided")
+        trace_execution_id = trace_execution_id.strip() or None
+
+    return QueueRequestPayload(
+        prompt=prompt,
+        workflow_meta=data.get("workflow"),
+        client_id=client_id,
+        delegate_master=delegate_master,
+        enabled_worker_ids=enabled_ids,
+        auto_prepare=auto_prepare,
+        trace_execution_id=trace_execution_id,
+    )
@@ -0,0 +1,54 @@
+def require_fields(data: dict, *fields) -> list[str]:
+    """Return field names that are missing or empty in a JSON object."""
+    if not isinstance(data, dict):
+        return list(fields)
+
+    missing = []
+    for field in fields:
+        if field not in data:
+            missing.append(field)
+            continue
+        value = data.get(field)
+        if value is None:
+            missing.append(field)
+            continue
+        if isinstance(value, str) and not value.strip():
+            missing.append(field)
+
+    return missing
+
+
+def validate_worker_id(worker_id: str, config: dict) -> bool:
+    """Return True when worker_id exists in config['workers']."""
+    worker_id_str = str(worker_id)
+    workers = (config or {}).get("workers", [])
+    return any(str(worker.get("id")) == worker_id_str for worker in workers)
+
+
+def validate_positive_int(value, field_name: str) -> str | None:
+    """Validate positive integers and return an error string when invalid."""
+    try:
+        parsed = int(value)
+    except (TypeError, ValueError):
+        return f"Field '{field_name}' must be a positive integer."
+    if parsed <= 0:
+        return f"Field '{field_name}' must be a positive integer."
+    return None
+
+
+def parse_positive_int(value, default: int) -> int:
+    """Parse value as positive int, returning default on failure."""
+    try:
+        parsed = int(value)
+    except (TypeError, ValueError):
+        return max(1, int(default))
+    return max(1, parsed)
+
+
+def parse_positive_float(value, default: float) -> float:
+    """Parse value as positive float, returning default on failure."""
+    try:
+        parsed = float(value)
+    except (TypeError, ValueError):
+        return max(0.0, float(default))
+    return max(0.0, parsed)
@@ -0,0 +1,51 @@
+from aiohttp import web
+import server
+
+from ..utils.cloudflare import cloudflare_tunnel_manager
+from ..utils.config import load_config
+from ..utils.logging import debug_log, log
+from ..utils.network import handle_api_error
+
+
+@server.PromptServer.instance.routes.get("/distributed/tunnel/status")
+async def tunnel_status_endpoint(request):
+    """Return Cloudflare tunnel status and last known details."""
+    try:
+        status = cloudflare_tunnel_manager.get_status()
+        config = load_config()
+        master_host = (config.get("master") or {}).get("host")
+        return web.json_response({
+            "status": "success",
+            "tunnel": status,
+            "master_host": master_host
+        })
+    except Exception as e:
+        return await handle_api_error(request, e, 500)
+
+@server.PromptServer.instance.routes.post("/distributed/tunnel/start")
+async def tunnel_start_endpoint(request):
+    """Start a Cloudflare tunnel pointing at the current ComfyUI server."""
+    try:
+        result = await cloudflare_tunnel_manager.start_tunnel()
+        config = load_config()
+        return web.json_response({
+            "status": "success",
+            "tunnel": result,
+            "master_host": (config.get("master") or {}).get("host")
+        })
+    except Exception as e:
+        return await handle_api_error(request, e, 500)
+
+@server.PromptServer.instance.routes.post("/distributed/tunnel/stop")
+async def tunnel_stop_endpoint(request):
+    """Stop the managed Cloudflare tunnel if running."""
+    try:
+        result = await cloudflare_tunnel_manager.stop_tunnel()
+        config = load_config()
+        return web.json_response({
+            "status": "success",
+            "tunnel": result,
+            "master_host": (config.get("master") or {}).get("host")
+        })
+    except Exception as e:
+        return await handle_api_error(request, e, 500)
@@ -0,0 +1,228 @@
+import asyncio
+import io
+import time
+
+from aiohttp import web
+from PIL import Image
+import server
+
+from ..upscale.job_models import BaseJobState, ImageJobState, TileJobState
+from ..upscale.job_store import MAX_PAYLOAD_SIZE, ensure_tile_jobs_initialized
+from ..upscale.payload_parsers import _parse_tiles_from_form
+from ..utils.logging import debug_log
+from ..utils.network import handle_api_error
+
+
+@server.PromptServer.instance.routes.post("/distributed/heartbeat")
+async def heartbeat_endpoint(request):
+    try:
+        data = await request.json()
+        worker_id = data.get('worker_id')
+        multi_job_id = data.get('multi_job_id')
+
+        if not worker_id or not multi_job_id:
+            return await handle_api_error(request, "Missing worker_id or multi_job_id", 400)
+
+        prompt_server = ensure_tile_jobs_initialized()
+        async with prompt_server.distributed_tile_jobs_lock:
+            if multi_job_id in prompt_server.distributed_pending_tile_jobs:
+                job_data = prompt_server.distributed_pending_tile_jobs[multi_job_id]
+                if isinstance(job_data, BaseJobState):
+                    job_data.worker_status[worker_id] = time.time()
+                    debug_log(f"Heartbeat from worker {worker_id}")
+                    return web.json_response({"status": "success"})
+                return await handle_api_error(request, "Worker status tracking not available", 400)
+            return await handle_api_error(request, "Job not found", 404)
+    except Exception as e:
+        return await handle_api_error(request, e, 500)
+
+
+@server.PromptServer.instance.routes.post("/distributed/submit_tiles")
+async def submit_tiles_endpoint(request):
+    """Endpoint for workers to submit processed tiles in static mode."""
+    try:
+        content_length = request.headers.get('content-length')
+        if content_length and int(content_length) > MAX_PAYLOAD_SIZE:
+            return await handle_api_error(request, f"Payload too large: {content_length} bytes", 413)
+
+        data = await request.post()
+        multi_job_id = data.get('multi_job_id')
+        worker_id = data.get('worker_id')
+        is_last = data.get('is_last', 'False').lower() == 'true'
+
+        if multi_job_id is None or worker_id is None:
+            return await handle_api_error(request, "Missing multi_job_id or worker_id", 400)
+
+        prompt_server = ensure_tile_jobs_initialized()
+
+        batch_size = int(data.get('batch_size', 0))
+
+        # Handle completion signal
+        if batch_size == 0 and is_last:
+            async with prompt_server.distributed_tile_jobs_lock:
+                if multi_job_id in prompt_server.distributed_pending_tile_jobs:
+                    job_data = prompt_server.distributed_pending_tile_jobs[multi_job_id]
+                    if not isinstance(job_data, TileJobState):
+                        return await handle_api_error(request, "Job not configured for tile submissions", 400)
+                    await job_data.queue.put({
+                        'worker_id': worker_id,
+                        'is_last': True,
+                        'tiles': [],
+                    })
+                    debug_log(f"Received completion signal from worker {worker_id}")
+                    return web.json_response({"status": "success"})
+
+        try:
+            tiles = _parse_tiles_from_form(data)
+        except ValueError as e:
+            return await handle_api_error(request, str(e), 400)
+
+        # Submit tiles to queue
+        async with prompt_server.distributed_tile_jobs_lock:
+            if multi_job_id in prompt_server.distributed_pending_tile_jobs:
+                job_data = prompt_server.distributed_pending_tile_jobs[multi_job_id]
+                if not isinstance(job_data, TileJobState):
+                    return await handle_api_error(request, "Job not configured for tile submissions", 400)
+
+                q = job_data.queue
+                if batch_size > 0 or len(tiles) > 0:
+                    await q.put({
+                        'worker_id': worker_id,
+                        'tiles': tiles,
+                        'is_last': is_last,
+                    })
+                    debug_log(f"Received {len(tiles)} tiles from worker {worker_id} (is_last={is_last})")
+                else:
+                    await q.put({
+                        'worker_id': worker_id,
+                        'is_last': True,
+                        'tiles': [],
+                    })
+
+                return web.json_response({"status": "success"})
+            return await handle_api_error(request, "Job not found", 404)
+    except Exception as e:
+        return await handle_api_error(request, e, 500)
+
+
+@server.PromptServer.instance.routes.post("/distributed/submit_image")
+async def submit_image_endpoint(request):
+    """Endpoint for workers to submit processed images in dynamic mode."""
+    try:
+        content_length = request.headers.get('content-length')
+        if content_length and int(content_length) > MAX_PAYLOAD_SIZE:
+            return await handle_api_error(request, f"Payload too large: {content_length} bytes", 413)
+
+        data = await request.post()
+        multi_job_id = data.get('multi_job_id')
+        worker_id = data.get('worker_id')
+        is_last = data.get('is_last', 'False').lower() == 'true'
+
+        if multi_job_id is None or worker_id is None:
+            return await handle_api_error(request, "Missing multi_job_id or worker_id", 400)
+
+        prompt_server = ensure_tile_jobs_initialized()
+
+        # Handle image submission
+        if 'full_image' in data and 'image_idx' in data:
+            image_idx = int(data.get('image_idx'))
+            img_data = data['full_image'].file.read()
+            img = Image.open(io.BytesIO(img_data)).convert("RGB")
+
+            debug_log(f"Received full image {image_idx} from worker {worker_id}")
+
+            async with prompt_server.distributed_tile_jobs_lock:
+                if multi_job_id in prompt_server.distributed_pending_tile_jobs:
+                    job_data = prompt_server.distributed_pending_tile_jobs[multi_job_id]
+                    if not isinstance(job_data, ImageJobState):
+                        return await handle_api_error(request, "Job not configured for image submissions", 400)
+                    await job_data.queue.put({
+                        'worker_id': worker_id,
+                        'image_idx': image_idx,
+                        'image': img,
+                        'is_last': is_last,
+                    })
+                    return web.json_response({"status": "success"})
+
+        # Handle completion signal (no image data)
+        elif is_last:
+            async with prompt_server.distributed_tile_jobs_lock:
+                if multi_job_id in prompt_server.distributed_pending_tile_jobs:
+                    job_data = prompt_server.distributed_pending_tile_jobs[multi_job_id]
+                    if not isinstance(job_data, ImageJobState):
+                        return await handle_api_error(request, "Job not configured for image submissions", 400)
+                    await job_data.queue.put({
+                        'worker_id': worker_id,
+                        'is_last': True,
+                    })
+                    debug_log(f"Received completion signal from worker {worker_id}")
+                    return web.json_response({"status": "success"})
+        else:
+            return await handle_api_error(request, "Missing image data or invalid request", 400)
+
+        return await handle_api_error(request, "Job not found", 404)
+    except Exception as e:
+        return await handle_api_error(request, e, 500)
+
+
+@server.PromptServer.instance.routes.post("/distributed/request_image")
+async def request_image_endpoint(request):
+    """Endpoint for workers to request tasks (images in dynamic mode, tiles in static mode)."""
+    try:
+        data = await request.json()
+        worker_id = data.get('worker_id')
+        multi_job_id = data.get('multi_job_id')
+
+        if not worker_id or not multi_job_id:
+            return await handle_api_error(request, "Missing worker_id or multi_job_id", 400)
+
+        prompt_server = ensure_tile_jobs_initialized()
+        async with prompt_server.distributed_tile_jobs_lock:
+            if multi_job_id in prompt_server.distributed_pending_tile_jobs:
+                job_data = prompt_server.distributed_pending_tile_jobs[multi_job_id]
+                if not isinstance(job_data, BaseJobState):
+                    return await handle_api_error(request, "Invalid job data structure", 500)
+
+                mode = job_data.mode
+                if isinstance(job_data, ImageJobState):
+                    pending_queue = job_data.pending_images
+                elif isinstance(job_data, TileJobState):
+                    pending_queue = job_data.pending_tasks
+                else:
+                    return await handle_api_error(request, "Invalid job configuration", 400)
+
+                try:
+                    task_idx = await asyncio.wait_for(pending_queue.get(), timeout=0.1)
+                    job_data.assigned_to_workers.setdefault(worker_id, []).append(task_idx)
+                    job_data.worker_status[worker_id] = time.time()
+                    remaining = pending_queue.qsize()
+
+                    if mode == 'dynamic':
+                        debug_log(f"UltimateSDUpscale API - Assigned image {task_idx} to worker {worker_id}")
+                        return web.json_response({"image_idx": task_idx, "estimated_remaining": remaining})
+                    debug_log(f"UltimateSDUpscale API - Assigned tile {task_idx} to worker {worker_id}")
+                    return web.json_response({
+                        "tile_idx": task_idx,
+                        "estimated_remaining": remaining,
+                        "batched_static": job_data.batched_static,
+                    })
+                except asyncio.TimeoutError:
+                    if mode == 'dynamic':
+                        return web.json_response({"image_idx": None})
+                    return web.json_response({"tile_idx": None})
+            return await handle_api_error(request, "Job not found", 404)
+    except Exception as e:
+        return await handle_api_error(request, e, 500)
+
+
+@server.PromptServer.instance.routes.get("/distributed/job_status")
+async def job_status_endpoint(request):
+    """Endpoint to check if a job is ready."""
+    multi_job_id = request.query.get('multi_job_id')
+    if not multi_job_id:
+        return web.json_response({"ready": False})
+    prompt_server = ensure_tile_jobs_initialized()
+    async with prompt_server.distributed_tile_jobs_lock:
+        job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
+        ready = bool(isinstance(job_data, BaseJobState) and job_data.queue is not None)
+        return web.json_response({"ready": ready})
@@ -0,0 +1,695 @@
+import json
+import asyncio
+import os
+import time
+import platform
+import subprocess
+import socket
+
+import torch
+import aiohttp
+from aiohttp import web
+import server
+
+from ..utils.config import load_config
+from ..utils.logging import debug_log, log
+from ..utils.network import (
+    build_worker_url,
+    get_client_session,
+    handle_api_error,
+    normalize_host,
+    probe_worker,
+)
+from ..utils.constants import CHUNK_SIZE
+from ..workers import get_worker_manager
+from .schemas import require_fields, validate_worker_id
+from ..workers.detection import (
+    get_machine_id,
+    is_docker_environment,
+    is_runpod_environment,
+)
+try:
+    from ..utils.async_helpers import PromptValidationError, queue_prompt_payload
+except ImportError:
+    from ..utils.async_helpers import queue_prompt_payload
+
+    class PromptValidationError(RuntimeError):
+        def __init__(self, message, validation_error=None, node_errors=None):
+            super().__init__(str(message))
+            self.validation_error = validation_error if isinstance(validation_error, dict) else {}
+            self.node_errors = node_errors if isinstance(node_errors, dict) else {}
+
+
+@server.PromptServer.instance.routes.get("/distributed/worker_ws")
+async def worker_ws_endpoint(request):
+    """WebSocket endpoint for worker prompt dispatch."""
+    ws = web.WebSocketResponse(heartbeat=30)
+    await ws.prepare(request)
+
+    async for msg in ws:
+        if msg.type == aiohttp.WSMsgType.TEXT:
+            try:
+                data = json.loads(msg.data or "{}")
+            except json.JSONDecodeError:
+                await ws.send_json({
+                    "type": "dispatch_ack",
+                    "request_id": None,
+                    "ok": False,
+                    "error": "Invalid JSON payload.",
+                })
+                continue
+
+            if data.get("type") != "dispatch_prompt":
+                await ws.send_json({
+                    "type": "dispatch_ack",
+                    "request_id": data.get("request_id"),
+                    "ok": False,
+                    "error": "Unsupported websocket message type.",
+                })
+                continue
+
+            prompt = data.get("prompt")
+            if not isinstance(prompt, dict):
+                await ws.send_json({
+                    "type": "dispatch_ack",
+                    "request_id": data.get("request_id"),
+                    "ok": False,
+                    "error": "Field 'prompt' must be an object.",
+                })
+                continue
+
+            try:
+                prompt_id = await queue_prompt_payload(
+                    prompt,
+                    workflow_meta=data.get("workflow"),
+                    client_id=data.get("client_id"),
+                )
+                await ws.send_json({
+                    "type": "dispatch_ack",
+                    "request_id": data.get("request_id"),
+                    "ok": True,
+                    "prompt_id": prompt_id,
+                })
+            except PromptValidationError as exc:
+                await ws.send_json({
+                    "type": "dispatch_ack",
+                    "request_id": data.get("request_id"),
+                    "ok": False,
+                    "error": str(exc),
+                    "validation_error": exc.validation_error,
+                    "node_errors": exc.node_errors,
+                })
+            except Exception as exc:
+                await ws.send_json({
+                    "type": "dispatch_ack",
+                    "request_id": data.get("request_id"),
+                    "ok": False,
+                    "error": str(exc),
+                })
+        elif msg.type == aiohttp.WSMsgType.ERROR:
+            log(f"[Distributed] Worker websocket error: {ws.exception()}")
+
+    return ws
+
+
+@server.PromptServer.instance.routes.post("/distributed/worker/clear_launching")
+async def clear_launching_state(request):
+    """Clear the launching flag when worker is confirmed running."""
+    try:
+        wm = get_worker_manager()
+        data = await request.json()
+        missing = require_fields(data, "worker_id")
+        if missing:
+            return await handle_api_error(request, f"Missing required field(s): {', '.join(missing)}", 400)
+
+        worker_id = str(data.get("worker_id")).strip()
+        config = load_config()
+        if not validate_worker_id(worker_id, config):
+            return await handle_api_error(request, f"Worker {worker_id} not found", 404)
+        
+        # Clear launching flag in managed processes
+        if worker_id in wm.processes:
+            if 'launching' in wm.processes[worker_id]:
+                del wm.processes[worker_id]['launching']
+                wm.save_processes()
+                debug_log(f"Cleared launching state for worker {worker_id}")
+        
+        return web.json_response({"status": "success"})
+    except Exception as e:
+        return await handle_api_error(request, e, 500)
+
+
+def get_network_ips():
+    """Get all network IPs, trying multiple methods."""
+    ips = []
+    hostname = socket.gethostname()
+
+    # Method 1: Try socket.getaddrinfo
+    try:
+        addr_info = socket.getaddrinfo(hostname, None)
+        for info in addr_info:
+            ip = info[4][0]
+            if ip and ip not in ips and not ip.startswith('::'):  # Skip IPv6 for now
+                ips.append(ip)
+    except (socket.gaierror, OSError):
+        pass
+
+    # Method 2: Try to connect to external server and get local IP
+    try:
+        s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
+        s.connect(("8.8.8.8", 80))  # Google DNS
+        local_ip = s.getsockname()[0]
+        s.close()
+        if local_ip not in ips:
+            ips.append(local_ip)
+    except (OSError, socket.error):
+        pass
+
+    # Method 3: Platform-specific commands
+    try:
+        if platform.system() == "Windows":
+            # Windows ipconfig
+            result = subprocess.run(["ipconfig"], capture_output=True, text=True)
+            lines = result.stdout.split('\n')
+            for i, line in enumerate(lines):
+                if 'IPv4' in line and i + 1 < len(lines):
+                    ip = lines[i].split(':')[-1].strip()
+                    if ip and ip not in ips:
+                        ips.append(ip)
+        else:
+            # Unix/Linux/Mac ifconfig or ip addr
+            try:
+                result = subprocess.run(["ip", "addr"], capture_output=True, text=True)
+            except (FileNotFoundError, OSError):
+                try:
+                    result = subprocess.run(["ifconfig"], capture_output=True, text=True)
+                except (FileNotFoundError, OSError):
+                    result = None
+
+            import re
+            ip_pattern = re.compile(r'inet\s+(\d+\.\d+\.\d+\.\d+)')
+            if result is not None:
+                for match in ip_pattern.finditer(result.stdout):
+                    ip = match.group(1)
+                    if ip and ip not in ips:
+                        ips.append(ip)
+    except (OSError, subprocess.SubprocessError):
+        pass
+
+    return ips
+
+
+def get_recommended_ip(ips):
+    """Choose the best IP for master-worker communication."""
+    # Priority order:
+    # 1. Private network ranges (192.168.x.x, 10.x.x.x, 172.16-31.x.x)
+    # 2. Other non-localhost IPs
+    # 3. Localhost as last resort
+
+    private_ips = []
+    public_ips = []
+
+    for ip in ips:
+        if ip.startswith('127.') or ip == 'localhost':
+            continue
+        elif (ip.startswith('192.168.')
+                or ip.startswith('10.')
+                or (ip.startswith('172.') and 16 <= int(ip.split('.')[1]) <= 31)):
+            private_ips.append(ip)
+        else:
+            public_ips.append(ip)
+
+    # Prefer private IPs
+    if private_ips:
+        # Prefer 192.168 range as it's most common
+        for ip in private_ips:
+            if ip.startswith('192.168.'):
+                return ip
+        return private_ips[0]
+    elif public_ips:
+        return public_ips[0]
+    elif ips:
+        return ips[0]
+    else:
+        return None
+
+
+def _get_cuda_info():
+    """Detect CUDA device index and total physical GPU count.
+
+    Returns (cuda_device, cuda_device_count, physical_device_count).
+    All three are 0/None if CUDA is unavailable.
+    """
+    if not torch.cuda.is_available():
+        return None, 0, 0
+    try:
+        cuda_device_count = torch.cuda.device_count()
+        cuda_visible = os.environ.get('CUDA_VISIBLE_DEVICES', '')
+        if cuda_visible and cuda_visible.strip():
+            visible_devices = [int(d.strip()) for d in cuda_visible.split(',') if d.strip().isdigit()]
+            if visible_devices:
+                cuda_device = visible_devices[0]
+                try:
+                    result = subprocess.run(
+                        ['nvidia-smi', '--query-gpu=name', '--format=csv,noheader'],
+                        capture_output=True,
+                        text=True,
+                        timeout=5,
+                    )
+                    physical_device_count = (
+                        len(result.stdout.strip().split('\n'))
+                        if result.returncode == 0
+                        else max(visible_devices) + 1
+                    )
+                except (FileNotFoundError, OSError, subprocess.SubprocessError):
+                    physical_device_count = max(visible_devices) + 1
+                return cuda_device, cuda_device_count, physical_device_count
+            else:
+                return 0, cuda_device_count, cuda_device_count
+        else:
+            cuda_device = torch.cuda.current_device()
+            return cuda_device, cuda_device_count, cuda_device_count
+    except Exception as e:
+        debug_log(f"CUDA detection error: {e}")
+        return None, 0, 0
+
+
+def _collect_network_info_sync():
+    """Collect network/cuda info in a worker thread to avoid blocking route handlers."""
+    cuda_device, cuda_device_count, physical_device_count = _get_cuda_info()
+    hostname = socket.gethostname()
+    all_ips = get_network_ips()
+    recommended_ip = get_recommended_ip(all_ips)
+    return {
+        "hostname": hostname,
+        "all_ips": all_ips,
+        "recommended_ip": recommended_ip,
+        "cuda_device": cuda_device,
+        "cuda_device_count": physical_device_count if physical_device_count > 0 else cuda_device_count,
+    }
+
+
+def _read_worker_log_sync(log_file, lines_to_read):
+    """Read worker log content from disk in a threadpool worker."""
+    file_size = os.path.getsize(log_file)
+
+    with open(log_file, 'r', encoding='utf-8', errors='replace') as f:
+        if lines_to_read > 0 and file_size > 1024 * 1024:
+            # Read last N lines efficiently from end of file.
+            lines = []
+            f.seek(0, 2)
+            file_length = f.tell()
+            chunk_size = min(CHUNK_SIZE, file_length)
+
+            while len(lines) < lines_to_read and f.tell() > 0:
+                current_pos = max(0, f.tell() - chunk_size)
+                f.seek(current_pos)
+                chunk = f.read(chunk_size)
+                chunk_lines = chunk.splitlines()
+                if current_pos > 0:
+                    chunk_lines = chunk_lines[1:]
+                lines = chunk_lines + lines
+                f.seek(current_pos)
+
+            content = '\n'.join(lines[-lines_to_read:])
+            truncated = len(lines) > lines_to_read
+        else:
+            content = f.read()
+            truncated = False
+
+    return {
+        "content": content,
+        "file_size": file_size,
+        "truncated": truncated,
+        "lines_shown": lines_to_read if truncated else content.count('\n') + 1,
+    }
+
+
+def _parse_positive_int_query(value, default, minimum=1, maximum=10000):
+    """Parse bounded positive integer query params with sane fallback."""
+    try:
+        parsed = int(value)
+    except (TypeError, ValueError):
+        return default
+    parsed = max(minimum, parsed)
+    if maximum is not None:
+        parsed = min(maximum, parsed)
+    return parsed
+
+
+def _find_worker_by_id(config, worker_id):
+    worker_id_str = str(worker_id).strip()
+    for worker in config.get("workers", []):
+        if str(worker.get("id")).strip() == worker_id_str:
+            return worker
+    return None
+
+
+@server.PromptServer.instance.routes.get("/distributed/local_log")
+async def get_local_log_endpoint(request):
+    """Return this instance's in-memory ComfyUI log buffer."""
+    try:
+        from app.logger import get_logs
+    except Exception as e:
+        return await handle_api_error(request, f"Failed to import app.logger: {e}", 500)
+
+    try:
+        lines_to_read = _parse_positive_int_query(request.query.get("lines"), default=300, maximum=3000)
+        logs = get_logs()
+        if logs is None:
+            return web.json_response(
+                {
+                    "status": "success",
+                    "content": "",
+                    "entries": 0,
+                    "source": "memory",
+                    "truncated": False,
+                    "lines_shown": 0,
+                }
+            )
+
+        entries = list(logs)
+        selected_entries = entries[-lines_to_read:]
+        content = "".join(
+            entry.get("m", "") if isinstance(entry, dict) else str(entry)
+            for entry in selected_entries
+        )
+        lines_shown = content.count("\n") + (1 if content else 0)
+
+        return web.json_response(
+            {
+                "status": "success",
+                "content": content,
+                "entries": len(selected_entries),
+                "source": "memory",
+                "truncated": len(entries) > len(selected_entries),
+                "lines_shown": lines_shown,
+            }
+        )
+    except Exception as e:
+        return await handle_api_error(request, e, 500)
+
+
+@server.PromptServer.instance.routes.get("/distributed/network_info")
+async def get_network_info_endpoint(request):
+    """Get network interfaces and recommend best IP for master."""
+    try:
+        loop = asyncio.get_running_loop()
+        info = await loop.run_in_executor(None, _collect_network_info_sync)
+        
+        return web.json_response({
+            "status": "success",
+            **info,
+            "message": "Auto-detected network configuration"
+        })
+    except Exception as e:
+        return await handle_api_error(request, e, 500)
+
+@server.PromptServer.instance.routes.get("/distributed/system_info")
+async def get_system_info_endpoint(request):
+    """Get system information including machine ID for local worker detection."""
+    try:
+        import socket
+        
+        return web.json_response({
+            "status": "success",
+            "hostname": socket.gethostname(),
+            "machine_id": get_machine_id(),
+            "platform": {
+                "system": platform.system(),
+                "machine": platform.machine(),
+                "node": platform.node(),
+                "path_separator": os.sep,  # Add path separator
+                "os_name": os.name  # Add OS name (posix, nt, etc.)
+            },
+            "is_docker": is_docker_environment(),
+            "is_runpod": is_runpod_environment(),
+            "runpod_pod_id": os.environ.get('RUNPOD_POD_ID')
+        })
+    except Exception as e:
+        return await handle_api_error(request, e, 500)
+
+@server.PromptServer.instance.routes.post("/distributed/launch_worker")
+async def launch_worker_endpoint(request):
+    """Launch a worker process from the UI."""
+    try:
+        wm = get_worker_manager()
+        data = await request.json()
+        missing = require_fields(data, "worker_id")
+        if missing:
+            return await handle_api_error(request, f"Missing required field(s): {', '.join(missing)}", 400)
+
+        worker_id = str(data.get("worker_id")).strip()
+        
+        # Find worker config
+        config = load_config()
+        if not validate_worker_id(worker_id, config):
+            return await handle_api_error(request, f"Worker {worker_id} not found", 404)
+        worker = next((w for w in config.get("workers", []) if str(w.get("id")) == worker_id), None)
+        if not worker:
+            return await handle_api_error(request, f"Worker {worker_id} not found", 404)
+        
+        # Ensure consistent string ID
+        worker_id_str = worker_id
+        
+        # Check if already running (managed by this instance)
+        if worker_id_str in wm.processes:
+            proc_info = wm.processes[worker_id_str]
+            process = proc_info.get('process')
+            
+            # Check if still running
+            is_running = False
+            if process:
+                is_running = process.poll() is None
+            else:
+                # Restored process without subprocess object
+                is_running = wm._is_process_running(proc_info['pid'])
+            
+            if is_running:
+                return await handle_api_error(request, "Worker already running (managed by UI)", 409)
+            else:
+                # Process is dead, remove it
+                del wm.processes[worker_id_str]
+                wm.save_processes()
+        
+        # Launch the worker
+        try:
+            loop = asyncio.get_running_loop()
+            pid = await loop.run_in_executor(None, wm.launch_worker, worker)
+            log_file = wm.processes[worker_id_str].get('log_file')
+            return web.json_response({
+                "status": "success",
+                "pid": pid,
+                "message": f"Worker {worker['name']} launched",
+                "log_file": log_file
+            })
+        except Exception as e:
+            return await handle_api_error(request, f"Failed to launch worker: {str(e)}", 500)
+            
+    except Exception as e:
+        return await handle_api_error(request, e, 400)
+
+
+@server.PromptServer.instance.routes.post("/distributed/stop_worker")
+async def stop_worker_endpoint(request):
+    """Stop a worker process that was launched from the UI."""
+    try:
+        wm = get_worker_manager()
+        data = await request.json()
+        missing = require_fields(data, "worker_id")
+        if missing:
+            return await handle_api_error(request, f"Missing required field(s): {', '.join(missing)}", 400)
+
+        worker_id = str(data.get("worker_id")).strip()
+        config = load_config()
+        if not validate_worker_id(worker_id, config):
+            return await handle_api_error(request, f"Worker {worker_id} not found", 404)
+
+        success, message = wm.stop_worker(worker_id)
+        
+        if success:
+            return web.json_response({"status": "success", "message": message})
+        else:
+            return await handle_api_error(
+                request,
+                message,
+                404 if "not managed" in message else 409,
+            )
+            
+    except Exception as e:
+        return await handle_api_error(request, e, 400)
+
+
+@server.PromptServer.instance.routes.get("/distributed/managed_workers")
+async def get_managed_workers_endpoint(request):
+    """Get list of workers managed by this UI instance."""
+    try:
+        managed = get_worker_manager().get_managed_workers()
+        return web.json_response({
+            "status": "success",
+            "managed_workers": managed
+        })
+    except Exception as e:
+        return await handle_api_error(request, e, 500)
+
+
+@server.PromptServer.instance.routes.get("/distributed/local-worker-status")
+async def get_local_worker_status_endpoint(request):
+    """Check status of all local workers (localhost/no host specified)."""
+    try:
+        config = load_config()
+        worker_statuses = {}
+        
+        for worker in config.get("workers", []):
+            # Only check local workers
+            host = normalize_host(worker.get("host")) or ""
+            if not host or host in ["localhost", "127.0.0.1"]:
+                worker_id = worker["id"]
+                port = worker["port"]
+                
+                # Check if worker is enabled
+                if not worker.get("enabled", False):
+                    worker_statuses[worker_id] = {
+                        "online": False,
+                        "enabled": False,
+                        "processing": False,
+                        "queue_count": 0
+                    }
+                    continue
+                
+                # Try to connect to worker
+                try:
+                    worker_url = build_worker_url(worker)
+                    data = await probe_worker(worker_url, timeout=2.0)
+                    if data is None:
+                        worker_statuses[worker_id] = {
+                            "online": False,
+                            "enabled": True,
+                            "processing": False,
+                            "queue_count": 0,
+                            "error": "Unavailable",
+                        }
+                        continue
+                    queue_remaining = data.get("exec_info", {}).get("queue_remaining", 0)
+                    worker_statuses[worker_id] = {
+                        "online": True,
+                        "enabled": True,
+                        "processing": queue_remaining > 0,
+                        "queue_count": queue_remaining
+                    }
+                except asyncio.TimeoutError:
+                    worker_statuses[worker_id] = {
+                        "online": False,
+                        "enabled": True,
+                        "processing": False,
+                        "queue_count": 0,
+                        "error": "Timeout"
+                    }
+                except Exception as e:
+                    worker_statuses[worker_id] = {
+                        "online": False,
+                        "enabled": True,
+                        "processing": False,
+                        "queue_count": 0,
+                        "error": str(e)
+                    }
+        
+        return web.json_response({
+            "status": "success",
+            "worker_statuses": worker_statuses
+        })
+    except Exception as e:
+        debug_log(f"Error checking local worker status: {e}")
+        return await handle_api_error(request, e, 500)
+
+
+@server.PromptServer.instance.routes.get("/distributed/worker_log/{worker_id}")
+async def get_worker_log_endpoint(request):
+    """Get log content for a specific worker."""
+    try:
+        wm = get_worker_manager()
+        worker_id = request.match_info['worker_id']
+        
+        # Ensure worker_id is string
+        worker_id = str(worker_id)
+        
+        # Check if we manage this worker
+        if worker_id not in wm.processes:
+            return await handle_api_error(request, f"Worker {worker_id} not managed by UI", 404)
+        
+        proc_info = wm.processes[worker_id]
+        log_file = proc_info.get('log_file')
+        
+        if not log_file or not os.path.exists(log_file):
+            return await handle_api_error(request, "Log file not found", 404)
+        
+        # Read last N lines (or full file if small)
+        lines_to_read = _parse_positive_int_query(request.query.get('lines'), default=1000)
+        
+        try:
+            loop = asyncio.get_running_loop()
+            payload = await loop.run_in_executor(None, _read_worker_log_sync, log_file, lines_to_read)
+            
+            return web.json_response({
+                "status": "success",
+                "content": payload["content"],
+                "log_file": log_file,
+                "file_size": payload["file_size"],
+                "truncated": payload["truncated"],
+                "lines_shown": payload["lines_shown"],
+            })
+            
+        except Exception as e:
+            return await handle_api_error(request, f"Error reading log file: {str(e)}", 500)
+            
+    except Exception as e:
+        return await handle_api_error(request, e, 500)
+
+
+@server.PromptServer.instance.routes.get("/distributed/remote_worker_log/{worker_id}")
+async def get_remote_worker_log_endpoint(request):
+    """Proxy a remote worker log request to the worker's local in-memory log endpoint."""
+    try:
+        worker_id = str(request.match_info["worker_id"]).strip()
+        config = load_config()
+        worker = _find_worker_by_id(config, worker_id)
+        if not worker:
+            return await handle_api_error(request, f"Worker {worker_id} not found", 404)
+
+        # Remote log proxy is only meaningful for remote/cloud workers.
+        host = normalize_host(worker.get("host")) or ""
+        if not host:
+            return await handle_api_error(
+                request,
+                f"Worker {worker_id} is local; use /distributed/worker_log/{worker_id} instead.",
+                400,
+            )
+
+        lines_to_read = _parse_positive_int_query(request.query.get("lines"), default=300, maximum=3000)
+        worker_url = build_worker_url(worker, "/distributed/local_log")
+        session = await get_client_session()
+        async with session.get(
+            worker_url,
+            params={"lines": str(lines_to_read)},
+            timeout=aiohttp.ClientTimeout(total=5),
+        ) as resp:
+            if resp.status >= 400:
+                body = await resp.text()
+                return await handle_api_error(
+                    request,
+                    f"Remote worker {worker_id} returned HTTP {resp.status}: {body[:400]}",
+                    resp.status,
+                )
+
+            try:
+                data = await resp.json()
+            except Exception as e:
+                return await handle_api_error(
+                    request,
+                    f"Remote worker {worker_id} returned invalid JSON: {e}",
+                    502,
+                )
+
+        return web.json_response(data)
+    except Exception as e:
+        return await handle_api_error(request, e, 500)
@@ -0,0 +1,31 @@
+# conftest.py — project-level pytest configuration.
+#
+# Problem: custom_nodes/ComfyUI-Distributed/__init__.py uses relative imports
+# (from .distributed import ...) that fail when pytest tries to import it as a
+# standalone module during Package.setup() for the root package node.
+#
+# Fix: patch Package.setup() to skip the root-package's __init__.py import.
+# All actual package context is provided by each test module via
+# importlib.util.spec_from_file_location with synthetic stub packages.
+
+from _pytest.python import Package
+
+_orig_pkg_setup = Package.setup
+
+
+def _patched_pkg_setup(self) -> None:
+    # Skip the root package setup — its __init__.py uses relative imports
+    # that require a parent package (ComfyUI's plugin loader) which is not
+    # available in the test environment.
+    if self.path == self.config.rootpath:
+        return
+    _orig_pkg_setup(self)
+
+
+Package.setup = _patched_pkg_setup
+
+collect_ignore = [
+    "__init__.py",
+    "distributed.py",
+    "distributed_upscale.py",
+]
@@ -0,0 +1,279 @@
+# ComfyUI-Distributed API (Experimental)
+
+This document describes the **public HTTP API** added to ComfyUI-Distributed to allow queueing *distributed* workflows from external tools (scripts, services, CI jobs, render farms, etc.) without using the ComfyUI web UI.
+
+## Demo
+
+- Video walkthrough: https://youtu.be/yiQlPd0MzLk
+
+## Examples Repository
+
+- Examples repo: https://github.com/umanets/ComfyUI-Distributed-API-examples.git
+
+---
+
+## Overview
+
+### What this adds
+
+- `POST /distributed/queue` — queues a workflow using the same distributed orchestration rules as the UI:
+  - Detects distributed nodes in the prompt (`DistributedCollector`, `UltimateSDUpscaleDistributed`).
+  - Resolves enabled/selected workers.
+  - Pings workers (`GET /prompt`) to include only reachable ones.
+  - Dispatches the workflow to workers (`POST /prompt`).
+  - Queues the master workflow in ComfyUI’s prompt queue.
+  - If any `DistributedCollector` has `load_balance=true`, selects one least-busy participant for this run.
+
+### What it does *not* add
+
+- Authentication/authorization.
+- A separate “job status” API for distributed results (you still use ComfyUI’s normal prompt history / websocket flow, and the existing `/distributed/queue_status/{job_id}` behavior for collector queues).
+
+---
+
+## Endpoint: `POST /distributed/queue`
+
+Queue a workflow for distributed execution.
+
+### URL
+
+- `http://<master-host>:<master-port>/distributed/queue`
+
+### Headers
+
+- `Content-Type: application/json`
+
+### Request Body
+
+```json
+{
+  "prompt": { "<node_id>": { "class_type": "...", "inputs": { } } },
+  "workflow": { },
+  "client_id": "external-client",
+  "delegate_master": false,
+  "enabled_worker_ids": ["1", "2"],
+  "workers": ["1", "2"],
+  "auto_prepare": true,
+  "trace_execution_id": "exec_1700000000_ab12cd"
+}
+```
+
+#### Fields
+
+- `prompt` (required unless `workflow.prompt` is present, object)
+  - The ComfyUI prompt/workflow graph, same shape as used by `POST /prompt`.
+- `workflow` (optional, object)
+  - Workflow metadata that ComfyUI normally stores in `extra_pnginfo.workflow`.
+  - If you don’t care about UI metadata, you can omit it.
+- `client_id` (required, string)
+  - Passed through as `extra_data.client_id` (useful if you consume ComfyUI websocket events).
+- `delegate_master` (optional, boolean)
+  - If `true`, attempts “workers-only” execution for workflows based on `DistributedCollector`.
+  - Current limitation: delegate-only mode **does not support** `UltimateSDUpscaleDistributed` and will fall back to running the full prompt on master.
+- `enabled_worker_ids` (required, array of strings)
+  - The explicit worker IDs to consider for this run.
+- `workers` (optional, array of strings or objects with `id`)
+  - Transitional alias for `enabled_worker_ids` used by older clients.
+- `auto_prepare` (optional, boolean)
+  - Kept for wire compatibility.
+  - Backend orchestration always runs with auto-prepare semantics.
+  - If top-level `prompt` is omitted, backend will attempt `workflow.prompt`.
+- `trace_execution_id` (optional, string)
+  - Passed through to orchestration logs.
+  - Server log lines include the marker as `[exec:<trace_execution_id>]`.
+
+##### How to get `enabled_worker_ids`
+
+Worker IDs come from the plugin config (`GET /distributed/config`) under `workers[].id`.
+
+Example (bash + `jq`):
+
+```bash
+curl -s "http://127.0.0.1:8188/distributed/config" \
+  | jq -r '.workers[] | "id=\(.id)\tname=\(.name)\tenabled=\(.enabled)\thost=\(.host)\tport=\(.port)\ttype=\(.type)"'
+```
+
+Example (PowerShell):
+
+```powershell
+$cfg = Invoke-RestMethod "http://127.0.0.1:8188/distributed/config"
+$cfg.workers | Select-Object id,name,enabled,host,port,type | Format-Table -AutoSize
+```
+
+### Response Body
+
+```json
+{
+  "prompt_id": "<uuid>",
+  "worker_count": 2,
+  "auto_prepare_supported": true
+}
+```
+
+- `prompt_id` — the master prompt id queued into ComfyUI.
+- `worker_count` — number of workers that received a dispatched prompt (only those that passed the health check).
+
+### Status Codes
+
+- `200` — queued.
+- `400` — invalid JSON or invalid body.
+- `500` — orchestration/dispatch failure (see server logs for details).
+
+---
+
+## Worker requirements (important)
+
+For a worker to participate, it must be reachable from the master:
+
+- Health check: `GET <worker-base>/prompt` must return HTTP 200.
+- Dispatch: `POST <worker-base>/prompt` must accept the workflow.
+
+Also, for collector-based flows:
+
+- Workers will send results back to the master via `POST /distributed/job_complete` (that route must be reachable from workers).
+
+---
+
+## Endpoint: `POST /distributed/job_complete`
+
+Submit one completed worker image back to the master collector queue.
+
+### URL
+
+- `http://<master-host>:<master-port>/distributed/job_complete`
+
+### Request Body
+
+```json
+{
+  "job_id": "exec_1234567890_17",
+  "worker_id": "worker-1",
+  "batch_idx": 0,
+  "image": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA...",
+  "is_last": false
+}
+```
+
+### Canonical envelope (required fields)
+
+- `job_id` (string, required)
+- `worker_id` (string, required)
+- `batch_idx` (integer >= 0, required)
+- `image` (string, required)
+  - PNG payload as either:
+    - data URL: `data:image/png;base64,...`
+    - raw base64 PNG bytes
+- `is_last` (boolean, required)
+
+Legacy multipart/tensor payload formats are no longer accepted on this endpoint.
+
+### CORS note
+
+If you call the API from a browser (not from a backend), ensure the master ComfyUI is started with `--enable-cors-header`.
+
+---
+
+## Log Endpoints
+
+### `GET /distributed/worker_log/{worker_id}`
+
+Read log files for workers launched locally by the master UI process manager.
+
+- Intended for managed local workers.
+- Query param: `lines` (optional, default `1000`).
+
+### `GET /distributed/local_log`
+
+Read this ComfyUI instance's in-memory runtime log buffer.
+
+- Available on any ComfyUI-Distributed instance (master or worker).
+- Query param: `lines` (optional, default `300`, max `3000`).
+
+### `GET /distributed/remote_worker_log/{worker_id}`
+
+Proxy endpoint on master that fetches logs from a configured remote/cloud worker's
+`/distributed/local_log`.
+
+- Intended for remote/cloud workers in master config.
+- Query param: `lines` (optional, default `300`, max `3000`).
+
+---
+
+## Examples
+
+### 1) Minimal `curl`
+
+```bash
+curl -X POST "http://127.0.0.1:8188/distributed/queue" \
+  -H "Content-Type: application/json" \
+  -d @payload.json
+```
+
+Where `payload.json` contains at least:
+
+```json
+{
+  "prompt": {
+    "1": {"class_type": "KSampler", "inputs": {} }
+  },
+  "enabled_worker_ids": [],
+  "client_id": "external-client"
+}
+```
+
+### 2) Python (`requests`)
+
+```python
+import requests
+
+url = "http://127.0.0.1:8188/distributed/queue"
+payload = {
+    "prompt": {...},
+    "workflow": {...},
+    "client_id": "external-client",
+    "delegate_master": False,
+    "enabled_worker_ids": ["1", "2"],
+}
+
+r = requests.post(url, json=payload, timeout=60)
+r.raise_for_status()
+print(r.json())
+```
+
+### 3) JavaScript (`fetch`)
+
+```js
+const url = "http://127.0.0.1:8188/distributed/queue";
+
+const payload = {
+  prompt: {/* ... */},
+  workflow: {/* ... */},
+  client_id: "external-client",
+  delegate_master: false,
+  enabled_worker_ids: ["1", "2"],
+};
+
+const resp = await fetch(url, {
+  method: "POST",
+  headers: { "Content-Type": "application/json" },
+  body: JSON.stringify(payload),
+});
+
+if (!resp.ok) throw new Error(await resp.text());
+console.log(await resp.json());
+```
+
+---
+
+## Operational notes / gotchas
+
+- If the workflow contains **no distributed nodes**, the endpoint falls back to normal master queueing and returns `worker_count: 0`.
+- Worker selection is “best-effort”: offline workers are skipped.
+- For public URLs/tunnels: prefer configuring `master.host` with an explicit scheme (`https://...`) to avoid ambiguity.
+
+---
+
+## Changelog (this feature)
+
+- Added `POST /distributed/queue` endpoint.
+- Added orchestration module used by the endpoint.
@@ -1,15 +1,14 @@
 ## Automating ComfyUI Model Downloads
+> This guide will walk you through creating a shell script to automatically download the necessary models for your ComfyUI workflow, leveraging an advanced Large Language Model (LLM).

-This guide will walk you through creating a shell script to automatically download the necessary models for your ComfyUI workflow, leveraging an advanced Large Language Model (LLM).
+1. In ComfyUI (on your local machine), export your workflow as an API workflow
+2. Copy the below prompt and upload the API workflow to an LLM **that has access to the internet**
+
+<details>
+<summary><strong>📋 Click to expand the full prompt</strong></summary>

-1. Launched the Pod with these Environment Variables:
-  - CIVITAI_API_TOKEN ([get your token here](https://civitai.com/user/account))
-  - HF_API_TOKEN ([get your token here](https://huggingface.co/settings/tokens))
-2. In ComfyUI, export your workflow as an API workflow
-3. Copy the below prompt and upload the API workflow to a LLM **that has access to the internet**
 ```
 Create a sh script that will download the models from this workflow into the correct folders. For reference, these are the paths:
-
 base_path: /workspace/ComfyUI
 checkpoints: models/checkpoints/
 clip: models/clip/
@@ -25,32 +24,30 @@ text_encoders: models/text_encoders/
 unet: models/unet/
 upscale_models: models/upscale_models/
 vae: models/vae/
-
 ---
-
 Important:
 Make sure you find the correct URLs for the models online.
 Use comfy cli to download the models: `comfy model download --url <URL> [--relative-path <PATH>] [--set-civitai-api-token <TOKEN>] [--set-hf-api-token <TOKEN>]`
 Make sure you add `--set-civitai-api-token $CIVITAI_API_TOKEN` for CivitAI download and `--set-hf-api-token $HF_API_TOKEN` for Hugging Face downloads.
-
 ---
-
 Example:
-
 #!/bin/bash
-
 # Download from CivitAI
 comfy model download --url https://civitai.com/api/download/models/1759168 --relative-path /workspace/ComfyUI/models/checkpoints --set-civitai-api-token $CIVITAI_API_TOKEN
-
 # Download model from Hugging Face
 comfy model download --url https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/flux1-dev.safetensors --relative-path /workspace/ComfyUI/models/unet --set-hf-api-token $HF_API_TOKEN
-
 # If a model in the workflow was in a subfolder
 comfy model download --url https://civitai.com/api/download/models/1759168 --relative-path /workspace/ComfyUI/models/checkpoints/SDXL --set-civitai-api-token $CIVITAI_API_TOKEN
-
 ```
-4. Review the LLMs output to make sure all download links are correct and save it as a .sh file, for example `download_models.sh`
-5. Upload it onto your Runpod instance, into `/workspace`
+
+</details>
+
+3. Review the LLMs output to make sure all download links are correct and save it as a .sh file, for example `download_models.sh`
+4. Launch the [ComfyUI Distributed Pod](https://console.runpod.io/deploy?template=m21ynvo8yo&ref=ak218p52) with these Environment Variables:
+   - `CIVITAI_API_TOKEN`: [get your token here](https://civitai.com/user/account)
+   - `HF_API_TOKEN`: [get your token here](https://huggingface.co/settings/tokens)
+5. Upload the .sh file to your Runpod instance, into `/workspace`
 6. Then run these commands:
   - `chmod 755 /workspace/download_models.sh`
   - `/workspace/download_models.sh`
+7. Confirm each model name (sometimes you might need to rename them to match the name on your local machine)
@@ -0,0 +1,20 @@
+![Clipboard Image](https://github.com/user-attachments/assets/5dc5224f-3f47-442c-b94a-116afeb28132)
+
+**Accelerated Creative Video Upscaler On Runpod:**
+
+1. Use the [ComfyUI Distributed Pod](https://console.runpod.io/deploy?template=m21ynvo8yo&ref=0bw29uf3ug0p) template.
+2. Filter instances by CUDA 12.8 (add filter in Additional Filters at the top of the page).
+3. Choose 4x 5090s
+4. Press Edit Template to configure the pod's Environment Variables:
+	- CIVITAI_API_TOKEN: Not necessary for this workflow.
+	- HF_API_TOKEN: [get your token here](https://huggingface.co/settings/tokens)
+	- SAGE_ATTENTION: optional optimisation (set to true/false). Recommended for this workflow.
+	- PRESET_VIDEO_UPSCALER: set to true. This will download everything you need.
+5. Deploy your pod.
+6. Once pod setup is complete, connect to ComfyUI running on your pod.
+7. In ComfyUI, open the GPU panel on the left.
+> If you set SAGE_ATTENTION to true, add "--use-sage-attention" to Extra Args on the workers.
+8. Launch the workers.
+9. [Load the workflow.](https://github.com/robertvoy/ComfyUI-Distributed/blob/main/workflows/distributed-upscale-video.json)
+10. Upload video, add prompt and run workflow.
+11. Right-click the Video Combine node and click Save Preview to save the video.
@@ -1,11 +1,19 @@
-## ComfyUI Distributed Worker Setup Guide
-
-ComfyUI-Distributed allows you to distribute your workflows across multiple GPUs, whether they're in the same machine, on your local network, or in the cloud.
+## Worker Setup Guide

 **Master**: The main ComfyUI instance that coordinates and distributes work. This is where you load workflows, manage the queue, and view results.

 **Worker**: A ComfyUI instance that receives and processes tasks from the master. Workers handle just the GPU computation and send results back to the master. You can have multiple workers connected to a single master, each utilizing their own GPU.

+<img width="600" src="https://github.com/user-attachments/assets/609c42aa-8a1c-4a3f-939e-f3552fa1d54f" />
+
+### Master participation modes
+
+The master can either contribute GPU work or stay in **orchestrator-only** mode:
+
+- **Participating**: Master renders alongside workers, useful when you want every available GPU.
+- **Orchestrator-only**: Master sends jobs to selected workers but skips local rendering. Enable this by opening the Distributed panel and unchecking the master toggle. The master card will display *“Master disabled: running as orchestrator only.”*
+- **Fallback**: If orchestrator-only is enabled but no workers remain selected, the master automatically re-enables execution to guarantee the workflow still runs. The UI shows a green *“Master fallback execution active”* badge so you know work is executing locally again.
+
 ### Types of Workers

 - **Local workers**: Additional GPUs on the same machine as the master
@@ -13,8 +21,14 @@ ComfyUI-Distributed allows you to distribute your workflows across multiple GPUs
 - **Cloud workers**: GPUs hosted on cloud services like Runpod

 ## Local workers
+
+<img align="right" width="200" src="https://github.com/user-attachments/assets/651e4912-7c23-4e32-bd88-250f5175e129" />
+
 > These are added automatically on first launch, but you can add them manually if you need to.

+
+📺 [Watch Tutorial](https://youtu.be/p6eE3IlAbOs?si=K7Km0_flmPHwRQwz&t=43)
+
 1. **Open** the Distributed GPU panel.
 2. **Click** "Add Worker" in the UI.
 3. **Configure** your local worker:
@@ -25,8 +39,14 @@ ComfyUI-Distributed allows you to distribute your workflows across multiple GPUs
 4. **Save** and  launch the local worker.

 ## Remote workers
+
+<img align="right" width="200"  src="https://github.com/user-attachments/assets/84291921-c44e-4556-94f2-a3b16500f4f9" />
+
+
 > ComfyUI instances running on completely different computers on your network. These allow you to harness GPU power from other machines. Remote workers must be manually started on their respective computers and are connected via IP address.

+📺 [Watch Tutorial](https://youtu.be/p6eE3IlAbOs?si=Oxj3EzPyf4jKDvfG&t=140)
+
 **On the Remote Worker Machine:**
 1. **Launch** ComfyUI with the `--listen --enable-cors-header` arguments. ⚠️ **Required!**
   - This ComfyUI instance will serve as a worker for your main master.
@@ -38,47 +58,66 @@ ComfyUI-Distributed allows you to distribute your workflows across multiple GPUs
 3. **Open** the ComfyUI port (e.g., 8188) and any additional worker ports (e.g., 8189, 8190) in the firewall.
  
 **On the Main Machine:**
-1. **Open** the Distributed GPU panel (sidebar on the left).
-2. **Click** "Add Worker."
-3. **Choose** "Remote".
-4. **Configure** your remote worker:
+1. **Launch** ComfyUI with `--enable-cors-header` launch argument.
+2. **Open** the Distributed GPU panel (sidebar on the left).
+3. **Click** "Add Worker."
+4. **Choose** "Remote".
+5. **Configure** your remote worker:
   - **Name**: A descriptive name for the worker (e.g., "Server Rack GPU 0")
   - **Host**: The remote worker's IP address.
   - **Port**: The port number used when launching ComfyUI on the remote master/worker (e.g., 8188).
-5. **Save** the remote worker configuration.
+6. **Save** the remote worker configuration.
  
 ## Cloud workers
-> ComfyUI instances running on a cloud service like [Runpod](https://get.runpod.io/0bw29uf3ug0p). 
+
+<img align="right" width="200"  src="https://github.com/user-attachments/assets/a053f3ae-22f0-4e1c-8f2e-f26a1f660adf" />
+
+> ComfyUI instances running on a cloud service like Runpod. 

 ### Deploy Cloud Worker on Runpod

+📺 [Watch Tutorial](https://www.youtube.com/watch?v=wxKKWMQhYTk)
+
 **On Runpod:**
+> If using your own template, make sure you launch ComfyUI with the `--enable-cors-header` argument and you `git clone ComfyUI-Distributed` into custom_nodes. ⚠️ **Required!**
+
 1. Register a [Runpod](https://get.runpod.io/0bw29uf3ug0p) account.
-2. On Runpod, go to Storage > New Network Volume and create a volume that will store the models you need. Start with 40 GB, you can always add more later.
-3. Now go to Pods and find a suitable GPU for your workflows. 
-4. Choose the [ComfyUI Distributed Pod](https://console.runpod.io/deploy?template=m21ynvo8yo&ref=ak218p52) template and make sure your network drive is mounted.
-> To use the ComfyUI Distributed Pod template, you will need to filter instances by CUDA 12.8.
-6. Launch your pod.
-7. Access your pod using JupyterLabs.
-8. Download models into /workspaces/ComfyUI/models/ (these will remain on your network drive even after you terminate the pod).
-> You can use [this guide](model-download-script.md) to make this process easy for you. It will generate a shell script that will automatically download the models you need for a given workflow.
-8. If using your own template, make sure you launch ComfyUI with the `--enable-cors-header` argument and you git clone ComfyUI-Distributed into custom_nodes. ⚠️ **Required!**
-9. Download any additional custom nodes using the ComfyUI Manager.
+2. On Runpod, go to Storage > New Network Volume and create a volume that will store the models you need. Start with 40 GB, you can always add more later. Learn more [about Network Volumes](https://docs.runpod.io/pods/storage/create-network-volumes).
+3. Use the [ComfyUI Distributed Pod](https://console.runpod.io/deploy?template=m21ynvo8yo&ref=0bw29uf3ug0p) template.
+4. Make sure your Network Volume is mounted and choose a suitable GPU.
+> ⚠️ To use the ComfyUI Distributed Pod template, you will need to filter instances by CUDA 12.8 (add filter in Additional Filters).
+6. Press Edit Template to configure the pod's Environment Variables:
+	- CIVITAI_API_TOKEN: [get your token here](https://civitai.com/user/account)
+	- HF_API_TOKEN: [get your token here](https://huggingface.co/settings/tokens)
+	- SAGE_ATTENTION: optional optimisation (set to true/false)
+5. Deploy your pod.
+6. Connect to your pod using JupyterLabs. This gives us access to the pod's file system.
+7. Download models into /workspaces/ComfyUI/models/ (these will remain on your network drive even after you terminate the pod). Example commands below:
+```
+# Download from CivitAI
+comfy model download --url https://civitai.com/api/download/models/1759168 --relative-path /workspace/ComfyUI/models/checkpoints --set-civitai-api-token $CIVITAI_API_TOKEN
+# Download model from Hugging Face
+comfy model download --url https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/flux1-dev.safetensors --relative-path /workspace/ComfyUI/models/unet --set-hf-api-token $HF_API_TOKEN
+```
+> ℹ️ Use [this guide](model-download-script.md) to make this process easy. It will generate a shell script that automatically downloads the models for a given workflow.
+9. Access ComfyUI through the Runpod URL.
+10. Download any additional custom nodes you need using the ComfyUI Manager.

 **On the Main Machine:**
-1. Launch a Cloudflare tunnel
+1. **Launch** a Cloudflare tunnel.
   - Download from here: [https://github.com/cloudflare/cloudflared/releases](https://github.com/cloudflare/cloudflared/releases)
 	- Then run, for example: `cloudflared-windows-amd64.exe tunnel --url http://localhost:8188`
-> Cloudflare tunnels create secure connections without exposing ports directly to the internet
-2. Copy the Cloudflare address
-3. **Open** the Distributed GPU panel (sidebar on the left).
-4. **Edit** the Master's host address and replace it with the Cloudflare address.
-   - **Click** "Add Worker."
-   - **Choose** "Cloud".
-   - **Configure** your cloud worker:
-     - **Host**: The Runpod address
-     - **Port**: 443
-   - **Save** the remote worker configuration.
+> ℹ️ Cloudflare tunnels create secure connections without exposing ports directly to the internet and are required for Cloud Workers.
+2. **Copy** the Cloudflare address
+3. **Launch** ComfyUI with `--enable-cors-header` launch argument.
+4. **Open** the Distributed GPU panel (sidebar on the left).
+5. **Edit** the Master's settings to change the host address to the Cloudflare address.
+6. **Click** "Add Worker."
+7. **Choose** "Cloud".
+8. **Configure** your cloud worker:
+	- **Host**: The ComfyUI Runpod address. For example: `wcegfo9tbbml9l-8188.proxy.runpod.net`
+	- **Port**: 443
+9. **Save** the remote worker configuration.

 ---

@@ -89,7 +128,7 @@ ComfyUI-Distributed allows you to distribute your workflows across multiple GPUs
   - If your cloud platform doesn't provide a secure connection, use Cloudflare to create a tunnel for the worker. Each GPU needs their own tunnel for their respective port.
 	- For example: `./cloudflared tunnel --url http://localhost:8188`
 1. **Launch** ComfyUI with the `--listen --enable-cors-header` arguments. ⚠️ **Required!**
-2. **Add** workers in the UI panel if the remote machine has more than one GPU.
+2. **Add** workers in the UI panel if the cloud machine has more than one GPU.
   - Make sure that they also have `--listen` set in `Extra Args`.
   - Then launch them.
  
@@ -98,11 +137,12 @@ ComfyUI-Distributed allows you to distribute your workflows across multiple GPUs
   - Download from here: [https://github.com/cloudflare/cloudflared/releases](https://github.com/cloudflare/cloudflared/releases)
   - Then run, for example: `cloudflared-windows-amd64.exe tunnel --url http://localhost:8188`
 2. **Copy** the Cloudflare address
-3. **Open** the Distributed GPU panel (sidebar on the left).
-4. **Edit** the Master's host address and replace it with the Cloudflare address.
-5. **Click** "Add Worker."
-6. **Choose** "Cloud".
-7. **Configure** your cloud worker:
+3. **Launch** ComfyUI with `--enable-cors-header` launch argument.
+4. **Open** the Distributed GPU panel (sidebar on the left).
+5. **Edit** the Master's host address and replace it with the Cloudflare address.
+6. **Click** "Add Worker."
+7. **Choose** "Cloud".
+8. **Configure** your cloud worker:
   - **Host**: The remote worker's IP address/domain
   - **Port**: 443
-8. **Save** the remote worker configuration.
+9. **Save** the remote worker configuration.
@@ -0,0 +1,31 @@
+from .utilities import (
+    DistributedSeed,
+    DistributedModelName,
+    DistributedValue,
+    ImageBatchDivider,
+    AudioBatchDivider,
+    DistributedEmptyImage,
+    AnyType,
+    ByPassTypeTuple,
+    any_type,
+)
+from .collector import DistributedCollectorNode
+
+NODE_CLASS_MAPPINGS = {
+    "DistributedCollector": DistributedCollectorNode,
+    "DistributedSeed": DistributedSeed,
+    "DistributedModelName": DistributedModelName,
+    "DistributedValue": DistributedValue,
+    "ImageBatchDivider": ImageBatchDivider,
+    "AudioBatchDivider": AudioBatchDivider,
+    "DistributedEmptyImage": DistributedEmptyImage,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "DistributedCollector": "Distributed Collector",
+    "DistributedSeed": "Distributed Seed",
+    "DistributedModelName": "Distributed Model Name",
+    "DistributedValue": "Distributed Value",
+    "ImageBatchDivider": "Image Batch Divider",
+    "AudioBatchDivider": "Audio Batch Divider",
+    "DistributedEmptyImage": "Distributed Empty Image",
+}
@@ -0,0 +1,469 @@
+import torch
+import io
+import json
+import asyncio
+import time
+import base64
+
+import aiohttp
+import server as _server
+import comfy.model_management
+from comfy.utils import ProgressBar
+
+from ..utils.logging import debug_log, log
+from ..utils.config import get_worker_timeout_seconds, load_config, is_master_delegate_only
+from ..utils.constants import HEARTBEAT_INTERVAL
+from ..utils.image import tensor_to_pil, pil_to_tensor, ensure_contiguous
+from ..utils.network import build_worker_url, get_client_session, probe_worker
+from ..utils.audio_payload import encode_audio_payload
+from ..utils.async_helpers import run_async_in_server_loop
+
+prompt_server = _server.PromptServer.instance
+
+
+class DistributedCollectorNode:
+    EMPTY_AUDIO = {"waveform": torch.zeros(1, 2, 1), "sample_rate": 44100}
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "load_balance": (
+                    "BOOLEAN",
+                    {
+                        "default": False,
+                        "tooltip": "Run this workflow on one least-busy participant (master included when participating).",
+                    },
+                ),
+            },
+            "optional": { "audio": ("AUDIO",) },
+            "hidden": {
+                "multi_job_id": ("STRING", {"default": ""}),
+                "is_worker": ("BOOLEAN", {"default": False}),
+                "master_url": ("STRING", {"default": ""}),
+                "enabled_worker_ids": ("STRING", {"default": "[]"}),
+                "worker_batch_size": ("INT", {"default": 1, "min": 1, "max": 1024}),
+                "worker_id": ("STRING", {"default": ""}),
+                "pass_through": ("BOOLEAN", {"default": False}),
+                "delegate_only": ("BOOLEAN", {"default": False}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "AUDIO")
+    RETURN_NAMES = ("images", "audio")
+    FUNCTION = "run"
+    CATEGORY = "image"
+    
+    def run(self, images, load_balance=False, audio=None, multi_job_id="", is_worker=False, master_url="", enabled_worker_ids="[]", worker_batch_size=1, worker_id="", pass_through=False, delegate_only=False):
+        # Create empty audio if not provided
+        empty_audio = {"waveform": torch.zeros(1, 2, 1), "sample_rate": 44100}
+
+        if not multi_job_id or pass_through:
+            if pass_through:
+                debug_log("Collector: pass-through mode enabled, returning images unchanged")
+            return (images, audio if audio is not None else empty_audio)
+
+        # Use async helper to run in server loop
+        result = run_async_in_server_loop(
+            self.execute(
+                images,
+                audio,
+                load_balance,
+                multi_job_id,
+                is_worker,
+                master_url,
+                enabled_worker_ids,
+                worker_batch_size,
+                worker_id,
+                delegate_only,
+            )
+        )
+        return result
+
+    async def send_batch_to_master(self, image_batch, audio, multi_job_id, master_url, worker_id):
+        """Send image batch to master via canonical JSON envelopes."""
+        batch_size = image_batch.shape[0]
+        if batch_size == 0:
+            return
+
+        encoded_audio = encode_audio_payload(audio)
+
+        session = await get_client_session()
+        url = f"{master_url}/distributed/job_complete"
+        for batch_idx in range(batch_size):
+            img = tensor_to_pil(image_batch[batch_idx:batch_idx+1], 0)
+            byte_io = io.BytesIO()
+            img.save(byte_io, format='PNG', compress_level=0)
+            encoded_image = base64.b64encode(byte_io.getvalue()).decode('utf-8')
+            payload = {
+                "job_id": str(multi_job_id),
+                "worker_id": str(worker_id),
+                "batch_idx": int(batch_idx),
+                "image": f"data:image/png;base64,{encoded_image}",
+                "is_last": bool(batch_idx == batch_size - 1),
+            }
+            if payload["is_last"] and encoded_audio is not None:
+                payload["audio"] = encoded_audio
+
+            try:
+                async with session.post(
+                    url,
+                    json=payload,
+                    timeout=aiohttp.ClientTimeout(total=60),
+                ) as response:
+                    response.raise_for_status()
+            except Exception as e:
+                log(f"Worker - Failed to send canonical image envelope to master: {e}")
+                debug_log(f"Worker - Full error details: URL={url}")
+                raise  # Re-raise to handle at caller level
+
+    def _combine_audio(self, master_audio, worker_audio, empty_audio, worker_order=None):
+        """Combine audio from master and workers into a single audio output.
+
+        Ordering: master first, then workers in `worker_order` (if provided),
+        then any unexpected worker ids in sorted order.
+        """
+        audio_pieces = []
+        sample_rate = 44100
+
+        # Add master audio first if present
+        if master_audio is not None:
+            waveform = master_audio.get("waveform")
+            if waveform is not None and waveform.numel() > 0:
+                audio_pieces.append(waveform)
+                sample_rate = master_audio.get("sample_rate", 44100)
+
+        # Add worker audio in configured enabled-worker order first.
+        ordered_worker_ids = [str(worker_id) for worker_id in (worker_order or [])]
+        seen = set()
+        for worker_id_str in ordered_worker_ids:
+            seen.add(worker_id_str)
+            w_audio = worker_audio.get(worker_id_str)
+            if w_audio is not None:
+                waveform = w_audio.get("waveform")
+                if waveform is not None and waveform.numel() > 0:
+                    audio_pieces.append(waveform)
+                    # Use first available sample rate
+                    if sample_rate == 44100:
+                        sample_rate = w_audio.get("sample_rate", 44100)
+
+        # Append any audio from unexpected worker ids deterministically.
+        for worker_id_str in sorted(worker_audio.keys()):
+            if worker_id_str in seen:
+                continue
+            w_audio = worker_audio[worker_id_str]
+            if w_audio is not None:
+                waveform = w_audio.get("waveform")
+                if waveform is not None and waveform.numel() > 0:
+                    audio_pieces.append(waveform)
+                    if sample_rate == 44100:
+                        sample_rate = w_audio.get("sample_rate", 44100)
+
+        if not audio_pieces:
+            return empty_audio
+
+        try:
+            # Concatenate along the samples dimension (dim=-1)
+            # Ensure all pieces have same batch and channel dimensions
+            combined_waveform = torch.cat(audio_pieces, dim=-1)
+            debug_log(f"Master - Combined audio: {len(audio_pieces)} pieces, final shape={combined_waveform.shape}")
+            return {"waveform": combined_waveform, "sample_rate": sample_rate}
+        except Exception as e:
+            log(f"[Distributed] Master - Audio combination failed, returning silence: {e}")
+            return empty_audio
+
+    def _store_worker_result(self, worker_images: dict, item: dict) -> int:
+        """Store one canonical queue item in worker_images in-place.
+
+        Canonical format:
+        - item has 'worker_id', 'image_index', and 'tensor'
+        Returns 1 when stored, otherwise 0.
+        """
+        worker_id = item['worker_id']
+        tensor = item.get('tensor')
+        image_index = item.get('image_index')
+        if tensor is None or image_index is None:
+            return 0
+
+        worker_images.setdefault(worker_id, {})
+        worker_images[worker_id][image_index] = tensor
+        return 1
+
+    def _reorder_and_combine_tensors(
+        self,
+        worker_images: dict,
+        worker_order: list,
+        master_batch_size: int,
+        images_on_cpu,
+        delegate_mode: bool,
+        fallback_images,
+    ) -> torch.Tensor:
+        """Assemble final tensor: master first, then workers in enabled order."""
+        ordered_tensors = []
+        if not delegate_mode and images_on_cpu is not None:
+            for i in range(master_batch_size):
+                ordered_tensors.append(images_on_cpu[i:i+1])
+
+        ordered_worker_ids = [str(worker_id) for worker_id in (worker_order or [])]
+        seen = set()
+        for worker_id_str in ordered_worker_ids:
+            seen.add(worker_id_str)
+            if worker_id_str not in worker_images:
+                continue
+            for idx in sorted(worker_images[worker_id_str].keys()):
+                ordered_tensors.append(worker_images[worker_id_str][idx])
+
+        # Append any unexpected worker ids deterministically.
+        for worker_id_str in sorted(worker_images.keys()):
+            if worker_id_str in seen:
+                continue
+            for idx in sorted(worker_images[worker_id_str].keys()):
+                ordered_tensors.append(worker_images[worker_id_str][idx])
+
+        cpu_tensors = []
+        for t in ordered_tensors:
+            if t.is_cuda:
+                t = t.cpu()
+            t = ensure_contiguous(t)
+            cpu_tensors.append(t)
+
+        if cpu_tensors:
+            return ensure_contiguous(torch.cat(cpu_tensors, dim=0))
+        elif fallback_images is not None:
+            return ensure_contiguous(fallback_images)
+        else:
+            raise ValueError("No image data collected from master or workers")
+
+    async def execute(self, images, audio, load_balance=False, multi_job_id="", is_worker=False, master_url="", enabled_worker_ids="[]", worker_batch_size=1, worker_id="", delegate_only=False):
+        if is_worker:
+            # Worker mode: send images and audio to master in a single batch
+            debug_log(f"Worker - Job {multi_job_id} complete. Sending {images.shape[0]} image(s) to master")
+            await self.send_batch_to_master(images, audio, multi_job_id, master_url, worker_id)
+            return (images, audio if audio is not None else self.EMPTY_AUDIO)
+        else:
+            delegate_mode = delegate_only or is_master_delegate_only()
+            # Master mode: collect images and audio from workers
+            enabled_workers_raw = json.loads(enabled_worker_ids)
+            enabled_workers = []
+            seen_enabled = set()
+            for worker_id in enabled_workers_raw:
+                worker_id_str = str(worker_id)
+                if worker_id_str in seen_enabled:
+                    continue
+                seen_enabled.add(worker_id_str)
+                enabled_workers.append(worker_id_str)
+            expected_workers = set(enabled_workers)
+            num_workers = len(expected_workers)
+            if num_workers == 0:
+                return (images, audio if audio is not None else self.EMPTY_AUDIO)
+
+            # Create the queue before any expensive local work to avoid job_complete race.
+            async with prompt_server.distributed_jobs_lock:
+                if multi_job_id not in prompt_server.distributed_pending_jobs:
+                    prompt_server.distributed_pending_jobs[multi_job_id] = asyncio.Queue()
+                    debug_log(f"Master - Initialized queue early for job {multi_job_id}")
+                else:
+                    existing_size = prompt_server.distributed_pending_jobs[multi_job_id].qsize()
+                    debug_log(f"Master - Using existing queue for job {multi_job_id} (current size: {existing_size})")
+
+            if delegate_mode:
+                master_batch_size = 0
+                images_on_cpu = None
+                master_audio = None
+                debug_log(f"Master - Job {multi_job_id}: Delegate-only mode enabled, collecting exclusively from {num_workers} workers")
+            else:
+                images_on_cpu = images.cpu()
+                master_batch_size = images.shape[0]
+                master_audio = audio  # Keep master's audio for later
+                debug_log(f"Master - Job {multi_job_id}: Master has {master_batch_size} images, collecting from {num_workers} workers...")
+
+                # Ensure master images are contiguous
+                images_on_cpu = ensure_contiguous(images_on_cpu)
+
+
+            # Initialize storage for collected images and audio
+            worker_images = {}  # Dict to store images by worker_id and index
+            worker_audio = {}   # Dict to store audio by worker_id
+            
+            # Collect images until all workers report they're done
+            collected_count = 0
+            workers_done = set()
+            
+            # Use unified worker timeout from config/UI with simple sliced waits
+            base_timeout = float(get_worker_timeout_seconds())
+            slice_timeout = min(max(0.1, HEARTBEAT_INTERVAL / 20.0), base_timeout)
+            last_activity = time.time()
+            
+            
+            # Get queue size before starting
+            async with prompt_server.distributed_jobs_lock:
+                q = prompt_server.distributed_pending_jobs[multi_job_id]
+                initial_size = q.qsize()
+
+            # NEW: Initialize progress bar for workers (total = num_workers)
+            p = ProgressBar(num_workers)
+
+            def mark_worker_done(done_worker_id):
+                done_worker_id = str(done_worker_id)
+                if done_worker_id not in expected_workers:
+                    debug_log(
+                        f"Master - Ignoring completion from unexpected worker {done_worker_id} for job {multi_job_id}"
+                    )
+                    return
+                if done_worker_id in workers_done:
+                    debug_log(
+                        f"Master - Ignoring duplicate completion from worker {done_worker_id} for job {multi_job_id}"
+                    )
+                    return
+                workers_done.add(done_worker_id)
+                p.update(1)  # +1 per completed expected worker
+
+            try:
+                while len(workers_done) < num_workers:
+                    # Check for user interruption to abort collection promptly
+                    comfy.model_management.throw_exception_if_processing_interrupted()
+                    try:
+                        # Get the queue again each time to ensure we have the right reference
+                        async with prompt_server.distributed_jobs_lock:
+                            q = prompt_server.distributed_pending_jobs[multi_job_id]
+                            current_size = q.qsize()
+                        
+                        result = await asyncio.wait_for(q.get(), timeout=slice_timeout)
+                        worker_id = result['worker_id']
+                        is_last = result.get('is_last', False)
+                        count = self._store_worker_result(worker_images, result)
+                        collected_count += count
+                        debug_log(
+                            f"Master - Got canonical result from worker {worker_id}, "
+                            f"image {result.get('image_index', 0)}, is_last={is_last}"
+                        )
+
+                        # Collect audio data if present
+                        result_audio = result.get('audio')
+                        if result_audio is not None:
+                            worker_audio[worker_id] = result_audio
+                            debug_log(f"Master - Got audio from worker {worker_id}")
+
+                        # Record activity and refresh timeout baseline
+                        last_activity = time.time()
+                        base_timeout = float(get_worker_timeout_seconds())
+
+                        if is_last:
+                            mark_worker_done(worker_id)
+                        
+                    except asyncio.TimeoutError:
+                        # If we still have time, continue polling; otherwise handle timeout
+                        if (time.time() - last_activity) < base_timeout:
+                            comfy.model_management.throw_exception_if_processing_interrupted()
+                            continue
+                        # Re-check for user interruption after timeout expiry
+                        comfy.model_management.throw_exception_if_processing_interrupted()
+                        missing_workers = set(str(w) for w in enabled_workers) - workers_done
+                        elapsed = time.time() - last_activity
+                        for missing_worker_id in sorted(missing_workers):
+                            log(
+                                "Master - Heartbeat timeout: "
+                                f"worker={missing_worker_id}, elapsed={elapsed:.1f}s"
+                            )
+                        log(
+                            f"Master - Heartbeat timeout. Still waiting for workers: {list(missing_workers)} "
+                            f"(elapsed={elapsed:.1f}s)"
+                        )
+
+                        # Probe missing workers' /prompt endpoints to check if they are actively processing
+                        any_busy = False
+                        try:
+                            cfg = load_config()
+                            cfg_workers = cfg.get('workers', [])
+                            for wid in list(missing_workers):
+                                wrec = next((w for w in cfg_workers if str(w.get('id')) == str(wid)), None)
+                                if not wrec:
+                                    debug_log(f"Collector probe: worker {wid} not found in config")
+                                    continue
+                                worker_url = build_worker_url(wrec)
+                                try:
+                                    payload = await probe_worker(worker_url, timeout=2.0)
+                                    queue_remaining = None
+                                    if payload is not None:
+                                        queue_remaining = int(payload.get('exec_info', {}).get('queue_remaining', 0))
+                                    debug_log(
+                                        "Collector probe: worker "
+                                        f"{wid} online={payload is not None} queue_remaining={queue_remaining}"
+                                    )
+                                    if payload is not None and queue_remaining and queue_remaining > 0:
+                                        any_busy = True
+                                        log(
+                                            f"Master - Probe grace: worker {wid} appears busy "
+                                            f"(queue_remaining={queue_remaining}). Continuing to wait."
+                                        )
+                                        break
+                                except Exception as e:
+                                    debug_log(f"Collector probe failed for worker {wid}: {e}")
+                        except Exception as e:
+                            debug_log(f"Collector probe setup error: {e}")
+
+                        if any_busy:
+                            # Refresh last_activity and continue waiting
+                            last_activity = time.time()
+                            # Refresh base timeout in case the user changed it in UI
+                            base_timeout = float(get_worker_timeout_seconds())
+                            continue
+                        
+                        # Check queue size again with lock
+                        async with prompt_server.distributed_jobs_lock:
+                            if multi_job_id in prompt_server.distributed_pending_jobs:
+                                final_q = prompt_server.distributed_pending_jobs[multi_job_id]
+                                final_size = final_q.qsize()
+                                
+                                # Try to drain any remaining items
+                                remaining_items = []
+                                while not final_q.empty():
+                                    try:
+                                        item = final_q.get_nowait()
+                                        remaining_items.append(item)
+                                    except asyncio.QueueEmpty:
+                                        break
+                                
+                                if remaining_items:
+                                    # Process them
+                                    for item in remaining_items:
+                                        worker_id = item['worker_id']
+                                        is_last = item.get('is_last', False)
+
+                                        collected_count += self._store_worker_result(worker_images, item)
+                                        
+                                        if is_last:
+                                            mark_worker_done(worker_id)
+                            else:
+                                log(f"Master - Queue {multi_job_id} no longer exists!")
+                        break
+            except comfy.model_management.InterruptProcessingException:
+                # Cleanup queue on interruption and re-raise to abort prompt cleanly
+                async with prompt_server.distributed_jobs_lock:
+                    if multi_job_id in prompt_server.distributed_pending_jobs:
+                        del prompt_server.distributed_pending_jobs[multi_job_id]
+                raise
+            
+            total_collected = sum(len(imgs) for imgs in worker_images.values())
+            
+            # Clean up job queue
+            async with prompt_server.distributed_jobs_lock:
+                if multi_job_id in prompt_server.distributed_pending_jobs:
+                    del prompt_server.distributed_pending_jobs[multi_job_id]
+
+            try:
+                combined = self._reorder_and_combine_tensors(
+                    worker_images, enabled_workers, master_batch_size, images_on_cpu, delegate_mode, images
+                )
+                debug_log(f"Master - Job {multi_job_id} complete. Combined {combined.shape[0]} images total "
+                          f"(master: {master_batch_size}, workers: {combined.shape[0] - master_batch_size})")
+
+                # Combine audio from master and workers
+                combined_audio = self._combine_audio(master_audio, worker_audio, self.EMPTY_AUDIO, enabled_workers)
+
+                return (combined, combined_audio)
+            except Exception as e:
+                log(f"Master - Error combining images: {e}")
+                # Return just the master images as fallback
+                return (images, audio if audio is not None else self.EMPTY_AUDIO)
@@ -0,0 +1,279 @@
+import json
+import math
+from functools import wraps
+
+import comfy.samplers
+
+from ..utils.logging import debug_log, log
+from ..utils.async_helpers import run_async_in_server_loop
+from ..upscale.job_store import ensure_tile_jobs_initialized
+
+from ..upscale.tile_ops import TileOpsMixin
+from ..upscale.result_collector import ResultCollectorMixin
+from ..upscale.worker_comms import WorkerCommsMixin
+from ..upscale.job_state import JobStateMixin
+from ..upscale.modes.single_gpu import SingleGpuModeMixin
+from ..upscale.modes.static import StaticModeMixin
+from ..upscale.modes.dynamic import DynamicModeMixin
+
+def sync_wrapper(async_func):
+    """Decorator to wrap async methods for synchronous execution."""
+    @wraps(async_func)
+    def sync_func(self, *args, **kwargs):
+        # Use run_async_in_server_loop for ComfyUI compatibility
+        return run_async_in_server_loop(
+            async_func(self, *args, **kwargs),
+            timeout=600.0  # 10 minute timeout for long operations
+        )
+    return sync_func
+
+def _parse_enabled_worker_ids(enabled_worker_ids):
+    """Parse enabled worker IDs from either JSON or list input."""
+    if isinstance(enabled_worker_ids, list):
+        return [str(worker_id) for worker_id in enabled_worker_ids]
+    if not enabled_worker_ids:
+        return []
+    if isinstance(enabled_worker_ids, str):
+        try:
+            parsed = json.loads(enabled_worker_ids)
+        except json.JSONDecodeError:
+            log("USDU Dist: Invalid enabled_worker_ids JSON; defaulting to no workers.")
+            return []
+        if isinstance(parsed, list):
+            return [str(wid) for wid in parsed]
+    return []
+
+class UltimateSDUpscaleDistributed(
+    DynamicModeMixin,
+    StaticModeMixin,
+    SingleGpuModeMixin,
+    ResultCollectorMixin,
+    WorkerCommsMixin,
+    JobStateMixin,
+    TileOpsMixin,
+):
+
+    """
+    Distributed version of Ultimate SD Upscale (No Upscale).
+    
+    Supports three processing modes:
+    1. Single GPU: No workers available, process everything locally
+    2. Static Mode: Small batches, distributes tiles across workers (flattened)
+    3. Dynamic Mode: Large batches, assigns whole images to workers dynamically
+    
+    Features:
+    - Multi-mode batch handling for efficient video/image upscaling
+    - Tiled VAE support for memory efficiency
+    - Dynamic load balancing for large batches
+    - Backward compatible with single-image workflows
+    
+    Environment Variables:
+    - COMFYUI_MAX_BATCH: Chunk size for tile sending (default 20)
+    - COMFYUI_MAX_PAYLOAD_SIZE: Max API payload bytes (default 50MB)
+    
+    Threshold: dynamic_threshold input controls mode switch (default 8)
+    """
+
+    def __init__(self):
+        """Initialize the node and ensure persistent storage exists."""
+        # Pre-initialize the persistent storage on node creation
+        ensure_tile_jobs_initialized()
+        debug_log("UltimateSDUpscaleDistributed - Node initialized")
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "upscaled_image": ("IMAGE",),
+                "model": ("MODEL",),
+                "positive": ("CONDITIONING",),
+                "negative": ("CONDITIONING",),
+                "vae": ("VAE",),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
+                "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
+                "scheduler": (comfy.samplers.KSampler.SCHEDULERS,),
+                "denoise": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "tile_width": ("INT", {"default": 512, "min": 64, "max": 2048, "step": 8}),
+                "tile_height": ("INT", {"default": 512, "min": 64, "max": 2048, "step": 8}),
+                "padding": ("INT", {"default": 32, "min": 0, "max": 256, "step": 8}),
+                "mask_blur": ("INT", {"default": 8, "min": 0, "max": 256}),
+                "force_uniform_tiles": ("BOOLEAN", {"default": True}),
+                "tiled_decode": ("BOOLEAN", {"default": False}),
+            },
+            "hidden": {
+                "multi_job_id": ("STRING", {"default": ""}),
+                "is_worker": ("BOOLEAN", {"default": False}),
+                "master_url": ("STRING", {"default": ""}),
+                "enabled_worker_ids": ("STRING", {"default": "[]"}),
+                "worker_id": ("STRING", {"default": ""}),
+                "tile_indices": ("STRING", {"default": ""}),  # Unused - kept for compatibility
+                "dynamic_threshold": ("INT", {"default": 8, "min": 1, "max": 64}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "run"
+    CATEGORY = "image/upscaling"
+
+    @classmethod
+    def IS_CHANGED(cls, **kwargs):
+        """Force re-execution."""
+        return float("nan")  # Always re-execute
+
+    def run(self, upscaled_image, model, positive, negative, vae, seed, steps, cfg, 
+            sampler_name, scheduler, denoise, tile_width, tile_height, padding, 
+            mask_blur, force_uniform_tiles, tiled_decode,
+            multi_job_id="", is_worker=False, master_url="", enabled_worker_ids="[]", 
+            worker_id="", tile_indices="", dynamic_threshold=8):
+        """Entry point - runs SYNCHRONOUSLY like Ultimate SD Upscaler."""
+        # Strict WAN/FLOW batching: error if batch is not 4n+1 (except allow 1)
+        try:
+            batch_size = int(getattr(upscaled_image, 'shape', [1])[0])
+        except Exception:
+            batch_size = 1
+        # Enforce 4n+1 batches globally for any model when batch > 1 (master only)
+        if not is_worker and batch_size != 1 and (batch_size % 4 != 1):
+            raise ValueError(
+                f"Batch size {batch_size} is not of the form 4n+1. "
+                "This node requires batch sizes of 1 or 4n+1 (1, 5, 9, 13, ...). "
+                "Please adjust the batch size."
+            )
+        if not multi_job_id:
+            # No distributed processing, run single GPU version
+            return self.process_single_gpu(upscaled_image, model, positive, negative, vae,
+                                          seed, steps, cfg, sampler_name, scheduler, denoise,
+                                          tile_width, tile_height, padding, mask_blur, force_uniform_tiles, tiled_decode)
+        
+        if is_worker:
+            # Worker mode: process tiles synchronously
+            return self.process_worker(upscaled_image, model, positive, negative, vae,
+                                      seed, steps, cfg, sampler_name, scheduler, denoise,
+                                      tile_width, tile_height, padding, mask_blur,
+                                      force_uniform_tiles, tiled_decode, multi_job_id, master_url,
+                                      worker_id, enabled_worker_ids, dynamic_threshold)
+        else:
+            # Master mode: distribute and collect synchronously
+            return self.process_master(upscaled_image, model, positive, negative, vae,
+                                     seed, steps, cfg, sampler_name, scheduler, denoise,
+                                     tile_width, tile_height, padding, mask_blur,
+                                     force_uniform_tiles, tiled_decode, multi_job_id, enabled_worker_ids, 
+                                     dynamic_threshold)
+
+    def process_worker(self, upscaled_image, model, positive, negative, vae,
+                      seed, steps, cfg, sampler_name, scheduler, denoise,
+                      tile_width, tile_height, padding, mask_blur,
+                      force_uniform_tiles, tiled_decode, multi_job_id, master_url,
+                      worker_id, enabled_worker_ids, dynamic_threshold):
+        """Unified worker processing - handles both static and dynamic modes."""
+        # Get batch size to determine mode
+        batch_size = upscaled_image.shape[0]
+        
+        # Ensure mode consistency across master/workers via shared threshold
+        # Determine mode (must match master's logic)
+        enabled_workers = json.loads(enabled_worker_ids)
+        num_workers = len(enabled_workers)
+        # Compute number of tiles for this image to decide if tile distribution makes sense
+        _, height, width, _ = upscaled_image.shape
+        all_tiles = self.calculate_tiles(width, height, self.round_to_multiple(tile_width), self.round_to_multiple(tile_height), force_uniform_tiles)
+        num_tiles_per_image = len(all_tiles)
+
+        mode = self._determine_processing_mode(batch_size, num_workers, dynamic_threshold)
+        # For USDU-style processing, we want tile distribution whenever workers are available
+        # and there is more than one tile to process, even if batch == 1.
+        if num_workers > 0 and num_tiles_per_image > 1:
+            mode = "static"
+            
+        debug_log(f"USDU Dist Worker - Batch size {batch_size}")
+        
+        if mode == "dynamic":
+            return self.process_worker_dynamic(upscaled_image, model, positive, negative, vae,
+                                             seed, steps, cfg, sampler_name, scheduler, denoise,
+                                             tile_width, tile_height, padding, mask_blur,
+                                             force_uniform_tiles, tiled_decode, multi_job_id, master_url,
+                                             worker_id, enabled_worker_ids, dynamic_threshold)
+        
+        # Static mode - enhanced with health monitoring and retry logic
+        return self._process_worker_static_sync(upscaled_image, model, positive, negative, vae,
+                                               seed, steps, cfg, sampler_name, scheduler, denoise,
+                                               tile_width, tile_height, padding, mask_blur,
+                                               force_uniform_tiles, tiled_decode, multi_job_id, master_url,
+                                               worker_id, enabled_workers)
+
+    def process_master(self, upscaled_image, model, positive, negative, vae,
+                      seed, steps, cfg, sampler_name, scheduler, denoise,
+                      tile_width, tile_height, padding, mask_blur,
+                      force_uniform_tiles, tiled_decode, multi_job_id, enabled_worker_ids, 
+                      dynamic_threshold):
+        """Unified master processing with enhanced monitoring and failure handling."""
+        # Round tile dimensions
+        tile_width = self.round_to_multiple(tile_width)
+        tile_height = self.round_to_multiple(tile_height)
+        
+        # Get image dimensions and batch size
+        batch_size, height, width, _ = upscaled_image.shape
+        
+        # Calculate all tiles and grid
+        all_tiles = self.calculate_tiles(width, height, tile_width, tile_height, force_uniform_tiles)
+        num_tiles_per_image = len(all_tiles)
+        rows = math.ceil(height / tile_height)
+        cols = math.ceil(width / tile_width)
+        log(
+            f"USDU Dist: Canvas {width}x{height} | Tile {tile_width}x{tile_height} | Grid {rows}x{cols} ({num_tiles_per_image} tiles/image) | Batch {batch_size}"
+        )
+        
+        # Parse enabled workers
+        enabled_workers = json.loads(enabled_worker_ids)
+        num_workers = len(enabled_workers)
+        
+        # Determine processing mode
+        mode = self._determine_processing_mode(batch_size, num_workers, dynamic_threshold)
+        # Prefer tile-based static distribution when workers are available and there are multiple tiles,
+        # even for batch == 1, to spread tiles across GPUs like the legacy dynamic tile queue.
+        if num_workers > 0 and num_tiles_per_image > 1:
+            mode = "static"
+        
+        log(f"USDU Dist: Workers {num_workers} | Mode {mode} | Threshold {dynamic_threshold}")
+
+        if mode == "single_gpu":
+            # No workers, process all tiles locally
+            return self.process_single_gpu(upscaled_image, model, positive, negative, vae,
+                                         seed, steps, cfg, sampler_name, scheduler, denoise,
+                                         tile_width, tile_height, padding, mask_blur, force_uniform_tiles, tiled_decode)
+        
+        elif mode == "dynamic":
+            # Dynamic mode for large batches
+            return self.process_master_dynamic(upscaled_image, model, positive, negative, vae,
+                                             seed, steps, cfg, sampler_name, scheduler, denoise,
+                                             tile_width, tile_height, padding, mask_blur,
+                                             force_uniform_tiles, tiled_decode, multi_job_id, enabled_workers)
+        
+        # Static mode - enhanced with unified job management
+        return self._process_master_static_sync(upscaled_image, model, positive, negative, vae,
+                                               seed, steps, cfg, sampler_name, scheduler, denoise,
+                                               tile_width, tile_height, padding, mask_blur,
+                                               force_uniform_tiles, tiled_decode, multi_job_id, enabled_workers,
+                                               all_tiles, num_tiles_per_image)
+
+    def _determine_processing_mode(self, batch_size: int, num_workers: int, dynamic_threshold: int) -> str:
+        """Determines processing mode per requested policy:
+        - any workers     => prefer static (tile-based) for USDU
+        - no workers      => single_gpu
+        """
+        if num_workers == 0:
+            return "single_gpu"
+        # Default to static when distributed; master/worker may still override if special cases arise
+        return "static"
+
+# Ensure initialization before registering routes
+ensure_tile_jobs_initialized()
+
+# Node registration
+NODE_CLASS_MAPPINGS = {
+    "UltimateSDUpscaleDistributed": UltimateSDUpscaleDistributed,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "UltimateSDUpscaleDistributed": "Ultimate SD Upscale Distributed (No Upscale)",
+}
@@ -0,0 +1,354 @@
+import torch
+import json
+
+from ..utils.logging import debug_log, log
+
+
+def _chunk_bounds(total_items: int, n_splits: int) -> list[tuple[int, int]]:
+    """Return contiguous [start, end) bounds for n_splits chunks."""
+    split_count = max(1, int(n_splits))
+    total = max(0, int(total_items))
+    base, remainder = divmod(total, split_count)
+
+    bounds: list[tuple[int, int]] = []
+    start = 0
+    for idx in range(split_count):
+        size = base + (1 if idx < remainder else 0)
+        end = start + size
+        bounds.append((start, end))
+        start = end
+    return bounds
+
+
+class DistributedSeed:
+    """
+    Distributes seed values across multiple GPUs.
+    On master: passes through the original seed.
+    On workers: adds offset based on worker ID.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "seed": ("INT", {
+                    "default": 1125899906842, 
+                    "min": 0,
+                    "max": 1125899906842624,
+                    "forceInput": False  # Widget by default, can be converted to input
+                }),
+            },
+            "hidden": {
+                "is_worker": ("BOOLEAN", {"default": False}),
+                "worker_id": ("STRING", {"default": ""}),
+            },
+        }
+    
+    RETURN_TYPES = ("INT",)
+    RETURN_NAMES = ("seed",)
+    FUNCTION = "distribute"
+    CATEGORY = "utils"
+    
+    def distribute(self, seed, is_worker=False, worker_id=""):
+        if not is_worker:
+            # Master node: pass through original values
+            debug_log(f"Distributor - Master: seed={seed}")
+            return (seed,)
+        else:
+            # Worker node: apply offset based on worker index
+            # Find worker index from enabled_worker_ids
+            try:
+                # Worker IDs are passed as "worker_0", "worker_1", etc.
+                if worker_id.startswith("worker_"):
+                    worker_index = int(worker_id.split("_")[1])
+                else:
+                    # Fallback: try to parse as direct index
+                    worker_index = int(worker_id)
+                
+                offset = worker_index + 1
+                new_seed = seed + offset
+                debug_log(f"Distributor - Worker {worker_index}: seed={seed} → {new_seed}")
+                return (new_seed,)
+            except (ValueError, IndexError) as e:
+                debug_log(f"Distributor - Error parsing worker_id '{worker_id}': {e}")
+                # Fallback: return original seed
+                return (seed,)
+
+
+# Define ByPassTypeTuple for flexible return types
+class AnyType(str):
+    def __ne__(self, __value: object) -> bool:
+        return False
+
+any_type = AnyType("*")
+
+
+class DistributedValue:
+    """
+    Outputs a different value per worker.
+    On master: returns default_value.
+    On workers: looks up the worker-specific value from a JSON map,
+    falling back to default_value if not set.
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "default_value": ("STRING", {"default": ""}),
+                "worker_values": ("STRING", {"default": "{}"}),
+            },
+            "hidden": {
+                "is_worker": ("BOOLEAN", {"default": False}),
+                "worker_id": ("STRING", {"default": ""}),
+            },
+        }
+
+    RETURN_TYPES = (any_type,)
+    RETURN_NAMES = ("value",)
+    FUNCTION = "distribute"
+    CATEGORY = "utils"
+
+    @staticmethod
+    def _coerce(value, value_type):
+        """Convert a string value to the requested type."""
+        if value_type == "INT":
+            return int(float(value))
+        if value_type == "FLOAT":
+            return float(value)
+        return value  # STRING and COMBO stay as strings
+
+    @staticmethod
+    def _coerce_safe(value, value_type):
+        """Best-effort coercion with graceful fallback to original value."""
+        try:
+            return DistributedValue._coerce(value, value_type)
+        except (TypeError, ValueError):
+            return value
+
+    def distribute(self, default_value, worker_values="{}", is_worker=False, worker_id=""):
+        values = {}
+        value_type = "STRING"
+
+        try:
+            values = json.loads(worker_values) if isinstance(worker_values, str) else worker_values
+            if not isinstance(values, dict):
+                values = {}
+        except json.JSONDecodeError as e:
+            debug_log(f"DistributedValue - Error parsing worker_values: {e}")
+            values = {}
+
+        value_type = values.get("_type", "STRING")
+        coerced_default = self._coerce_safe(default_value, value_type)
+
+        if not is_worker:
+            debug_log(f"DistributedValue - Master: returning default '{coerced_default}'")
+            return (coerced_default,)
+
+        try:
+            if worker_id.startswith("worker_"):
+                idx = int(worker_id.split("_")[1])
+            else:
+                idx = int(worker_id)
+            key = str(idx + 1)  # worker_0 → key "1" (1-indexed)
+            raw = values.get(key, "")
+            if raw:
+                coerced = self._coerce(raw, value_type)
+                debug_log(f"DistributedValue - Worker {idx}: returning '{coerced}'")
+                return (coerced,)
+        except (ValueError, IndexError) as e:
+            debug_log(f"DistributedValue - Error: {e}")
+        debug_log(f"DistributedValue - Worker fallback: returning default '{coerced_default}'")
+        return (coerced_default,)
+
+class DistributedModelName:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "text": ("STRING", {"default": ""}),
+            },
+            "hidden": {
+                "unique_id": "UNIQUE_ID",
+                "extra_pnginfo": "EXTRA_PNGINFO",
+            },
+        }
+
+    RETURN_TYPES = (any_type,)
+    RETURN_NAMES = ("output",)
+    FUNCTION = "log_input"
+    OUTPUT_NODE = True
+    CATEGORY = "utils"
+
+    def _stringify(self, value):
+        if isinstance(value, str):
+            return value
+        if isinstance(value, (int, float, bool)):
+            return str(value)
+        try:
+            return json.dumps(value, indent=4)
+        except Exception:
+            return str(value)
+
+    def _update_workflow(self, extra_pnginfo, unique_id, values):
+        if not extra_pnginfo:
+            return
+        info = extra_pnginfo[0] if isinstance(extra_pnginfo, list) else extra_pnginfo
+        if not isinstance(info, dict) or "workflow" not in info:
+            return
+        node_id = None
+        if isinstance(unique_id, list) and unique_id:
+            node_id = str(unique_id[0])
+        elif unique_id is not None:
+            node_id = str(unique_id)
+        if not node_id:
+            return
+        workflow = info["workflow"]
+        node = next((x for x in workflow["nodes"] if str(x.get("id")) == node_id), None)
+        if node:
+            node["widgets_values"] = [values]
+
+    def log_input(self, text, unique_id=None, extra_pnginfo=None):
+        values = []
+        if isinstance(text, list):
+            for val in text:
+                values.append(self._stringify(val))
+        else:
+            values.append(self._stringify(text))
+
+        # Keep widget display in workflow metadata if available.
+        self._update_workflow(extra_pnginfo, unique_id, values)
+
+        if isinstance(values, list) and len(values) == 1:
+            return {"ui": {"text": values}, "result": (values[0],)}
+        return {"ui": {"text": values}, "result": (values,)}
+
+class ByPassTypeTuple(tuple):
+    def __getitem__(self, index):
+        if index > 0:
+            index = 0
+        item = super().__getitem__(index)
+        if isinstance(item, str):
+            return any_type
+        return item
+
+class ImageBatchDivider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "divide_by": ("INT", {
+                    "default": 2, 
+                    "min": 1, 
+                    "max": 10, 
+                    "step": 1,
+                    "display": "number",
+                    "tooltip": "Number of parts to divide the batch into"
+                }),
+            }
+        }
+    
+    RETURN_TYPES = ByPassTypeTuple(("IMAGE", ))  # Flexible for variable outputs
+    RETURN_NAMES = ByPassTypeTuple(tuple([f"batch_{i+1}" for i in range(10)]))
+    FUNCTION = "divide_batch"
+    OUTPUT_NODE = True
+    CATEGORY = "image"
+    
+    def divide_batch(self, images, divide_by):
+        total_splits = max(1, min(int(divide_by), 10))
+        total_frames = images.shape[0]
+        empty_tensor = images[:0]
+        bounds = _chunk_bounds(total_frames, total_splits)
+        outputs = [images[start:end] if end > start else empty_tensor for start, end in bounds]
+
+        while len(outputs) < 10:
+            outputs.append(empty_tensor)
+
+        return tuple(outputs[:10])
+
+
+class AudioBatchDivider:
+    """Divides an audio waveform into multiple parts along the time/samples dimension."""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "audio": ("AUDIO",),
+                "divide_by": ("INT", {
+                    "default": 2,
+                    "min": 1,
+                    "max": 10,
+                    "step": 1,
+                    "display": "number",
+                    "tooltip": "Number of parts to divide the audio into"
+                }),
+            }
+        }
+
+    RETURN_TYPES = ByPassTypeTuple(("AUDIO",))  # Flexible for variable outputs
+    RETURN_NAMES = ByPassTypeTuple(tuple([f"audio_{i+1}" for i in range(10)]))
+    FUNCTION = "divide_audio"
+    OUTPUT_NODE = True
+    CATEGORY = "audio"
+
+    def divide_audio(self, audio, divide_by):
+        import torch
+
+        waveform = audio.get("waveform")
+        sample_rate = audio.get("sample_rate", 44100)
+
+        if waveform is None or waveform.numel() == 0:
+            # Return empty audio for all outputs
+            empty_audio = {"waveform": torch.zeros(1, 2, 1), "sample_rate": sample_rate}
+            return tuple([empty_audio] * 10)
+
+        total_splits = max(1, min(int(divide_by), 10))
+        total_samples = int(waveform.shape[-1])
+        bounds = _chunk_bounds(total_samples, total_splits)
+
+        outputs = []
+        empty_waveform = waveform[..., :0]
+        for start, end in bounds:
+            split_waveform = waveform[..., start:end] if end > start else empty_waveform
+            outputs.append({
+                "waveform": split_waveform,
+                "sample_rate": sample_rate
+            })
+
+        # Pad with empty audio up to max (10) to match RETURN_TYPES length
+        empty_audio = {
+            "waveform": empty_waveform,
+            "sample_rate": sample_rate
+        }
+
+        while len(outputs) < 10:
+            outputs.append(empty_audio)
+
+        return tuple(outputs)
+
+
+class DistributedEmptyImage:
+    """Produces an empty IMAGE batch used when the master delegates all work."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "height": ("INT", {"default": 64, "min": 1, "max": 4096, "step": 1}),
+                "width": ("INT", {"default": 64, "min": 1, "max": 4096, "step": 1}),
+                "channels": ("INT", {"default": 3, "min": 1, "max": 4, "step": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "create"
+    CATEGORY = "image"
+
+    def create(self, height, width, channels):
+        import torch
+
+        shape = (0, height, width, channels)
+        tensor = torch.zeros(shape, dtype=torch.float32)
+        return (tensor,)
@@ -0,0 +1,12 @@
+{
+  "name": "comfyui-distributed-web-tests",
+  "private": true,
+  "type": "module",
+  "scripts": {
+    "test:web": "bash ./scripts/test-web.sh",
+    "test:web:watch": "bash ./scripts/test-web.sh --watch"
+  },
+  "devDependencies": {
+    "vitest": "^2.1.9"
+  }
+}
@@ -1,7 +1,7 @@
 [project]
 name = "ComfyUI-Distributed"
-description = "ComfyUI extension that enables multi-GPU processing locally and remotely "
-version = "1.0.1"
+description = "ComfyUI extension that enables multi-GPU processing locally, remotely and in the cloud"
+version = "1.4.0"
 license = {file = "LICENSE"}
 dependencies  = []

@@ -12,4 +12,8 @@ Repository = "https://github.com/robertvoy/ComfyUI-Distributed"
 [tool.comfy]
 PublisherId = "robertvoy"
 DisplayName = "ComfyUI-Distributed"
-Icon = ""
+Icon = "https://raw.githubusercontent.com/robertvoy/ComfyUI-Distributed/refs/heads/main/web/distributed-logo-icon.png"
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+pythonpath = ["."]
@@ -0,0 +1,29 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
+REPO_ROOT="$(cd -- "${SCRIPT_DIR}/.." && pwd)"
+
+export NVM_DIR="${NVM_DIR:-$HOME/.nvm}"
+if [[ -s "${NVM_DIR}/nvm.sh" ]]; then
+    # shellcheck source=/dev/null
+    . "${NVM_DIR}/nvm.sh"
+fi
+
+if ! command -v node >/dev/null 2>&1 || ! command -v npm >/dev/null 2>&1; then
+    echo "[test-web] node/npm are not available." >&2
+    echo "[test-web] Install nvm and Node, or ensure node/npm are on PATH." >&2
+    exit 1
+fi
+
+if [[ -f "${REPO_ROOT}/.nvmrc" ]] && command -v nvm >/dev/null 2>&1; then
+    nvm use >/dev/null
+fi
+
+cd "${REPO_ROOT}"
+
+if [[ "${1:-}" == "--watch" ]]; then
+    exec npx vitest web/tests
+fi
+
+exec npx vitest run web/tests
@@ -0,0 +1,148 @@
+import copy
+import importlib.util
+import sys
+import types
+import unittest
+from pathlib import Path
+from unittest.mock import patch
+
+
+class _FakeResponse:
+    def __init__(self, payload, status=200):
+        self.payload = payload
+        self.status = status
+
+
+class _FakeRequest:
+    def __init__(self, payload=None):
+        self._payload = payload
+
+    async def json(self):
+        return self._payload
+
+
+def _load_config_routes_module():
+    module_path = Path(__file__).resolve().parents[2] / "api" / "config_routes.py"
+    package_name = "dist_api_config_testpkg"
+
+    for mod_name in list(sys.modules):
+        if mod_name == package_name or mod_name.startswith(f"{package_name}."):
+            del sys.modules[mod_name]
+
+    root_pkg = types.ModuleType(package_name)
+    root_pkg.__path__ = []
+    sys.modules[package_name] = root_pkg
+
+    api_pkg = types.ModuleType(f"{package_name}.api")
+    api_pkg.__path__ = []
+    sys.modules[f"{package_name}.api"] = api_pkg
+
+    utils_pkg = types.ModuleType(f"{package_name}.utils")
+    utils_pkg.__path__ = []
+    sys.modules[f"{package_name}.utils"] = utils_pkg
+
+    created_aiohttp_stub = False
+    if "aiohttp" not in sys.modules:
+        created_aiohttp_stub = True
+        aiohttp_module = types.ModuleType("aiohttp")
+        aiohttp_module.web = types.SimpleNamespace(
+            json_response=lambda payload, status=200: _FakeResponse(payload, status=status)
+        )
+        sys.modules["aiohttp"] = aiohttp_module
+
+    class _Routes:
+        def get(self, _path):
+            def _decorator(fn):
+                return fn
+            return _decorator
+
+        def post(self, _path):
+            def _decorator(fn):
+                return fn
+            return _decorator
+
+    server_module = types.ModuleType("server")
+    server_module.PromptServer = types.SimpleNamespace(instance=types.SimpleNamespace(routes=_Routes()))
+    sys.modules["server"] = server_module
+
+    logging_module = types.ModuleType(f"{package_name}.utils.logging")
+    logging_module.debug_log = lambda *_args, **_kwargs: None
+    logging_module.log = lambda *_args, **_kwargs: None
+    sys.modules[f"{package_name}.utils.logging"] = logging_module
+
+    network_module = types.ModuleType(f"{package_name}.utils.network")
+
+    async def _handle_api_error(_request, error, status=500):
+        return _FakeResponse({"status": "error", "message": str(error)}, status=status)
+
+    network_module.handle_api_error = _handle_api_error
+    network_module.normalize_host = lambda value: value
+    sys.modules[f"{package_name}.utils.network"] = network_module
+
+    default_config = {
+        "workers": [],
+        "master": {"host": ""},
+        "settings": {"debug": False},
+        "tunnel": {},
+    }
+
+    config_module = types.ModuleType(f"{package_name}.utils.config")
+    config_module.load_config = lambda: copy.deepcopy(default_config)
+    config_module.save_config = lambda _cfg: True
+    sys.modules[f"{package_name}.utils.config"] = config_module
+
+    spec = importlib.util.spec_from_file_location(f"{package_name}.api.config_routes", module_path)
+    module = importlib.util.module_from_spec(spec)
+    assert spec is not None and spec.loader is not None
+    spec.loader.exec_module(module)
+
+    if created_aiohttp_stub:
+        sys.modules.pop("aiohttp", None)
+
+    return module
+
+
+config_routes = _load_config_routes_module()
+
+
+class ConfigRoutesTests(unittest.IsolatedAsyncioTestCase):
+    async def test_get_config_returns_core_sections(self):
+        cfg = {"workers": [], "master": {}, "settings": {}, "tunnel": {}}
+        with patch.object(config_routes, "load_config", return_value=cfg):
+            response = await config_routes.get_config_endpoint(_FakeRequest())
+
+        self.assertEqual(response.status, 200)
+        self.assertIn("workers", response.payload)
+        self.assertIn("master", response.payload)
+        self.assertIn("settings", response.payload)
+
+    async def test_update_config_valid_field_persists(self):
+        cfg = {"workers": [], "master": {}, "settings": {"debug": False}, "tunnel": {}}
+        with patch.object(config_routes, "load_config", return_value=cfg), patch.object(
+            config_routes, "save_config", return_value=True
+        ):
+            response = await config_routes.update_config_endpoint(_FakeRequest({"debug": True}))
+
+        self.assertEqual(response.status, 200)
+        self.assertEqual(response.payload["status"], "success")
+        self.assertTrue(response.payload["config"]["settings"]["debug"])
+
+    async def test_update_config_unknown_field_returns_400(self):
+        cfg = {"workers": [], "master": {}, "settings": {"debug": False}, "tunnel": {}}
+        with patch.object(config_routes, "load_config", return_value=cfg):
+            response = await config_routes.update_config_endpoint(_FakeRequest({"unknown_field": 1}))
+
+        self.assertEqual(response.status, 400)
+        self.assertIn("unknown_field", " ".join(response.payload.get("error", [])).lower())
+
+    async def test_update_config_wrong_type_returns_400(self):
+        cfg = {"workers": [], "master": {}, "settings": {"debug": False}, "tunnel": {}}
+        with patch.object(config_routes, "load_config", return_value=cfg):
+            response = await config_routes.update_config_endpoint(_FakeRequest({"debug": "true"}))
+
+        self.assertEqual(response.status, 400)
+        self.assertIn("debug", " ".join(response.payload.get("error", [])).lower())
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,325 @@
+import importlib.util
+import sys
+import types
+import unittest
+import asyncio
+import base64
+from dataclasses import dataclass
+from pathlib import Path
+from unittest.mock import AsyncMock, patch
+
+import numpy as np
+import torch
+
+
+class _FakeResponse:
+    def __init__(self, payload, status=200):
+        self.payload = payload
+        self.status = status
+
+
+class _FakeRequest:
+    def __init__(self, payload):
+        self._payload = payload
+
+    async def json(self):
+        return self._payload
+
+
+def _load_job_routes_module():
+    module_path = Path(__file__).resolve().parents[2] / "api" / "job_routes.py"
+    package_name = "dist_api_queue_testpkg"
+
+    # Reset package namespace to avoid stale module state across test runs.
+    for mod_name in list(sys.modules):
+        if mod_name == package_name or mod_name.startswith(f"{package_name}."):
+            del sys.modules[mod_name]
+
+    root_pkg = types.ModuleType(package_name)
+    root_pkg.__path__ = []
+    sys.modules[package_name] = root_pkg
+
+    api_pkg = types.ModuleType(f"{package_name}.api")
+    api_pkg.__path__ = []
+    sys.modules[f"{package_name}.api"] = api_pkg
+
+    utils_pkg = types.ModuleType(f"{package_name}.utils")
+    utils_pkg.__path__ = []
+    sys.modules[f"{package_name}.utils"] = utils_pkg
+
+    # aiohttp.web stub
+    created_aiohttp_stub = False
+    if "aiohttp" not in sys.modules:
+        created_aiohttp_stub = True
+        aiohttp_module = types.ModuleType("aiohttp")
+        aiohttp_module.web = types.SimpleNamespace(
+            json_response=lambda payload, status=200: _FakeResponse(payload, status=status)
+        )
+        sys.modules["aiohttp"] = aiohttp_module
+
+    # server module stub with route decorators
+    class _Routes:
+        def get(self, _path):
+            def _decorator(fn):
+                return fn
+            return _decorator
+
+        def post(self, _path):
+            def _decorator(fn):
+                return fn
+            return _decorator
+
+    prompt_server_instance = types.SimpleNamespace(
+        routes=_Routes(),
+        distributed_jobs_lock=None,
+        distributed_pending_jobs={},
+    )
+    server_module = types.ModuleType("server")
+    server_module.PromptServer = types.SimpleNamespace(instance=prompt_server_instance)
+    sys.modules["server"] = server_module
+
+    # torch stub (only needed to satisfy import)
+    created_torch_stub = False
+    if "torch" not in sys.modules:
+        created_torch_stub = True
+        torch_module = types.ModuleType("torch")
+        torch_module.cuda = types.SimpleNamespace(
+            is_available=lambda: False,
+            empty_cache=lambda: None,
+            ipc_collect=lambda: None,
+        )
+        sys.modules["torch"] = torch_module
+
+    # PIL stub (only needed to satisfy import)
+    created_pil_stub = False
+    if "PIL" not in sys.modules:
+        created_pil_stub = True
+        pil_module = types.ModuleType("PIL")
+        image_module = types.ModuleType("PIL.Image")
+        pil_module.Image = image_module
+        sys.modules["PIL"] = pil_module
+        sys.modules["PIL.Image"] = image_module
+
+    logging_module = types.ModuleType(f"{package_name}.utils.logging")
+    logging_module.debug_log = lambda *_args, **_kwargs: None
+    logging_module.log = lambda *_args, **_kwargs: None
+    sys.modules[f"{package_name}.utils.logging"] = logging_module
+
+    image_module = types.ModuleType(f"{package_name}.utils.image")
+    image_module.pil_to_tensor = lambda *_args, **_kwargs: None
+    image_module.ensure_contiguous = lambda tensor: tensor
+    sys.modules[f"{package_name}.utils.image"] = image_module
+
+    audio_payload_module = types.ModuleType(f"{package_name}.utils.audio_payload")
+
+    def _decode_audio_payload(payload):
+        if payload is None:
+            return None
+        if not isinstance(payload, dict):
+            raise ValueError("Field 'audio' must be an object when provided.")
+
+        encoded = payload.get("data")
+        shape = payload.get("shape")
+        dtype = payload.get("dtype", "float32")
+        sample_rate = payload.get("sample_rate", 44100)
+        if not isinstance(encoded, str) or not encoded.strip():
+            raise ValueError("Field 'audio.data' must be a non-empty base64 string.")
+        if not isinstance(shape, list) or len(shape) != 3:
+            raise ValueError("Field 'audio.shape' must be a 3-item list.")
+        if dtype != "float32":
+            raise ValueError("Field 'audio.dtype' must be 'float32'.")
+        try:
+            shape_tuple = tuple(int(dim) for dim in shape)
+        except Exception as exc:
+            raise ValueError("Field 'audio.shape' must contain integers.") from exc
+
+        raw = base64.b64decode(encoded, validate=True)
+        expected_bytes = int(np.prod(shape_tuple, dtype=np.int64)) * 4
+        if len(raw) != expected_bytes:
+            raise ValueError("Field 'audio.data' byte size mismatch.")
+
+        waveform = torch.from_numpy(np.frombuffer(raw, dtype=np.float32).reshape(shape_tuple).copy())
+        return {"waveform": waveform, "sample_rate": int(sample_rate)}
+
+    audio_payload_module.decode_audio_payload = _decode_audio_payload
+    sys.modules[f"{package_name}.utils.audio_payload"] = audio_payload_module
+
+    network_module = types.ModuleType(f"{package_name}.utils.network")
+
+    async def _handle_api_error(_request, error, status=500):
+        return _FakeResponse({"status": "error", "message": str(error)}, status=status)
+
+    network_module.handle_api_error = _handle_api_error
+    sys.modules[f"{package_name}.utils.network"] = network_module
+
+    constants_module = types.ModuleType(f"{package_name}.utils.constants")
+    constants_module.MEMORY_CLEAR_DELAY = 0.0
+    constants_module.JOB_INIT_GRACE_PERIOD = 10.0
+    sys.modules[f"{package_name}.utils.constants"] = constants_module
+
+    async_helpers_module = types.ModuleType(f"{package_name}.utils.async_helpers")
+    async_helpers_module.queue_prompt_payload = AsyncMock(return_value="prompt_local")
+    sys.modules[f"{package_name}.utils.async_helpers"] = async_helpers_module
+
+    queue_orchestration_module = types.ModuleType(f"{package_name}.api.queue_orchestration")
+    queue_orchestration_module.orchestrate_distributed_execution = AsyncMock(return_value=("prompt_dist", 1))
+    sys.modules[f"{package_name}.api.queue_orchestration"] = queue_orchestration_module
+
+    @dataclass(frozen=True)
+    class _QueuePayload:
+        prompt: dict
+        workflow_meta: object
+        client_id: str
+        delegate_master: object
+        enabled_worker_ids: list
+        auto_prepare: bool
+        trace_execution_id: object
+
+    def _parse_queue_request_payload(data):
+        if not isinstance(data, dict):
+            raise ValueError("Expected a JSON object body")
+        prompt = data.get("prompt")
+        if not isinstance(prompt, dict):
+            raise ValueError("Field 'prompt' must be an object")
+        enabled = data.get("enabled_worker_ids")
+        if not isinstance(enabled, list):
+            raise ValueError("enabled_worker_ids required")
+        client_id = data.get("client_id")
+        if not isinstance(client_id, str) or not client_id.strip():
+            raise ValueError("client_id required")
+        return _QueuePayload(
+            prompt=prompt,
+            workflow_meta=data.get("workflow"),
+            client_id=client_id,
+            delegate_master=data.get("delegate_master"),
+            enabled_worker_ids=enabled,
+            auto_prepare=bool(data.get("auto_prepare", True)),
+            trace_execution_id=data.get("trace_execution_id"),
+        )
+
+    queue_request_module = types.ModuleType(f"{package_name}.api.queue_request")
+    queue_request_module.parse_queue_request_payload = _parse_queue_request_payload
+    sys.modules[f"{package_name}.api.queue_request"] = queue_request_module
+
+    spec = importlib.util.spec_from_file_location(f"{package_name}.api.job_routes", module_path)
+    module = importlib.util.module_from_spec(spec)
+    assert spec is not None and spec.loader is not None
+    spec.loader.exec_module(module)
+
+    if created_aiohttp_stub:
+        sys.modules.pop("aiohttp", None)
+    if created_torch_stub:
+        sys.modules.pop("torch", None)
+    if created_pil_stub:
+        sys.modules.pop("PIL.Image", None)
+        sys.modules.pop("PIL", None)
+
+    return module
+
+
+job_routes = _load_job_routes_module()
+
+
+class DistributedQueueEndpointTests(unittest.IsolatedAsyncioTestCase):
+    async def test_distributed_queue_happy_path_returns_prompt_id(self):
+        request = _FakeRequest(
+            {
+                "prompt": {"1": {"class_type": "Node"}},
+                "enabled_worker_ids": ["w1"],
+                "client_id": "client-1",
+                "auto_prepare": True,
+            }
+        )
+        with patch.object(
+            job_routes,
+            "orchestrate_distributed_execution",
+            new=AsyncMock(return_value=("prompt_123", 2)),
+        ):
+            response = await job_routes.distributed_queue_endpoint(request)
+
+        self.assertEqual(response.status, 200)
+        self.assertEqual(response.payload.get("prompt_id"), "prompt_123")
+        self.assertTrue(response.payload.get("auto_prepare_supported"))
+
+    async def test_distributed_queue_missing_prompt_returns_400(self):
+        request = _FakeRequest(
+            {
+                "enabled_worker_ids": ["w1"],
+                "client_id": "client-1",
+            }
+        )
+        response = await job_routes.distributed_queue_endpoint(request)
+        self.assertEqual(response.status, 400)
+        self.assertIn("prompt", response.payload.get("message", "").lower())
+
+    async def test_distributed_queue_missing_enabled_worker_ids_returns_400(self):
+        request = _FakeRequest(
+            {
+                "prompt": {"1": {"class_type": "Node"}},
+                "client_id": "client-1",
+            }
+        )
+        response = await job_routes.distributed_queue_endpoint(request)
+        self.assertEqual(response.status, 400)
+        self.assertIn("enabled_worker_ids", response.payload.get("message", "").lower())
+
+
+class JobCompleteAudioPayloadTests(unittest.IsolatedAsyncioTestCase):
+    def _encoded_audio_payload(self):
+        waveform = np.arange(8, dtype=np.float32).reshape(1, 2, 4)
+        return {
+            "sample_rate": 44100,
+            "shape": [1, 2, 4],
+            "dtype": "float32",
+            "data": base64.b64encode(waveform.tobytes()).decode("ascii"),
+        }
+
+    async def test_job_complete_accepts_audio_payload(self):
+        queue = asyncio.Queue()
+        job_routes.prompt_server.distributed_jobs_lock = asyncio.Lock()
+        job_routes.prompt_server.distributed_pending_jobs = {"job-1": queue}
+        request = _FakeRequest(
+            {
+                "job_id": "job-1",
+                "worker_id": "worker-1",
+                "batch_idx": 0,
+                "image": "data:image/png;base64,AAAA",
+                "audio": self._encoded_audio_payload(),
+                "is_last": True,
+            }
+        )
+
+        with patch.object(job_routes, "_decode_canonical_png_tensor", return_value="tensor-data"):
+            response = await job_routes.job_complete_endpoint(request)
+
+        self.assertEqual(response.status, 200)
+        queued = await queue.get()
+        self.assertEqual(queued["worker_id"], "worker-1")
+        self.assertTrue(queued["is_last"])
+        self.assertIsNotNone(queued["audio"])
+        self.assertEqual(queued["audio"]["sample_rate"], 44100)
+        self.assertEqual(tuple(queued["audio"]["waveform"].shape), (1, 2, 4))
+
+    def test_decode_audio_payload_rejects_bad_shape(self):
+        bad = {
+            "sample_rate": 44100,
+            "shape": [1, 2],
+            "dtype": "float32",
+            "data": base64.b64encode(b"\x00\x00\x00\x00").decode("ascii"),
+        }
+        with self.assertRaises(ValueError):
+            job_routes._decode_audio_payload(bad)
+
+    def test_decode_audio_payload_rejects_bad_dtype(self):
+        payload = {
+            "sample_rate": 44100,
+            "shape": [1, 2, 4],
+            "dtype": "float16",
+            "data": base64.b64encode((np.zeros((1, 2, 4), dtype=np.float32)).tobytes()).decode("ascii"),
+        }
+        with self.assertRaises(ValueError):
+            job_routes._decode_audio_payload(payload)
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,262 @@
+import importlib.util
+import sys
+import types
+import unittest
+from pathlib import Path
+
+
+def _load_media_sync_module():
+    module_path = Path(__file__).resolve().parents[2] / "api" / "orchestration" / "media_sync.py"
+    package_name = "dist_ms_testpkg"
+
+    for mod_name in list(sys.modules):
+        if mod_name == package_name or mod_name.startswith(f"{package_name}."):
+            del sys.modules[mod_name]
+
+    root_pkg = types.ModuleType(package_name)
+    root_pkg.__path__ = []
+    sys.modules[package_name] = root_pkg
+
+    api_pkg = types.ModuleType(f"{package_name}.api")
+    api_pkg.__path__ = []
+    sys.modules[f"{package_name}.api"] = api_pkg
+
+    orch_pkg = types.ModuleType(f"{package_name}.api.orchestration")
+    orch_pkg.__path__ = []
+    sys.modules[f"{package_name}.api.orchestration"] = orch_pkg
+
+    utils_pkg = types.ModuleType(f"{package_name}.utils")
+    utils_pkg.__path__ = []
+    sys.modules[f"{package_name}.utils"] = utils_pkg
+
+    logging_module = types.ModuleType(f"{package_name}.utils.logging")
+    logging_module.debug_log = lambda *_args, **_kwargs: None
+    logging_module.log = lambda *_args, **_kwargs: None
+    sys.modules[f"{package_name}.utils.logging"] = logging_module
+
+    network_module = types.ModuleType(f"{package_name}.utils.network")
+    network_module.build_worker_url = lambda worker, endpoint="": f"http://localhost{endpoint}"
+
+    async def _fake_session():
+        raise RuntimeError("network calls not used in pure-function tests")
+
+    network_module.get_client_session = _fake_session
+    sys.modules[f"{package_name}.utils.network"] = network_module
+
+    trace_module = types.ModuleType(f"{package_name}.utils.trace_logger")
+    trace_module.trace_debug = lambda *_args, **_kwargs: None
+    trace_module.trace_info = lambda *_args, **_kwargs: None
+    sys.modules[f"{package_name}.utils.trace_logger"] = trace_module
+
+    created_aiohttp_stub = False
+    if "aiohttp" not in sys.modules:
+        created_aiohttp_stub = True
+        aiohttp_module = types.ModuleType("aiohttp")
+
+        class _ClientTimeout:
+            def __init__(self, total=None):
+                pass
+
+        class _FormData:
+            def add_field(self, *args, **kwargs):
+                pass
+
+        aiohttp_module.ClientTimeout = _ClientTimeout
+        aiohttp_module.FormData = _FormData
+        sys.modules["aiohttp"] = aiohttp_module
+
+    spec = importlib.util.spec_from_file_location(
+        f"{package_name}.api.orchestration.media_sync",
+        module_path,
+    )
+    module = importlib.util.module_from_spec(spec)
+    assert spec is not None and spec.loader is not None
+    spec.loader.exec_module(module)
+
+    if created_aiohttp_stub:
+        sys.modules.pop("aiohttp", None)
+
+    return module
+
+
+ms = _load_media_sync_module()
+
+
+# ---------------------------------------------------------------------------
+# convert_paths_for_platform
+# ---------------------------------------------------------------------------
+
+class ConvertPathsForPlatformTests(unittest.TestCase):
+    def test_forward_slash_target_normalises_backslashes(self):
+        obj = {"ckpt_name": "C:\\Models\\model.safetensors"}
+        result = ms.convert_paths_for_platform(obj, "/")
+        self.assertEqual(result["ckpt_name"], "C:/Models/model.safetensors")
+
+    def test_backslash_target_normalises_forward_slashes(self):
+        obj = {"ckpt_name": "/models/checkpoints/model.safetensors"}
+        result = ms.convert_paths_for_platform(obj, "\\")
+        self.assertIn("\\", result["ckpt_name"])
+        self.assertNotIn("/", result["ckpt_name"])
+
+    def test_relative_media_paths_always_stay_forward_slash(self):
+        """Relative image/video/audio paths (Comfy annotated style) must not be backslash-ified."""
+        obj = {"image": "subfolder/my_photo.png"}
+        result = ms.convert_paths_for_platform(obj, "\\")
+        self.assertEqual(result["image"], "subfolder/my_photo.png")
+
+    def test_relative_audio_paths_stay_forward_slash(self):
+        obj = {"audio": "subfolder/my_track.wav"}
+        result = ms.convert_paths_for_platform(obj, "\\")
+        self.assertEqual(result["audio"], "subfolder/my_track.wav")
+
+    def test_annotated_relative_media_path_stays_forward_slash(self):
+        obj = {"image": "input/frame.jpg [abc123]"}
+        result = ms.convert_paths_for_platform(obj, "\\")
+        self.assertIn("/", result["image"])
+        self.assertNotIn("\\", result["image"].split("[")[0])
+
+    def test_non_filename_strings_are_untouched(self):
+        obj = {"prompt": "a beautiful cat", "count": 5}
+        result = ms.convert_paths_for_platform(obj, "\\")
+        self.assertEqual(result["prompt"], "a beautiful cat")
+        self.assertEqual(result["count"], 5)
+
+    def test_url_strings_are_untouched(self):
+        obj = {"url": "https://example.com/model.safetensors"}
+        result = ms.convert_paths_for_platform(obj, "\\")
+        self.assertEqual(result["url"], "https://example.com/model.safetensors")
+
+    def test_invalid_separator_returns_obj_unchanged(self):
+        obj = {"ckpt_name": "/models/model.safetensors"}
+        result = ms.convert_paths_for_platform(obj, "|")
+        self.assertEqual(result, obj)
+
+    def test_nested_dict_is_processed_recursively(self):
+        obj = {"node": {"ckpt_name": "C:\\Models\\model.safetensors"}}
+        result = ms.convert_paths_for_platform(obj, "/")
+        self.assertEqual(result["node"]["ckpt_name"], "C:/Models/model.safetensors")
+
+    def test_list_items_are_processed_recursively(self):
+        obj = [{"ckpt_name": "C:\\Models\\model.safetensors"}, "plain string"]
+        result = ms.convert_paths_for_platform(obj, "/")
+        self.assertEqual(result[0]["ckpt_name"], "C:/Models/model.safetensors")
+        self.assertEqual(result[1], "plain string")
+
+    def test_non_string_scalar_values_are_untouched(self):
+        obj = {"seed": 42, "enabled": True, "ratio": 1.5}
+        result = ms.convert_paths_for_platform(obj, "/")
+        self.assertEqual(result["seed"], 42)
+        self.assertTrue(result["enabled"])
+
+    def test_absolute_unix_path_to_windows(self):
+        obj = {"lora": "/home/user/loras/my_lora.safetensors"}
+        result = ms.convert_paths_for_platform(obj, "\\")
+        self.assertNotIn("/", result["lora"])
+
+    def test_already_normalised_path_is_idempotent(self):
+        obj = {"ckpt": "C:/Models/model.safetensors"}
+        result = ms.convert_paths_for_platform(obj, "/")
+        self.assertEqual(result["ckpt"], "C:/Models/model.safetensors")
+
+
+# ---------------------------------------------------------------------------
+# _find_media_references
+# ---------------------------------------------------------------------------
+
+class FindMediaReferencesTests(unittest.TestCase):
+    def test_finds_image_input(self):
+        prompt = {"1": {"class_type": "LoadImage", "inputs": {"image": "photo.png"}}}
+        refs = ms._find_media_references(prompt)
+        self.assertIn("photo.png", refs)
+
+    def test_finds_video_input(self):
+        prompt = {"1": {"class_type": "LoadVideo", "inputs": {"video": "clip.mp4"}}}
+        refs = ms._find_media_references(prompt)
+        self.assertIn("clip.mp4", refs)
+
+    def test_finds_file_input_for_load_video(self):
+        prompt = {"1": {"class_type": "LoadVideo", "inputs": {"file": "1 - Copy.mp4"}}}
+        refs = ms._find_media_references(prompt)
+        self.assertIn("1 - Copy.mp4", refs)
+
+    def test_finds_audio_input(self):
+        prompt = {"1": {"class_type": "LoadAudio", "inputs": {"audio": "track.wav"}}}
+        refs = ms._find_media_references(prompt)
+        self.assertIn("track.wav", refs)
+
+    def test_strips_annotation_suffix(self):
+        prompt = {"1": {"class_type": "LoadImage", "inputs": {"image": "photo.jpg [abc123]"}}}
+        refs = ms._find_media_references(prompt)
+        self.assertIn("photo.jpg", refs)
+        self.assertFalse(any("[" in r for r in refs))
+
+    def test_normalises_backslashes_in_path(self):
+        prompt = {"1": {"class_type": "LoadImage", "inputs": {"image": "sub\\img.png"}}}
+        refs = ms._find_media_references(prompt)
+        self.assertIn("sub/img.png", refs)
+
+    def test_ignores_non_media_text_inputs(self):
+        prompt = {"1": {"class_type": "CLIPTextEncode", "inputs": {"text": "a cat"}}}
+        refs = ms._find_media_references(prompt)
+        self.assertEqual(refs, [])
+
+    def test_ignores_node_link_values(self):
+        """Inputs that are [node_id, slot] lists should be ignored."""
+        prompt = {"1": {"class_type": "Anything", "inputs": {"image": ["2", 0]}}}
+        refs = ms._find_media_references(prompt)
+        self.assertEqual(refs, [])
+
+    def test_deduplicates_same_file_across_nodes(self):
+        prompt = {
+            "1": {"class_type": "LoadImage", "inputs": {"image": "cat.png"}},
+            "2": {"class_type": "LoadImage", "inputs": {"image": "cat.png"}},
+        }
+        refs = ms._find_media_references(prompt)
+        self.assertEqual(len(refs), 1)
+
+    def test_returns_sorted_list(self):
+        prompt = {
+            "1": {"class_type": "LoadImage", "inputs": {"image": "z_image.png"}},
+            "2": {"class_type": "LoadImage", "inputs": {"image": "a_image.jpg"}},
+        }
+        refs = ms._find_media_references(prompt)
+        self.assertEqual(refs, sorted(refs))
+
+    def test_ignores_non_dict_nodes(self):
+        prompt = {"1": "not a node dict", "2": {"class_type": "LoadImage", "inputs": {"image": "img.png"}}}
+        refs = ms._find_media_references(prompt)
+        self.assertIn("img.png", refs)
+
+    def test_empty_prompt_returns_empty_list(self):
+        self.assertEqual(ms._find_media_references({}), [])
+
+    def test_multiple_media_types_all_found(self):
+        prompt = {
+            "1": {"class_type": "LoadImage", "inputs": {"image": "frame.png"}},
+            "2": {"class_type": "LoadVideo", "inputs": {"video": "clip.mp4"}},
+            "3": {"class_type": "LoadAudio", "inputs": {"audio": "track.wav"}},
+        }
+        refs = ms._find_media_references(prompt)
+        self.assertIn("frame.png", refs)
+        self.assertIn("clip.mp4", refs)
+        self.assertIn("track.wav", refs)
+
+
+class RewritePromptMediaInputsTests(unittest.TestCase):
+    def test_rewrites_video_file_input_to_worker_path(self):
+        prompt = {
+            "79": {"class_type": "LoadVideo", "inputs": {"file": "1 - Copy.mp4"}},
+        }
+        ms._rewrite_prompt_media_inputs(prompt, {"1 - Copy.mp4": "videos/1 - Copy.mp4"})
+        self.assertEqual(prompt["79"]["inputs"]["file"], "videos/1 - Copy.mp4")
+
+    def test_rewrites_audio_input_and_strips_annotation_when_matching(self):
+        prompt = {
+            "1": {"class_type": "LoadAudio", "inputs": {"audio": "song.wav [input]"}},
+        }
+        ms._rewrite_prompt_media_inputs(prompt, {"song.wav": "song.wav"})
+        self.assertEqual(prompt["1"]["inputs"]["audio"], "song.wav")
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,304 @@
+import asyncio
+import importlib.util
+import io
+import sys
+import types
+import unittest
+from dataclasses import dataclass, field
+from pathlib import Path
+
+from PIL import Image
+
+
+class _FakeResponse:
+    def __init__(self, payload, status=200):
+        self.payload = payload
+        self.status = status
+
+
+class _FakeRequest:
+    def __init__(self, json_payload=None, post_payload=None, headers=None, query=None):
+        self._json_payload = json_payload
+        self._post_payload = post_payload or {}
+        self.headers = headers or {}
+        self.query = query or {}
+
+    async def json(self):
+        return self._json_payload
+
+    async def post(self):
+        return self._post_payload
+
+
+class _Routes:
+    def post(self, _path):
+        def _decorator(fn):
+            return fn
+
+        return _decorator
+
+    def get(self, _path):
+        def _decorator(fn):
+            return fn
+
+        return _decorator
+
+
+def _load_usdu_routes_module():
+    module_path = Path(__file__).resolve().parents[2] / "api" / "usdu_routes.py"
+    package_name = "dist_api_usdu_testpkg"
+
+    for mod_name in list(sys.modules):
+        if mod_name == package_name or mod_name.startswith(f"{package_name}."):
+            del sys.modules[mod_name]
+
+    root_pkg = types.ModuleType(package_name)
+    root_pkg.__path__ = []
+    sys.modules[package_name] = root_pkg
+
+    api_pkg = types.ModuleType(f"{package_name}.api")
+    api_pkg.__path__ = []
+    sys.modules[f"{package_name}.api"] = api_pkg
+
+    upscale_pkg = types.ModuleType(f"{package_name}.upscale")
+    upscale_pkg.__path__ = []
+    sys.modules[f"{package_name}.upscale"] = upscale_pkg
+
+    utils_pkg = types.ModuleType(f"{package_name}.utils")
+    utils_pkg.__path__ = []
+    sys.modules[f"{package_name}.utils"] = utils_pkg
+
+    prompt_server_holder = {
+        "value": types.SimpleNamespace(
+            distributed_tile_jobs_lock=asyncio.Lock(),
+            distributed_pending_tile_jobs={},
+        )
+    }
+
+    created_aiohttp_stub = False
+    if "aiohttp" not in sys.modules:
+        created_aiohttp_stub = True
+        aiohttp_module = types.ModuleType("aiohttp")
+        aiohttp_module.web = types.SimpleNamespace(
+            json_response=lambda payload, status=200: _FakeResponse(payload, status=status)
+        )
+        sys.modules["aiohttp"] = aiohttp_module
+
+    server_module = types.ModuleType("server")
+    server_module.PromptServer = types.SimpleNamespace(instance=types.SimpleNamespace(routes=_Routes()))
+    sys.modules["server"] = server_module
+
+    logging_module = types.ModuleType(f"{package_name}.utils.logging")
+    logging_module.debug_log = lambda *_args, **_kwargs: None
+    sys.modules[f"{package_name}.utils.logging"] = logging_module
+
+    network_module = types.ModuleType(f"{package_name}.utils.network")
+
+    async def _handle_api_error(_request, error, status=500):
+        return _FakeResponse({"status": "error", "message": str(error)}, status=status)
+
+    network_module.handle_api_error = _handle_api_error
+    sys.modules[f"{package_name}.utils.network"] = network_module
+
+    job_store_module = types.ModuleType(f"{package_name}.upscale.job_store")
+    job_store_module.MAX_PAYLOAD_SIZE = 1024
+    job_store_module.ensure_tile_jobs_initialized = lambda: prompt_server_holder["value"]
+    sys.modules[f"{package_name}.upscale.job_store"] = job_store_module
+
+    job_models_module = types.ModuleType(f"{package_name}.upscale.job_models")
+
+    class BaseJobState:
+        pass
+
+    @dataclass
+    class TileJobState(BaseJobState):
+        multi_job_id: str
+        mode: str = field(default="static", init=False)
+        queue: asyncio.Queue = field(default_factory=asyncio.Queue)
+        pending_tasks: asyncio.Queue = field(default_factory=asyncio.Queue)
+        completed_tasks: dict = field(default_factory=dict)
+        worker_status: dict = field(default_factory=dict)
+        assigned_to_workers: dict = field(default_factory=dict)
+        batch_size: int = 0
+        num_tiles_per_image: int = 0
+        batched_static: bool = False
+
+    @dataclass
+    class ImageJobState(BaseJobState):
+        multi_job_id: str
+        mode: str = field(default="dynamic", init=False)
+        queue: asyncio.Queue = field(default_factory=asyncio.Queue)
+        pending_images: asyncio.Queue = field(default_factory=asyncio.Queue)
+        completed_images: dict = field(default_factory=dict)
+        worker_status: dict = field(default_factory=dict)
+        assigned_to_workers: dict = field(default_factory=dict)
+        batch_size: int = 0
+        num_tiles_per_image: int = 0
+        batched_static: bool = False
+
+        @property
+        def pending_tasks(self):
+            return self.pending_images
+
+        @property
+        def completed_tasks(self):
+            return self.completed_images
+
+    job_models_module.BaseJobState = BaseJobState
+    job_models_module.TileJobState = TileJobState
+    job_models_module.ImageJobState = ImageJobState
+    sys.modules[f"{package_name}.upscale.job_models"] = job_models_module
+
+    parsers_module = types.ModuleType(f"{package_name}.upscale.payload_parsers")
+    parsers_module._parse_tiles_from_form = lambda _data: []
+    sys.modules[f"{package_name}.upscale.payload_parsers"] = parsers_module
+
+    spec = importlib.util.spec_from_file_location(f"{package_name}.api.usdu_routes", module_path)
+    module = importlib.util.module_from_spec(spec)
+    assert spec is not None and spec.loader is not None
+    spec.loader.exec_module(module)
+
+    if created_aiohttp_stub:
+        sys.modules.pop("aiohttp", None)
+
+    module.web = types.SimpleNamespace(
+        json_response=lambda payload, status=200: _FakeResponse(payload, status=status)
+    )
+
+    module._prompt_server_holder = prompt_server_holder
+    module._TileJobState = TileJobState
+    module._ImageJobState = ImageJobState
+    return module
+
+
+usdu_routes = _load_usdu_routes_module()
+
+
+class _UploadField:
+    def __init__(self, data):
+        self.file = io.BytesIO(data)
+
+
+def _tiny_png_bytes():
+    image = Image.new("RGB", (1, 1), (255, 0, 0))
+    buf = io.BytesIO()
+    image.save(buf, format="PNG")
+    return buf.getvalue()
+
+
+class USDURoutesTests(unittest.IsolatedAsyncioTestCase):
+    async def asyncSetUp(self):
+        usdu_routes._prompt_server_holder["value"] = types.SimpleNamespace(
+            distributed_tile_jobs_lock=asyncio.Lock(),
+            distributed_pending_tile_jobs={},
+        )
+
+    async def test_heartbeat_updates_worker_status(self):
+        prompt_server = usdu_routes._prompt_server_holder["value"]
+        job_data = usdu_routes._TileJobState("job-1")
+        prompt_server.distributed_pending_tile_jobs["job-1"] = job_data
+
+        request = _FakeRequest(json_payload={"worker_id": "worker-a", "multi_job_id": "job-1"})
+        response = await usdu_routes.heartbeat_endpoint(request)
+
+        self.assertEqual(response.status, 200)
+        self.assertEqual(response.payload.get("status"), "success")
+        self.assertIn("worker-a", job_data.worker_status)
+
+    async def test_heartbeat_missing_fields_returns_400(self):
+        request = _FakeRequest(json_payload={"worker_id": "worker-a"})
+        response = await usdu_routes.heartbeat_endpoint(request)
+
+        self.assertEqual(response.status, 400)
+        self.assertIn("missing", response.payload.get("message", "").lower())
+
+    async def test_request_image_dynamic_assigns_next_image(self):
+        prompt_server = usdu_routes._prompt_server_holder["value"]
+        job_data = usdu_routes._ImageJobState("job-2")
+        await job_data.pending_images.put(7)
+        prompt_server.distributed_pending_tile_jobs["job-2"] = job_data
+
+        request = _FakeRequest(json_payload={"worker_id": "worker-a", "multi_job_id": "job-2"})
+        response = await usdu_routes.request_image_endpoint(request)
+
+        self.assertEqual(response.status, 200)
+        self.assertEqual(response.payload.get("image_idx"), 7)
+        self.assertEqual(response.payload.get("estimated_remaining"), 0)
+        self.assertEqual(job_data.assigned_to_workers["worker-a"], [7])
+        self.assertIn("worker-a", job_data.worker_status)
+
+    async def test_request_image_static_assigns_tile_and_batched_flag(self):
+        prompt_server = usdu_routes._prompt_server_holder["value"]
+        job_data = usdu_routes._TileJobState("job-3")
+        job_data.batched_static = True
+        await job_data.pending_tasks.put(4)
+        prompt_server.distributed_pending_tile_jobs["job-3"] = job_data
+
+        request = _FakeRequest(json_payload={"worker_id": "worker-a", "multi_job_id": "job-3"})
+        response = await usdu_routes.request_image_endpoint(request)
+
+        self.assertEqual(response.status, 200)
+        self.assertEqual(response.payload.get("tile_idx"), 4)
+        self.assertTrue(response.payload.get("batched_static"))
+        self.assertEqual(job_data.assigned_to_workers["worker-a"], [4])
+
+    async def test_submit_tiles_completion_signal_enqueues_last_marker(self):
+        prompt_server = usdu_routes._prompt_server_holder["value"]
+        job_data = usdu_routes._TileJobState("job-4")
+        prompt_server.distributed_pending_tile_jobs["job-4"] = job_data
+
+        request = _FakeRequest(
+            post_payload={
+                "multi_job_id": "job-4",
+                "worker_id": "worker-a",
+                "batch_size": "0",
+                "is_last": "true",
+            },
+            headers={"content-length": "128"},
+        )
+
+        response = await usdu_routes.submit_tiles_endpoint(request)
+
+        self.assertEqual(response.status, 200)
+        queued = await job_data.queue.get()
+        self.assertEqual(queued["worker_id"], "worker-a")
+        self.assertTrue(queued["is_last"])
+        self.assertEqual(queued["tiles"], [])
+
+    async def test_submit_image_enqueues_processed_image_payload(self):
+        prompt_server = usdu_routes._prompt_server_holder["value"]
+        job_data = usdu_routes._ImageJobState("job-5")
+        prompt_server.distributed_pending_tile_jobs["job-5"] = job_data
+
+        request = _FakeRequest(
+            post_payload={
+                "multi_job_id": "job-5",
+                "worker_id": "worker-a",
+                "image_idx": "2",
+                "full_image": _UploadField(_tiny_png_bytes()),
+                "is_last": "false",
+            },
+            headers={"content-length": "256"},
+        )
+
+        response = await usdu_routes.submit_image_endpoint(request)
+
+        self.assertEqual(response.status, 200)
+        queued = await job_data.queue.get()
+        self.assertEqual(queued["worker_id"], "worker-a")
+        self.assertEqual(queued["image_idx"], 2)
+        self.assertIn("image", queued)
+
+    async def test_job_status_endpoint_reports_ready(self):
+        prompt_server = usdu_routes._prompt_server_holder["value"]
+        prompt_server.distributed_pending_tile_jobs["job-6"] = usdu_routes._TileJobState("job-6")
+
+        request = _FakeRequest(query={"multi_job_id": "job-6"})
+        response = await usdu_routes.job_status_endpoint(request)
+
+        self.assertEqual(response.status, 200)
+        self.assertTrue(response.payload.get("ready"))
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,402 @@
+import importlib.util
+import os
+import sys
+import tempfile
+import types
+import unittest
+from collections import deque
+from pathlib import Path
+from unittest.mock import patch
+
+
+class _FakeResponse:
+    def __init__(self, payload, status=200):
+        self.payload = payload
+        self.status = status
+
+
+class _FakeRequest:
+    def __init__(self, payload=None, match_info=None, query=None):
+        self._payload = payload
+        self.match_info = match_info or {}
+        self.query = query or {}
+
+    async def json(self):
+        return self._payload
+
+
+class _FakeHTTPClientResponse:
+    def __init__(self, payload, status=200):
+        self._payload = payload
+        self.status = status
+
+    async def __aenter__(self):
+        return self
+
+    async def __aexit__(self, _exc_type, _exc, _tb):
+        return False
+
+    async def json(self):
+        return self._payload
+
+    async def text(self):
+        return str(self._payload)
+
+
+class _FakeHTTPClientSession:
+    def __init__(self, payload, status=200):
+        self._payload = payload
+        self._status = status
+        self.calls = []
+
+    def get(self, url, params=None, timeout=None):
+        self.calls.append({"url": url, "params": params, "timeout": timeout})
+        return _FakeHTTPClientResponse(self._payload, status=self._status)
+
+
+class _DummyWorkerManager:
+    def __init__(self):
+        self.processes = {}
+
+    def launch_worker(self, worker):
+        worker_id = str(worker["id"])
+        self.processes[worker_id] = {
+            "pid": 12345,
+            "log_file": f"/tmp/distributed_worker_{worker_id}.log",
+            "process": None,
+        }
+        return 12345
+
+    def _is_process_running(self, _pid):
+        return False
+
+    def save_processes(self):
+        return None
+
+    def stop_worker(self, _worker_id):
+        return True, "Stopped"
+
+    def get_managed_workers(self):
+        return []
+
+
+class _ImmediateLoop:
+    async def run_in_executor(self, _executor, func, *args):
+        return func(*args)
+
+
+def _load_worker_routes_module():
+    module_path = Path(__file__).resolve().parents[2] / "api" / "worker_routes.py"
+    package_name = "dist_api_worker_testpkg"
+
+    for mod_name in list(sys.modules):
+        if mod_name == package_name or mod_name.startswith(f"{package_name}."):
+            del sys.modules[mod_name]
+
+    root_pkg = types.ModuleType(package_name)
+    root_pkg.__path__ = []
+    sys.modules[package_name] = root_pkg
+
+    api_pkg = types.ModuleType(f"{package_name}.api")
+    api_pkg.__path__ = []
+    sys.modules[f"{package_name}.api"] = api_pkg
+
+    utils_pkg = types.ModuleType(f"{package_name}.utils")
+    utils_pkg.__path__ = []
+    sys.modules[f"{package_name}.utils"] = utils_pkg
+
+    workers_pkg = types.ModuleType(f"{package_name}.workers")
+    workers_pkg.__path__ = []
+    workers_pkg.get_worker_manager = lambda: _DummyWorkerManager()
+    sys.modules[f"{package_name}.workers"] = workers_pkg
+
+    detection_module = types.ModuleType(f"{package_name}.workers.detection")
+    detection_module.is_local_worker = lambda *_args, **_kwargs: True
+    detection_module.is_same_physical_host = lambda *_args, **_kwargs: True
+    detection_module.get_machine_id = lambda: "machine-id"
+    detection_module.is_docker_environment = lambda: False
+    detection_module.is_runpod_environment = lambda: False
+    detection_module.get_comms_channel = lambda *_args, **_kwargs: "lan"
+    sys.modules[f"{package_name}.workers.detection"] = detection_module
+
+    created_aiohttp_stub = False
+    if "aiohttp" not in sys.modules:
+        created_aiohttp_stub = True
+        aiohttp_module = types.ModuleType("aiohttp")
+
+        class _ClientTimeout:
+            def __init__(self, total=None):
+                self.total = total
+
+        class _WSMsgType:
+            TEXT = "TEXT"
+            ERROR = "ERROR"
+            CLOSED = "CLOSED"
+
+        class _WebSocketResponse:
+            def __init__(self, *args, **kwargs):
+                self.args = args
+                self.kwargs = kwargs
+
+            async def prepare(self, _request):
+                return None
+
+            async def send_json(self, _payload):
+                return None
+
+            def __aiter__(self):
+                async def _empty():
+                    if False:
+                        yield None
+                return _empty()
+
+        aiohttp_module.ClientTimeout = _ClientTimeout
+        aiohttp_module.WSMsgType = _WSMsgType
+        aiohttp_module.web = types.SimpleNamespace(
+            json_response=lambda payload, status=200: _FakeResponse(payload, status=status),
+            WebSocketResponse=_WebSocketResponse,
+        )
+        sys.modules["aiohttp"] = aiohttp_module
+
+    class _Routes:
+        def get(self, _path):
+            def _decorator(fn):
+                return fn
+            return _decorator
+
+        def post(self, _path):
+            def _decorator(fn):
+                return fn
+            return _decorator
+
+    server_module = types.ModuleType("server")
+    server_module.PromptServer = types.SimpleNamespace(instance=types.SimpleNamespace(routes=_Routes()))
+    sys.modules["server"] = server_module
+
+    created_torch_stub = False
+    if "torch" not in sys.modules:
+        created_torch_stub = True
+        torch_module = types.ModuleType("torch")
+        torch_module.cuda = types.SimpleNamespace(
+            is_available=lambda: False,
+            empty_cache=lambda: None,
+            ipc_collect=lambda: None,
+            current_device=lambda: 0,
+            device_count=lambda: 0,
+        )
+        sys.modules["torch"] = torch_module
+
+    logging_module = types.ModuleType(f"{package_name}.utils.logging")
+    logging_module.debug_log = lambda *_args, **_kwargs: None
+    logging_module.log = lambda *_args, **_kwargs: None
+    sys.modules[f"{package_name}.utils.logging"] = logging_module
+
+    config_module = types.ModuleType(f"{package_name}.utils.config")
+    config_module.load_config = lambda: {"workers": []}
+    sys.modules[f"{package_name}.utils.config"] = config_module
+
+    network_module = types.ModuleType(f"{package_name}.utils.network")
+
+    async def _handle_api_error(_request, error, status=500):
+        return _FakeResponse({"status": "error", "message": str(error)}, status=status)
+
+    network_module.handle_api_error = _handle_api_error
+    network_module.normalize_host = lambda value: value
+    network_module.build_worker_url = lambda worker, endpoint="": f"http://localhost:{worker.get('port', 8188)}{endpoint}"
+
+    async def _probe_worker(*_args, **_kwargs):
+        return None
+
+    network_module.probe_worker = _probe_worker
+
+    async def _get_client_session():
+        raise RuntimeError("not used in these tests")
+
+    network_module.get_client_session = _get_client_session
+    sys.modules[f"{package_name}.utils.network"] = network_module
+
+    constants_module = types.ModuleType(f"{package_name}.utils.constants")
+    constants_module.CHUNK_SIZE = 8192
+    sys.modules[f"{package_name}.utils.constants"] = constants_module
+
+    async_helpers_module = types.ModuleType(f"{package_name}.utils.async_helpers")
+
+    async def _queue_prompt_payload(*_args, **_kwargs):
+        return "prompt-id"
+
+    async_helpers_module.queue_prompt_payload = _queue_prompt_payload
+
+    class _PromptValidationError(RuntimeError):
+        def __init__(self, message="invalid prompt", validation_error=None, node_errors=None):
+            super().__init__(message)
+            self.validation_error = validation_error if isinstance(validation_error, dict) else {}
+            self.node_errors = node_errors if isinstance(node_errors, dict) else {}
+
+    async_helpers_module.PromptValidationError = _PromptValidationError
+    sys.modules[f"{package_name}.utils.async_helpers"] = async_helpers_module
+
+    schemas_module = types.ModuleType(f"{package_name}.api.schemas")
+
+    def _require_fields(data, *fields):
+        missing = []
+        for field in fields:
+            value = data.get(field) if isinstance(data, dict) else None
+            if value is None or (isinstance(value, str) and not value.strip()):
+                missing.append(field)
+        return missing
+
+    def _validate_worker_id(worker_id, config):
+        return any(str(worker.get("id")) == str(worker_id) for worker in config.get("workers", []))
+
+    schemas_module.require_fields = _require_fields
+    schemas_module.validate_worker_id = _validate_worker_id
+    sys.modules[f"{package_name}.api.schemas"] = schemas_module
+
+    spec = importlib.util.spec_from_file_location(f"{package_name}.api.worker_routes", module_path)
+    module = importlib.util.module_from_spec(spec)
+    assert spec is not None and spec.loader is not None
+    spec.loader.exec_module(module)
+
+    if created_aiohttp_stub:
+        sys.modules.pop("aiohttp", None)
+    if created_torch_stub:
+        sys.modules.pop("torch", None)
+
+    return module
+
+
+worker_routes = _load_worker_routes_module()
+
+
+class WorkerRoutesTests(unittest.IsolatedAsyncioTestCase):
+    async def test_launch_worker_valid_id_returns_200(self):
+        manager = _DummyWorkerManager()
+        config = {"workers": [{"id": "worker-a", "name": "Worker A", "port": 8188}]}
+        request = _FakeRequest({"worker_id": "worker-a"})
+
+        with patch.object(worker_routes, "get_worker_manager", return_value=manager), patch.object(
+            worker_routes, "load_config", return_value=config
+        ), patch.object(
+            worker_routes.asyncio, "get_running_loop", return_value=_ImmediateLoop()
+        ):
+            response = await worker_routes.launch_worker_endpoint(request)
+
+        self.assertEqual(response.status, 200)
+        self.assertEqual(response.payload.get("status"), "success")
+        self.assertEqual(response.payload.get("pid"), 12345)
+
+    async def test_launch_worker_unknown_id_returns_404(self):
+        manager = _DummyWorkerManager()
+        config = {"workers": [{"id": "worker-a", "name": "Worker A", "port": 8188}]}
+        request = _FakeRequest({"worker_id": "missing-worker"})
+
+        with patch.object(worker_routes, "get_worker_manager", return_value=manager), patch.object(
+            worker_routes, "load_config", return_value=config
+        ):
+            response = await worker_routes.launch_worker_endpoint(request)
+
+        self.assertEqual(response.status, 404)
+        self.assertIn("not found", response.payload.get("message", "").lower())
+
+    async def test_worker_log_returns_content_json(self):
+        manager = _DummyWorkerManager()
+        with tempfile.NamedTemporaryFile("w", delete=False, encoding="utf-8") as handle:
+            handle.write("line-1\nline-2\nline-3\n")
+            log_path = handle.name
+
+        manager.processes["worker-a"] = {
+            "pid": 9999,
+            "log_file": log_path,
+            "process": None,
+        }
+
+        request = _FakeRequest(match_info={"worker_id": "worker-a"}, query={"lines": "2"})
+        try:
+            with patch.object(worker_routes, "get_worker_manager", return_value=manager), patch.object(
+                worker_routes.asyncio, "get_running_loop", return_value=_ImmediateLoop()
+            ):
+                response = await worker_routes.get_worker_log_endpoint(request)
+        finally:
+            if os.path.exists(log_path):
+                os.remove(log_path)
+
+        self.assertEqual(response.status, 200)
+        self.assertEqual(response.payload.get("status"), "success")
+        self.assertIn("content", response.payload)
+        self.assertIn("line-3", response.payload["content"])
+
+    async def test_local_log_reads_memory_buffer(self):
+        request = _FakeRequest(query={"lines": "2"})
+        fake_logs = deque(
+            [
+                {"m": "line-1\n"},
+                {"m": "line-2\n"},
+                {"m": "line-3\n"},
+            ],
+            maxlen=300,
+        )
+        app_module = types.ModuleType("app")
+        app_module.__path__ = []
+        logger_module = types.ModuleType("app.logger")
+        logger_module.get_logs = lambda: fake_logs
+        app_module.logger = logger_module
+
+        with patch.dict(sys.modules, {"app": app_module, "app.logger": logger_module}):
+            response = await worker_routes.get_local_log_endpoint(request)
+
+        self.assertEqual(response.status, 200)
+        self.assertEqual(response.payload.get("status"), "success")
+        self.assertEqual(response.payload.get("source"), "memory")
+        self.assertEqual(response.payload.get("entries"), 2)
+        self.assertIn("line-3", response.payload.get("content", ""))
+
+    async def test_remote_worker_log_proxies_to_worker_local_log_endpoint(self):
+        config = {
+            "workers": [
+                {
+                    "id": "worker-remote",
+                    "name": "Remote Worker",
+                    "host": "worker.example.com",
+                    "port": 8188,
+                    "type": "remote",
+                }
+            ]
+        }
+        request = _FakeRequest(match_info={"worker_id": "worker-remote"}, query={"lines": "120"})
+        proxied_payload = {
+            "status": "success",
+            "content": "remote-log-content\n",
+            "entries": 1,
+            "source": "memory",
+            "truncated": False,
+            "lines_shown": 1,
+        }
+        fake_session = _FakeHTTPClientSession(proxied_payload)
+
+        async def _fake_get_client_session():
+            return fake_session
+
+        with patch.object(worker_routes, "load_config", return_value=config), patch.object(
+            worker_routes, "get_client_session", side_effect=_fake_get_client_session
+        ):
+            response = await worker_routes.get_remote_worker_log_endpoint(request)
+
+        self.assertEqual(response.status, 200)
+        self.assertEqual(response.payload.get("content"), "remote-log-content\n")
+        self.assertEqual(len(fake_session.calls), 1)
+        self.assertEqual(fake_session.calls[0]["params"], {"lines": "120"})
+        self.assertTrue(fake_session.calls[0]["url"].endswith("/distributed/local_log"))
+
+    async def test_remote_worker_log_rejects_local_workers(self):
+        config = {"workers": [{"id": "worker-local", "name": "Local Worker", "port": 8188}]}
+        request = _FakeRequest(match_info={"worker_id": "worker-local"})
+
+        with patch.object(worker_routes, "load_config", return_value=config):
+            response = await worker_routes.get_remote_worker_log_endpoint(request)
+
+        self.assertEqual(response.status, 400)
+        self.assertIn("local", response.payload.get("message", "").lower())
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,2 @@
+# This conftest.py marks the tests/ directory as the pytest collection root,
+# preventing pytest from traversing into the parent package's __init__.py.
@@ -0,0 +1,102 @@
+import importlib.util
+import sys
+import types
+import unittest
+from pathlib import Path
+
+import torch
+
+
+def _load_utilities_module():
+    module_path = Path(__file__).resolve().parents[1] / "nodes" / "utilities.py"
+    package_name = "dist_divider_testpkg"
+
+    for mod_name in list(sys.modules):
+        if mod_name == package_name or mod_name.startswith(f"{package_name}."):
+            del sys.modules[mod_name]
+
+    root_pkg = types.ModuleType(package_name)
+    root_pkg.__path__ = []
+    sys.modules[package_name] = root_pkg
+
+    nodes_pkg = types.ModuleType(f"{package_name}.nodes")
+    nodes_pkg.__path__ = []
+    sys.modules[f"{package_name}.nodes"] = nodes_pkg
+
+    utils_pkg = types.ModuleType(f"{package_name}.utils")
+    utils_pkg.__path__ = []
+    sys.modules[f"{package_name}.utils"] = utils_pkg
+
+    logging_module = types.ModuleType(f"{package_name}.utils.logging")
+    logging_module.debug_log = lambda *_args, **_kwargs: None
+    logging_module.log = lambda *_args, **_kwargs: None
+    sys.modules[f"{package_name}.utils.logging"] = logging_module
+
+    spec = importlib.util.spec_from_file_location(
+        f"{package_name}.nodes.utilities",
+        module_path,
+    )
+    module = importlib.util.module_from_spec(spec)
+    assert spec is not None and spec.loader is not None
+    spec.loader.exec_module(module)
+    return module
+
+
+utils = _load_utilities_module()
+
+
+class ImageBatchDividerTests(unittest.TestCase):
+    def test_divides_images_into_contiguous_chunks(self):
+        divider = utils.ImageBatchDivider()
+        images = torch.arange(10, dtype=torch.float32).reshape(10, 1, 1, 1)
+
+        outputs = divider.divide_batch(images, 3)
+
+        self.assertEqual(outputs[0].shape[0], 4)
+        self.assertEqual(outputs[1].shape[0], 3)
+        self.assertEqual(outputs[2].shape[0], 3)
+        self.assertEqual(outputs[0][:, 0, 0, 0].tolist(), [0.0, 1.0, 2.0, 3.0])
+        self.assertEqual(outputs[1][:, 0, 0, 0].tolist(), [4.0, 5.0, 6.0])
+        self.assertEqual(outputs[2][:, 0, 0, 0].tolist(), [7.0, 8.0, 9.0])
+
+    def test_unused_image_outputs_are_empty(self):
+        divider = utils.ImageBatchDivider()
+        images = torch.arange(4, dtype=torch.float32).reshape(4, 1, 1, 1)
+
+        outputs = divider.divide_batch(images, 2)
+
+        self.assertEqual(len(outputs), 10)
+        for idx in range(2, 10):
+            self.assertEqual(outputs[idx].shape[0], 0)
+
+
+class AudioBatchDividerTests(unittest.TestCase):
+    def test_divides_audio_samples_into_contiguous_chunks(self):
+        divider = utils.AudioBatchDivider()
+        audio = {
+            "waveform": torch.arange(10, dtype=torch.float32).reshape(1, 1, 10),
+            "sample_rate": 24000,
+        }
+
+        outputs = divider.divide_audio(audio, 3)
+
+        self.assertEqual(outputs[0]["waveform"][0, 0].tolist(), [0.0, 1.0, 2.0, 3.0])
+        self.assertEqual(outputs[1]["waveform"][0, 0].tolist(), [4.0, 5.0, 6.0])
+        self.assertEqual(outputs[2]["waveform"][0, 0].tolist(), [7.0, 8.0, 9.0])
+
+    def test_unused_audio_outputs_are_empty(self):
+        divider = utils.AudioBatchDivider()
+        audio = {
+            "waveform": torch.arange(8, dtype=torch.float32).reshape(1, 1, 8),
+            "sample_rate": 24000,
+        }
+
+        outputs = divider.divide_audio(audio, 2)
+
+        self.assertEqual(len(outputs), 10)
+        for idx in range(2, 10):
+            self.assertEqual(outputs[idx]["waveform"].shape[-1], 0)
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,293 @@
+import importlib.util
+import json
+import os
+import sys
+import tempfile
+import types
+import unittest
+from pathlib import Path
+from unittest.mock import patch
+
+
+def _load_config_module():
+    module_path = Path(__file__).resolve().parents[1] / "utils" / "config.py"
+    package_name = "dist_cfg_testpkg"
+
+    for mod_name in list(sys.modules):
+        if mod_name == package_name or mod_name.startswith(f"{package_name}."):
+            del sys.modules[mod_name]
+
+    root_pkg = types.ModuleType(package_name)
+    root_pkg.__path__ = []
+    sys.modules[package_name] = root_pkg
+
+    logging_module = types.ModuleType(f"{package_name}.logging")
+    logging_module.log = lambda *_args, **_kwargs: None
+    logging_module.debug_log = lambda *_args, **_kwargs: None
+    sys.modules[f"{package_name}.logging"] = logging_module
+
+    constants_module = types.ModuleType(f"{package_name}.constants")
+    constants_module.HEARTBEAT_TIMEOUT = 30
+    sys.modules[f"{package_name}.constants"] = constants_module
+
+    spec = importlib.util.spec_from_file_location(f"{package_name}.config", module_path)
+    module = importlib.util.module_from_spec(spec)
+    assert spec is not None and spec.loader is not None
+    spec.loader.exec_module(module)
+    return module
+
+
+config = _load_config_module()
+
+
+# ---------------------------------------------------------------------------
+# _merge_with_defaults
+# ---------------------------------------------------------------------------
+
+class MergeWithDefaultsTests(unittest.TestCase):
+    def test_non_dict_input_returns_defaults(self):
+        result = config._merge_with_defaults("not a dict", {"key": "default"})
+        self.assertEqual(result, {"key": "default"})
+
+    def test_fills_missing_keys_with_defaults(self):
+        result = config._merge_with_defaults({}, {"a": 1, "b": 2})
+        self.assertEqual(result, {"a": 1, "b": 2})
+
+    def test_loaded_value_overrides_default(self):
+        result = config._merge_with_defaults({"a": 99}, {"a": 1, "b": 2})
+        self.assertEqual(result["a"], 99)
+        self.assertEqual(result["b"], 2)
+
+    def test_nested_dict_merges_recursively(self):
+        defaults = {"settings": {"debug": False, "count": 5}}
+        loaded = {"settings": {"debug": True}}
+        result = config._merge_with_defaults(loaded, defaults)
+        self.assertTrue(result["settings"]["debug"])
+        self.assertEqual(result["settings"]["count"], 5)
+
+    def test_preserves_unknown_keys_for_forward_compatibility(self):
+        result = config._merge_with_defaults({"extra_key": "extra"}, {"a": 1})
+        self.assertEqual(result["extra_key"], "extra")
+
+    def test_none_loaded_value_overrides_default(self):
+        """Explicitly set None in config should override non-None default."""
+        result = config._merge_with_defaults({"a": None}, {"a": "default"})
+        self.assertIsNone(result["a"])
+
+    def test_non_dict_nested_loaded_value_replaces_dict_default(self):
+        """If loaded has a scalar where default has a dict, use the scalar."""
+        defaults = {"settings": {"debug": False}}
+        loaded = {"settings": "flat_string"}
+        result = config._merge_with_defaults(loaded, defaults)
+        self.assertEqual(result["settings"], "flat_string")
+
+
+# ---------------------------------------------------------------------------
+# load_config
+# ---------------------------------------------------------------------------
+
+class LoadConfigTests(unittest.TestCase):
+    def setUp(self):
+        config.invalidate_config_cache()
+
+    def tearDown(self):
+        config.invalidate_config_cache()
+
+    def test_returns_defaults_when_file_missing(self):
+        with patch.object(config, "CONFIG_FILE", "/nonexistent/path/config.json"):
+            cfg = config.load_config()
+        defaults = config.get_default_config()
+        self.assertEqual(cfg["settings"]["debug"], defaults["settings"]["debug"])
+        self.assertIn("workers", cfg)
+
+    def test_loads_valid_json_file(self):
+        data = {
+            "workers": [{"id": "w1"}],
+            "master": {"host": "test.host"},
+            "settings": {},
+            "tunnel": {},
+        }
+        with tempfile.NamedTemporaryFile("w", suffix=".json", delete=False, encoding="utf-8") as f:
+            json.dump(data, f)
+            tmp_path = f.name
+        try:
+            with patch.object(config, "CONFIG_FILE", tmp_path):
+                cfg = config.load_config()
+            self.assertEqual(cfg["master"]["host"], "test.host")
+            self.assertEqual(len(cfg["workers"]), 1)
+        finally:
+            os.unlink(tmp_path)
+
+    def test_merges_loaded_file_with_defaults(self):
+        """Loaded file with partial settings should be filled in from defaults."""
+        data = {"master": {"host": "h"}, "workers": [], "settings": {"debug": True}, "tunnel": {}}
+        with tempfile.NamedTemporaryFile("w", suffix=".json", delete=False, encoding="utf-8") as f:
+            json.dump(data, f)
+            tmp_path = f.name
+        try:
+            with patch.object(config, "CONFIG_FILE", tmp_path):
+                cfg = config.load_config()
+            # debug was set to True
+            self.assertTrue(cfg["settings"]["debug"])
+            # auto_launch_workers is a default key and should be present
+            self.assertIn("auto_launch_workers", cfg["settings"])
+        finally:
+            os.unlink(tmp_path)
+
+    def test_falls_back_to_defaults_on_invalid_json(self):
+        with tempfile.NamedTemporaryFile("w", suffix=".json", delete=False, encoding="utf-8") as f:
+            f.write("{invalid json{{")
+            tmp_path = f.name
+        try:
+            with patch.object(config, "CONFIG_FILE", tmp_path):
+                cfg = config.load_config()
+            self.assertIn("settings", cfg)
+            self.assertIn("workers", cfg)
+        finally:
+            os.unlink(tmp_path)
+
+    def test_second_call_returns_cached_object(self):
+        with tempfile.NamedTemporaryFile("w", suffix=".json", delete=False, encoding="utf-8") as f:
+            json.dump(config.get_default_config(), f)
+            tmp_path = f.name
+        try:
+            with patch.object(config, "CONFIG_FILE", tmp_path):
+                cfg1 = config.load_config()
+                cfg2 = config.load_config()
+            self.assertIs(cfg1, cfg2)
+        finally:
+            os.unlink(tmp_path)
+
+    def test_invalidate_cache_forces_reload(self):
+        data = config.get_default_config()
+        data["master"]["host"] = "first"
+        with tempfile.NamedTemporaryFile("w", suffix=".json", delete=False, encoding="utf-8") as f:
+            json.dump(data, f)
+            tmp_path = f.name
+        try:
+            with patch.object(config, "CONFIG_FILE", tmp_path):
+                cfg1 = config.load_config()
+                config.invalidate_config_cache()
+                data["master"]["host"] = "second"
+                with open(tmp_path, "w", encoding="utf-8") as fh:
+                    json.dump(data, fh)
+                cfg2 = config.load_config()
+            self.assertEqual(cfg1["master"]["host"], "first")
+            self.assertEqual(cfg2["master"]["host"], "second")
+        finally:
+            os.unlink(tmp_path)
+
+
+# ---------------------------------------------------------------------------
+# save_config
+# ---------------------------------------------------------------------------
+
+class SaveConfigTests(unittest.TestCase):
+    def setUp(self):
+        config.invalidate_config_cache()
+
+    def tearDown(self):
+        config.invalidate_config_cache()
+
+    def test_saves_and_reloads_correctly(self):
+        data = config.get_default_config()
+        data["master"]["host"] = "saved.host"
+        with tempfile.TemporaryDirectory() as tmpdir:
+            tmp_path = os.path.join(tmpdir, "config.json")
+            with patch.object(config, "CONFIG_FILE", tmp_path):
+                result = config.save_config(data)
+                self.assertTrue(result)
+                loaded = config.load_config()
+        self.assertEqual(loaded["master"]["host"], "saved.host")
+
+    def test_returns_false_when_path_unwritable(self):
+        with patch.object(config, "CONFIG_FILE", "/nonexistent_dir/config.json"):
+            result = config.save_config({})
+        self.assertFalse(result)
+
+    def test_save_invalidates_cache(self):
+        """After saving, the cache should be cleared so next load re-reads."""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            tmp_path = os.path.join(tmpdir, "config.json")
+            with patch.object(config, "CONFIG_FILE", tmp_path):
+                data = config.get_default_config()
+                config.save_config(data)
+                # Cache is now None; load_config should re-read
+                self.assertIsNone(config._config_cache)
+
+    def test_written_file_is_valid_json(self):
+        data = config.get_default_config()
+        with tempfile.TemporaryDirectory() as tmpdir:
+            tmp_path = os.path.join(tmpdir, "config.json")
+            with patch.object(config, "CONFIG_FILE", tmp_path):
+                config.save_config(data)
+            with open(tmp_path, encoding="utf-8") as fh:
+                parsed = json.load(fh)
+        self.assertEqual(parsed["master"], data["master"])
+
+
+# ---------------------------------------------------------------------------
+# get_worker_timeout_seconds
+# ---------------------------------------------------------------------------
+
+class GetWorkerTimeoutSecondsTests(unittest.TestCase):
+    def test_returns_configured_value(self):
+        cfg = config.get_default_config()
+        cfg["settings"]["worker_timeout_seconds"] = 120
+        with patch.object(config, "load_config", return_value=cfg):
+            self.assertEqual(config.get_worker_timeout_seconds(), 120)
+
+    def test_clamps_zero_to_one(self):
+        cfg = config.get_default_config()
+        cfg["settings"]["worker_timeout_seconds"] = 0
+        with patch.object(config, "load_config", return_value=cfg):
+            self.assertEqual(config.get_worker_timeout_seconds(), 1)
+
+    def test_clamps_negative_to_one(self):
+        cfg = config.get_default_config()
+        cfg["settings"]["worker_timeout_seconds"] = -10
+        with patch.object(config, "load_config", return_value=cfg):
+            self.assertEqual(config.get_worker_timeout_seconds(), 1)
+
+    def test_falls_back_to_provided_default_when_key_missing(self):
+        cfg = config.get_default_config()
+        # worker_timeout_seconds is not present in default config
+        cfg["settings"].pop("worker_timeout_seconds", None)
+        with patch.object(config, "load_config", return_value=cfg):
+            result = config.get_worker_timeout_seconds(default=45)
+        self.assertEqual(result, 45)
+
+    def test_fallback_also_clamped_to_one(self):
+        cfg = config.get_default_config()
+        cfg["settings"].pop("worker_timeout_seconds", None)
+        with patch.object(config, "load_config", return_value=cfg):
+            result = config.get_worker_timeout_seconds(default=0)
+        self.assertEqual(result, 1)
+
+
+# ---------------------------------------------------------------------------
+# is_master_delegate_only
+# ---------------------------------------------------------------------------
+
+class IsMasterDelegateOnlyTests(unittest.TestCase):
+    def test_returns_false_by_default(self):
+        cfg = config.get_default_config()
+        with patch.object(config, "load_config", return_value=cfg):
+            self.assertFalse(config.is_master_delegate_only())
+
+    def test_returns_true_when_enabled(self):
+        cfg = config.get_default_config()
+        cfg["settings"]["master_delegate_only"] = True
+        with patch.object(config, "load_config", return_value=cfg):
+            self.assertTrue(config.is_master_delegate_only())
+
+    def test_returns_false_on_exception(self):
+        def _raise():
+            raise RuntimeError("config exploded")
+
+        with patch.object(config, "load_config", side_effect=RuntimeError("boom")):
+            self.assertFalse(config.is_master_delegate_only())
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,196 @@
+import importlib.util
+import sys
+import types
+import unittest
+from pathlib import Path
+from unittest.mock import patch
+
+
+def _load_detection_module():
+    module_path = Path(__file__).resolve().parents[1] / "workers" / "detection.py"
+    package_name = "dist_det_testpkg"
+
+    for mod_name in list(sys.modules):
+        if mod_name == package_name or mod_name.startswith(f"{package_name}."):
+            del sys.modules[mod_name]
+
+    root_pkg = types.ModuleType(package_name)
+    root_pkg.__path__ = []
+    sys.modules[package_name] = root_pkg
+
+    workers_pkg = types.ModuleType(f"{package_name}.workers")
+    workers_pkg.__path__ = []
+    sys.modules[f"{package_name}.workers"] = workers_pkg
+
+    utils_pkg = types.ModuleType(f"{package_name}.utils")
+    utils_pkg.__path__ = []
+    sys.modules[f"{package_name}.utils"] = utils_pkg
+
+    logging_module = types.ModuleType(f"{package_name}.utils.logging")
+    logging_module.debug_log = lambda *_args, **_kwargs: None
+    sys.modules[f"{package_name}.utils.logging"] = logging_module
+
+    network_module = types.ModuleType(f"{package_name}.utils.network")
+    network_module.normalize_host = lambda value: value
+
+    async def _fake_session():
+        raise RuntimeError("network calls not used in these tests")
+
+    network_module.get_client_session = _fake_session
+    sys.modules[f"{package_name}.utils.network"] = network_module
+
+    created_aiohttp_stub = False
+    if "aiohttp" not in sys.modules:
+        created_aiohttp_stub = True
+        aiohttp_module = types.ModuleType("aiohttp")
+
+        class _ClientTimeout:
+            def __init__(self, total=None):
+                pass
+
+        aiohttp_module.ClientTimeout = _ClientTimeout
+        sys.modules["aiohttp"] = aiohttp_module
+
+    spec = importlib.util.spec_from_file_location(
+        f"{package_name}.workers.detection",
+        module_path,
+    )
+    module = importlib.util.module_from_spec(spec)
+    assert spec is not None and spec.loader is not None
+    spec.loader.exec_module(module)
+
+    if created_aiohttp_stub:
+        sys.modules.pop("aiohttp", None)
+
+    return module
+
+
+detection = _load_detection_module()
+
+
+# ---------------------------------------------------------------------------
+# is_docker_environment
+# ---------------------------------------------------------------------------
+
+class IsDockerEnvironmentTests(unittest.TestCase):
+    def test_true_when_dockerenv_file_exists(self):
+        with patch.object(detection.os.path, "exists", return_value=True), \
+             patch.dict(detection.os.environ, {}, clear=True), \
+             patch.object(detection.platform, "node", return_value="my-laptop"):
+            self.assertTrue(detection.is_docker_environment())
+
+    def test_true_when_docker_container_env_var_is_set(self):
+        with patch.object(detection.os.path, "exists", return_value=False), \
+             patch.dict(detection.os.environ, {"DOCKER_CONTAINER": "1"}, clear=True), \
+             patch.object(detection.platform, "node", return_value="my-laptop"):
+            self.assertTrue(detection.is_docker_environment())
+
+    def test_true_when_platform_node_contains_docker(self):
+        with patch.object(detection.os.path, "exists", return_value=False), \
+             patch.dict(detection.os.environ, {}, clear=True), \
+             patch.object(detection.platform, "node", return_value="my-docker-host"):
+            self.assertTrue(detection.is_docker_environment())
+
+    def test_false_when_none_of_the_signals_are_present(self):
+        with patch.object(detection.os.path, "exists", return_value=False), \
+             patch.dict(detection.os.environ, {}, clear=True), \
+             patch.object(detection.platform, "node", return_value="my-laptop"):
+            self.assertFalse(detection.is_docker_environment())
+
+    def test_docker_node_name_is_case_insensitive(self):
+        with patch.object(detection.os.path, "exists", return_value=False), \
+             patch.dict(detection.os.environ, {}, clear=True), \
+             patch.object(detection.platform, "node", return_value="My-Docker-Box"):
+            self.assertTrue(detection.is_docker_environment())
+
+    def test_docker_env_var_empty_string_is_falsy(self):
+        """An empty DOCKER_CONTAINER env var should NOT trigger docker detection."""
+        with patch.object(detection.os.path, "exists", return_value=False), \
+             patch.dict(detection.os.environ, {"DOCKER_CONTAINER": ""}, clear=True), \
+             patch.object(detection.platform, "node", return_value="my-laptop"):
+            self.assertFalse(detection.is_docker_environment())
+
+
+# ---------------------------------------------------------------------------
+# is_runpod_environment
+# ---------------------------------------------------------------------------
+
+class IsRunpodEnvironmentTests(unittest.TestCase):
+    def test_true_when_runpod_pod_id_is_set(self):
+        with patch.dict(detection.os.environ, {"RUNPOD_POD_ID": "pod-abc"}, clear=True):
+            self.assertTrue(detection.is_runpod_environment())
+
+    def test_true_when_runpod_api_key_is_set(self):
+        with patch.dict(detection.os.environ, {"RUNPOD_API_KEY": "key-xyz"}, clear=True):
+            self.assertTrue(detection.is_runpod_environment())
+
+    def test_true_when_both_vars_are_set(self):
+        with patch.dict(
+            detection.os.environ,
+            {"RUNPOD_POD_ID": "pod-abc", "RUNPOD_API_KEY": "key-xyz"},
+            clear=True,
+        ):
+            self.assertTrue(detection.is_runpod_environment())
+
+    def test_false_when_neither_var_is_set(self):
+        with patch.dict(detection.os.environ, {}, clear=True):
+            self.assertFalse(detection.is_runpod_environment())
+
+    def test_true_when_pod_id_is_empty_string(self):
+        """is not None check means even empty string counts as detected."""
+        with patch.dict(detection.os.environ, {"RUNPOD_POD_ID": ""}, clear=True):
+            self.assertTrue(detection.is_runpod_environment())
+
+
+# ---------------------------------------------------------------------------
+# is_local_worker (synchronous paths only)
+# ---------------------------------------------------------------------------
+
+class IsLocalWorkerTests(unittest.IsolatedAsyncioTestCase):
+    async def test_true_for_localhost_host(self):
+        result = await detection.is_local_worker({"host": "localhost", "port": 8188})
+        self.assertTrue(result)
+
+    async def test_true_for_127_0_0_1(self):
+        result = await detection.is_local_worker({"host": "127.0.0.1", "port": 8188})
+        self.assertTrue(result)
+
+    async def test_true_for_0_0_0_0(self):
+        result = await detection.is_local_worker({"host": "0.0.0.0", "port": 8188})
+        self.assertTrue(result)
+
+    async def test_true_when_type_is_local(self):
+        result = await detection.is_local_worker({"type": "local", "host": "remote.example.com"})
+        self.assertTrue(result)
+
+    async def test_false_for_remote_host(self):
+        result = await detection.is_local_worker({"host": "remote.example.com", "port": 8188})
+        self.assertFalse(result)
+
+    async def test_true_when_no_host_key(self):
+        """Missing host defaults to 'localhost'."""
+        result = await detection.is_local_worker({"port": 8188})
+        self.assertTrue(result)
+
+
+# ---------------------------------------------------------------------------
+# get_machine_id
+# ---------------------------------------------------------------------------
+
+class GetMachineIdTests(unittest.TestCase):
+    def test_returns_a_string(self):
+        result = detection.get_machine_id()
+        self.assertIsInstance(result, str)
+
+    def test_returns_non_empty_string(self):
+        result = detection.get_machine_id()
+        self.assertTrue(len(result) > 0)
+
+    def test_stable_across_calls(self):
+        r1 = detection.get_machine_id()
+        r2 = detection.get_machine_id()
+        self.assertEqual(r1, r2)
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,254 @@
+import asyncio
+import importlib.util
+import sys
+import types
+import unittest
+from pathlib import Path
+from unittest.mock import patch
+
+
+def _load_dispatch_module():
+    module_path = Path(__file__).resolve().parents[1] / "api" / "orchestration" / "dispatch.py"
+
+    package_name = "dist_dispatch_testpkg"
+    root_pkg = types.ModuleType(package_name)
+    root_pkg.__path__ = []
+    sys.modules[package_name] = root_pkg
+
+    api_pkg = types.ModuleType(f"{package_name}.api")
+    api_pkg.__path__ = []
+    sys.modules[f"{package_name}.api"] = api_pkg
+
+    orch_pkg = types.ModuleType(f"{package_name}.api.orchestration")
+    orch_pkg.__path__ = []
+    sys.modules[f"{package_name}.api.orchestration"] = orch_pkg
+
+    utils_pkg = types.ModuleType(f"{package_name}.utils")
+    utils_pkg.__path__ = []
+    sys.modules[f"{package_name}.utils"] = utils_pkg
+
+    logging_module = types.ModuleType(f"{package_name}.utils.logging")
+    logging_module.debug_log = lambda *_args, **_kwargs: None
+    logging_module.log = lambda *_args, **_kwargs: None
+    sys.modules[f"{package_name}.utils.logging"] = logging_module
+
+    network_module = types.ModuleType(f"{package_name}.utils.network")
+    network_module.build_worker_url = lambda *_args, **_kwargs: "http://example.invalid"
+
+    async def _probe_worker(*_args, **_kwargs):
+        return None
+
+    network_module.probe_worker = _probe_worker
+
+    async def _fake_session():
+        raise RuntimeError("get_client_session should be mocked in these tests")
+
+    network_module.get_client_session = _fake_session
+    sys.modules[f"{package_name}.utils.network"] = network_module
+
+    created_aiohttp_stub = False
+    if "aiohttp" not in sys.modules:
+        created_aiohttp_stub = True
+        aiohttp_module = types.ModuleType("aiohttp")
+
+        class _ClientTimeout:
+            def __init__(self, total=None):
+                self.total = total
+
+        class _ClientConnectorError(Exception):
+            pass
+
+        class _WSMsgType:
+            TEXT = "TEXT"
+            ERROR = "ERROR"
+            CLOSED = "CLOSED"
+
+        class _TCPConnector:
+            def __init__(self, *args, **kwargs):
+                self.args = args
+                self.kwargs = kwargs
+
+        class _ClientSession:
+            def __init__(self, *args, **kwargs):
+                self.closed = False
+
+            async def close(self):
+                self.closed = True
+
+        aiohttp_module.ClientTimeout = _ClientTimeout
+        aiohttp_module.ClientConnectorError = _ClientConnectorError
+        aiohttp_module.WSMsgType = _WSMsgType
+        aiohttp_module.TCPConnector = _TCPConnector
+        aiohttp_module.ClientSession = _ClientSession
+        aiohttp_module.web = types.SimpleNamespace(
+            json_response=lambda payload, status=200: {"payload": payload, "status": status}
+        )
+        sys.modules["aiohttp"] = aiohttp_module
+
+    spec = importlib.util.spec_from_file_location(
+        f"{package_name}.api.orchestration.dispatch",
+        module_path,
+    )
+    module = importlib.util.module_from_spec(spec)
+    assert spec is not None and spec.loader is not None
+    spec.loader.exec_module(module)
+    if created_aiohttp_stub:
+        sys.modules.pop("aiohttp", None)
+    return module
+
+
+dispatch = _load_dispatch_module()
+
+
+class DispatchSelectionTests(unittest.IsolatedAsyncioTestCase):
+    async def test_select_active_workers_filters_offline(self):
+        workers = [
+            {"id": "w1", "name": "Worker 1"},
+            {"id": "w2", "name": "Worker 2"},
+            {"id": "w3", "name": "Worker 3"},
+        ]
+
+        async def fake_probe(worker):
+            return worker["id"] != "w2"
+
+        with patch.object(dispatch, "worker_is_active", side_effect=fake_probe):
+            active_workers, delegate_master = await dispatch.select_active_workers(
+                workers,
+                use_websocket=False,
+                delegate_master=False,
+                probe_concurrency=3,
+            )
+
+        self.assertEqual([w["id"] for w in active_workers], ["w1", "w3"])
+        self.assertFalse(delegate_master)
+
+    async def test_select_active_workers_disables_delegate_when_all_offline(self):
+        workers = [{"id": "w1", "name": "Worker 1"}]
+
+        async def fake_probe(_worker):
+            return False
+
+        with patch.object(dispatch, "worker_is_active", side_effect=fake_probe):
+            active_workers, delegate_master = await dispatch.select_active_workers(
+                workers,
+                use_websocket=False,
+                delegate_master=True,
+                probe_concurrency=1,
+            )
+
+        self.assertEqual(active_workers, [])
+        self.assertFalse(delegate_master)
+
+    async def test_select_active_workers_uses_websocket_probe_when_enabled(self):
+        workers = [{"id": "w1", "name": "Worker 1"}, {"id": "w2", "name": "Worker 2"}]
+
+        async def fake_http_probe(_worker):
+            return False
+
+        async def fake_ws_probe(_worker):
+            return True
+
+        with patch.object(dispatch, "worker_is_active", side_effect=fake_http_probe) as http_probe, patch.object(
+            dispatch,
+            "worker_ws_is_active",
+            side_effect=fake_ws_probe,
+        ) as ws_probe:
+            active_workers, _ = await dispatch.select_active_workers(
+                workers,
+                use_websocket=True,
+                delegate_master=False,
+                probe_concurrency=4,
+            )
+
+        self.assertEqual([w["id"] for w in active_workers], ["w1", "w2"])
+        self.assertEqual(ws_probe.call_count, 2)
+        self.assertEqual(http_probe.call_count, 0)
+
+    async def test_probe_concurrency_is_bounded(self):
+        workers = [{"id": f"w{i}", "name": f"Worker {i}"} for i in range(6)]
+        state = {"in_flight": 0, "max_in_flight": 0}
+
+        async def fake_probe(_worker):
+            state["in_flight"] += 1
+            state["max_in_flight"] = max(state["max_in_flight"], state["in_flight"])
+            await asyncio.sleep(0.01)
+            state["in_flight"] -= 1
+            return True
+
+        with patch.object(dispatch, "worker_is_active", side_effect=fake_probe):
+            active_workers, _ = await dispatch.select_active_workers(
+                workers,
+                use_websocket=False,
+                delegate_master=False,
+                probe_concurrency=2,
+            )
+
+        self.assertEqual(len(active_workers), len(workers))
+        self.assertLessEqual(state["max_in_flight"], 2)
+        self.assertGreaterEqual(state["max_in_flight"], 2)
+
+    async def test_select_least_busy_worker_round_robins_idle_workers(self):
+        workers = [
+            {"id": "w1", "name": "Worker 1"},
+            {"id": "w2", "name": "Worker 2"},
+            {"id": "w3", "name": "Worker 3"},
+        ]
+        queue_map = {"w1": 0, "w2": 0, "w3": 2}
+
+        async def fake_probe(worker_url, timeout=3.0):
+            worker_id = worker_url.rsplit("/", 1)[-1]
+            return {"exec_info": {"queue_remaining": queue_map[worker_id]}}
+
+        with patch.object(dispatch, "build_worker_url", side_effect=lambda worker: f"http://host/{worker['id']}"), patch.object(
+            dispatch,
+            "probe_worker",
+            side_effect=fake_probe,
+        ):
+            dispatch._least_busy_rr_index = 0
+            selected1 = await dispatch.select_least_busy_worker(workers, probe_concurrency=3)
+            selected2 = await dispatch.select_least_busy_worker(workers, probe_concurrency=3)
+            selected3 = await dispatch.select_least_busy_worker(workers, probe_concurrency=3)
+
+        self.assertEqual(selected1["id"], "w1")
+        self.assertEqual(selected2["id"], "w2")
+        self.assertEqual(selected3["id"], "w1")
+
+    async def test_select_least_busy_worker_chooses_smallest_queue_when_all_busy(self):
+        workers = [
+            {"id": "w1", "name": "Worker 1"},
+            {"id": "w2", "name": "Worker 2"},
+            {"id": "w3", "name": "Worker 3"},
+        ]
+        queue_map = {"w1": 5, "w2": 2, "w3": 4}
+
+        async def fake_probe(worker_url, timeout=3.0):
+            worker_id = worker_url.rsplit("/", 1)[-1]
+            return {"exec_info": {"queue_remaining": queue_map[worker_id]}}
+
+        with patch.object(dispatch, "build_worker_url", side_effect=lambda worker: f"http://host/{worker['id']}"), patch.object(
+            dispatch,
+            "probe_worker",
+            side_effect=fake_probe,
+        ):
+            selected = await dispatch.select_least_busy_worker(workers, probe_concurrency=2)
+
+        self.assertEqual(selected["id"], "w2")
+
+    async def test_select_least_busy_worker_returns_none_when_all_probes_fail(self):
+        workers = [{"id": "w1", "name": "Worker 1"}]
+
+        async def fake_probe(_worker_url, timeout=3.0):
+            return None
+
+        with patch.object(dispatch, "build_worker_url", side_effect=lambda worker: f"http://host/{worker['id']}"), patch.object(
+            dispatch,
+            "probe_worker",
+            side_effect=fake_probe,
+        ):
+            selected = await dispatch.select_least_busy_worker(workers, probe_concurrency=1)
+
+        self.assertIsNone(selected)
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,208 @@
+import json
+import unittest
+
+
+class DistributedValueTests(unittest.TestCase):
+    """Unit tests for the DistributedValue node's distribute() method."""
+
+    def _make_node(self):
+        # Import inline to avoid plugin-level imports
+        import importlib.util
+        import sys
+        import types
+        from pathlib import Path
+        from unittest.mock import MagicMock
+
+        module_path = Path(__file__).resolve().parents[1] / "nodes" / "utilities.py"
+        pkg_name = "dv_test_pkg"
+
+        for mod_name in list(sys.modules):
+            if mod_name == pkg_name or mod_name.startswith(f"{pkg_name}."):
+                del sys.modules[mod_name]
+
+        # Mock torch if not available
+        if "torch" not in sys.modules:
+            sys.modules["torch"] = MagicMock()
+
+        root_pkg = types.ModuleType(pkg_name)
+        root_pkg.__path__ = []
+        sys.modules[pkg_name] = root_pkg
+
+        utils_pkg = types.ModuleType(f"{pkg_name}.utils")
+        utils_pkg.__path__ = []
+        sys.modules[f"{pkg_name}.utils"] = utils_pkg
+
+        logging_mod = types.ModuleType(f"{pkg_name}.utils.logging")
+        logging_mod.debug_log = lambda *_a, **_k: None
+        logging_mod.log = lambda *_a, **_k: None
+        sys.modules[f"{pkg_name}.utils.logging"] = logging_mod
+
+        spec = importlib.util.spec_from_file_location(
+            f"{pkg_name}.nodes.utilities", module_path
+        )
+        mod = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(mod)
+        return mod.DistributedValue()
+
+    def setUp(self):
+        self.node = self._make_node()
+
+    def test_master_returns_default(self):
+        result = self.node.distribute(
+            default_value="model_a",
+            worker_values="{}",
+            is_worker=False,
+            worker_id="",
+        )
+        self.assertEqual(result, ("model_a",))
+
+    def test_master_coerces_default_int(self):
+        values = json.dumps({"_type": "INT"})
+        result = self.node.distribute(
+            default_value="42",
+            worker_values=values,
+            is_worker=False,
+            worker_id="",
+        )
+        self.assertEqual(result, (42,))
+        self.assertIsInstance(result[0], int)
+
+    def test_master_coerces_default_float(self):
+        values = json.dumps({"_type": "FLOAT"})
+        result = self.node.distribute(
+            default_value="2.5",
+            worker_values=values,
+            is_worker=False,
+            worker_id="",
+        )
+        self.assertEqual(result, (2.5,))
+        self.assertIsInstance(result[0], float)
+
+    def test_worker_returns_specific_value(self):
+        values = json.dumps({"1": "model_x", "2": "model_y"})
+        result = self.node.distribute(
+            default_value="default",
+            worker_values=values,
+            is_worker=True,
+            worker_id="worker_0",
+        )
+        self.assertEqual(result, ("model_x",))
+
+    def test_worker_second_index(self):
+        values = json.dumps({"1": "model_x", "2": "model_y"})
+        result = self.node.distribute(
+            default_value="default",
+            worker_values=values,
+            is_worker=True,
+            worker_id="worker_1",
+        )
+        self.assertEqual(result, ("model_y",))
+
+    def test_worker_falls_back_to_default_when_key_missing(self):
+        values = json.dumps({"_type": "INT", "1": "3"})
+        result = self.node.distribute(
+            default_value="9",
+            worker_values=values,
+            is_worker=True,
+            worker_id="worker_5",
+        )
+        self.assertEqual(result, (9,))
+        self.assertIsInstance(result[0], int)
+
+    def test_worker_falls_back_to_default_on_empty_value(self):
+        values = json.dumps({"1": ""})
+        result = self.node.distribute(
+            default_value="fallback",
+            worker_values=values,
+            is_worker=True,
+            worker_id="worker_0",
+        )
+        self.assertEqual(result, ("fallback",))
+
+    def test_worker_falls_back_on_invalid_json(self):
+        result = self.node.distribute(
+            default_value="safe",
+            worker_values="not-json",
+            is_worker=True,
+            worker_id="worker_0",
+        )
+        self.assertEqual(result, ("safe",))
+
+    def test_worker_falls_back_on_invalid_worker_id(self):
+        values = json.dumps({"1": "model_x"})
+        result = self.node.distribute(
+            default_value="safe",
+            worker_values=values,
+            is_worker=True,
+            worker_id="bad_id",
+        )
+        self.assertEqual(result, ("safe",))
+
+    def test_worker_id_as_direct_integer(self):
+        values = json.dumps({"1": "model_x"})
+        result = self.node.distribute(
+            default_value="default",
+            worker_values=values,
+            is_worker=True,
+            worker_id="0",
+        )
+        self.assertEqual(result, ("model_x",))
+
+    def test_type_int_coerces_value(self):
+        values = json.dumps({"_type": "INT", "1": "42"})
+        result = self.node.distribute(
+            default_value="0",
+            worker_values=values,
+            is_worker=True,
+            worker_id="worker_0",
+        )
+        self.assertEqual(result, (42,))
+        self.assertIsInstance(result[0], int)
+
+    def test_type_float_coerces_value(self):
+        values = json.dumps({"_type": "FLOAT", "1": "3.14"})
+        result = self.node.distribute(
+            default_value="0",
+            worker_values=values,
+            is_worker=True,
+            worker_id="worker_0",
+        )
+        self.assertAlmostEqual(result[0], 3.14)
+        self.assertIsInstance(result[0], float)
+
+    def test_type_combo_stays_string(self):
+        values = json.dumps({"_type": "COMBO", "1": "model_v2"})
+        result = self.node.distribute(
+            default_value="model_v1",
+            worker_values=values,
+            is_worker=True,
+            worker_id="worker_0",
+        )
+        self.assertEqual(result, ("model_v2",))
+        self.assertIsInstance(result[0], str)
+
+    def test_type_string_default_stays_string(self):
+        values = json.dumps({"1": "hello"})
+        result = self.node.distribute(
+            default_value="default",
+            worker_values=values,
+            is_worker=True,
+            worker_id="worker_0",
+        )
+        self.assertEqual(result, ("hello",))
+        self.assertIsInstance(result[0], str)
+
+    def test_int_coerce_handles_float_string(self):
+        """INT coercion of '3.7' should truncate to 3."""
+        values = json.dumps({"_type": "INT", "1": "3.7"})
+        result = self.node.distribute(
+            default_value="0",
+            worker_values=values,
+            is_worker=True,
+            worker_id="worker_0",
+        )
+        self.assertEqual(result, (3,))
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,197 @@
+import asyncio
+import importlib.util
+import sys
+import time
+import types
+import unittest
+from dataclasses import dataclass, field
+from pathlib import Path
+
+
+def _load_job_timeout_module():
+    module_path = Path(__file__).resolve().parents[1] / "upscale" / "job_timeout.py"
+    package_name = "dist_job_timeout_testpkg"
+
+    for mod_name in list(sys.modules):
+        if mod_name == package_name or mod_name.startswith(f"{package_name}."):
+            del sys.modules[mod_name]
+
+    root_pkg = types.ModuleType(package_name)
+    root_pkg.__path__ = []
+    sys.modules[package_name] = root_pkg
+
+    upscale_pkg = types.ModuleType(f"{package_name}.upscale")
+    upscale_pkg.__path__ = []
+    sys.modules[f"{package_name}.upscale"] = upscale_pkg
+
+    utils_pkg = types.ModuleType(f"{package_name}.utils")
+    utils_pkg.__path__ = []
+    sys.modules[f"{package_name}.utils"] = utils_pkg
+
+    config_holder = {"value": {"settings": {}, "workers": []}}
+    probe_holder = {"fn": None}
+    prompt_server_holder = {
+        "value": types.SimpleNamespace(
+            distributed_tile_jobs_lock=asyncio.Lock(),
+            distributed_pending_tile_jobs={},
+        )
+    }
+
+    config_module = types.ModuleType(f"{package_name}.utils.config")
+    config_module.load_config = lambda: config_holder["value"]
+    sys.modules[f"{package_name}.utils.config"] = config_module
+
+    constants_module = types.ModuleType(f"{package_name}.utils.constants")
+    constants_module.HEARTBEAT_TIMEOUT = 60
+    sys.modules[f"{package_name}.utils.constants"] = constants_module
+
+    logging_module = types.ModuleType(f"{package_name}.utils.logging")
+    logging_module.debug_log = lambda *_args, **_kwargs: None
+    logging_module.log = lambda *_args, **_kwargs: None
+    sys.modules[f"{package_name}.utils.logging"] = logging_module
+
+    network_module = types.ModuleType(f"{package_name}.utils.network")
+    network_module.build_worker_url = lambda worker: f"http://{worker.get('host', '127.0.0.1')}:{worker.get('port', 8188)}"
+
+    async def _probe_worker(url, timeout=2.0):
+        fn = probe_holder["fn"]
+        if fn is None:
+            return None
+        return await fn(url, timeout)
+
+    network_module.probe_worker = _probe_worker
+    sys.modules[f"{package_name}.utils.network"] = network_module
+
+    job_store_module = types.ModuleType(f"{package_name}.upscale.job_store")
+    job_store_module.ensure_tile_jobs_initialized = lambda: prompt_server_holder["value"]
+    sys.modules[f"{package_name}.upscale.job_store"] = job_store_module
+
+    job_models_module = types.ModuleType(f"{package_name}.upscale.job_models")
+
+    class BaseJobState:
+        pass
+
+    @dataclass
+    class ImageJobState(BaseJobState):
+        multi_job_id: str
+        mode: str = field(default="dynamic", init=False)
+        queue: asyncio.Queue = field(default_factory=asyncio.Queue)
+        pending_images: asyncio.Queue = field(default_factory=asyncio.Queue)
+        completed_images: dict = field(default_factory=dict)
+        worker_status: dict = field(default_factory=dict)
+        assigned_to_workers: dict = field(default_factory=dict)
+        batch_size: int = 0
+        num_tiles_per_image: int = 0
+        batched_static: bool = False
+
+        @property
+        def pending_tasks(self):
+            return self.pending_images
+
+        @property
+        def completed_tasks(self):
+            return self.completed_images
+
+    @dataclass
+    class TileJobState(BaseJobState):
+        multi_job_id: str
+        mode: str = field(default="static", init=False)
+        queue: asyncio.Queue = field(default_factory=asyncio.Queue)
+        pending_tasks: asyncio.Queue = field(default_factory=asyncio.Queue)
+        completed_tasks: dict = field(default_factory=dict)
+        worker_status: dict = field(default_factory=dict)
+        assigned_to_workers: dict = field(default_factory=dict)
+        batch_size: int = 0
+        num_tiles_per_image: int = 0
+        batched_static: bool = False
+
+    job_models_module.BaseJobState = BaseJobState
+    job_models_module.ImageJobState = ImageJobState
+    job_models_module.TileJobState = TileJobState
+    sys.modules[f"{package_name}.upscale.job_models"] = job_models_module
+
+    spec = importlib.util.spec_from_file_location(f"{package_name}.upscale.job_timeout", module_path)
+    module = importlib.util.module_from_spec(spec)
+    assert spec is not None and spec.loader is not None
+    spec.loader.exec_module(module)
+
+    module._config_holder = config_holder
+    module._probe_holder = probe_holder
+    module._prompt_server_holder = prompt_server_holder
+    module._ImageJobState = ImageJobState
+    module._TileJobState = TileJobState
+    return module
+
+
+jt = _load_job_timeout_module()
+
+
+class JobTimeoutRequeueTests(unittest.IsolatedAsyncioTestCase):
+    async def asyncSetUp(self):
+        jt._prompt_server_holder["value"] = types.SimpleNamespace(
+            distributed_tile_jobs_lock=asyncio.Lock(),
+            distributed_pending_tile_jobs={},
+        )
+        jt._config_holder["value"] = {
+            "settings": {"worker_timeout_seconds": 5},
+            "workers": [{"id": "worker-1", "host": "worker.local", "port": 8188}],
+        }
+
+    async def test_requeues_only_incomplete_dynamic_tasks_for_timed_out_worker(self):
+        async def _offline_probe(_url, _timeout):
+            return None
+
+        jt._probe_holder["fn"] = _offline_probe
+        prompt_server = jt._prompt_server_holder["value"]
+        job_data = jt._ImageJobState("job-1")
+        job_data.worker_status["worker-1"] = time.time() - 60.0
+        job_data.assigned_to_workers["worker-1"] = [0, 1]
+        job_data.completed_images[1] = "done"
+        prompt_server.distributed_pending_tile_jobs["job-1"] = job_data
+
+        requeued = await jt._check_and_requeue_timed_out_workers("job-1", total_tasks=2)
+
+        self.assertEqual(requeued, 1)
+        self.assertEqual(await job_data.pending_images.get(), 0)
+        self.assertNotIn("worker-1", job_data.worker_status)
+        self.assertEqual(job_data.assigned_to_workers["worker-1"], [])
+
+    async def test_busy_probe_graces_worker_and_skips_requeue(self):
+        async def _busy_probe(_url, _timeout):
+            return {"exec_info": {"queue_remaining": 3}}
+
+        jt._probe_holder["fn"] = _busy_probe
+        prompt_server = jt._prompt_server_holder["value"]
+        job_data = jt._ImageJobState("job-2")
+        old_heartbeat = time.time() - 60.0
+        job_data.worker_status["worker-1"] = old_heartbeat
+        job_data.assigned_to_workers["worker-1"] = [0]
+        prompt_server.distributed_pending_tile_jobs["job-2"] = job_data
+
+        requeued = await jt._check_and_requeue_timed_out_workers("job-2", total_tasks=1)
+
+        self.assertEqual(requeued, 0)
+        self.assertIn("worker-1", job_data.worker_status)
+        self.assertGreaterEqual(job_data.worker_status["worker-1"], old_heartbeat)
+        self.assertTrue(job_data.pending_images.empty())
+
+    async def test_completed_dynamic_task_is_not_requeued(self):
+        async def _offline_probe(_url, _timeout):
+            return None
+
+        jt._probe_holder["fn"] = _offline_probe
+        prompt_server = jt._prompt_server_holder["value"]
+        job_data = jt._ImageJobState("job-3")
+        job_data.worker_status["worker-1"] = time.time() - 60.0
+        job_data.assigned_to_workers["worker-1"] = [7]
+        job_data.completed_images[7] = "complete"
+        prompt_server.distributed_pending_tile_jobs["job-3"] = job_data
+
+        requeued = await jt._check_and_requeue_timed_out_workers("job-3", total_tasks=1)
+
+        self.assertEqual(requeued, 0)
+        self.assertTrue(job_data.pending_images.empty())
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,103 @@
+import importlib.util
+import sys
+import types
+import unittest
+from pathlib import Path
+
+
+def _load_network_module():
+    module_path = Path(__file__).resolve().parents[1] / "utils" / "network.py"
+
+    package_name = "dist_utils_testpkg"
+    package_module = types.ModuleType(package_name)
+    package_module.__path__ = []  # mark as package
+    sys.modules[package_name] = package_module
+
+    logging_module = types.ModuleType(f"{package_name}.logging")
+    logging_module.debug_log = lambda *_args, **_kwargs: None
+    sys.modules[f"{package_name}.logging"] = logging_module
+
+    server_module = types.ModuleType("server")
+    server_module.PromptServer = types.SimpleNamespace(
+        instance=types.SimpleNamespace(address="127.0.0.1", port=8188, loop=None)
+    )
+    sys.modules["server"] = server_module
+
+    if "aiohttp" not in sys.modules:
+        aiohttp_module = types.ModuleType("aiohttp")
+
+        class _TCPConnector:
+            def __init__(self, *args, **kwargs):
+                self.args = args
+                self.kwargs = kwargs
+
+        class _ClientSession:
+            def __init__(self, *args, **kwargs):
+                self.closed = False
+
+            async def close(self):
+                self.closed = True
+
+        aiohttp_module.TCPConnector = _TCPConnector
+        aiohttp_module.ClientSession = _ClientSession
+        aiohttp_module.web = types.SimpleNamespace(
+            json_response=lambda payload, status=200: {"payload": payload, "status": status}
+        )
+        sys.modules["aiohttp"] = aiohttp_module
+
+    spec = importlib.util.spec_from_file_location(f"{package_name}.network", module_path)
+    module = importlib.util.module_from_spec(spec)
+    assert spec is not None and spec.loader is not None
+    spec.loader.exec_module(module)
+    return module
+
+
+network = _load_network_module()
+
+
+class NetworkHelpersTests(unittest.TestCase):
+    def test_normalize_host_strips_protocol_and_path(self):
+        self.assertEqual(network.normalize_host("  https://example.com/a/b  "), "example.com")
+
+    def test_normalize_host_keeps_none(self):
+        self.assertIsNone(network.normalize_host(None))
+
+    def test_build_worker_url_defaults_to_server_address(self):
+        worker = {"id": "w1", "port": 8189}
+        self.assertEqual(network.build_worker_url(worker, "/prompt"), "http://127.0.0.1:8189/prompt")
+
+    def test_build_worker_url_cloud_defaults_to_https(self):
+        worker = {"id": "w2", "host": "foo.proxy.runpod.net", "port": 443}
+        self.assertEqual(network.build_worker_url(worker), "https://foo.proxy.runpod.net")
+
+    def test_build_worker_url_keeps_explicit_scheme(self):
+        worker = {"id": "w3", "host": "https://worker.example.com", "port": 1234}
+        self.assertEqual(network.build_worker_url(worker, "/prompt"), "https://worker.example.com/prompt")
+
+    def test_build_master_url_uses_https_for_cloud_host(self):
+        cfg = {"master": {"host": "demo.proxy.runpod.net"}}
+        prompt_server = types.SimpleNamespace(address="127.0.0.1", port=8188)
+        self.assertEqual(
+            network.build_master_url(config=cfg, prompt_server_instance=prompt_server),
+            "https://demo.proxy.runpod.net",
+        )
+
+    def test_build_master_url_keeps_explicit_scheme(self):
+        cfg = {"master": {"host": "https://master.example.com/"}}
+        prompt_server = types.SimpleNamespace(address="127.0.0.1", port=8188)
+        self.assertEqual(
+            network.build_master_url(config=cfg, prompt_server_instance=prompt_server),
+            "https://master.example.com",
+        )
+
+    def test_build_master_url_falls_back_to_server_address(self):
+        cfg = {"master": {"host": ""}}
+        prompt_server = types.SimpleNamespace(address="0.0.0.0", port=8190)
+        self.assertEqual(
+            network.build_master_url(config=cfg, prompt_server_instance=prompt_server),
+            "http://127.0.0.1:8190",
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,208 @@
+import importlib.util
+import io
+import json
+import sys
+import types
+import unittest
+from pathlib import Path
+
+try:
+    from PIL import Image as PILImage
+    PIL_AVAILABLE = True
+except ImportError:
+    PIL_AVAILABLE = False
+
+
+def _load_payload_parsers_module():
+    # payload_parsers.py has no relative imports; only stdlib + PIL
+    module_path = Path(__file__).resolve().parents[1] / "upscale" / "payload_parsers.py"
+    spec = importlib.util.spec_from_file_location("upscale_payload_parsers", module_path)
+    module = importlib.util.module_from_spec(spec)
+    assert spec is not None and spec.loader is not None
+    spec.loader.exec_module(module)
+    return module
+
+
+if PIL_AVAILABLE:
+    pp = _load_payload_parsers_module()
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _make_png_bytes(width=64, height=64, color=(128, 64, 32)):
+    """Return raw PNG bytes for a solid-colour image."""
+    img = PILImage.new("RGB", (width, height), color=color)
+    buf = io.BytesIO()
+    img.save(buf, format="PNG")
+    return buf.getvalue()
+
+
+class _MockFileField:
+    """Minimal multipart file-field stub."""
+
+    class _MockFile:
+        def __init__(self, data: bytes):
+            self._buf = io.BytesIO(data)
+
+        def read(self) -> bytes:
+            return self._buf.read()
+
+    def __init__(self, data: bytes):
+        self.file = self._MockFile(data)
+
+
+def _make_form(n_tiles, *, padding=None, extra_meta=None, image_color=(128, 64, 32)):
+    """Build a minimal form-data dict with `n_tiles` tile entries."""
+    image_bytes = _make_png_bytes(color=image_color)
+    metadata = []
+    for i in range(n_tiles):
+        entry = {
+            "tile_idx": i,
+            "x": i * 64,
+            "y": 0,
+            "extracted_width": 64,
+            "extracted_height": 64,
+        }
+        if extra_meta and i < len(extra_meta):
+            entry.update(extra_meta[i])
+        metadata.append(entry)
+
+    form = {"tiles_metadata": json.dumps(metadata)}
+    if padding is not None:
+        form["padding"] = str(padding)
+    for i in range(n_tiles):
+        form[f"tile_{i}"] = _MockFileField(image_bytes)
+    return form
+
+
+# ---------------------------------------------------------------------------
+# Tests
+# ---------------------------------------------------------------------------
+
+@unittest.skipUnless(PIL_AVAILABLE, "PIL not installed")
+class ParseTilesFromFormTests(unittest.TestCase):
+
+    # --- happy paths ---
+
+    def test_single_tile_returns_one_entry(self):
+        tiles = pp._parse_tiles_from_form(_make_form(1))
+        self.assertEqual(len(tiles), 1)
+
+    def test_multiple_tiles_all_returned(self):
+        tiles = pp._parse_tiles_from_form(_make_form(3))
+        self.assertEqual(len(tiles), 3)
+
+    def test_tile_image_is_pil_image(self):
+        tiles = pp._parse_tiles_from_form(_make_form(1))
+        self.assertIsInstance(tiles[0]["image"], PILImage.Image)
+
+    def test_tile_metadata_fields_are_parsed(self):
+        tiles = pp._parse_tiles_from_form(_make_form(1))
+        tile = tiles[0]
+        self.assertEqual(tile["tile_idx"], 0)
+        self.assertEqual(tile["x"], 0)
+        self.assertEqual(tile["y"], 0)
+        self.assertEqual(tile["extracted_width"], 64)
+        self.assertEqual(tile["extracted_height"], 64)
+
+    def test_padding_is_parsed_from_form(self):
+        tiles = pp._parse_tiles_from_form(_make_form(1, padding=16))
+        self.assertEqual(tiles[0]["padding"], 16)
+
+    def test_default_padding_is_zero(self):
+        form = _make_form(1)
+        form.pop("padding", None)
+        tiles = pp._parse_tiles_from_form(form)
+        self.assertEqual(tiles[0]["padding"], 0)
+
+    def test_invalid_padding_string_falls_back_to_zero(self):
+        form = _make_form(1)
+        form["padding"] = "not_a_number"
+        tiles = pp._parse_tiles_from_form(form)
+        self.assertEqual(tiles[0]["padding"], 0)
+
+    def test_optional_batch_idx_included_when_present(self):
+        extra = [{"batch_idx": 2}]
+        tiles = pp._parse_tiles_from_form(_make_form(1, extra_meta=extra))
+        self.assertEqual(tiles[0]["batch_idx"], 2)
+
+    def test_optional_global_idx_included_when_present(self):
+        extra = [{"global_idx": 5}]
+        tiles = pp._parse_tiles_from_form(_make_form(1, extra_meta=extra))
+        self.assertEqual(tiles[0]["global_idx"], 5)
+
+    def test_batch_idx_and_global_idx_absent_when_not_in_metadata(self):
+        tiles = pp._parse_tiles_from_form(_make_form(1))
+        self.assertNotIn("batch_idx", tiles[0])
+        self.assertNotIn("global_idx", tiles[0])
+
+    def test_tile_indices_match_metadata_order(self):
+        tiles = pp._parse_tiles_from_form(_make_form(3))
+        for i, tile in enumerate(tiles):
+            self.assertEqual(tile["tile_idx"], i)
+
+    def test_x_coordinates_reflect_metadata(self):
+        tiles = pp._parse_tiles_from_form(_make_form(3))
+        self.assertEqual(tiles[1]["x"], 64)
+        self.assertEqual(tiles[2]["x"], 128)
+
+    # --- error cases ---
+
+    def test_missing_tiles_metadata_raises_value_error(self):
+        with self.assertRaises(ValueError, msg="Missing tiles_metadata"):
+            pp._parse_tiles_from_form({})
+
+    def test_invalid_json_metadata_raises_value_error(self):
+        form = {"tiles_metadata": "{not valid json}"}
+        with self.assertRaises(ValueError):
+            pp._parse_tiles_from_form(form)
+
+    def test_non_list_metadata_raises_value_error(self):
+        form = {"tiles_metadata": json.dumps({"not": "a list"})}
+        with self.assertRaises(ValueError):
+            pp._parse_tiles_from_form(form)
+
+    def test_missing_tile_file_field_raises_value_error(self):
+        form = {
+            "tiles_metadata": json.dumps([{"tile_idx": 0, "x": 0, "y": 0}]),
+            # tile_0 intentionally omitted
+        }
+        with self.assertRaises(ValueError):
+            pp._parse_tiles_from_form(form)
+
+    def test_tile_field_without_file_attr_raises_value_error(self):
+        form = {
+            "tiles_metadata": json.dumps([{"tile_idx": 0, "x": 0, "y": 0}]),
+            "tile_0": "plain string without .file",
+        }
+        with self.assertRaises(ValueError):
+            pp._parse_tiles_from_form(form)
+
+    def test_non_image_bytes_raises_value_error(self):
+        class _BadFileField:
+            class _BadFile:
+                def read(self):
+                    return b"this is definitely not image data"
+            file = _BadFile()
+
+        form = {
+            "tiles_metadata": json.dumps([{"tile_idx": 0, "x": 0, "y": 0}]),
+            "tile_0": _BadFileField(),
+        }
+        with self.assertRaises(ValueError):
+            pp._parse_tiles_from_form(form)
+
+    def test_invalid_metadata_value_type_raises_value_error(self):
+        """Non-integer metadata fields (x, y, etc.) should raise ValueError."""
+        form = {
+            "tiles_metadata": json.dumps([{"tile_idx": 0, "x": "not_int", "y": 0}]),
+            "tile_0": _MockFileField(_make_png_bytes()),
+        }
+        with self.assertRaises(ValueError):
+            pp._parse_tiles_from_form(form)
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,493 @@
+import importlib.util
+import json
+import sys
+import types
+import unittest
+from pathlib import Path
+
+
+def _load_prompt_transform_module():
+    module_path = Path(__file__).resolve().parents[1] / "api" / "orchestration" / "prompt_transform.py"
+    package_name = "dist_pt_testpkg"
+
+    for mod_name in list(sys.modules):
+        if mod_name == package_name or mod_name.startswith(f"{package_name}."):
+            del sys.modules[mod_name]
+
+    root_pkg = types.ModuleType(package_name)
+    root_pkg.__path__ = []
+    sys.modules[package_name] = root_pkg
+
+    api_pkg = types.ModuleType(f"{package_name}.api")
+    api_pkg.__path__ = []
+    sys.modules[f"{package_name}.api"] = api_pkg
+
+    orch_pkg = types.ModuleType(f"{package_name}.api.orchestration")
+    orch_pkg.__path__ = []
+    sys.modules[f"{package_name}.api.orchestration"] = orch_pkg
+
+    utils_pkg = types.ModuleType(f"{package_name}.utils")
+    utils_pkg.__path__ = []
+    sys.modules[f"{package_name}.utils"] = utils_pkg
+
+    logging_module = types.ModuleType(f"{package_name}.utils.logging")
+    logging_module.debug_log = lambda *_args, **_kwargs: None
+    logging_module.log = lambda *_args, **_kwargs: None
+    sys.modules[f"{package_name}.utils.logging"] = logging_module
+
+    spec = importlib.util.spec_from_file_location(
+        f"{package_name}.api.orchestration.prompt_transform",
+        module_path,
+    )
+    module = importlib.util.module_from_spec(spec)
+    assert spec is not None and spec.loader is not None
+    spec.loader.exec_module(module)
+    return module
+
+
+pt = _load_prompt_transform_module()
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _linear_prompt():
+    """1 → 2 → 3 → 4(DistributedCollector) → 5(SaveImage)"""
+    return {
+        "1": {"class_type": "CheckpointLoaderSimple", "inputs": {}},
+        "2": {"class_type": "CLIPTextEncode", "inputs": {"clip": ["1", 1]}},
+        "3": {"class_type": "KSampler", "inputs": {"model": ["1", 0], "positive": ["2", 0]}},
+        "4": {"class_type": "DistributedCollector", "inputs": {"images": ["3", 0]}},
+        "5": {"class_type": "SaveImage", "inputs": {"images": ["4", 0]}},
+    }
+
+
+def _collector_only_prompt():
+    """1(Checkpoint) → 2(DistributedCollector) [no downstream from 2]"""
+    return {
+        "1": {"class_type": "CheckpointLoaderSimple", "inputs": {}},
+        "2": {"class_type": "DistributedCollector", "inputs": {"images": ["1", 0]}},
+    }
+
+
+def _delegate_prompt():
+    """1 → 2 → 3(DistributedCollector) → 4(SaveImage)"""
+    return {
+        "1": {"class_type": "CheckpointLoaderSimple", "inputs": {}},
+        "2": {"class_type": "KSampler", "inputs": {"model": ["1", 0]}},
+        "3": {"class_type": "DistributedCollector", "inputs": {"images": ["2", 0]}},
+        "4": {"class_type": "SaveImage", "inputs": {"images": ["3", 0]}},
+    }
+
+
+def _apply(prompt, participant_id, enabled_worker_ids=None, delegate_master=False):
+    if enabled_worker_ids is None:
+        enabled_worker_ids = ["worker-a", "worker-b"]
+    idx = pt.PromptIndex(prompt)
+    job_id_map = pt.generate_job_id_map(idx, "run")
+    return pt.apply_participant_overrides(
+        prompt,
+        participant_id=participant_id,
+        enabled_worker_ids=enabled_worker_ids,
+        job_id_map=job_id_map,
+        master_url="http://master.example.com",
+        delegate_master=delegate_master,
+        prompt_index=idx,
+    )
+
+
+# ---------------------------------------------------------------------------
+# PromptIndex
+# ---------------------------------------------------------------------------
+
+class PromptIndexTests(unittest.TestCase):
+    def test_nodes_by_class_groups_correctly(self):
+        prompt = {
+            "1": {"class_type": "CheckpointLoaderSimple", "inputs": {}},
+            "2": {"class_type": "DistributedCollector", "inputs": {}},
+            "3": {"class_type": "DistributedCollector", "inputs": {}},
+        }
+        idx = pt.PromptIndex(prompt)
+        self.assertCountEqual(idx.nodes_for_class("DistributedCollector"), ["2", "3"])
+        self.assertEqual(idx.nodes_for_class("CheckpointLoaderSimple"), ["1"])
+
+    def test_nodes_for_class_unknown_returns_empty(self):
+        idx = pt.PromptIndex({"1": {"class_type": "KSampler", "inputs": {}}})
+        self.assertEqual(idx.nodes_for_class("Nonexistent"), [])
+
+    def test_nodes_without_class_type_are_indexed_under_none(self):
+        prompt = {"1": {"inputs": {}}}
+        idx = pt.PromptIndex(prompt)
+        # Should not raise; nodes_for_class with None key or missing class_type
+        self.assertEqual(idx.nodes_for_class("KSampler"), [])
+
+    def test_copy_prompt_is_a_deep_copy(self):
+        prompt = {"1": {"class_type": "KSampler", "inputs": {"seed": 42}}}
+        idx = pt.PromptIndex(prompt)
+        copy = idx.copy_prompt()
+        copy["1"]["inputs"]["seed"] = 999
+        self.assertEqual(prompt["1"]["inputs"]["seed"], 42)
+
+    def test_has_upstream_direct_connection(self):
+        """Node 4 reads directly from node 3 (KSampler)."""
+        idx = pt.PromptIndex(_linear_prompt())
+        self.assertTrue(idx.has_upstream("4", "KSampler"))
+
+    def test_has_upstream_transitive_connection(self):
+        """Node 4 → 3 → 2 → 1 (CheckpointLoaderSimple)."""
+        idx = pt.PromptIndex(_linear_prompt())
+        self.assertTrue(idx.has_upstream("4", "CheckpointLoaderSimple"))
+
+    def test_has_upstream_returns_false_when_no_path(self):
+        idx = pt.PromptIndex(_linear_prompt())
+        # CheckpointLoaderSimple has no upstream nodes
+        self.assertFalse(idx.has_upstream("1", "DistributedCollector"))
+
+    def test_has_upstream_result_is_cached(self):
+        idx = pt.PromptIndex(_linear_prompt())
+        r1 = idx.has_upstream("4", "KSampler")
+        r2 = idx.has_upstream("4", "KSampler")
+        self.assertEqual(r1, r2)
+        self.assertIn(("4", "KSampler"), idx._upstream_cache)
+
+    def test_has_upstream_does_not_infinite_loop_on_cycle(self):
+        """Cyclic references in inputs should not cause infinite recursion."""
+        prompt = {
+            "1": {"class_type": "A", "inputs": {"x": ["2", 0]}},
+            "2": {"class_type": "B", "inputs": {"x": ["1", 0]}},
+        }
+        idx = pt.PromptIndex(prompt)
+        # Should terminate without error
+        result = idx.has_upstream("1", "NonExistent")
+        self.assertFalse(result)
+
+
+# ---------------------------------------------------------------------------
+# find_nodes_by_class
+# ---------------------------------------------------------------------------
+
+class FindNodesByClassTests(unittest.TestCase):
+    def test_finds_matching_nodes(self):
+        prompt = {
+            "1": {"class_type": "KSampler", "inputs": {}},
+            "2": {"class_type": "DistributedCollector", "inputs": {}},
+        }
+        result = pt.find_nodes_by_class(prompt, "KSampler")
+        self.assertEqual(result, ["1"])
+
+    def test_returns_empty_when_no_match(self):
+        prompt = {"1": {"class_type": "KSampler", "inputs": {}}}
+        self.assertEqual(pt.find_nodes_by_class(prompt, "DistributedCollector"), [])
+
+    def test_skips_non_dict_nodes(self):
+        prompt = {"1": "not a dict", "2": {"class_type": "KSampler", "inputs": {}}}
+        result = pt.find_nodes_by_class(prompt, "KSampler")
+        self.assertEqual(result, ["2"])
+
+
+# ---------------------------------------------------------------------------
+# prune_prompt_for_worker
+# ---------------------------------------------------------------------------
+
+class PrunePromptForWorkerTests(unittest.TestCase):
+    def test_no_distributed_nodes_returns_prompt_unchanged(self):
+        prompt = {
+            "1": {"class_type": "CheckpointLoaderSimple", "inputs": {}},
+            "2": {"class_type": "SaveImage", "inputs": {"images": ["1", 0]}},
+        }
+        result = pt.prune_prompt_for_worker(prompt)
+        self.assertCountEqual(result.keys(), ["1", "2"])
+
+    def test_keeps_collector_and_upstream(self):
+        prompt = _linear_prompt()
+        result = pt.prune_prompt_for_worker(prompt)
+        for node_id in ("1", "2", "3", "4"):
+            self.assertIn(node_id, result)
+
+    def test_removes_downstream_of_collector(self):
+        prompt = _linear_prompt()
+        result = pt.prune_prompt_for_worker(prompt)
+        self.assertNotIn("5", result)
+
+    def test_injects_preview_image_when_downstream_exists(self):
+        prompt = _linear_prompt()
+        result = pt.prune_prompt_for_worker(prompt)
+        preview_nodes = [n for n in result.values() if n.get("class_type") == "PreviewImage"]
+        self.assertEqual(len(preview_nodes), 1)
+        self.assertEqual(preview_nodes[0]["inputs"]["images"], ["4", 0])
+
+    def test_no_preview_image_when_no_downstream(self):
+        result = pt.prune_prompt_for_worker(_collector_only_prompt())
+        preview_nodes = [n for n in result.values() if n.get("class_type") == "PreviewImage"]
+        self.assertEqual(len(preview_nodes), 0)
+
+    def test_unrelated_nodes_are_pruned(self):
+        prompt = {
+            "1": {"class_type": "DistributedCollector", "inputs": {}},
+            "2": {"class_type": "UnrelatedNode", "inputs": {}},  # no connection to 1
+        }
+        result = pt.prune_prompt_for_worker(prompt)
+        self.assertIn("1", result)
+        self.assertNotIn("2", result)
+
+    def test_result_is_a_copy_not_same_object(self):
+        prompt = _linear_prompt()
+        result = pt.prune_prompt_for_worker(prompt)
+        # Mutating the result should not affect the original
+        original_keys = set(prompt.keys())
+        result["NEW"] = {"class_type": "Test", "inputs": {}}
+        self.assertEqual(set(prompt.keys()), original_keys)
+
+    def test_upscale_node_is_treated_as_distributed(self):
+        prompt = {
+            "1": {"class_type": "KSampler", "inputs": {}},
+            "2": {"class_type": "UltimateSDUpscaleDistributed", "inputs": {"image": ["1", 0]}},
+            "3": {"class_type": "SaveImage", "inputs": {"images": ["2", 0]}},
+        }
+        result = pt.prune_prompt_for_worker(prompt)
+        self.assertIn("1", result)
+        self.assertIn("2", result)
+        self.assertNotIn("3", result)
+
+
+# ---------------------------------------------------------------------------
+# prepare_delegate_master_prompt
+# ---------------------------------------------------------------------------
+
+class PrepareDelegateMasterPromptTests(unittest.TestCase):
+    def test_keeps_collector_and_downstream(self):
+        prompt = _delegate_prompt()
+        result = pt.prepare_delegate_master_prompt(prompt, ["3"])
+        self.assertIn("3", result)
+        self.assertIn("4", result)
+        self.assertNotIn("1", result)
+        self.assertNotIn("2", result)
+
+    def test_removes_dangling_upstream_refs(self):
+        """Collector must not retain dangling refs to pruned upstream nodes."""
+        prompt = _delegate_prompt()
+        result = pt.prepare_delegate_master_prompt(prompt, ["3"])
+        collector_inputs = result["3"].get("inputs", {})
+        # Original "images" pointed at node 2, which is pruned.
+        # It should now point at a newly injected placeholder node.
+        self.assertIn("images", collector_inputs)
+        source_id = str(collector_inputs["images"][0])
+        self.assertNotEqual(source_id, "2")
+        self.assertIn(source_id, result)
+        self.assertEqual(result[source_id].get("class_type"), "DistributedEmptyImage")
+
+    def test_injects_empty_image_placeholder(self):
+        prompt = _delegate_prompt()
+        result = pt.prepare_delegate_master_prompt(prompt, ["3"])
+        empty_nodes = [(nid, n) for nid, n in result.items() if n.get("class_type") == "DistributedEmptyImage"]
+        self.assertEqual(len(empty_nodes), 1)
+        placeholder_id = empty_nodes[0][0]
+        self.assertEqual(result["3"]["inputs"]["images"], [placeholder_id, 0])
+
+    def test_one_placeholder_per_collector(self):
+        """Two collectors → two placeholders."""
+        prompt = {
+            "1": {"class_type": "DistributedCollector", "inputs": {}},
+            "2": {"class_type": "DistributedCollector", "inputs": {}},
+            "3": {"class_type": "SaveImage", "inputs": {"images": ["1", 0]}},
+        }
+        result = pt.prepare_delegate_master_prompt(prompt, ["1", "2"])
+        empty_nodes = [n for n in result.values() if n.get("class_type") == "DistributedEmptyImage"]
+        self.assertEqual(len(empty_nodes), 2)
+
+    def test_result_is_independent_copy(self):
+        prompt = _delegate_prompt()
+        result = pt.prepare_delegate_master_prompt(prompt, ["3"])
+        result["3"]["inputs"]["NEW"] = "injected"
+        # Original should be untouched
+        self.assertNotIn("NEW", prompt["3"].get("inputs", {}))
+
+
+# ---------------------------------------------------------------------------
+# generate_job_id_map
+# ---------------------------------------------------------------------------
+
+class GenerateJobIdMapTests(unittest.TestCase):
+    def test_maps_collector_nodes(self):
+        prompt = {
+            "1": {"class_type": "DistributedCollector", "inputs": {}},
+            "2": {"class_type": "KSampler", "inputs": {}},
+        }
+        idx = pt.PromptIndex(prompt)
+        job_map = pt.generate_job_id_map(idx, "prefix")
+        self.assertEqual(job_map["1"], "prefix_1")
+        self.assertNotIn("2", job_map)
+
+    def test_maps_upscale_nodes(self):
+        prompt = {
+            "5": {"class_type": "UltimateSDUpscaleDistributed", "inputs": {}},
+        }
+        idx = pt.PromptIndex(prompt)
+        job_map = pt.generate_job_id_map(idx, "run")
+        self.assertEqual(job_map["5"], "run_5")
+
+    def test_empty_prompt_returns_empty_map(self):
+        idx = pt.PromptIndex({})
+        self.assertEqual(pt.generate_job_id_map(idx, "prefix"), {})
+
+    def test_stable_ids_across_calls(self):
+        prompt = {"1": {"class_type": "DistributedCollector", "inputs": {}}}
+        idx = pt.PromptIndex(prompt)
+        m1 = pt.generate_job_id_map(idx, "run")
+        m2 = pt.generate_job_id_map(idx, "run")
+        self.assertEqual(m1, m2)
+
+
+# ---------------------------------------------------------------------------
+# apply_participant_overrides – DistributedCollector
+# ---------------------------------------------------------------------------
+
+class ApplyOverridesCollectorTests(unittest.TestCase):
+    def _collector_prompt(self):
+        return {"1": {"class_type": "DistributedCollector", "inputs": {}}}
+
+    def test_worker_sets_is_worker_true(self):
+        result = _apply(self._collector_prompt(), "worker-a")
+        self.assertTrue(result["1"]["inputs"]["is_worker"])
+
+    def test_worker_sets_master_url(self):
+        result = _apply(self._collector_prompt(), "worker-a")
+        self.assertEqual(result["1"]["inputs"]["master_url"], "http://master.example.com")
+
+    def test_worker_sets_worker_id(self):
+        result = _apply(self._collector_prompt(), "worker-a")
+        self.assertEqual(result["1"]["inputs"]["worker_id"], "worker-a")
+
+    def test_worker_sets_delegate_only_false(self):
+        result = _apply(self._collector_prompt(), "worker-a")
+        self.assertFalse(result["1"]["inputs"]["delegate_only"])
+
+    def test_master_sets_is_worker_false(self):
+        result = _apply(self._collector_prompt(), "master")
+        self.assertFalse(result["1"]["inputs"]["is_worker"])
+
+    def test_master_clears_stale_master_url(self):
+        prompt = {"1": {"class_type": "DistributedCollector", "inputs": {"master_url": "stale"}}}
+        result = _apply(prompt, "master")
+        self.assertNotIn("master_url", result["1"]["inputs"])
+
+    def test_master_clears_stale_worker_id(self):
+        prompt = {"1": {"class_type": "DistributedCollector", "inputs": {"worker_id": "stale"}}}
+        result = _apply(prompt, "master")
+        self.assertNotIn("worker_id", result["1"]["inputs"])
+
+    def test_master_with_delegate_master_sets_delegate_only_true(self):
+        result = _apply(self._collector_prompt(), "master", delegate_master=True)
+        self.assertTrue(result["1"]["inputs"]["delegate_only"])
+
+    def test_master_without_delegate_master_sets_delegate_only_false(self):
+        result = _apply(self._collector_prompt(), "master", delegate_master=False)
+        self.assertFalse(result["1"]["inputs"]["delegate_only"])
+
+    def test_enabled_worker_ids_serialized_as_json(self):
+        enabled = ["worker-a", "worker-b"]
+        result = _apply(self._collector_prompt(), "master", enabled_worker_ids=enabled)
+        self.assertEqual(result["1"]["inputs"]["enabled_worker_ids"], json.dumps(enabled))
+
+    def test_multi_job_id_is_set_from_job_map(self):
+        prompt = {"1": {"class_type": "DistributedCollector", "inputs": {}}}
+        idx = pt.PromptIndex(prompt)
+        job_id_map = {"1": "run_abc_1"}
+        result = pt.apply_participant_overrides(
+            prompt,
+            participant_id="worker-a",
+            enabled_worker_ids=["worker-a"],
+            job_id_map=job_id_map,
+            master_url="http://master",
+            delegate_master=False,
+            prompt_index=idx,
+        )
+        self.assertEqual(result["1"]["inputs"]["multi_job_id"], "run_abc_1")
+
+
+# ---------------------------------------------------------------------------
+# apply_participant_overrides – DistributedSeed
+# ---------------------------------------------------------------------------
+
+class ApplyOverridesSeedTests(unittest.TestCase):
+    def _seed_prompt(self):
+        return {"1": {"class_type": "DistributedSeed", "inputs": {}}}
+
+    def test_worker_sets_is_worker_true(self):
+        result = _apply(self._seed_prompt(), "worker-a")
+        self.assertTrue(result["1"]["inputs"]["is_worker"])
+
+    def test_worker_id_reflects_index_in_enabled_list(self):
+        result = _apply(self._seed_prompt(), "worker-b", enabled_worker_ids=["worker-a", "worker-b"])
+        self.assertEqual(result["1"]["inputs"]["worker_id"], "worker_1")
+
+    def test_master_sets_is_worker_false(self):
+        result = _apply(self._seed_prompt(), "master")
+        self.assertFalse(result["1"]["inputs"]["is_worker"])
+
+    def test_master_sets_empty_worker_id(self):
+        result = _apply(self._seed_prompt(), "master")
+        self.assertEqual(result["1"]["inputs"]["worker_id"], "")
+
+
+# ---------------------------------------------------------------------------
+# apply_participant_overrides – UltimateSDUpscaleDistributed
+# ---------------------------------------------------------------------------
+
+class ApplyOverridesUpscaleTests(unittest.TestCase):
+    def _upscale_prompt(self):
+        return {"1": {"class_type": "UltimateSDUpscaleDistributed", "inputs": {}}}
+
+    def test_worker_sets_is_worker_true(self):
+        result = _apply(self._upscale_prompt(), "worker-a")
+        self.assertTrue(result["1"]["inputs"]["is_worker"])
+
+    def test_worker_sets_master_url_and_worker_id(self):
+        result = _apply(self._upscale_prompt(), "worker-a")
+        self.assertEqual(result["1"]["inputs"]["master_url"], "http://master.example.com")
+        self.assertEqual(result["1"]["inputs"]["worker_id"], "worker-a")
+
+    def test_master_clears_master_url_and_worker_id(self):
+        prompt = {"1": {"class_type": "UltimateSDUpscaleDistributed", "inputs": {"master_url": "x", "worker_id": "y"}}}
+        result = _apply(prompt, "master")
+        self.assertNotIn("master_url", result["1"]["inputs"])
+        self.assertNotIn("worker_id", result["1"]["inputs"])
+
+    def test_collector_downstream_of_upscale_gets_pass_through(self):
+        """A DistributedCollector that is downstream of UltimateSDUpscaleDistributed → pass_through=True."""
+        prompt = {
+            "1": {"class_type": "UltimateSDUpscaleDistributed", "inputs": {}},
+            "2": {"class_type": "DistributedCollector", "inputs": {"images": ["1", 0]}},
+        }
+        result = _apply(prompt, "worker-a", enabled_worker_ids=["worker-a"])
+        self.assertTrue(result["2"]["inputs"].get("pass_through"))
+
+
+# ---------------------------------------------------------------------------
+# apply_participant_overrides – DistributedValue
+# ---------------------------------------------------------------------------
+
+class ApplyOverridesValueTests(unittest.TestCase):
+    def _value_prompt(self):
+        return {"1": {"class_type": "DistributedValue", "inputs": {}}}
+
+    def test_worker_sets_is_worker_true(self):
+        result = _apply(self._value_prompt(), "worker-a")
+        self.assertTrue(result["1"]["inputs"]["is_worker"])
+
+    def test_worker_id_reflects_index_in_enabled_list(self):
+        result = _apply(self._value_prompt(), "worker-b", enabled_worker_ids=["worker-a", "worker-b"])
+        self.assertEqual(result["1"]["inputs"]["worker_id"], "worker_1")
+
+    def test_master_sets_is_worker_false(self):
+        result = _apply(self._value_prompt(), "master")
+        self.assertFalse(result["1"]["inputs"]["is_worker"])
+
+    def test_master_sets_empty_worker_id(self):
+        result = _apply(self._value_prompt(), "master")
+        self.assertEqual(result["1"]["inputs"]["worker_id"], "")
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,151 @@
+import importlib.util
+import unittest
+from pathlib import Path
+
+
+def _load_queue_request_module():
+    module_path = Path(__file__).resolve().parents[1] / "api" / "queue_request.py"
+    spec = importlib.util.spec_from_file_location("queue_request", module_path)
+    module = importlib.util.module_from_spec(spec)
+    assert spec is not None and spec.loader is not None
+    spec.loader.exec_module(module)
+    return module
+
+
+queue_request = _load_queue_request_module()
+parse_queue_request_payload = queue_request.parse_queue_request_payload
+
+
+class QueueRequestPayloadTests(unittest.TestCase):
+    def _base_payload(self):
+        return {
+            "prompt": {"1": {"class_type": "Anything"}},
+            "enabled_worker_ids": ["worker-1"],
+            "client_id": "client-1",
+        }
+
+    def test_normalizes_enabled_worker_ids(self):
+        payload_data = self._base_payload()
+        payload_data["enabled_worker_ids"] = ["a", 2, 3]
+        payload_data["delegate_master"] = True
+        payload = parse_queue_request_payload(
+            payload_data
+        )
+        self.assertEqual(payload.enabled_worker_ids, ["a", "2", "3"])
+        self.assertTrue(payload.delegate_master)
+
+    def test_supports_legacy_workers_field(self):
+        payload_data = self._base_payload()
+        payload_data.pop("enabled_worker_ids", None)
+        payload_data["workers"] = [{"id": "w1"}, "w2", {"id": 3}, {"name": "no-id"}]
+        payload = parse_queue_request_payload(
+            payload_data
+        )
+        self.assertEqual(payload.enabled_worker_ids, ["w1", "w2", "3"])
+
+    def test_supports_auto_prepare_prompt_fallback(self):
+        payload_data = self._base_payload()
+        payload_data.pop("prompt", None)
+        payload_data["auto_prepare"] = True
+        payload_data["workflow"] = {
+            "prompt": {
+                "10": {"class_type": "DistributedCollector"},
+            }
+        }
+        payload = parse_queue_request_payload(
+            payload_data
+        )
+        self.assertIn("10", payload.prompt)
+        self.assertTrue(payload.auto_prepare)
+
+    def test_normalizes_trace_execution_id(self):
+        payload_data = self._base_payload()
+        payload_data["trace_execution_id"] = "  exec_123  "
+        payload = parse_queue_request_payload(
+            payload_data
+        )
+        self.assertEqual(payload.trace_execution_id, "exec_123")
+
+    def test_blank_trace_execution_id_normalizes_to_none(self):
+        payload_data = self._base_payload()
+        payload_data["trace_execution_id"] = "   "
+        payload = parse_queue_request_payload(
+            payload_data
+        )
+        self.assertIsNone(payload.trace_execution_id)
+
+    def test_auto_prepare_defaults_true(self):
+        payload = parse_queue_request_payload(self._base_payload())
+        self.assertTrue(payload.auto_prepare)
+
+    def test_workers_field_must_be_list(self):
+        payload_data = self._base_payload()
+        payload_data.pop("enabled_worker_ids", None)
+        payload_data["workers"] = "worker-a"
+        with self.assertRaisesRegex(ValueError, "Field 'workers' must be a list"):
+            parse_queue_request_payload(payload_data)
+
+    def test_trace_execution_id_must_be_string(self):
+        payload_data = self._base_payload()
+        payload_data["trace_execution_id"] = 123
+        with self.assertRaisesRegex(ValueError, "trace_execution_id must be a string"):
+            parse_queue_request_payload(payload_data)
+
+    def test_auto_prepare_false_still_falls_back_to_workflow_prompt(self):
+        payload_data = self._base_payload()
+        payload_data.pop("prompt", None)
+        payload_data["auto_prepare"] = False
+        payload_data["workflow"] = {
+            "prompt": {"10": {"class_type": "DistributedCollector"}},
+        }
+        payload = parse_queue_request_payload(payload_data)
+        self.assertIn("10", payload.prompt)
+        self.assertFalse(payload.auto_prepare)
+
+    def test_auto_prepare_must_be_boolean(self):
+        payload_data = self._base_payload()
+        payload_data["auto_prepare"] = "true"
+        with self.assertRaisesRegex(ValueError, "auto_prepare must be a boolean"):
+            parse_queue_request_payload(payload_data)
+
+    def test_invalid_delegate_master_type_raises(self):
+        payload_data = self._base_payload()
+        payload_data["delegate_master"] = "yes"
+        with self.assertRaisesRegex(ValueError, "delegate_master must be a boolean"):
+            parse_queue_request_payload(payload_data)
+
+    def test_invalid_enabled_worker_ids_type_raises(self):
+        payload_data = self._base_payload()
+        payload_data["enabled_worker_ids"] = "worker-a"
+        with self.assertRaisesRegex(ValueError, "enabled_worker_ids must be a list"):
+            parse_queue_request_payload(payload_data)
+
+    def test_invalid_top_level_payload_raises(self):
+        with self.assertRaisesRegex(ValueError, "Expected a JSON object body"):
+            parse_queue_request_payload(["not", "an", "object"])
+
+    def test_missing_prompt_raises(self):
+        with self.assertRaisesRegex(ValueError, "Field 'prompt' must be an object"):
+            parse_queue_request_payload(
+                {
+                    "workflow": {},
+                    "enabled_worker_ids": ["worker-1"],
+                    "client_id": "client-1",
+                }
+            )
+
+    def test_missing_enabled_worker_ids_raises(self):
+        payload_data = self._base_payload()
+        payload_data.pop("enabled_worker_ids", None)
+        with self.assertRaisesRegex(ValueError, "enabled_worker_ids required"):
+            parse_queue_request_payload(payload_data)
+
+    def test_missing_client_id_raises(self):
+        payload_data = self._base_payload()
+        payload_data.pop("client_id", None)
+        with self.assertRaisesRegex(ValueError, "client_id required"):
+            parse_queue_request_payload(payload_data)
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,260 @@
+import asyncio
+import importlib.util
+import sys
+import types
+import unittest
+from pathlib import Path
+
+import torch
+
+
+def _load_static_mode_module():
+    module_path = Path(__file__).resolve().parents[1] / "upscale" / "modes" / "static.py"
+    package_name = "dist_static_mode_testpkg"
+
+    for mod_name in list(sys.modules):
+        if mod_name == package_name or mod_name.startswith(f"{package_name}."):
+            del sys.modules[mod_name]
+
+    root_pkg = types.ModuleType(package_name)
+    root_pkg.__path__ = []
+    sys.modules[package_name] = root_pkg
+
+    upscale_pkg = types.ModuleType(f"{package_name}.upscale")
+    upscale_pkg.__path__ = []
+    sys.modules[f"{package_name}.upscale"] = upscale_pkg
+
+    modes_pkg = types.ModuleType(f"{package_name}.upscale.modes")
+    modes_pkg.__path__ = []
+    sys.modules[f"{package_name}.upscale.modes"] = modes_pkg
+
+    utils_pkg = types.ModuleType(f"{package_name}.utils")
+    utils_pkg.__path__ = []
+    sys.modules[f"{package_name}.utils"] = utils_pkg
+
+    created_comfy_stub = False
+    if "comfy" not in sys.modules:
+        created_comfy_stub = True
+        comfy_module = types.ModuleType("comfy")
+        model_mgmt = types.ModuleType("comfy.model_management")
+
+        class _InterruptProcessingException(Exception):
+            pass
+
+        model_mgmt.processing_interrupted = lambda: False
+        model_mgmt.throw_exception_if_processing_interrupted = lambda: None
+        model_mgmt.InterruptProcessingException = _InterruptProcessingException
+
+        comfy_module.model_management = model_mgmt
+        sys.modules["comfy"] = comfy_module
+        sys.modules["comfy.model_management"] = model_mgmt
+
+    logging_module = types.ModuleType(f"{package_name}.utils.logging")
+    logging_module.debug_log = lambda *_args, **_kwargs: None
+    logging_module.log = lambda *_args, **_kwargs: None
+    sys.modules[f"{package_name}.utils.logging"] = logging_module
+
+    image_module = types.ModuleType(f"{package_name}.utils.image")
+    from PIL import Image as PILImage
+    import numpy as np
+
+    def _tensor_to_pil(img_tensor, batch_index=0):
+        return PILImage.fromarray((255 * img_tensor[batch_index].cpu().numpy()).astype(np.uint8))
+
+    def _pil_to_tensor(image):
+        arr = np.array(image).astype(np.float32) / 255.0
+        return torch.from_numpy(arr).unsqueeze(0)
+
+    image_module.tensor_to_pil = _tensor_to_pil
+    image_module.pil_to_tensor = _pil_to_tensor
+    sys.modules[f"{package_name}.utils.image"] = image_module
+
+    async_helpers_module = types.ModuleType(f"{package_name}.utils.async_helpers")
+
+    def _run_async_in_server_loop(coro, timeout=None):
+        if timeout is not None:
+            return asyncio.run(asyncio.wait_for(coro, timeout=timeout))
+        return asyncio.run(coro)
+
+    async_helpers_module.run_async_in_server_loop = _run_async_in_server_loop
+    sys.modules[f"{package_name}.utils.async_helpers"] = async_helpers_module
+
+    config_module = types.ModuleType(f"{package_name}.utils.config")
+    config_module.get_worker_timeout_seconds = lambda: 60
+    sys.modules[f"{package_name}.utils.config"] = config_module
+
+    constants_module = types.ModuleType(f"{package_name}.utils.constants")
+    constants_module.HEARTBEAT_INTERVAL = 10.0
+    constants_module.JOB_POLL_INTERVAL = 0.0
+    constants_module.JOB_POLL_MAX_ATTEMPTS = 3
+    constants_module.MAX_BATCH = 20
+    constants_module.TILE_SEND_TIMEOUT = 1.0
+    constants_module.TILE_WAIT_TIMEOUT = 1.0
+    sys.modules[f"{package_name}.utils.constants"] = constants_module
+
+    job_store_module = types.ModuleType(f"{package_name}.upscale.job_store")
+
+    async def _noop(*_args, **_kwargs):
+        return None
+
+    job_store_module.ensure_tile_jobs_initialized = lambda: types.SimpleNamespace(
+        distributed_tile_jobs_lock=asyncio.Lock(),
+        distributed_pending_tile_jobs={},
+    )
+    job_store_module.init_static_job_batched = _noop
+    job_store_module._mark_task_completed = _noop
+    job_store_module._cleanup_job = _noop
+    job_store_module._drain_results_queue = _noop
+    job_store_module._get_completed_count = _noop
+    sys.modules[f"{package_name}.upscale.job_store"] = job_store_module
+
+    job_models_module = types.ModuleType(f"{package_name}.upscale.job_models")
+
+    class _TileJobState:
+        pass
+
+    job_models_module.TileJobState = _TileJobState
+    sys.modules[f"{package_name}.upscale.job_models"] = job_models_module
+
+    spec = importlib.util.spec_from_file_location(f"{package_name}.upscale.modes.static", module_path)
+    module = importlib.util.module_from_spec(spec)
+    assert spec is not None and spec.loader is not None
+    spec.loader.exec_module(module)
+
+    if created_comfy_stub:
+        sys.modules.pop("comfy.model_management", None)
+        sys.modules.pop("comfy", None)
+
+    return module
+
+
+static_mode = _load_static_mode_module()
+
+
+class _FakeStaticWorker(static_mode.StaticModeMixin):
+    def __init__(self):
+        self.sent_batches = []
+        self.request_calls = 0
+        self.heartbeat_calls = 0
+        self.job_ready = True
+        self.tile_sequence = [(0, 0, True), (None, 0, True)]
+
+    def round_to_multiple(self, value):
+        return value
+
+    def calculate_tiles(self, _width, _height, _tile_width, _tile_height, _force_uniform_tiles):
+        return [(0, 0)]
+
+    def _poll_job_ready(self, *_args, **_kwargs):
+        return self.job_ready
+
+    async def _request_tile_from_master(self, *_args, **_kwargs):
+        self.request_calls += 1
+        return self.tile_sequence.pop(0)
+
+    async def _send_heartbeat_to_master(self, *_args, **_kwargs):
+        self.heartbeat_calls += 1
+
+    async def send_tiles_batch_to_master(
+        self,
+        processed_tiles,
+        _multi_job_id,
+        _master_url,
+        _padding,
+        _worker_id,
+        is_final_flush=False,
+    ):
+        self.sent_batches.append(
+            {
+                "tiles": list(processed_tiles),
+                "is_final_flush": bool(is_final_flush),
+            }
+        )
+
+    def _extract_and_process_tile(self, upscaled_image, *_args, **_kwargs):
+        batch_size = upscaled_image.shape[0]
+        processed_batch = torch.zeros((batch_size, 2, 2, 3), dtype=torch.float32)
+        return processed_batch, 0, 0, 2, 2
+
+    def create_tile_mask(self, *_args, **_kwargs):
+        from PIL import Image
+        return Image.new("L", (4, 4), 255)
+
+    def blend_tile(self, base_image, *_args, **_kwargs):
+        return base_image
+
+
+def _call_worker_static(fake_worker):
+    image = torch.zeros((1, 4, 4, 3), dtype=torch.float32)
+    return fake_worker._process_worker_static_sync(
+        image,
+        model=None,
+        positive=None,
+        negative=None,
+        vae=None,
+        seed=1,
+        steps=1,
+        cfg=1.0,
+        sampler_name="euler",
+        scheduler="normal",
+        denoise=0.5,
+        tile_width=4,
+        tile_height=4,
+        padding=8,
+        mask_blur=4,
+        force_uniform_tiles=True,
+        tiled_decode=False,
+        multi_job_id="job-1",
+        master_url="http://master:8188",
+        worker_id="worker-1",
+        enabled_workers=["worker-1"],
+    )
+
+
+class StaticModeWorkerFlowTests(unittest.TestCase):
+    def test_worker_static_aborts_when_job_not_ready(self):
+        worker = _FakeStaticWorker()
+        worker.job_ready = False
+
+        result = _call_worker_static(worker)
+
+        self.assertEqual(result[0].shape[0], 1)
+        self.assertEqual(worker.request_calls, 0)
+        self.assertEqual(worker.heartbeat_calls, 0)
+        self.assertEqual(worker.sent_batches, [])
+
+    def test_worker_static_requests_tiles_and_flushes_final_batch(self):
+        worker = _FakeStaticWorker()
+
+        _call_worker_static(worker)
+
+        self.assertEqual(worker.request_calls, 2)  # one tile, then sentinel
+        self.assertEqual(worker.heartbeat_calls, 1)
+        self.assertEqual(len(worker.sent_batches), 1)
+        self.assertTrue(worker.sent_batches[0]["is_final_flush"])
+        tiles = worker.sent_batches[0]["tiles"]
+        self.assertEqual(len(tiles), 1)
+        self.assertEqual(tiles[0]["tile_idx"], 0)
+        self.assertEqual(tiles[0]["global_idx"], 0)
+        self.assertEqual(tiles[0]["batch_idx"], 0)
+
+    def test_flush_empty_final_still_sends_completion_signal(self):
+        worker = _FakeStaticWorker()
+
+        returned = worker._flush_tiles_to_master(
+            [],
+            "job-1",
+            "http://master:8188",
+            8,
+            "worker-1",
+            is_final_flush=True,
+        )
+
+        self.assertEqual(returned, [])
+        self.assertEqual(len(worker.sent_batches), 1)
+        self.assertEqual(worker.sent_batches[0]["tiles"], [])
+        self.assertTrue(worker.sent_batches[0]["is_final_flush"])
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -0,0 +1,32 @@
+import copy
+
+
+def clone_control_chain(control, clone_hint=True):
+    """Shallow copy the ControlNet chain, optionally cloning hints but sharing models."""
+    if control is None:
+        return None
+    new_control = copy.copy(control)
+    if clone_hint and hasattr(control, 'cond_hint_original'):
+        hint = getattr(control, 'cond_hint_original', None)
+        new_control.cond_hint_original = hint.clone() if hint is not None else None
+    if hasattr(control, 'previous_controlnet'):
+        new_control.previous_controlnet = clone_control_chain(control.previous_controlnet, clone_hint)
+    return new_control
+
+
+def clone_conditioning(cond_list, clone_hints=True):
+    """Clone conditioning without duplicating ControlNet models."""
+    new_cond = []
+    for emb, cond_dict in cond_list:
+        new_emb = emb.clone() if emb is not None else None
+        new_dict = cond_dict.copy()
+        if 'control' in new_dict:
+            new_dict['control'] = clone_control_chain(new_dict['control'], clone_hints)
+        if 'mask' in new_dict and new_dict['mask'] is not None:
+            new_dict['mask'] = new_dict['mask'].clone()
+        if 'pooled_output' in new_dict and new_dict['pooled_output'] is not None:
+            new_dict['pooled_output'] = new_dict['pooled_output'].clone()
+        if 'area' in new_dict:
+            new_dict['area'] = new_dict['area'][:]
+        new_cond.append([new_emb, new_dict])
+    return new_cond
@@ -0,0 +1,49 @@
+from dataclasses import dataclass, field
+import asyncio
+import time
+
+
+class BaseJobState:
+    """Marker base class for typed USDU job state containers."""
+
+
+@dataclass
+class TileJobState(BaseJobState):
+    """Typed state container for static (tile) USDU jobs."""
+
+    multi_job_id: str
+    mode: str = field(default="static", init=False)
+    queue: asyncio.Queue = field(default_factory=asyncio.Queue)
+    pending_tasks: asyncio.Queue = field(default_factory=asyncio.Queue)
+    completed_tasks: dict = field(default_factory=dict)
+    worker_status: dict = field(default_factory=dict)
+    assigned_to_workers: dict = field(default_factory=dict)
+    batch_size: int = 0
+    num_tiles_per_image: int = 0
+    batched_static: bool = False
+    created_at: float = field(default_factory=time.monotonic)
+
+
+@dataclass
+class ImageJobState(BaseJobState):
+    """Typed state container for dynamic (per-image) USDU jobs."""
+
+    multi_job_id: str
+    mode: str = field(default="dynamic", init=False)
+    queue: asyncio.Queue = field(default_factory=asyncio.Queue)
+    pending_images: asyncio.Queue = field(default_factory=asyncio.Queue)
+    completed_images: dict = field(default_factory=dict)
+    worker_status: dict = field(default_factory=dict)
+    assigned_to_workers: dict = field(default_factory=dict)
+    batch_size: int = 0
+    num_tiles_per_image: int = 0
+    batched_static: bool = False
+    created_at: float = field(default_factory=time.monotonic)
+
+    @property
+    def pending_tasks(self):
+        return self.pending_images
+
+    @property
+    def completed_tasks(self):
+        return self.completed_images
@@ -0,0 +1,135 @@
+import asyncio
+
+from ..utils.logging import debug_log
+from .job_store import ensure_tile_jobs_initialized
+from .job_timeout import _check_and_requeue_timed_out_workers as _requeue_usdu
+from .job_models import ImageJobState, TileJobState
+
+
+class JobStateMixin:
+    async def _get_job_data(self, multi_job_id):
+        """Return current job data reference while holding lock briefly."""
+        prompt_server = ensure_tile_jobs_initialized()
+        async with prompt_server.distributed_tile_jobs_lock:
+            return prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
+
+    async def _get_all_completed_tasks(self, multi_job_id):
+        """Helper to retrieve all completed tasks from the job data."""
+        job_data = await self._get_job_data(multi_job_id)
+        if isinstance(job_data, TileJobState):
+            return dict(job_data.completed_tasks)
+        if isinstance(job_data, ImageJobState):
+            return dict(job_data.completed_images)
+        return {}
+
+    async def _get_next_image_index(self, multi_job_id):
+        """Get next image index from pending queue for master."""
+        prompt_server = ensure_tile_jobs_initialized()
+        pending_queue = None
+        async with prompt_server.distributed_tile_jobs_lock:
+            job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
+            if isinstance(job_data, ImageJobState):
+                pending_queue = job_data.pending_images
+
+        if pending_queue is None:
+            return None
+
+        try:
+            return await asyncio.wait_for(pending_queue.get(), timeout=1.0)
+        except asyncio.TimeoutError:
+            return None
+
+    async def _get_next_tile_index(self, multi_job_id):
+        """Get next tile index from pending queue for master in static mode."""
+        prompt_server = ensure_tile_jobs_initialized()
+        pending_queue = None
+        async with prompt_server.distributed_tile_jobs_lock:
+            job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
+            if isinstance(job_data, TileJobState):
+                pending_queue = job_data.pending_tasks
+
+        if pending_queue is None:
+            return None
+
+        try:
+            return await asyncio.wait_for(pending_queue.get(), timeout=0.1)
+        except asyncio.TimeoutError:
+            return None
+
+    async def _get_total_completed_count(self, multi_job_id):
+        """Get total count of all completed images (master + workers)."""
+        prompt_server = ensure_tile_jobs_initialized()
+        async with prompt_server.distributed_tile_jobs_lock:
+            job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
+            if isinstance(job_data, ImageJobState):
+                return len(job_data.completed_images)
+            if isinstance(job_data, TileJobState):
+                return len(job_data.completed_tasks)
+            return 0
+
+    async def _get_all_completed_images(self, multi_job_id):
+        """Get all completed images."""
+        prompt_server = ensure_tile_jobs_initialized()
+        async with prompt_server.distributed_tile_jobs_lock:
+            job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
+            if isinstance(job_data, ImageJobState):
+                return job_data.completed_images.copy()
+            return {}
+
+    async def _get_pending_count(self, multi_job_id):
+        """Get count of pending images in the queue."""
+        prompt_server = ensure_tile_jobs_initialized()
+        async with prompt_server.distributed_tile_jobs_lock:
+            job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
+            if isinstance(job_data, ImageJobState):
+                return job_data.pending_images.qsize()
+            if isinstance(job_data, TileJobState):
+                return job_data.pending_tasks.qsize()
+            return 0
+
+    async def _drain_worker_results_queue(self, multi_job_id):
+        """Drain pending worker results from queue and update completed images."""
+        prompt_server = ensure_tile_jobs_initialized()
+        worker_queue = None
+        async with prompt_server.distributed_tile_jobs_lock:
+            job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
+            if isinstance(job_data, ImageJobState):
+                worker_queue = job_data.queue
+
+        if worker_queue is None:
+            return 0
+
+        drained_results = []
+        while True:
+            try:
+                drained_results.append(worker_queue.get_nowait())
+            except asyncio.QueueEmpty:
+                break
+
+        if not drained_results:
+            return 0
+
+        collected = 0
+        async with prompt_server.distributed_tile_jobs_lock:
+            job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
+            if not isinstance(job_data, ImageJobState):
+                return 0
+
+            for result in drained_results:
+                worker_id = result.get("worker_id")
+                if "image_idx" in result and "image" in result:
+                    image_idx = result["image_idx"]
+                    image_pil = result["image"]
+                    if image_idx not in job_data.completed_images:
+                        job_data.completed_images[image_idx] = image_pil
+                        collected += 1
+                        debug_log(f"Drained image {image_idx} from worker {worker_id}")
+
+        if collected > 0:
+            debug_log(f"Drained {collected} worker images during retry")
+
+        return collected
+
+    async def _check_and_requeue_timed_out_workers(self, multi_job_id, batch_size):
+        """Check for timed out workers and requeue their assigned images."""
+        return await _requeue_usdu(multi_job_id, batch_size)
@@ -0,0 +1,180 @@
+import asyncio
+import os
+import time
+from typing import List, Optional
+
+import server
+
+from ..utils.logging import debug_log
+from .job_models import BaseJobState, ImageJobState, TileJobState
+
+# Configure maximum payload size (50MB default, configurable via environment variable)
+MAX_PAYLOAD_SIZE = int(os.environ.get('COMFYUI_MAX_PAYLOAD_SIZE', str(50 * 1024 * 1024)))
+
+
+def ensure_tile_jobs_initialized():
+    """Ensure tile job storage is initialized on the server instance."""
+    prompt_server = server.PromptServer.instance
+    if not hasattr(prompt_server, 'distributed_pending_tile_jobs'):
+        debug_log("Initializing persistent tile job queue on server instance.")
+        prompt_server.distributed_pending_tile_jobs = {}
+        prompt_server.distributed_tile_jobs_lock = asyncio.Lock()
+    else:
+        invalid_job_ids = [
+            job_id
+            for job_id, job_data in prompt_server.distributed_pending_tile_jobs.items()
+            if not isinstance(job_data, BaseJobState)
+        ]
+        for job_id in invalid_job_ids:
+            debug_log(f"Removing invalid job state for {job_id}")
+            del prompt_server.distributed_pending_tile_jobs[job_id]
+    return prompt_server
+
+
+async def _init_job_queue(
+    multi_job_id,
+    mode,
+    batch_size=None,
+    num_tiles_per_image=None,
+    all_indices=None,
+    enabled_workers=None,
+    batched_static: bool = False,
+):
+    """Unified initialization for job queues in static and dynamic modes."""
+    prompt_server = ensure_tile_jobs_initialized()
+    async with prompt_server.distributed_tile_jobs_lock:
+        if multi_job_id in prompt_server.distributed_pending_tile_jobs:
+            debug_log(f"Queue already exists for {multi_job_id}")
+            return
+
+        if mode == 'dynamic':
+            job_data = ImageJobState(multi_job_id=multi_job_id)
+        elif mode == 'static':
+            job_data = TileJobState(multi_job_id=multi_job_id)
+        else:
+            raise ValueError(f"Unknown mode: {mode}")
+
+        job_data.worker_status = {w: time.time() for w in enabled_workers or []}
+        job_data.assigned_to_workers = {w: [] for w in enabled_workers or []}
+
+        if mode == 'dynamic':
+            job_data.batch_size = int(batch_size or 0)
+            pending_queue = job_data.pending_images
+            for i in (all_indices or range(int(batch_size or 0))):
+                await pending_queue.put(i)
+            debug_log(f"Initialized image queue with {batch_size} pending items")
+        elif mode == 'static':
+            job_data.num_tiles_per_image = int(num_tiles_per_image or 0)
+            job_data.batch_size = int(batch_size or 0)
+            job_data.batched_static = bool(batched_static)
+            # For batched static distribution, populate only tile ids [0..num_tiles_per_image-1]
+            pending_queue = job_data.pending_tasks
+            if batched_static and num_tiles_per_image is not None:
+                for i in range(num_tiles_per_image):
+                    await pending_queue.put(i)
+            else:
+                total_tiles = int(batch_size or 0) * int(num_tiles_per_image or 0)
+                for i in range(total_tiles):
+                    await pending_queue.put(i)
+
+        prompt_server.distributed_pending_tile_jobs[multi_job_id] = job_data
+
+
+async def init_dynamic_job(
+    multi_job_id: str,
+    batch_size: int,
+    enabled_workers: List[str],
+    all_indices: Optional[List[int]] = None,
+):
+    """Initialize queue for dynamic mode (per-image), with collector fields."""
+    await _init_job_queue(
+        multi_job_id,
+        'dynamic',
+        batch_size=batch_size,
+        all_indices=all_indices or list(range(batch_size)),
+        enabled_workers=enabled_workers,
+    )
+    debug_log(f"Job {multi_job_id} initialized with {batch_size} images")
+
+
+async def init_static_job_batched(
+    multi_job_id: str,
+    batch_size: int,
+    num_tiles_per_image: int,
+    enabled_workers: List[str],
+):
+    """Initialize queue for static mode (batched-per-tile)."""
+    await _init_job_queue(
+        multi_job_id,
+        'static',
+        batch_size=batch_size,
+        num_tiles_per_image=num_tiles_per_image,
+        enabled_workers=enabled_workers,
+        batched_static=True,
+    )
+
+
+async def _drain_results_queue(multi_job_id):
+    """Drain pending results from queue and update completed_tasks. Returns count drained."""
+    prompt_server = ensure_tile_jobs_initialized()
+    async with prompt_server.distributed_tile_jobs_lock:
+        job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
+        if not isinstance(job_data, BaseJobState):
+            return 0
+        q = job_data.queue
+        completed_tasks = job_data.completed_tasks
+
+        collected = 0
+        while True:
+            try:
+                result = q.get_nowait()
+            except asyncio.QueueEmpty:
+                break
+
+            worker_id = result['worker_id']
+            is_last = result.get('is_last', False)
+
+            if 'image_idx' in result and 'image' in result:
+                task_id = result['image_idx']
+                if task_id not in completed_tasks:
+                    completed_tasks[task_id] = result['image']
+                    collected += 1
+            elif 'tiles' in result:
+                for tile_data in result['tiles']:
+                    task_id = tile_data.get('global_idx', tile_data['tile_idx'])
+                    if task_id not in completed_tasks:
+                        completed_tasks[task_id] = tile_data
+                        collected += 1
+            if is_last:
+                if worker_id in job_data.worker_status:
+                    del job_data.worker_status[worker_id]
+
+        return collected
+
+
+async def _get_completed_count(multi_job_id):
+    """Get count of completed tasks."""
+    prompt_server = ensure_tile_jobs_initialized()
+    async with prompt_server.distributed_tile_jobs_lock:
+        job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
+        if isinstance(job_data, BaseJobState):
+            return len(job_data.completed_tasks)
+        return 0
+
+
+async def _mark_task_completed(multi_job_id, task_id, result):
+    """Mark a task as completed."""
+    prompt_server = ensure_tile_jobs_initialized()
+    async with prompt_server.distributed_tile_jobs_lock:
+        job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
+        if isinstance(job_data, BaseJobState):
+            job_data.completed_tasks[task_id] = result
+
+
+async def _cleanup_job(multi_job_id):
+    """Cleanup the job data."""
+    prompt_server = ensure_tile_jobs_initialized()
+    async with prompt_server.distributed_tile_jobs_lock:
+        if multi_job_id in prompt_server.distributed_pending_tile_jobs:
+            del prompt_server.distributed_pending_tile_jobs[multi_job_id]
+            debug_log(f"Cleaned up job {multi_job_id}")
@@ -0,0 +1,150 @@
+import time
+
+from ..utils.config import load_config
+from ..utils.constants import HEARTBEAT_TIMEOUT
+from ..utils.logging import debug_log, log
+from ..utils.network import build_worker_url, probe_worker
+from .job_models import BaseJobState
+from .job_store import ensure_tile_jobs_initialized
+
+
+def _find_worker_record(worker_id):
+    """Return worker config entry by id, or None when missing."""
+    workers = load_config().get("workers", [])
+    return next((w for w in workers if str(w.get("id")) == str(worker_id)), None)
+
+
+async def _check_and_requeue_timed_out_workers(multi_job_id, total_tasks):
+    """Check timed out workers and requeue their tasks. Returns requeued count."""
+    prompt_server = ensure_tile_jobs_initialized()
+    current_time = time.time()
+
+    # Allow override via config setting 'worker_timeout_seconds'
+    cfg = load_config()
+    hb_timeout = int(cfg.get("settings", {}).get("worker_timeout_seconds", HEARTBEAT_TIMEOUT))
+
+    # Snapshot timed-out workers and job details under lock.
+    async with prompt_server.distributed_tile_jobs_lock:
+        job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
+        if not isinstance(job_data, BaseJobState):
+            return 0
+
+        completed_tasks_snapshot = set(job_data.completed_tasks.keys())
+        batched_static_snapshot = bool(job_data.batched_static)
+        num_tiles_per_image_snapshot = int(job_data.num_tiles_per_image or 1)
+        batch_size_snapshot = int(job_data.batch_size or 1)
+
+        timed_out_workers = []
+        for worker, last_heartbeat in list(job_data.worker_status.items()):
+            age = current_time - float(last_heartbeat)
+            debug_log(f"Timeout check: worker={worker} age={age:.1f}s threshold={hb_timeout}s")
+            if age > hb_timeout:
+                timed_out_workers.append(
+                    {
+                        "worker_id": worker,
+                        "last_heartbeat": float(last_heartbeat),
+                        "assigned_tasks": list(job_data.assigned_to_workers.get(worker, [])),
+                    }
+                )
+
+    if not timed_out_workers:
+        return 0
+
+    # Probe outside lock to avoid lock contention on network latency.
+    workers_to_requeue = []
+    workers_graced = []
+    for worker_info in timed_out_workers:
+        worker = worker_info["worker_id"]
+        assigned = worker_info["assigned_tasks"]
+        age = current_time - worker_info["last_heartbeat"]
+
+        incomplete_assigned = 0
+        try:
+            if assigned:
+                if batched_static_snapshot:
+                    for task_id in assigned:
+                        for b in range(batch_size_snapshot):
+                            gidx = b * num_tiles_per_image_snapshot + task_id
+                            if gidx not in completed_tasks_snapshot:
+                                incomplete_assigned += 1
+                                break
+                else:
+                    for task_id in assigned:
+                        if task_id not in completed_tasks_snapshot:
+                            incomplete_assigned += 1
+            debug_log(
+                f"Assigned diagnostics: total_assigned={len(assigned)} "
+                f"incomplete_assigned={incomplete_assigned}"
+            )
+        except Exception as e:
+            debug_log(f"Assigned diagnostics failed for worker {worker}: {e}")
+
+        busy = False
+        probe_queue = None
+        try:
+            worker_record = _find_worker_record(worker)
+            if worker_record:
+                worker_url = build_worker_url(worker_record)
+                debug_log(f"Probing worker {worker} at {worker_url}/prompt")
+                payload = await probe_worker(worker_url, timeout=2.0)
+                if payload is not None:
+                    probe_queue = int(payload.get("exec_info", {}).get("queue_remaining", 0))
+                    busy = probe_queue is not None and probe_queue > 0
+            else:
+                debug_log(f"Probe skipped; worker {worker} not found in config")
+        except Exception as e:
+            debug_log(f"Probe failed for worker {worker}: {e}")
+        finally:
+            debug_log(
+                f"Probe diagnostics: online={probe_queue is not None} queue_remaining={probe_queue}"
+            )
+
+        if busy:
+            workers_graced.append(worker)
+            debug_log(f"Heartbeat grace: worker {worker} busy via probe; skipping requeue")
+            continue
+
+        log(f"Worker {worker} heartbeat timed out after {age:.1f}s")
+        workers_to_requeue.append((worker, assigned))
+
+    # Re-acquire lock and apply requeue/cleanup decisions.
+    async with prompt_server.distributed_tile_jobs_lock:
+        job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
+        if not isinstance(job_data, BaseJobState):
+            return 0
+
+        # Refresh heartbeat for workers that we proved are still busy.
+        for worker in workers_graced:
+            if worker in job_data.worker_status:
+                job_data.worker_status[worker] = current_time
+
+        requeued_count = 0
+        completed_tasks = job_data.completed_tasks
+        batched_static = bool(job_data.batched_static)
+        num_tiles_per_image = int(job_data.num_tiles_per_image or 1)
+        batch_size = int(job_data.batch_size or 1)
+        for worker, assigned_snapshot in workers_to_requeue:
+            # Use current assignments if present, falling back to the snapshot.
+            assigned_tasks = list(job_data.assigned_to_workers.get(worker, assigned_snapshot))
+            for task_id in assigned_tasks:
+                # If batched_static, task_id is a tile_idx; consider it complete only if
+                # all corresponding global_idx entries are present in completed_tasks.
+                if batched_static:
+                    all_done = True
+                    for b in range(batch_size):
+                        gidx = b * num_tiles_per_image + task_id
+                        if gidx not in completed_tasks:
+                            all_done = False
+                            break
+                    if not all_done:
+                        await job_data.pending_tasks.put(task_id)
+                        requeued_count += 1
+                else:
+                    if task_id not in completed_tasks:
+                        await job_data.pending_tasks.put(task_id)
+                        requeued_count += 1
+            job_data.worker_status.pop(worker, None)
+            if worker in job_data.assigned_to_workers:
+                job_data.assigned_to_workers[worker] = []
+
+        return requeued_count
@@ -0,0 +1,313 @@
+import asyncio, torch
+from PIL import Image
+import comfy.model_management
+from ...utils.logging import debug_log, log
+from ...utils.image import tensor_to_pil, pil_to_tensor
+from ...utils.async_helpers import run_async_in_server_loop
+from ...utils.config import get_worker_timeout_seconds
+from ...utils.constants import TILE_WAIT_TIMEOUT, TILE_SEND_TIMEOUT
+from ..job_store import ensure_tile_jobs_initialized, init_dynamic_job
+
+
+class DynamicModeMixin:
+    """
+    Dynamic (per-image queue) USDU mode behaviors for master and worker roles.
+
+    Expected co-mixins on `self`:
+    - TileOpsMixin (`calculate_tiles`, `_slice_conditioning`, `_process_and_blend_tile`).
+    - JobStateMixin (image queue/task completion helpers).
+    - WorkerCommsMixin (`_request_image_from_master`, `_send_full_image_to_master`, `_send_heartbeat_to_master`).
+    """
+
+    def process_master_dynamic(self, upscaled_image, model, positive, negative, vae,
+                              seed, steps, cfg, sampler_name, scheduler, denoise,
+                              tile_width, tile_height, padding, mask_blur,
+                              force_uniform_tiles, tiled_decode, multi_job_id, enabled_workers):
+        """Dynamic mode for large batches - assigns whole images to workers dynamically, including master."""
+        # Get batch size and dimensions
+        batch_size, height, width, _ = upscaled_image.shape
+        num_workers = len(enabled_workers)
+        
+        log(f"USDU Dist: Image queue distribution | Batch {batch_size} | Workers {num_workers} | Canvas {width}x{height} | Tile {tile_width}x{tile_height}")
+
+        # No fixed share - all images are dynamic
+        all_indices = list(range(batch_size))
+        
+        debug_log(f"Processing {batch_size} images dynamically across master + {num_workers} workers.")
+        
+        # Calculate tiles for processing
+        all_tiles = self.calculate_tiles(width, height, tile_width, tile_height, force_uniform_tiles)
+        
+        # Initialize job queue for communication
+        try:
+            run_async_in_server_loop(
+                init_dynamic_job(multi_job_id, batch_size, enabled_workers, all_indices),
+                timeout=2.0
+            )
+        except Exception as e:
+            debug_log(f"UltimateSDUpscale Master - Queue initialization error: {e}")
+            raise RuntimeError(f"Failed to initialize dynamic mode queue: {e}")
+        
+        # Convert batch to PIL list
+        result_images = [tensor_to_pil(upscaled_image[b:b+1], 0).convert('RGB').copy() for b in range(batch_size)]
+        
+        # Process images dynamically with master participating
+        prompt_server = ensure_tile_jobs_initialized()
+        processed_count = 0
+        consecutive_retries = 0
+        max_consecutive_retries = 10
+        
+        # Process loop - master pulls from queue and processes synchronously
+        while processed_count < batch_size:
+            # Try to get an image to process
+            image_idx = run_async_in_server_loop(
+                self._get_next_image_index(multi_job_id),
+                timeout=5.0  # Short timeout to allow frequent checks
+            )
+
+            if image_idx is not None:
+                # Reset retry counter and process locally
+                consecutive_retries = 0
+                debug_log(f"Master processing image {image_idx} dynamically")
+                processed_count += 1
+
+                # Process locally
+                single_tensor = upscaled_image[image_idx:image_idx+1]
+                local_image = result_images[image_idx]
+                image_seed = seed
+                
+                # Pre-slice conditioning once per image (not per tile)
+                positive_sliced, negative_sliced = self._slice_conditioning(positive, negative, image_idx)
+                
+                for tile_idx, pos in enumerate(all_tiles):
+                    source_tensor = pil_to_tensor(local_image)
+                    if single_tensor.is_cuda:
+                        source_tensor = source_tensor.cuda()
+                    local_image = self._process_and_blend_tile(
+                        tile_idx, pos, source_tensor, local_image,
+                        model, positive_sliced, negative_sliced, vae, image_seed, steps, cfg,
+                        sampler_name, scheduler, denoise, tile_width, tile_height,
+                        padding, mask_blur, width, height, force_uniform_tiles,
+                        tiled_decode, batch_idx=image_idx
+                    )
+                    
+                    # Yield after each tile to minimize worker downtime
+                    run_async_in_server_loop(self._async_yield(), timeout=0.1)
+                    # Note: No per-tile drain here – that's what makes this "per-image"
+                
+                result_images[image_idx] = local_image
+                
+                # Mark as completed
+                run_async_in_server_loop(
+                    self._mark_image_completed(multi_job_id, image_idx, local_image),
+                    timeout=5.0
+                )
+                
+                # NEW: Drain after the full image is marked complete (catches workers who finished during master's processing)
+                drained_count = run_async_in_server_loop(
+                    self._drain_worker_results_queue(multi_job_id),
+                    timeout=5.0
+                )
+                
+                if drained_count > 0:
+                    debug_log(f"Drained {drained_count} worker images after master's image {image_idx}")
+                
+                # NEW: Log overall progress (includes master's image + any drained workers)
+                completed_now = run_async_in_server_loop(
+                    self._get_total_completed_count(multi_job_id),
+                    timeout=1.0
+                )
+                log(f"USDU Dist: Images progress {completed_now}/{batch_size}")
+                
+                # Yield to allow workers to get new images after completing one
+                run_async_in_server_loop(self._async_yield(), timeout=0.1)
+            else:
+                # Queue empty: collect any queued worker results to update progress
+                drained_count = run_async_in_server_loop(
+                    self._drain_worker_results_queue(multi_job_id),
+                    timeout=5.0
+                )
+                run_async_in_server_loop(self._async_yield(), timeout=0.1)  # Yield after drain
+                
+                # Check for timed out workers and requeue their images
+                requeued_count = run_async_in_server_loop(
+                    self._check_and_requeue_timed_out_workers(multi_job_id, batch_size),
+                    timeout=5.0
+                )
+                run_async_in_server_loop(self._async_yield(), timeout=0.1)  # Yield after requeue
+                
+                if requeued_count > 0:
+                    log(f"Requeued {requeued_count} images from timed out workers")
+                    consecutive_retries = 0  # Reset since we have work to do
+                    continue
+
+                # Now check total completed (includes newly collected)
+                completed_now = run_async_in_server_loop(
+                    self._get_total_completed_count(multi_job_id),
+                    timeout=1.0
+                )
+                
+                log(f"USDU Dist: Images progress {completed_now}/{batch_size}")
+                
+                if completed_now >= batch_size:
+                    break
+
+                run_async_in_server_loop(self._async_yield(), timeout=0.1)  # Yield before pending check
+                
+                # Check if there are pending images in the queue (could be requeued)
+                pending_count = run_async_in_server_loop(
+                    self._get_pending_count(multi_job_id),
+                    timeout=1.0
+                )
+                
+                if pending_count > 0:
+                    consecutive_retries = 0  # Reset retries since there's work to do
+                    continue
+
+                consecutive_retries += 1
+                if consecutive_retries >= max_consecutive_retries:
+                    log(f"Max retries ({max_consecutive_retries}) reached. Forcing collection of remaining results.")
+                    break  # Force exit to collection phase
+
+                debug_log("Waiting for workers")
+                # Use async sleep to allow event loop to process worker requests
+                run_async_in_server_loop(asyncio.sleep(2), timeout=3.0)
+        
+        debug_log(f"Master processed {processed_count} images locally")
+        
+        # Get all completed images to check what needs to be collected
+        all_completed = run_async_in_server_loop(
+            self._get_all_completed_images(multi_job_id),
+            timeout=5.0
+        )
+        
+        # Calculate how many we still need to collect
+        remaining_to_collect = batch_size - len(all_completed)
+        
+        if remaining_to_collect > 0:
+            debug_log(f"Waiting for {remaining_to_collect} more images from workers")
+            # Use the unified worker timeout for the collection phase
+            collection_timeout = float(get_worker_timeout_seconds())
+            collected_images = run_async_in_server_loop(
+                self._async_collect_dynamic_images(multi_job_id, remaining_to_collect, num_workers, batch_size, processed_count),
+                timeout=collection_timeout
+            )
+            
+            # Merge collected with already completed
+            all_completed.update(collected_images)
+        
+        # Update result images with all completed images
+        for idx, processed_img in all_completed.items():
+            if idx < batch_size:
+                result_images[idx] = processed_img
+        
+        # Convert back to tensor
+        result_tensor = torch.cat([pil_to_tensor(img) for img in result_images], dim=0) if batch_size > 1 else pil_to_tensor(result_images[0])
+        if upscaled_image.is_cuda:
+            result_tensor = result_tensor.cuda()
+        
+        debug_log(f"UltimateSDUpscale Master - Job {multi_job_id} complete")
+        log(f"Completed processing all {batch_size} images")
+        return (result_tensor,)
+
+    def process_worker_dynamic(self, upscaled_image, model, positive, negative, vae,
+                               seed, steps, cfg, sampler_name, scheduler, denoise,
+                               tile_width, tile_height, padding, mask_blur,
+                               force_uniform_tiles, tiled_decode, multi_job_id, master_url,
+                               worker_id, enabled_worker_ids, dynamic_threshold):
+        """Worker processing in dynamic mode - processes whole images."""
+        # Round tile dimensions
+        tile_width = self.round_to_multiple(tile_width)
+        tile_height = self.round_to_multiple(tile_height)
+
+        # Get dimensions and tile grid
+        batch_size, height, width, _ = upscaled_image.shape
+        all_tiles = self.calculate_tiles(width, height, tile_width, tile_height, force_uniform_tiles)
+        log(f"USDU Dist Worker[{worker_id[:8]}]: Processing image queue | Batch {batch_size}")
+
+        # Keep track of processed images for is_last detection
+        processed_count = 0
+
+        # Poll for job readiness to avoid races during master init
+        max_poll_attempts = 20  # ~20s at 1s sleep
+        if not self._poll_job_ready(multi_job_id, master_url, worker_id=worker_id, max_attempts=max_poll_attempts):
+            log(f"Job {multi_job_id} not ready after {max_poll_attempts} attempts, aborting")
+            return (upscaled_image,)
+
+        # Loop to request and process images
+        while True:
+            # Request an image to process
+            image_idx, estimated_remaining = run_async_in_server_loop(
+                self._request_image_from_master(multi_job_id, master_url, worker_id),
+                timeout=TILE_WAIT_TIMEOUT
+            )
+
+            if image_idx is None:
+                debug_log(f"USDU Dist Worker - No more images to process")
+                break
+
+            debug_log(f"Worker[{worker_id[:8]}] - Assigned image {image_idx}")
+            processed_count += 1
+
+            # Determine if this should be marked as last for this worker
+            is_last_for_worker = (estimated_remaining == 0)
+
+            # Extract single image tensor
+            single_tensor = upscaled_image[image_idx:image_idx+1]
+
+            # Convert to PIL for processing
+            local_image = tensor_to_pil(single_tensor, 0).copy()
+
+            # Process all tiles for this image
+            image_seed = seed
+
+            # Pre-slice conditioning once per image (not per tile)
+            positive_sliced, negative_sliced = self._slice_conditioning(positive, negative, image_idx)
+
+            for tile_idx, pos in enumerate(all_tiles):
+                source_tensor = pil_to_tensor(local_image)
+                if single_tensor.is_cuda:
+                    source_tensor = source_tensor.cuda()
+                local_image = self._process_and_blend_tile(
+                    tile_idx, pos, source_tensor, local_image,
+                    model, positive_sliced, negative_sliced, vae, image_seed, steps, cfg,
+                    sampler_name, scheduler, denoise, tile_width, tile_height,
+                    padding, mask_blur, width, height, force_uniform_tiles,
+                    tiled_decode, batch_idx=image_idx
+                )
+                run_async_in_server_loop(
+                    self._send_heartbeat_to_master(multi_job_id, master_url, worker_id),
+                    timeout=5.0
+                )
+
+            # Send processed image back to master
+            try:
+                # Use the estimated remaining to determine if this is the last image
+                is_last = is_last_for_worker
+                run_async_in_server_loop(
+                    self._send_full_image_to_master(local_image, image_idx, multi_job_id,
+                                                    master_url, worker_id, is_last),
+                    timeout=TILE_SEND_TIMEOUT
+                )
+                # Send heartbeat after processing
+                run_async_in_server_loop(
+                    self._send_heartbeat_to_master(multi_job_id, master_url, worker_id),
+                    timeout=5.0
+                )
+                if is_last:
+                    break
+            except Exception as e:
+                log(f"USDU Dist Worker[{worker_id[:8]}] - Error sending image {image_idx}: {e}")
+                # Continue processing other images
+
+        # Send final is_last signal
+        debug_log(f"Worker[{worker_id[:8]}] processed {processed_count} images, sending completion signal")
+        try:
+            run_async_in_server_loop(
+                self._send_worker_complete_signal(multi_job_id, master_url, worker_id),
+                timeout=TILE_SEND_TIMEOUT
+            )
+        except Exception as e:
+            log(f"USDU Dist Worker[{worker_id[:8]}] - Error sending completion signal: {e}")
+
+        return (upscaled_image,)
@@ -0,0 +1,72 @@
+import math, torch
+from PIL import Image
+from ...utils.logging import debug_log, log
+from ...utils.image import tensor_to_pil, pil_to_tensor
+
+
+class SingleGpuModeMixin:
+    def process_single_gpu(self, upscaled_image, model, positive, negative, vae,
+                          seed, steps, cfg, sampler_name, scheduler, denoise,
+                          tile_width, tile_height, padding, mask_blur, force_uniform_tiles, tiled_decode):
+        """Process all tiles on a single GPU (no distribution), batching per tile like USDU."""
+        # Round tile dimensions
+        tile_width = self.round_to_multiple(tile_width)
+        tile_height = self.round_to_multiple(tile_height)
+
+        # Get image dimensions and batch size
+        batch_size, height, width, _ = upscaled_image.shape
+
+        # Calculate all tiles
+        all_tiles = self.calculate_tiles(width, height, tile_width, tile_height, force_uniform_tiles)
+
+        rows = math.ceil(height / tile_height)
+        cols = math.ceil(width / tile_width)
+        log(
+            f"USDU Dist: Single GPU | Canvas {width}x{height} | Tile {tile_width}x{tile_height} | Grid {rows}x{cols} ({len(all_tiles)} tiles/image) | Batch {batch_size}"
+        )
+
+        # Prepare result images list
+        result_images = []
+        for b in range(batch_size):
+            image_pil = tensor_to_pil(upscaled_image[b:b+1], 0).convert('RGB')
+            result_images.append(image_pil.copy())
+
+        # Precompute tile masks once
+        tile_masks = []
+        for tx, ty in all_tiles:
+            tile_masks.append(self.create_tile_mask(width, height, tx, ty, tile_width, tile_height, mask_blur))
+
+        # Process tiles batched across images
+        for tile_idx, (tx, ty) in enumerate(all_tiles):
+            # Progressive state parity: extract each tile from the current updated image batch.
+            source_batch = torch.cat([pil_to_tensor(img) for img in result_images], dim=0)
+            if upscaled_image.is_cuda:
+                source_batch = source_batch.cuda()
+
+            # Extract batched tile
+            tile_batch, x1, y1, ew, eh = self.extract_batch_tile_with_padding(
+                source_batch, tx, ty, tile_width, tile_height, padding, force_uniform_tiles
+            )
+
+            # Process batch
+            region = (x1, y1, x1 + ew, y1 + eh)
+            processed_batch = self.process_tiles_batch(tile_batch, model, positive, negative, vae,
+                                                       seed, steps, cfg, sampler_name, scheduler, denoise,
+                                                       tiled_decode, region, (width, height))
+
+            # Blend results back into each image using cached mask
+            tile_mask = tile_masks[tile_idx]
+            for b in range(batch_size):
+                tile_pil = tensor_to_pil(processed_batch, b)
+                # Resize back to extracted size
+                if tile_pil.size != (ew, eh):
+                    tile_pil = tile_pil.resize((ew, eh), Image.LANCZOS)
+                result_images[b] = self.blend_tile(result_images[b], tile_pil, x1, y1, (ew, eh), tile_mask, padding)
+
+        # Convert back to tensor
+        result_tensors = [pil_to_tensor(img) for img in result_images]
+        result_tensor = torch.cat(result_tensors, dim=0)
+        if upscaled_image.is_cuda:
+            result_tensor = result_tensor.cuda()
+
+        return (result_tensor,)
@@ -0,0 +1,570 @@
+import asyncio, time, torch
+from PIL import Image
+import comfy.model_management
+from ...utils.logging import debug_log, log
+from ...utils.image import tensor_to_pil, pil_to_tensor
+from ...utils.async_helpers import run_async_in_server_loop
+from ...utils.config import get_worker_timeout_seconds
+from ...utils.constants import (
+    HEARTBEAT_INTERVAL,
+    JOB_POLL_INTERVAL,
+    JOB_POLL_MAX_ATTEMPTS,
+    MAX_BATCH,
+    TILE_SEND_TIMEOUT,
+    TILE_WAIT_TIMEOUT,
+)
+from ..job_store import (
+    ensure_tile_jobs_initialized, init_static_job_batched,
+    _mark_task_completed, _cleanup_job, _drain_results_queue, _get_completed_count,
+)
+from ..job_models import TileJobState
+
+
+class StaticModeMixin:
+    """
+    Static (tile-queue) USDU mode behaviors for master and worker roles.
+
+    Expected co-mixins on `self`:
+    - TileOpsMixin (`calculate_tiles`, tile extract/blend helpers).
+    - JobStateMixin (`_get_next_tile_index`, `_get_all_completed_tasks`, requeue checks).
+    - WorkerCommsMixin (`send_tiles_batch_to_master`, `_request_tile_from_master`, `_send_heartbeat_to_master`).
+    """
+
+    def _poll_job_ready(self, multi_job_id, master_url, worker_id=None, max_attempts=JOB_POLL_MAX_ATTEMPTS):
+        """Poll master for job readiness to avoid worker/master initialization race."""
+        for attempt in range(max_attempts):
+            ready = run_async_in_server_loop(
+                self._check_job_status(multi_job_id, master_url),
+                timeout=5.0
+            )
+            if ready:
+                if worker_id:
+                    debug_log(f"Worker[{worker_id[:8]}] job {multi_job_id} ready after {attempt} attempts")
+                else:
+                    debug_log(f"Job {multi_job_id} ready after {attempt} attempts")
+                return True
+            time.sleep(JOB_POLL_INTERVAL)
+        return False
+
+    def _extract_and_process_tile(
+        self,
+        upscaled_image,
+        tile_id,
+        all_tiles,
+        tile_width,
+        tile_height,
+        padding,
+        force_uniform_tiles,
+        model,
+        positive,
+        negative,
+        vae,
+        seed,
+        steps,
+        cfg,
+        sampler_name,
+        scheduler,
+        denoise,
+        tiled_decode,
+        width,
+        height,
+    ):
+        """Extract one tile position for the whole batch and process it."""
+        tx, ty = all_tiles[tile_id]
+        tile_batch, x1, y1, ew, eh = self.extract_batch_tile_with_padding(
+            upscaled_image, tx, ty, tile_width, tile_height, padding, force_uniform_tiles
+        )
+        region = (x1, y1, x1 + ew, y1 + eh)
+        processed_batch = self.process_tiles_batch(
+            tile_batch, model, positive, negative, vae,
+            seed, steps, cfg, sampler_name, scheduler, denoise, tiled_decode,
+            region, (width, height)
+        )
+        return processed_batch, x1, y1, ew, eh
+
+    def _flush_tiles_to_master(
+        self,
+        processed_tiles,
+        multi_job_id,
+        master_url,
+        padding,
+        worker_id,
+        is_final_flush=False,
+    ):
+        """Send accumulated tile payloads to master and return a fresh accumulator."""
+        if not processed_tiles:
+            if is_final_flush:
+                run_async_in_server_loop(
+                    self.send_tiles_batch_to_master(
+                        [],
+                        multi_job_id,
+                        master_url,
+                        padding,
+                        worker_id,
+                        is_final_flush=True,
+                    ),
+                    timeout=TILE_SEND_TIMEOUT,
+                )
+            return processed_tiles
+        run_async_in_server_loop(
+            self.send_tiles_batch_to_master(
+                processed_tiles,
+                multi_job_id,
+                master_url,
+                padding,
+                worker_id,
+                is_final_flush=is_final_flush,
+            ),
+            timeout=TILE_SEND_TIMEOUT
+        )
+        return []
+
+    def _master_process_one_tile(
+        self,
+        tile_id,
+        all_tiles,
+        upscaled_image,
+        result_images,
+        tile_masks,
+        multi_job_id,
+        batch_size,
+        num_tiles_per_image,
+        tile_width,
+        tile_height,
+        padding,
+        force_uniform_tiles,
+        model,
+        positive,
+        negative,
+        vae,
+        seed,
+        steps,
+        cfg,
+        sampler_name,
+        scheduler,
+        denoise,
+        tiled_decode,
+        width,
+        height,
+    ):
+        """Process one tile_id across the batch and blend into result_images."""
+        source_batch = torch.cat([pil_to_tensor(img) for img in result_images], dim=0)
+        if upscaled_image.is_cuda:
+            source_batch = source_batch.cuda()
+        processed_batch, x1, y1, ew, eh = self._extract_and_process_tile(
+            source_batch,
+            tile_id,
+            all_tiles,
+            tile_width,
+            tile_height,
+            padding,
+            force_uniform_tiles,
+            model,
+            positive,
+            negative,
+            vae,
+            seed,
+            steps,
+            cfg,
+            sampler_name,
+            scheduler,
+            denoise,
+            tiled_decode,
+            width,
+            height,
+        )
+        tile_mask = tile_masks[tile_id]
+        out_bs = processed_batch.shape[0] if hasattr(processed_batch, "shape") else batch_size
+        processed_items = min(batch_size, out_bs)
+        for b in range(processed_items):
+            tile_pil = tensor_to_pil(processed_batch, b)
+            if tile_pil.size != (ew, eh):
+                tile_pil = tile_pil.resize((ew, eh), Image.LANCZOS)
+            result_images[b] = self.blend_tile(result_images[b], tile_pil, x1, y1, (ew, eh), tile_mask, padding)
+            global_idx = b * num_tiles_per_image + tile_id
+            run_async_in_server_loop(
+                _mark_task_completed(multi_job_id, global_idx, {'batch_idx': b, 'tile_idx': tile_id}),
+                timeout=5.0
+            )
+        return processed_items
+
+    def _process_worker_static_sync(self, upscaled_image, model, positive, negative, vae,
+                                    seed, steps, cfg, sampler_name, scheduler, denoise,
+                                    tile_width, tile_height, padding, mask_blur,
+                                    force_uniform_tiles, tiled_decode, multi_job_id, master_url,
+                                    worker_id, enabled_workers):
+        """Worker static mode processing with optional dynamic queue pulling."""
+        # Round tile dimensions
+        tile_width = self.round_to_multiple(tile_width)
+        tile_height = self.round_to_multiple(tile_height)
+        
+        # Get dimensions and calculate tiles
+        _, height, width, _ = upscaled_image.shape
+        all_tiles = self.calculate_tiles(width, height, tile_width, tile_height, force_uniform_tiles)
+        num_tiles_per_image = len(all_tiles)
+        batch_size = upscaled_image.shape[0]
+        total_tiles = batch_size * num_tiles_per_image
+        
+        processed_tiles = []
+        working_images = []
+        for b in range(batch_size):
+            image_pil = tensor_to_pil(upscaled_image[b:b+1], 0)
+            working_images.append(image_pil.copy())
+        tile_masks = []
+        for tx, ty in all_tiles:
+            tile_masks.append(self.create_tile_mask(width, height, tx, ty, tile_width, tile_height, mask_blur))
+        
+        # Dynamic queue mode (static processing): process batched-per-tile
+        log(f"USDU Dist Worker[{worker_id[:8]}]: Canvas {width}x{height} | Tile {tile_width}x{tile_height} | Tiles/image {num_tiles_per_image} | Batch {batch_size}")
+        processed_count = 0
+
+        max_poll_attempts = JOB_POLL_MAX_ATTEMPTS
+        if not self._poll_job_ready(multi_job_id, master_url, worker_id=worker_id, max_attempts=max_poll_attempts):
+            log(f"Job {multi_job_id} not ready after {max_poll_attempts} attempts, aborting")
+            return (upscaled_image,)
+
+        # Main processing loop - pull tile ids from queue
+        while True:
+            # Request a tile to process
+            tile_idx, estimated_remaining, batched_static = run_async_in_server_loop(
+                self._request_tile_from_master(multi_job_id, master_url, worker_id),
+                timeout=TILE_WAIT_TIMEOUT
+            )
+
+            if tile_idx is None:
+                debug_log(f"Worker[{worker_id[:8]}] - No more tiles to process")
+                break
+
+            # Always batched-per-tile in static mode
+            debug_log(f"Worker[{worker_id[:8]}] - Assigned tile_id {tile_idx}")
+            processed_count += batch_size
+            tile_id = tile_idx
+            source_batch = torch.cat([pil_to_tensor(img) for img in working_images], dim=0)
+            if upscaled_image.is_cuda:
+                source_batch = source_batch.cuda()
+            processed_batch, x1, y1, ew, eh = self._extract_and_process_tile(
+                source_batch,
+                tile_id,
+                all_tiles,
+                tile_width,
+                tile_height,
+                padding,
+                force_uniform_tiles,
+                model,
+                positive,
+                negative,
+                vae,
+                seed,
+                steps,
+                cfg,
+                sampler_name,
+                scheduler,
+                denoise,
+                tiled_decode,
+                width,
+                height,
+            )
+            # Queue results
+            for b in range(batch_size):
+                tile_pil = tensor_to_pil(processed_batch, b)
+                if tile_pil.size != (ew, eh):
+                    tile_pil = tile_pil.resize((ew, eh), Image.LANCZOS)
+                working_images[b] = self.blend_tile(
+                    working_images[b],
+                    tile_pil,
+                    x1,
+                    y1,
+                    (ew, eh),
+                    tile_masks[tile_id],
+                    padding,
+                )
+                processed_tiles.append({
+                    'tile': processed_batch[b:b+1],
+                    'tile_idx': tile_id,
+                    'x': x1,
+                    'y': y1,
+                    'extracted_width': ew,
+                    'extracted_height': eh,
+                    'padding': padding,
+                    'batch_idx': b,
+                    'global_idx': b * num_tiles_per_image + tile_id
+                })
+
+            # Send heartbeat
+            try:
+                run_async_in_server_loop(
+                    self._send_heartbeat_to_master(multi_job_id, master_url, worker_id),
+                    timeout=5.0
+                )
+            except Exception as e:
+                debug_log(f"Worker[{worker_id[:8]}] heartbeat failed: {e}")
+
+            # Send tiles in batches within loop
+            if len(processed_tiles) >= MAX_BATCH:
+                processed_tiles = self._flush_tiles_to_master(
+                    processed_tiles, multi_job_id, master_url, padding, worker_id, is_final_flush=False
+                )
+
+        # Send any remaining tiles
+        processed_tiles = self._flush_tiles_to_master(
+            processed_tiles, multi_job_id, master_url, padding, worker_id, is_final_flush=True
+        )
+        
+        debug_log(f"Worker {worker_id} completed all assigned and requeued tiles")
+        return (upscaled_image,)
+
+    async def _async_collect_and_monitor_static(self, multi_job_id, total_tiles, expected_total):
+        """Async helper for collection and monitoring in static mode.
+        Returns collected tasks dict. Caller should check if all tasks are complete."""
+        last_progress_log = time.time()
+        progress_interval = 5.0
+        last_heartbeat_check = time.time()
+        last_completed_count = 0
+        
+        while True:
+            # Check for user interruption
+            if comfy.model_management.processing_interrupted():
+                log("Processing interrupted by user")
+                raise comfy.model_management.InterruptProcessingException()
+            
+            # Drain any pending results
+            collected_count = await _drain_results_queue(multi_job_id)
+            
+            # Check and requeue timed-out workers periodically
+            current_time = time.time()
+            if current_time - last_heartbeat_check >= HEARTBEAT_INTERVAL:
+                requeued_count = await self._check_and_requeue_timed_out_workers(multi_job_id, expected_total)
+                if requeued_count > 0:
+                    log(f"Requeued {requeued_count} tasks from timed-out workers")
+                last_heartbeat_check = current_time
+            
+            # Get current completion count
+            completed_count = await _get_completed_count(multi_job_id)
+            
+            # Progress logging
+            if current_time - last_progress_log >= progress_interval:
+                log(f"Progress: {completed_count}/{expected_total} tasks completed")
+                last_progress_log = current_time
+            
+            # Check if all tasks are completed
+            if completed_count >= expected_total:
+                debug_log(f"All {expected_total} tasks completed")
+                break
+            
+            # If no active workers remain and there are pending tasks, return for local processing
+            prompt_server = ensure_tile_jobs_initialized()
+            async with prompt_server.distributed_tile_jobs_lock:
+                job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
+                if isinstance(job_data, TileJobState):
+                    pending_queue = job_data.pending_tasks
+                    active_workers = list(job_data.worker_status.keys())
+                    if pending_queue and not pending_queue.empty() and len(active_workers) == 0:
+                        log(f"No active workers remaining with {expected_total - completed_count} tasks pending. Returning for local processing.")
+                        break
+            
+            # Wait a bit before next check
+            await asyncio.sleep(0.1)
+        
+        # Get all completed tasks for return
+        return await self._get_all_completed_tasks(multi_job_id)
+
+    def _process_master_static_sync(self, upscaled_image, model, positive, negative, vae,
+                                    seed, steps, cfg, sampler_name, scheduler, denoise,
+                                    tile_width, tile_height, padding, mask_blur,
+                                    force_uniform_tiles, tiled_decode, multi_job_id, enabled_workers,
+                                    all_tiles, num_tiles_per_image):
+        """Static mode master processing with optional dynamic queue pulling."""
+        batch_size = upscaled_image.shape[0]
+        _, height, width, _ = upscaled_image.shape
+        total_tiles = batch_size * num_tiles_per_image
+        
+        # Convert batch to PIL list for processing
+        result_images = []
+        for b in range(batch_size):
+            image_pil = tensor_to_pil(upscaled_image[b:b+1], 0)
+            result_images.append(image_pil.copy())
+        
+        # Initialize queue: pending queue holds tile ids (batched per tile)
+        log("USDU Dist: Using tile queue distribution")
+        run_async_in_server_loop(
+            init_static_job_batched(multi_job_id, batch_size, num_tiles_per_image, enabled_workers),
+            timeout=10.0
+        )
+        debug_log(
+            f"Initialized tile-id queue with {num_tiles_per_image} ids for batch {batch_size}"
+        )
+
+        # Precompute masks for all tile positions to avoid repeated Gaussian blur work during blending
+        tile_masks = []
+        for idx, (tx, ty) in enumerate(all_tiles):
+            tile_masks.append(self.create_tile_mask(width, height, tx, ty, tile_width, tile_height, mask_blur))
+
+        processed_count = 0
+        consecutive_no_tile = 0
+        max_consecutive_no_tile = 2
+
+        while processed_count < total_tiles:
+            comfy.model_management.throw_exception_if_processing_interrupted()
+            tile_idx = run_async_in_server_loop(
+                self._get_next_tile_index(multi_job_id),
+                timeout=5.0
+            )
+            if tile_idx is not None:
+                consecutive_no_tile = 0
+                tile_id = tile_idx
+                processed_count += self._master_process_one_tile(
+                    tile_id,
+                    all_tiles,
+                    upscaled_image,
+                    result_images,
+                    tile_masks,
+                    multi_job_id,
+                    batch_size,
+                    num_tiles_per_image,
+                    tile_width,
+                    tile_height,
+                    padding,
+                    force_uniform_tiles,
+                    model,
+                    positive,
+                    negative,
+                    vae,
+                    seed,
+                    steps,
+                    cfg,
+                    sampler_name,
+                    scheduler,
+                    denoise,
+                    tiled_decode,
+                    width,
+                    height,
+                )
+                log(f"USDU Dist: Tiles progress {processed_count}/{total_tiles} (tile {tile_id})")
+            else:
+                consecutive_no_tile += 1
+                if consecutive_no_tile >= max_consecutive_no_tile:
+                    debug_log(f"Master processed {processed_count} tiles, moving to collection phase")
+                    break
+                time.sleep(0.1)
+        master_processed_count = processed_count
+        
+        # Continue processing any remaining tiles while collecting worker results
+        remaining_tiles = total_tiles - master_processed_count
+        if remaining_tiles > 0:
+            debug_log(f"Master waiting for {remaining_tiles} tiles from workers")
+            
+            # Collect worker results using async operations
+            try:
+                # Wait until either all tasks are collected or there are no active workers left
+                collected_tasks = run_async_in_server_loop(
+                    self._async_collect_and_monitor_static(multi_job_id, total_tiles, expected_total=total_tiles),
+                    timeout=None
+                )
+            except comfy.model_management.InterruptProcessingException:
+                # Clean up job on interruption
+                run_async_in_server_loop(_cleanup_job(multi_job_id), timeout=5.0)
+                raise
+            
+            # Check if we need to process any remaining tasks locally after collection
+            completed_count = len(collected_tasks)
+            if completed_count < total_tiles:
+                log(f"Processing remaining {total_tiles - completed_count} tasks locally after worker failures")
+                
+                # Process any remaining pending tasks (batched-per-tile)
+                while True:
+                    # Check for user interruption
+                    comfy.model_management.throw_exception_if_processing_interrupted()
+
+                    # Get next tile_id from pending queue
+                    tile_id = run_async_in_server_loop(
+                        self._get_next_tile_index(multi_job_id),
+                        timeout=5.0
+                    )
+
+                    if tile_id is None:
+                        break
+
+                    self._master_process_one_tile(
+                        tile_id,
+                        all_tiles,
+                        upscaled_image,
+                        result_images,
+                        tile_masks,
+                        multi_job_id,
+                        batch_size,
+                        num_tiles_per_image,
+                        tile_width,
+                        tile_height,
+                        padding,
+                        force_uniform_tiles,
+                        model,
+                        positive,
+                        negative,
+                        vae,
+                        seed,
+                        steps,
+                        cfg,
+                        sampler_name,
+                        scheduler,
+                        denoise,
+                        tiled_decode,
+                        width,
+                        height,
+                    )
+        else:
+            # Master processed all tiles
+            collected_tasks = run_async_in_server_loop(
+                self._get_all_completed_tasks(multi_job_id),
+                timeout=5.0
+            )
+        
+        # Blend worker tiles synchronously in deterministic tile order.
+        def _sort_key(item):
+            global_idx, tile_data = item
+            batch_idx = tile_data.get('batch_idx', global_idx // num_tiles_per_image)
+            tile_idx = tile_data.get('tile_idx', global_idx % num_tiles_per_image)
+            return (tile_idx, batch_idx, global_idx)
+
+        for global_idx, tile_data in sorted(collected_tasks.items(), key=_sort_key):
+            # Skip tiles that don't have tensor data (already processed)
+            if 'tensor' not in tile_data and 'image' not in tile_data:
+                continue
+            
+            batch_idx = tile_data.get('batch_idx', global_idx // num_tiles_per_image)
+            tile_idx = tile_data.get('tile_idx', global_idx % num_tiles_per_image)
+            
+            if batch_idx >= batch_size:
+                continue
+            
+            # Blend tile synchronously
+            x = tile_data.get('x', 0)
+            y = tile_data.get('y', 0)
+            # Prefer PIL image if present to avoid reconversion
+            if 'image' in tile_data:
+                tile_pil = tile_data['image']
+            else:
+                tile_tensor = tile_data['tensor']
+                tile_pil = tensor_to_pil(tile_tensor, 0)
+            orig_x, orig_y = all_tiles[tile_idx]
+            tile_mask = tile_masks[tile_idx]
+            extracted_width = tile_data.get('extracted_width', tile_width + 2 * padding)
+            extracted_height = tile_data.get('extracted_height', tile_height + 2 * padding)
+            result_images[batch_idx] = self.blend_tile(result_images[batch_idx], tile_pil,
+                                                      x, y, (extracted_width, extracted_height), tile_mask, padding)
+        
+        try:
+            # Convert back to tensor
+            if batch_size == 1:
+                result_tensor = pil_to_tensor(result_images[0])
+            else:
+                result_tensors = [pil_to_tensor(img) for img in result_images]
+                result_tensor = torch.cat(result_tensors, dim=0)
+            
+            if upscaled_image.is_cuda:
+                result_tensor = result_tensor.cuda()
+            
+            log(f"UltimateSDUpscale Master - Job {multi_job_id} complete")
+            return (result_tensor,)
+        finally:
+            # Cleanup (async operation) - always execute
+            run_async_in_server_loop(_cleanup_job(multi_job_id), timeout=5.0)
@@ -0,0 +1,64 @@
+import io
+import json
+
+from PIL import Image
+
+
+def _parse_tiles_from_form(data):
+    """Parse tiles submitted via multipart/form-data into a list of tile dicts."""
+    try:
+        padding = int(data.get('padding', 0)) if data.get('padding') is not None else 0
+    except Exception:
+        padding = 0
+
+    meta_raw = data.get('tiles_metadata')
+    if meta_raw is None:
+        raise ValueError("Missing tiles_metadata")
+
+    try:
+        metadata = json.loads(meta_raw)
+    except Exception as e:
+        raise ValueError(f"Invalid tiles_metadata JSON: {e}")
+
+    if not isinstance(metadata, list):
+        raise ValueError("tiles_metadata must be a list")
+
+    tiles = []
+    for i, meta in enumerate(metadata):
+        file_field = data.get(f'tile_{i}')
+        if file_field is None or not hasattr(file_field, 'file'):
+            raise ValueError(f"Missing tile data for index {i}")
+
+        raw = file_field.file.read()
+        try:
+            img = Image.open(io.BytesIO(raw)).convert("RGB")
+        except Exception as e:
+            raise ValueError(f"Invalid image data for tile {i}: {e}")
+
+        try:
+            tile_info = {
+                'image': img,
+                'tile_idx': int(meta.get('tile_idx', i)),
+                'x': int(meta.get('x', 0)),
+                'y': int(meta.get('y', 0)),
+                'extracted_width': int(meta.get('extracted_width', img.width)),
+                'extracted_height': int(meta.get('extracted_height', img.height)),
+                'padding': int(padding),
+            }
+        except Exception as e:
+            raise ValueError(f"Invalid metadata values for tile {i}: {e}")
+
+        if 'batch_idx' in meta:
+            try:
+                tile_info['batch_idx'] = int(meta['batch_idx'])
+            except Exception:
+                pass
+        if 'global_idx' in meta:
+            try:
+                tile_info['global_idx'] = int(meta['global_idx'])
+            except Exception:
+                pass
+
+        tiles.append(tile_info)
+
+    return tiles
@@ -0,0 +1,198 @@
+import asyncio, time
+import comfy.model_management
+import server
+from ..utils.constants import DYNAMIC_MODE_MAX_POLL_TIMEOUT, HEARTBEAT_INTERVAL
+from ..utils.logging import debug_log, log
+from ..utils.config import get_worker_timeout_seconds
+from .job_store import ensure_tile_jobs_initialized, _mark_task_completed
+from .job_timeout import _check_and_requeue_timed_out_workers
+from .job_models import BaseJobState, ImageJobState, TileJobState
+
+
+class ResultCollectorMixin:
+    """
+    Mixin for master-side result collection in USDU distributed jobs.
+
+    Expected co-mixins/attributes:
+    - JobStateMixin methods for queue/task access.
+    - `self._check_and_requeue_timed_out_workers(...)` coroutine.
+    - `self._async_yield(...)` optional helper from WorkerCommsMixin.
+    """
+
+    def _log_worker_timeout_status(self, job_data, current_time: float, multi_job_id: str) -> list[str]:
+        """Log timeout elapsed seconds for each tracked worker and return worker ids."""
+        if not isinstance(job_data, BaseJobState):
+            return []
+
+        worker_status = dict(job_data.worker_status)
+        for worker_id, last_seen in worker_status.items():
+            elapsed = max(0.0, current_time - float(last_seen))
+            log(
+                "UltimateSDUpscale Master - Heartbeat timeout: "
+                f"job={multi_job_id}, worker={worker_id}, elapsed={elapsed:.1f}s"
+            )
+        return list(worker_status.keys())
+
+    async def _async_collect_results(self, multi_job_id, num_workers, mode='static', 
+                                   remaining_to_collect=None, batch_size=None):
+        """Unified async helper to collect results from workers (tiles or images)."""
+        # Get the already initialized queue
+        prompt_server = ensure_tile_jobs_initialized()
+        async with prompt_server.distributed_tile_jobs_lock:
+            if multi_job_id not in prompt_server.distributed_pending_tile_jobs:
+                raise RuntimeError(f"Job queue not initialized for {multi_job_id}")
+            job_data = prompt_server.distributed_pending_tile_jobs[multi_job_id]
+            if mode == 'dynamic':
+                if not isinstance(job_data, ImageJobState):
+                    raise RuntimeError(
+                        f"Mode mismatch: expected dynamic, got {getattr(job_data, 'mode', 'unknown')}"
+                    )
+                q = job_data.queue
+                completed_images = job_data.completed_images
+                expected_count = remaining_to_collect or batch_size
+            elif mode == 'static':
+                if not isinstance(job_data, TileJobState):
+                    raise RuntimeError(
+                        f"Mode mismatch: expected static, got {getattr(job_data, 'mode', 'unknown')}"
+                    )
+                q = job_data.queue
+                expected_count = len(job_data.completed_tasks) + job_data.pending_tasks.qsize()
+            else:
+                raise RuntimeError(f"Unsupported mode: {mode}")
+        
+        item_type = "images" if mode == 'dynamic' else "tiles"
+        debug_log(f"UltimateSDUpscale Master - Starting collection, expecting {expected_count} {item_type} from {num_workers} workers")
+        
+        collected_results = {}
+        workers_done = set()
+        # Unify collector/upscaler wait behavior with the UI worker timeout
+        timeout = float(get_worker_timeout_seconds())
+        last_heartbeat_check = time.time()
+        wait_started_at = time.time()
+        collected_count = 0
+        
+        while len(workers_done) < num_workers:
+            # Check for user interruption
+            if comfy.model_management.processing_interrupted():
+                log("Processing interrupted by user")
+                raise comfy.model_management.InterruptProcessingException()
+                
+            # For dynamic mode with remaining_to_collect, check if we've collected enough
+            if mode == 'dynamic' and remaining_to_collect and collected_count >= remaining_to_collect:
+                break
+
+            job_data_snapshot = None
+            async with prompt_server.distributed_tile_jobs_lock:
+                current_job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
+                if isinstance(current_job_data, BaseJobState):
+                    job_data_snapshot = current_job_data
+
+            try:
+                # Shorter poll for dynamic mode, but never exceed the configured timeout
+                wait_timeout = (min(DYNAMIC_MODE_MAX_POLL_TIMEOUT, timeout) if mode == 'dynamic' else timeout)
+                result = await asyncio.wait_for(q.get(), timeout=wait_timeout)
+                worker_id = result['worker_id']
+                is_last = result.get('is_last', False)
+                
+                if mode == 'static':
+                    # Handle tiles
+                    tiles = result.get('tiles', [])
+                    debug_log(
+                        f"UltimateSDUpscale Master - Received batch of {len(tiles)} tiles from worker "
+                        f"'{worker_id}' (is_last={is_last})"
+                    )
+
+                    for tile_data in tiles:
+                        if 'batch_idx' not in tile_data:
+                            log("UltimateSDUpscale Master - Missing batch_idx in tile data, skipping")
+                            continue
+
+                        tile_idx = tile_data['tile_idx']
+                        key = tile_data.get('global_idx', tile_idx)
+                        entry = {
+                            'tile_idx': tile_idx,
+                            'x': tile_data['x'],
+                            'y': tile_data['y'],
+                            'extracted_width': tile_data['extracted_width'],
+                            'extracted_height': tile_data['extracted_height'],
+                            'padding': tile_data['padding'],
+                            'worker_id': worker_id,
+                            'batch_idx': tile_data.get('batch_idx', 0),
+                            'global_idx': tile_data.get('global_idx', tile_idx),
+                        }
+                        if 'image' in tile_data:
+                            entry['image'] = tile_data['image']
+                        elif 'tensor' in tile_data:
+                            entry['tensor'] = tile_data['tensor']
+                        collected_results[key] = entry
+                
+                elif mode == 'dynamic':
+                    # Handle full images
+                    if 'image_idx' in result and 'image' in result:
+                        image_idx = result['image_idx']
+                        image_pil = result['image']
+                        completed_images[image_idx] = image_pil
+                        collected_results[image_idx] = image_pil
+                        collected_count += 1
+                        debug_log(f"UltimateSDUpscale Master - Received image {image_idx} from worker {worker_id}")
+                
+                if is_last:
+                    workers_done.add(worker_id)
+                    debug_log(f"UltimateSDUpscale Master - Worker {worker_id} completed")
+                    
+            except asyncio.TimeoutError:
+                current_time = time.time()
+                waiting_workers = self._log_worker_timeout_status(job_data_snapshot, current_time, multi_job_id)
+                if mode == 'dynamic':
+                    # Check for worker timeouts periodically
+                    if current_time - last_heartbeat_check >= HEARTBEAT_INTERVAL:
+                        # Use the class method to check and requeue
+                        requeued = await self._check_and_requeue_timed_out_workers(multi_job_id, batch_size)
+                        if requeued > 0:
+                            log(f"UltimateSDUpscale Master - Requeued {requeued} images from timed out workers")
+                        last_heartbeat_check = current_time
+                    
+                    # Check if we've been waiting too long overall
+                    if current_time - wait_started_at > timeout:
+                        elapsed = current_time - wait_started_at
+                        log(
+                            "UltimateSDUpscale Master - Heartbeat timeout while waiting for images; "
+                            f"workers={waiting_workers}, elapsed={elapsed:.1f}s"
+                        )
+                        break
+                else:
+                    elapsed = current_time - wait_started_at
+                    log(
+                        f"UltimateSDUpscale Master - Heartbeat timeout waiting for {item_type}; "
+                        f"workers={waiting_workers}, elapsed={elapsed:.1f}s"
+                    )
+                    break
+        
+        debug_log(f"UltimateSDUpscale Master - Collection complete. Got {len(collected_results)} {item_type} from {len(workers_done)} workers")
+        
+        # Clean up job queue
+        async with prompt_server.distributed_tile_jobs_lock:
+            if multi_job_id in prompt_server.distributed_pending_tile_jobs:
+                del prompt_server.distributed_pending_tile_jobs[multi_job_id]
+        
+        return collected_results if mode == 'static' else completed_images
+
+    async def _async_collect_worker_tiles(self, multi_job_id, num_workers):
+        """Async helper to collect tiles from workers."""
+        return await self._async_collect_results(multi_job_id, num_workers, mode='static')
+
+    async def _mark_image_completed(self, multi_job_id, image_idx, image_pil):
+        """Mark an image as completed in the job data."""
+        # Mark the image as completed with the image data
+        await _mark_task_completed(multi_job_id, image_idx, {'image': image_pil})
+        prompt_server = ensure_tile_jobs_initialized()
+        async with prompt_server.distributed_tile_jobs_lock:
+            job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
+            if isinstance(job_data, ImageJobState):
+                job_data.completed_images[image_idx] = image_pil
+
+    async def _async_collect_dynamic_images(self, multi_job_id, remaining_to_collect, num_workers, batch_size, master_processed_count):
+        """Collect remaining processed images from workers."""
+        return await self._async_collect_results(multi_job_id, num_workers, mode='dynamic', 
+                                               remaining_to_collect=remaining_to_collect, 
+                                               batch_size=batch_size)
@@ -0,0 +1,446 @@
+import math, torch
+from contextlib import nullcontext
+from PIL import Image, ImageFilter, ImageDraw
+from typing import List, Tuple
+import comfy.samplers, comfy.model_management
+from ..utils.logging import debug_log, log
+from ..utils.image import tensor_to_pil, pil_to_tensor
+from ..utils.usdu_utils import crop_cond, get_crop_region, expand_crop
+from ..utils.crop_model_patch import crop_model_cond
+from .conditioning import clone_conditioning
+
+
+class TileOpsMixin:
+    def round_to_multiple(self, value: int, multiple: int = 8) -> int:
+        """Round value to nearest multiple."""
+        return round(value / multiple) * multiple
+
+    def calculate_tiles(self, image_width: int, image_height: int,
+                       tile_width: int, tile_height: int, force_uniform_tiles: bool = True) -> List[Tuple[int, int]]:
+        """Calculate tile positions to match Ultimate SD Upscale.
+
+        Positions are a simple grid starting at (0,0) with steps of
+        `tile_width` and `tile_height`, using ceil(rows/cols) to cover edges.
+        Uniform vs non-uniform affects only crop/resize, not positions.
+        """
+        rows = math.ceil(image_height / tile_height)
+        cols = math.ceil(image_width / tile_width)
+        tiles: List[Tuple[int, int]] = []
+        for yi in range(rows):
+            for xi in range(cols):
+                tiles.append((xi * tile_width, yi * tile_height))
+        return tiles
+
+    def extract_tile_with_padding(self, image: torch.Tensor, x: int, y: int,
+                                 tile_width: int, tile_height: int, padding: int,
+                                 force_uniform_tiles: bool) -> Tuple[torch.Tensor, int, int, int, int]:
+        """Extract a tile region and resize to match USDU cropping logic.
+
+        Mirrors ComfyUI_UltimateSDUpscale processing:
+        - Build a mask with a white rectangle at the tile rect
+        - Compute crop_region via get_crop_region(mask, padding)
+        - If force_uniform_tiles: expand by crop/aspect ratio, then resize to
+          fixed processing size of round_to_multiple(tile + padding)
+        - Else: target is ceil(crop_size/8)*8 per dimension
+        - Extract the crop and resize to target tile_size
+        Returns the resized tensor and crop origin/size for blending.
+        """
+        _, h, w, _ = image.shape
+
+        # Create mask and compute initial padded crop region
+        mask = Image.new('L', (w, h), 0)
+        draw = ImageDraw.Draw(mask)
+        draw.rectangle([x, y, x + tile_width, y + tile_height], fill=255)
+        x1, y1, x2, y2 = get_crop_region(mask, padding)
+
+        # Determine crop + processing size
+        if force_uniform_tiles:
+            process_w = self.round_to_multiple(tile_width + padding, 8)
+            process_h = self.round_to_multiple(tile_height + padding, 8)
+            crop_w = x2 - x1
+            crop_h = y2 - y1
+            crop_ratio = crop_w / crop_h if crop_h != 0 else 1.0
+            process_ratio = process_w / process_h if process_h != 0 else 1.0
+            if crop_ratio > process_ratio:
+                target_w = crop_w
+                target_h = round(crop_w / process_ratio) if process_ratio != 0 else crop_h
+            else:
+                target_w = round(crop_h * process_ratio)
+                target_h = crop_h
+            (x1, y1, x2, y2), _ = expand_crop((x1, y1, x2, y2), w, h, target_w, target_h)
+            target_w = process_w
+            target_h = process_h
+        else:
+            crop_w = x2 - x1
+            crop_h = y2 - y1
+            target_w = max(8, math.ceil(crop_w / 8) * 8)
+            target_h = max(8, math.ceil(crop_h / 8) * 8)
+            (x1, y1, x2, y2), (target_w, target_h) = expand_crop((x1, y1, x2, y2), w, h, target_w, target_h)
+
+        # Actual extracted size before resizing (for blending)
+        extracted_width = x2 - x1
+        extracted_height = y2 - y1
+
+        # Extract tile and resize to processing size
+        tile = image[:, y1:y2, x1:x2, :]
+        tile_pil = tensor_to_pil(tile, 0)
+        if tile_pil.size != (target_w, target_h):
+            tile_pil = tile_pil.resize((target_w, target_h), Image.LANCZOS)
+
+        tile_tensor = pil_to_tensor(tile_pil)
+        if image.is_cuda:
+            tile_tensor = tile_tensor.cuda()
+
+        return tile_tensor, x1, y1, extracted_width, extracted_height
+
+    def extract_batch_tile_with_padding(self, images: torch.Tensor, x: int, y: int,
+                                        tile_width: int, tile_height: int, padding: int,
+                                        force_uniform_tiles: bool) -> Tuple[torch.Tensor, int, int, int, int]:
+        """Extract a tile region for the entire batch and resize to USDU logic.
+
+        - Computes a single crop region from a mask at (x,y,w,h) with padding
+        - force_uniform_tiles controls target processing size logic
+        - Returns a batched tensor [B,H',W',C] and crop origin/size for blending
+        """
+        batch, h, w, _ = images.shape
+
+        # Create mask and compute initial padded crop region (same for all images)
+        mask = Image.new('L', (w, h), 0)
+        draw = ImageDraw.Draw(mask)
+        draw.rectangle([x, y, x + tile_width, y + tile_height], fill=255)
+        x1, y1, x2, y2 = get_crop_region(mask, padding)
+
+        # Determine crop + processing size
+        if force_uniform_tiles:
+            process_w = self.round_to_multiple(tile_width + padding, 8)
+            process_h = self.round_to_multiple(tile_height + padding, 8)
+            crop_w = x2 - x1
+            crop_h = y2 - y1
+            crop_ratio = crop_w / crop_h if crop_h != 0 else 1.0
+            process_ratio = process_w / process_h if process_h != 0 else 1.0
+            if crop_ratio > process_ratio:
+                target_w = crop_w
+                target_h = round(crop_w / process_ratio) if process_ratio != 0 else crop_h
+            else:
+                target_w = round(crop_h * process_ratio)
+                target_h = crop_h
+            (x1, y1, x2, y2), _ = expand_crop((x1, y1, x2, y2), w, h, target_w, target_h)
+            target_w = process_w
+            target_h = process_h
+        else:
+            crop_w = x2 - x1
+            crop_h = y2 - y1
+            target_w = max(8, math.ceil(crop_w / 8) * 8)
+            target_h = max(8, math.ceil(crop_h / 8) * 8)
+            (x1, y1, x2, y2), (target_w, target_h) = expand_crop((x1, y1, x2, y2), w, h, target_w, target_h)
+
+        extracted_width = x2 - x1
+        extracted_height = y2 - y1
+
+        # Slice batch region
+        tiles = images[:, y1:y2, x1:x2, :]
+
+        # Resize each tile to target size
+        resized_tiles = []
+        for i in range(batch):
+            tile_pil = tensor_to_pil(tiles, i)
+            if tile_pil.size != (target_w, target_h):
+                tile_pil = tile_pil.resize((target_w, target_h), Image.LANCZOS)
+            resized_tiles.append(pil_to_tensor(tile_pil))
+        tile_batch = torch.cat(resized_tiles, dim=0)
+
+        if images.is_cuda:
+            tile_batch = tile_batch.cuda()
+
+        return tile_batch, x1, y1, extracted_width, extracted_height
+
+    def process_tile(self, tile_tensor: torch.Tensor, model, positive, negative, vae,
+                     seed: int, steps: int, cfg: float, sampler_name: str, 
+                     scheduler: str, denoise: float, tiled_decode: bool = False,
+                     batch_idx: int = 0, region: Tuple[int, int, int, int] = None,
+                     image_size: Tuple[int, int] = None) -> torch.Tensor:
+        """Process a single tile through SD sampling. 
+        Note: positive and negative should already be pre-sliced for the current batch_idx."""
+        debug_log(f"[process_tile] Processing tile for batch_idx={batch_idx}, seed={seed}, region={region}")
+        
+        
+        # Import here to avoid circular dependencies
+        from nodes import common_ksampler, VAEEncode, VAEDecode
+        
+        # Try to import tiled VAE nodes if available
+        try:
+            from nodes import VAEEncodeTiled, VAEDecodeTiled
+            tiled_vae_available = True
+        except ImportError:
+            tiled_vae_available = False
+            if tiled_decode:
+                debug_log("Tiled VAE nodes not available, falling back to standard VAE")
+        
+        # Convert to PIL and back to ensure clean tensor without gradient tracking
+        tile_pil = tensor_to_pil(tile_tensor, 0)
+        clean_tensor = pil_to_tensor(tile_pil)
+        
+        # Ensure tensor is detached and doesn't require gradients
+        clean_tensor = clean_tensor.detach()
+        if hasattr(clean_tensor, 'requires_grad_'):
+            clean_tensor.requires_grad_(False)
+        
+        # Move to correct device
+        if tile_tensor.is_cuda:
+            clean_tensor = clean_tensor.cuda()
+            clean_tensor = clean_tensor.detach()  # Detach again after device transfer
+        
+        # Clone conditioning per tile (shares models, clones hints for cropping)
+        positive_tile = clone_conditioning(positive, clone_hints=True)
+        negative_tile = clone_conditioning(negative, clone_hints=True)
+        
+        # Crop conditioning to tile region if provided (assumes hints at image resolution)
+        if region is not None and image_size is not None:
+            init_size = image_size  # (width, height) of full image
+            canvas_size = image_size
+            tile_size = (tile_tensor.shape[2], tile_tensor.shape[1])  # (width, height)
+            w_pad = 0  # No extra pad needed; region already includes padding
+            h_pad = 0
+            positive_cropped = crop_cond(positive_tile, region, init_size, canvas_size, tile_size, w_pad, h_pad)
+            negative_cropped = crop_cond(negative_tile, region, init_size, canvas_size, tile_size, w_pad, h_pad)
+        else:
+            # No region cropping needed, use cloned conditioning as-is
+            positive_cropped = positive_tile
+            negative_cropped = negative_tile
+        
+        # Encode to latent (always non-tiled, matching original node)
+        latent = VAEEncode().encode(vae, clean_tensor)[0]
+        
+        # Sample with model patch cropping parity (ControlNet patch hints)
+        if region is not None and image_size is not None:
+            model_ctx = crop_model_cond(
+                model,
+                region,
+                image_size,
+                image_size,
+                (clean_tensor.shape[2], clean_tensor.shape[1]),
+            )
+        else:
+            model_ctx = nullcontext(model)
+        with model_ctx as model_for_sampling:
+            samples = common_ksampler(
+                model_for_sampling, seed, steps, cfg, sampler_name, scheduler,
+                positive_cropped, negative_cropped, latent, denoise=denoise
+            )[0]
+        
+        # Decode back to image
+        if tiled_decode and tiled_vae_available:
+            image = VAEDecodeTiled().decode(vae, samples, tile_size=512)[0]
+        else:
+            image = VAEDecode().decode(vae, samples)[0]
+        
+        return image
+
+    def process_tiles_batch(self, tile_batch: torch.Tensor, model, positive, negative, vae,
+                            seed: int, steps: int, cfg: float, sampler_name: str,
+                            scheduler: str, denoise: float, tiled_decode: bool,
+                            region: Tuple[int, int, int, int], image_size: Tuple[int, int]) -> torch.Tensor:
+        """Process a batch of tiles together (USDU behavior).
+
+        tile_batch: [B, H, W, C]
+        Returns image batch tensor [B, H, W, C]
+        """
+        # Import locally to avoid circular deps
+        from nodes import common_ksampler, VAEEncode, VAEDecode
+        try:
+            from nodes import VAEEncodeTiled, VAEDecodeTiled
+            tiled_vae_available = True
+        except ImportError:
+            tiled_vae_available = False
+
+        # Detach and move device
+        clean = tile_batch.detach()
+        if hasattr(clean, 'requires_grad_'):
+            clean.requires_grad_(False)
+        if tile_batch.is_cuda:
+            clean = clean.cuda().detach()
+
+        # Clone/crop conditioning once for the region
+        positive_tile = clone_conditioning(positive, clone_hints=True)
+        negative_tile = clone_conditioning(negative, clone_hints=True)
+
+        init_size = image_size
+        canvas_size = image_size
+        tile_size = (clean.shape[2], clean.shape[1])  # (W,H)
+        w_pad = 0
+        h_pad = 0
+        positive_cropped = crop_cond(positive_tile, region, init_size, canvas_size, tile_size, w_pad, h_pad)
+        negative_cropped = crop_cond(negative_tile, region, init_size, canvas_size, tile_size, w_pad, h_pad)
+
+        # Encode -> Sample -> Decode
+        latent = VAEEncode().encode(vae, clean)[0]
+        with crop_model_cond(model, region, image_size, image_size, tile_size) as model_for_sampling:
+            samples = common_ksampler(
+                model_for_sampling, seed, steps, cfg, sampler_name, scheduler,
+                positive_cropped, negative_cropped, latent, denoise=denoise
+            )[0]
+        if tiled_decode and tiled_vae_available:
+            image = VAEDecodeTiled().decode(vae, samples, tile_size=512)[0]
+        else:
+            image = VAEDecode().decode(vae, samples)[0]
+
+        return image
+
+    def create_tile_mask(self, image_width: int, image_height: int,
+                        x: int, y: int, tile_width: int, tile_height: int, 
+                        mask_blur: int) -> Image.Image:
+        """Create a mask for blending tiles - matches Ultimate SD Upscale approach.
+        
+        Creates a black image with a white rectangle at the tile position,
+        then applies blur to create soft edges.
+        """
+        # Create a full-size mask matching the image dimensions
+        mask = Image.new('L', (image_width, image_height), 0)  # Black background
+        
+        # Draw white rectangle at tile position
+        draw = ImageDraw.Draw(mask)
+        draw.rectangle([x, y, x + tile_width, y + tile_height], fill=255)
+        
+        # Apply blur to soften edges
+        if mask_blur > 0:
+            mask = mask.filter(ImageFilter.GaussianBlur(mask_blur))
+        
+        return mask
+
+    def blend_tile(self, base_image: Image.Image, tile_image: Image.Image,
+                  x: int, y: int, extracted_size: Tuple[int, int], 
+                  mask: Image.Image, padding: int) -> Image.Image:
+        """Blend a processed tile back into the base image using Ultimate SD Upscale's exact approach.
+        
+        This follows the exact method from ComfyUI_UltimateSDUpscale/modules/processing.py
+        """
+        extracted_width, extracted_height = extracted_size
+        
+        # Debug logging (uncomment if needed)
+        # debug_log(f"[Blend] Placing tile at ({x}, {y}), size: {extracted_width}x{extracted_height}")
+        
+        # Calculate the crop region that was used for extraction
+        crop_region = (x, y, x + extracted_width, y + extracted_height)
+        
+        # The mask is already full-size, no need to crop
+        
+        # Resize the processed tile back to the extracted size
+        if tile_image.size != (extracted_width, extracted_height):
+            tile_resized = tile_image.resize((extracted_width, extracted_height), Image.LANCZOS)
+        else:
+            tile_resized = tile_image
+        
+        # Follow Ultimate SD Upscale blending approach:
+        # Put the tile into position
+        image_tile_only = Image.new('RGBA', base_image.size)
+        image_tile_only.paste(tile_resized, crop_region[:2])
+        
+        # Add the mask as an alpha channel
+        # Must make a copy due to the possibility of an edge becoming black
+        temp = image_tile_only.copy()
+        temp.putalpha(mask)  # Use the full image mask
+        image_tile_only.paste(temp, image_tile_only)
+        
+        # Add back the tile to the initial image according to the mask in the alpha channel
+        result = base_image.convert('RGBA')
+        result.alpha_composite(image_tile_only)
+        
+        # Convert back to RGB
+        return result.convert('RGB')
+
+    def _slice_conditioning(self, positive, negative, batch_idx):
+        """Helper to slice conditioning for a specific batch index."""
+        # Clone and slice conditioning properly, including ControlNet hints
+        positive_sliced = clone_conditioning(positive)
+        negative_sliced = clone_conditioning(negative)
+        
+        for cond_list in [positive_sliced, negative_sliced]:
+            for i in range(len(cond_list)):
+                emb, cond_dict = cond_list[i]
+                if emb.shape[0] > 1:
+                    cond_list[i][0] = emb[batch_idx:batch_idx+1]
+                if 'control' in cond_dict:
+                    control = cond_dict['control']
+                    while control is not None:
+                        hint = control.cond_hint_original
+                        if hint.shape[0] > 1:
+                            control.cond_hint_original = hint[batch_idx:batch_idx+1]
+                        control = control.previous_controlnet
+                if 'mask' in cond_dict and cond_dict['mask'].shape[0] > 1:
+                    cond_dict['mask'] = cond_dict['mask'][batch_idx:batch_idx+1]
+        
+        return positive_sliced, negative_sliced
+
+    def _process_and_blend_tile(self, tile_idx, tile_pos, upscaled_image, result_image,
+                               model, positive, negative, vae, seed, steps, cfg,
+                               sampler_name, scheduler, denoise, tile_width, tile_height,
+                               padding, mask_blur, image_width, image_height, force_uniform_tiles,
+                               tiled_decode, batch_idx: int = 0):
+        """Process a single tile and blend it into the result image."""
+        x, y = tile_pos
+        
+        # Extract and process tile
+        tile_tensor, x1, y1, ew, eh = self.extract_tile_with_padding(
+            upscaled_image, x, y, tile_width, tile_height, padding, force_uniform_tiles
+        )
+        
+        processed_tile = self.process_tile(tile_tensor, model, positive, negative, vae,
+                                         seed, steps, cfg, sampler_name, 
+                                         scheduler, denoise, tiled_decode, batch_idx=batch_idx,
+                                         region=(x1, y1, x1 + ew, y1 + eh), image_size=(image_width, image_height))
+        
+        # Convert and blend
+        processed_pil = tensor_to_pil(processed_tile, 0)
+        # Create mask for this specific tile (no cache here; only used in single-tile path)
+        tile_mask = self.create_tile_mask(image_width, image_height, x, y, tile_width, tile_height, mask_blur)
+        # Use extraction position and size for blending
+        result_image = self.blend_tile(result_image, processed_pil, 
+                                     x1, y1, (ew, eh), tile_mask, padding)
+        
+        return result_image
+
+    def _process_single_tile(self, global_idx, num_tiles_per_image, upscaled_image, all_tiles,
+                                  model, positive, negative, vae, seed, steps, cfg, sampler_name,
+                                  scheduler, denoise, tiled_decode, tile_width, tile_height, padding,
+                                  width, height, force_uniform_tiles, sliced_conditioning_cache):
+        """Process a single tile."""
+        # Calculate which image and tile this corresponds to
+        batch_idx = global_idx // num_tiles_per_image
+        tile_idx = global_idx % num_tiles_per_image
+        
+        # Skip if batch_idx is out of range
+        if batch_idx >= upscaled_image.shape[0]:
+            debug_log(f"Warning: Calculated batch_idx {batch_idx} exceeds batch size {upscaled_image.shape[0]}")
+            return None
+        
+        # Get or create sliced conditioning for this batch index
+        if batch_idx not in sliced_conditioning_cache:
+            positive_sliced, negative_sliced = self._slice_conditioning(positive, negative, batch_idx)
+            sliced_conditioning_cache[batch_idx] = (positive_sliced, negative_sliced)
+        else:
+            positive_sliced, negative_sliced = sliced_conditioning_cache[batch_idx]
+        
+        x, y = all_tiles[tile_idx]
+        
+        # Extract tile from the specific image in the batch
+        tile_tensor, x1, y1, ew, eh = self.extract_tile_with_padding(
+            upscaled_image[batch_idx:batch_idx+1], x, y, tile_width, tile_height, padding, force_uniform_tiles
+        )
+        
+        # Process tile through SD with the exact seed (USDU parity)
+        image_seed = seed
+        processed_tile = self.process_tile(tile_tensor, model, positive_sliced, negative_sliced, vae,
+                                         image_seed, steps, cfg, sampler_name,
+                                         scheduler, denoise, tiled_decode, batch_idx=batch_idx,
+                                         region=(x1, y1, x1 + ew, y1 + eh), image_size=(width, height))
+        
+        return {
+            'tile': processed_tile,
+            'global_idx': global_idx,
+            'batch_idx': batch_idx,
+            'tile_idx': tile_idx,
+            'x': x1,
+            'y': y1,
+            'extracted_width': ew,
+            'extracted_height': eh
+        }
@@ -0,0 +1,262 @@
+import asyncio, io, json, time
+import aiohttp
+from PIL import Image
+from ..utils.logging import debug_log, log
+from ..utils.network import get_client_session
+from ..utils.constants import TILE_SEND_TIMEOUT
+from ..utils.usdu_managment import MAX_PAYLOAD_SIZE, _send_heartbeat_to_master
+from ..utils.image import tensor_to_pil
+
+
+class WorkerCommsMixin:
+    async def _send_heartbeat_to_master(self, multi_job_id, master_url, worker_id):
+        """Proxy heartbeat helper used by worker processing mixins."""
+        await _send_heartbeat_to_master(multi_job_id, master_url, worker_id)
+
+    async def send_tiles_batch_to_master(self, processed_tiles, multi_job_id, master_url, 
+                                       padding, worker_id, is_final_flush=False):
+        """Send all processed tiles to master, chunked if large."""
+        if not processed_tiles:
+            if is_final_flush:
+                await self._send_tiles_completion_signal(multi_job_id, master_url, worker_id)
+            return  # Early exit if empty
+
+        total_tiles = len(processed_tiles)
+        debug_log(f"Worker[{worker_id[:8]}] - Preparing to send {total_tiles} tiles (size-aware chunks)")
+
+        # Prepare encoded images and sizes to enable size-aware chunking
+        encoded = []
+        for idx, tile_data in enumerate(processed_tiles):
+            img = tensor_to_pil(tile_data['tile'], 0)
+            bio = io.BytesIO()
+            # Keep compression low to balance speed and size; adjust if needed
+            img.save(bio, format='PNG', compress_level=0)
+            raw = bio.getvalue()
+            encoded.append({
+                'bytes': raw,
+                'meta': {
+                    'tile_idx': tile_data['tile_idx'],
+                    'x': tile_data['x'],
+                    'y': tile_data['y'],
+                    'extracted_width': tile_data['extracted_width'],
+                    'extracted_height': tile_data['extracted_height'],
+                    **({'batch_idx': tile_data['batch_idx']} if 'batch_idx' in tile_data else {}),
+                    **({'global_idx': tile_data['global_idx']} if 'global_idx' in tile_data else {}),
+                }
+            })
+
+        # Size-aware chunking
+        max_bytes = int(MAX_PAYLOAD_SIZE) - (1024 * 1024)  # 1MB headroom
+        i = 0
+        chunk_index = 0
+        while i < total_tiles:
+            data = aiohttp.FormData()
+            data.add_field('multi_job_id', multi_job_id)
+            data.add_field('worker_id', str(worker_id))
+            data.add_field('padding', str(padding))
+
+            metadata = []
+            used = 0
+            j = i
+            while j < total_tiles:
+                img_bytes = encoded[j]['bytes']
+                meta = encoded[j]['meta']
+                # Rough overhead for fields + JSON
+                overhead = 1024
+                if used + len(img_bytes) + overhead > max_bytes and j > i:
+                    break
+                # Accept this tile in this chunk
+                metadata.append(meta)
+                data.add_field(f'tile_{j - i}', io.BytesIO(img_bytes), filename=f'tile_{j}.png', content_type='image/png')
+                used += len(img_bytes) + overhead
+                j += 1
+
+            # Ensure at least one tile per chunk
+            if j == i:
+                # Single oversized tile, send anyway
+                meta = encoded[j]['meta']
+                metadata.append(meta)
+                data.add_field('tile_0', io.BytesIO(encoded[j]['bytes']), filename=f'tile_{j}.png', content_type='image/png')
+                j += 1
+
+            chunk_size = j - i
+            is_chunk_last = (j >= total_tiles)
+            data.add_field('is_last', str(bool(is_final_flush and is_chunk_last)))
+            data.add_field('batch_size', str(chunk_size))
+            data.add_field('tiles_metadata', json.dumps(metadata), content_type='application/json')
+
+            # Retry logic with exponential backoff
+            max_retries = 5
+            retry_delay = 0.5
+            for attempt in range(max_retries):
+                try:
+                    session = await get_client_session()
+                    url = f"{master_url}/distributed/submit_tiles"
+                    async with session.post(url, data=data) as response:
+                        response.raise_for_status()
+                        break
+                except Exception as e:
+                    if attempt < max_retries - 1:
+                        await asyncio.sleep(retry_delay)
+                        retry_delay = min(retry_delay * 2, 5.0)
+                    else:
+                        log(f"UltimateSDUpscale Worker - Failed to send chunk {chunk_index} after {max_retries} attempts: {e}")
+                        raise
+
+            debug_log(f"Worker[{worker_id[:8]}] - Sent chunk {chunk_index} ({chunk_size} tiles, ~{used/1e6:.2f} MB)")
+            chunk_index += 1
+            i = j
+
+    async def _send_tiles_completion_signal(self, multi_job_id, master_url, worker_id):
+        """Send completion signal to master in static mode when no tiles are left."""
+        data = aiohttp.FormData()
+        data.add_field('multi_job_id', multi_job_id)
+        data.add_field('worker_id', str(worker_id))
+        data.add_field('is_last', 'true')
+        data.add_field('batch_size', '0')
+
+        session = await get_client_session()
+        url = f"{master_url}/distributed/submit_tiles"
+        async with session.post(url, data=data) as response:
+            response.raise_for_status()
+            debug_log(f"Worker {worker_id} sent static completion signal")
+
+    async def _request_work_item_from_master(
+        self,
+        multi_job_id,
+        master_url,
+        worker_id,
+        endpoint="/distributed/request_image",
+    ):
+        """Request one work item from master with retry/backoff and total timeout."""
+        max_retries = 10
+        retry_delay = 0.5
+        start_time = time.monotonic()
+        url = f"{master_url}{endpoint}"
+
+        for attempt in range(max_retries):
+            if time.monotonic() - start_time > 30:
+                log(f"Total request timeout after 30s for worker {worker_id}")
+                return None
+
+            try:
+                session = await get_client_session()
+                async with session.post(url, json={
+                    'worker_id': str(worker_id),
+                    'multi_job_id': multi_job_id
+                }) as response:
+                    if response.status == 200:
+                        return await response.json()
+                    if response.status == 404:
+                        text = await response.text()
+                        debug_log(f"Job not found (404), will retry: {text}")
+                        await asyncio.sleep(1.0)
+                    else:
+                        text = await response.text()
+                        debug_log(
+                            f"Request work item failed ({response.status}) for worker {worker_id}: {text}"
+                        )
+
+            except Exception as exc:
+                if attempt < max_retries - 1:
+                    debug_log(f"Retry {attempt + 1}/{max_retries} after error: {exc}")
+                    await asyncio.sleep(retry_delay)
+                    retry_delay = min(retry_delay * 2, 5.0)
+                else:
+                    log(f"Failed to request work item after {max_retries} attempts: {exc}")
+                    raise
+
+        return None
+
+    async def _request_image_from_master(self, multi_job_id, master_url, worker_id):
+        """Request an image index to process from master in dynamic mode."""
+        data = await self._request_work_item_from_master(multi_job_id, master_url, worker_id)
+        if not data:
+            return None, 0
+        image_idx = data.get('image_idx')
+        estimated_remaining = data.get('estimated_remaining', 0)
+        return image_idx, estimated_remaining
+
+    async def _request_tile_from_master(self, multi_job_id, master_url, worker_id):
+        """Request a tile index to process from master in static mode (reusing dynamic infrastructure)."""
+        data = await self._request_work_item_from_master(multi_job_id, master_url, worker_id)
+        if not data:
+            return None, 0, False
+        tile_idx = data.get('tile_idx')
+        estimated_remaining = data.get('estimated_remaining', 0)
+        batched_static = data.get('batched_static', False)
+        return tile_idx, estimated_remaining, batched_static
+
+    async def _send_full_image_to_master(self, image_pil, image_idx, multi_job_id, 
+                                        master_url, worker_id, is_last):
+        """Send a processed full image back to master in dynamic mode."""
+        # Serialize image to PNG
+        byte_io = io.BytesIO()
+        image_pil.save(byte_io, format='PNG', compress_level=0)
+        byte_io.seek(0)
+        
+        # Prepare form data
+        data = aiohttp.FormData()
+        data.add_field('multi_job_id', multi_job_id)
+        data.add_field('worker_id', str(worker_id))
+        data.add_field('image_idx', str(image_idx))
+        data.add_field('is_last', str(is_last))
+        data.add_field('full_image', byte_io, filename=f'image_{image_idx}.png', 
+                      content_type='image/png')
+        
+        # Retry logic
+        max_retries = 5
+        retry_delay = 0.5
+        
+        for attempt in range(max_retries):
+            try:
+                session = await get_client_session()
+                url = f"{master_url}/distributed/submit_image"
+                
+                async with session.post(url, data=data) as response:
+                    response.raise_for_status()
+                    debug_log(f"Successfully sent image {image_idx} to master")
+                    return
+                    
+            except Exception as e:
+                if attempt < max_retries - 1:
+                    debug_log(f"Retry {attempt + 1}/{max_retries} after error: {e}")
+                    await asyncio.sleep(retry_delay)
+                    retry_delay *= 2
+                else:
+                    log(f"Failed to send image {image_idx} after {max_retries} attempts: {e}")
+                    raise
+
+    async def _send_worker_complete_signal(self, multi_job_id, master_url, worker_id):
+        """Send completion signal to master in dynamic mode."""
+        # Send a dummy request with is_last=True
+        data = aiohttp.FormData()
+        data.add_field('multi_job_id', multi_job_id)
+        data.add_field('worker_id', str(worker_id))
+        data.add_field('is_last', 'true')
+        # No image data - just completion signal
+        
+        session = await get_client_session()
+        url = f"{master_url}/distributed/submit_image"
+        
+        async with session.post(url, data=data) as response:
+            response.raise_for_status()
+            debug_log(f"Worker {worker_id} sent completion signal")
+
+    async def _check_job_status(self, multi_job_id, master_url):
+        """Check if job is ready on the master."""
+        try:
+            session = await get_client_session()
+            url = f"{master_url}/distributed/job_status?multi_job_id={multi_job_id}"
+            async with session.get(url) as response:
+                if response.status == 200:
+                    data = await response.json()
+                    return data.get('ready', False)
+                return False
+        except Exception as e:
+            debug_log(f"Job status check failed: {e}")
+            return False
+
+    async def _async_yield(self):
+        """Simple async yield to allow event loop processing."""
+        await asyncio.sleep(0)
@@ -3,6 +3,9 @@ Async helper utilities for ComfyUI-Distributed.
 """
 import asyncio
 import threading
+import uuid
+import execution
+import server
 from typing import Optional, Any, Coroutine
 from .network import get_server_loop

@@ -47,4 +50,86 @@ def run_async_in_server_loop(coro: Coroutine, timeout: Optional[float] = None) -
    
    if error:
        raise error
-    return result
+    return result
+
+
+prompt_server = server.PromptServer.instance
+
+
+def _summarize_node_errors(node_errors: dict) -> str:
+    if not isinstance(node_errors, dict) or not node_errors:
+        return ""
+
+    parts = []
+    for node_id, entry in node_errors.items():
+        if not isinstance(entry, dict):
+            continue
+        class_type = str(entry.get("class_type") or "UnknownNode")
+        for err in entry.get("errors", []):
+            if not isinstance(err, dict):
+                continue
+            message = str(err.get("message") or "validation error")
+            details = str(err.get("details") or "").strip()
+            parts.append(
+                f"{class_type}#{node_id}: {message}{f' ({details})' if details else ''}"
+            )
+            if len(parts) >= 5:
+                return " | ".join(parts)
+    return " | ".join(parts)
+
+
+class PromptValidationError(RuntimeError):
+    """Raised when a prompt fails ComfyUI validation with structured context."""
+
+    def __init__(self, error_payload, node_errors=None):
+        payload = error_payload if isinstance(error_payload, dict) else {
+            "type": "prompt_validation_failed",
+            "message": str(error_payload),
+            "details": "",
+            "extra_info": {},
+        }
+        self.validation_error = dict(payload)
+        self.node_errors = node_errors if isinstance(node_errors, dict) else {}
+
+        if self.node_errors:
+            details = str(self.validation_error.get("details") or "").strip()
+            if not details:
+                summary = _summarize_node_errors(self.node_errors)
+                if summary:
+                    self.validation_error["details"] = summary
+
+        merged = dict(self.validation_error)
+        if self.node_errors:
+            merged["node_errors"] = self.node_errors
+        super().__init__(f"Invalid prompt: {merged}")
+
+
+async def queue_prompt_payload(prompt_obj, workflow_meta=None, client_id=None):
+    """Validate and queue a prompt via ComfyUI's prompt queue."""
+    payload = {"prompt": prompt_obj}
+    payload = prompt_server.trigger_on_prompt(payload)
+    prompt = payload["prompt"]
+
+    prompt_id = str(uuid.uuid4())
+    valid = await execution.validate_prompt(prompt_id, prompt, None)
+    if not valid[0]:
+        error_payload = valid[1] if len(valid) > 1 else "Prompt outputs failed validation"
+        node_errors = valid[3] if len(valid) > 3 else {}
+        raise PromptValidationError(error_payload, node_errors)
+
+    extra_data = {}
+    if workflow_meta:
+        extra_data.setdefault("extra_pnginfo", {})["workflow"] = workflow_meta
+    if client_id:
+        extra_data["client_id"] = client_id
+
+    sensitive = {}
+    for key in getattr(execution, "SENSITIVE_EXTRA_DATA_KEYS", []):
+        if key in extra_data:
+            sensitive[key] = extra_data.pop(key)
+
+    number = getattr(prompt_server, "number", 0)
+    prompt_server.number = number + 1
+    prompt_queue_item = (number, prompt_id, prompt, extra_data, valid[2], sensitive)
+    prompt_server.prompt_queue.put(prompt_queue_item)
+    return prompt_id
@@ -0,0 +1,103 @@
+import base64
+import binascii
+import os
+
+import numpy as np
+import torch
+
+from .image import ensure_contiguous
+
+
+MAX_AUDIO_PAYLOAD_BYTES = int(
+    os.environ.get("COMFYUI_MAX_AUDIO_PAYLOAD_BYTES", str(256 * 1024 * 1024))
+)
+
+
+def encode_audio_payload(audio_payload):
+    """Serialize an AUDIO dict into JSON-safe canonical envelope payload."""
+    if not isinstance(audio_payload, dict):
+        return None
+
+    waveform = audio_payload.get("waveform")
+    if waveform is None or not isinstance(waveform, torch.Tensor) or waveform.numel() == 0:
+        return None
+
+    sample_rate = audio_payload.get("sample_rate", 44100)
+    try:
+        sample_rate = int(sample_rate)
+    except (TypeError, ValueError):
+        sample_rate = 44100
+
+    waveform_cpu = waveform.detach().to(device="cpu", dtype=torch.float32).contiguous()
+    data_bytes = waveform_cpu.numpy().tobytes()
+    if len(data_bytes) > MAX_AUDIO_PAYLOAD_BYTES:
+        raise ValueError(
+            f"Audio payload too large: {len(data_bytes)} bytes exceeds {MAX_AUDIO_PAYLOAD_BYTES}."
+        )
+
+    return {
+        "sample_rate": sample_rate,
+        "shape": [int(dim) for dim in waveform_cpu.shape],
+        "dtype": "float32",
+        "data": base64.b64encode(data_bytes).decode("ascii"),
+    }
+
+
+def decode_audio_payload(audio_payload):
+    """Decode canonical envelope audio payload into an AUDIO dict."""
+    if audio_payload is None:
+        return None
+    if not isinstance(audio_payload, dict):
+        raise ValueError("Field 'audio' must be an object when provided.")
+
+    encoded = audio_payload.get("data")
+    shape = audio_payload.get("shape")
+    sample_rate = audio_payload.get("sample_rate", 44100)
+    dtype = audio_payload.get("dtype", "float32")
+
+    if not isinstance(encoded, str) or not encoded.strip():
+        raise ValueError("Field 'audio.data' must be a non-empty base64 string.")
+    if not isinstance(shape, list) or len(shape) != 3:
+        raise ValueError("Field 'audio.shape' must be a 3-item list [batch, channels, samples].")
+    if dtype != "float32":
+        raise ValueError("Field 'audio.dtype' must be 'float32'.")
+
+    try:
+        shape_tuple = tuple(int(dim) for dim in shape)
+    except (TypeError, ValueError) as exc:
+        raise ValueError("Field 'audio.shape' must contain integers.") from exc
+
+    if shape_tuple[0] <= 0 or shape_tuple[1] <= 0 or shape_tuple[2] < 0:
+        raise ValueError(
+            "Field 'audio.shape' must be [batch>0, channels>0, samples>=0]."
+        )
+
+    try:
+        sample_rate = int(sample_rate)
+    except (TypeError, ValueError) as exc:
+        raise ValueError("Field 'audio.sample_rate' must be an integer.") from exc
+    if sample_rate <= 0:
+        raise ValueError("Field 'audio.sample_rate' must be positive.")
+
+    try:
+        raw = base64.b64decode(encoded, validate=True)
+    except (binascii.Error, ValueError) as exc:
+        raise ValueError("Field 'audio.data' is not valid base64.") from exc
+
+    if len(raw) > MAX_AUDIO_PAYLOAD_BYTES:
+        raise ValueError(
+            f"Field 'audio.data' too large: {len(raw)} bytes exceeds {MAX_AUDIO_PAYLOAD_BYTES}."
+        )
+
+    expected_bytes = int(np.prod(shape_tuple, dtype=np.int64)) * 4
+    if len(raw) != expected_bytes:
+        raise ValueError(
+            f"Field 'audio.data' byte size mismatch: expected {expected_bytes}, got {len(raw)}."
+        )
+
+    array = np.frombuffer(raw, dtype=np.float32).reshape(shape_tuple)
+    waveform = torch.from_numpy(array.copy())
+    return {
+        "waveform": ensure_contiguous(waveform),
+        "sample_rate": sample_rate,
+    }
@@ -0,0 +1,5 @@
+from .tunnel import CloudflareTunnelManager
+
+cloudflare_tunnel_manager = CloudflareTunnelManager()
+
+__all__ = ["CloudflareTunnelManager", "cloudflare_tunnel_manager"]
@@ -0,0 +1,83 @@
+"""Cloudflared binary discovery and download helpers."""
+
+import os
+import platform
+import shutil
+import stat
+from urllib import error as urlerror
+from urllib import request
+
+from ..logging import debug_log
+
+
+def _get_project_root():
+    return os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
+
+
+def _get_cloudflared_dir():
+    return os.path.join(_get_project_root(), "bin")
+
+
+def _get_platform_binary_name():
+    system = platform.system().lower()
+    machine = platform.machine().lower()
+
+    if system == "windows":
+        if "arm" in machine:
+            return "cloudflared-windows-arm64.exe"
+        return "cloudflared-windows-amd64.exe"
+    if system == "darwin":
+        if machine in ("arm64", "aarch64"):
+            return "cloudflared-darwin-arm64"
+        return "cloudflared-darwin-amd64"
+    if system == "linux":
+        if machine in ("arm64", "aarch64"):
+            return "cloudflared-linux-arm64"
+        return "cloudflared-linux-amd64"
+
+    raise RuntimeError(f"Unsupported platform for cloudflared: {system}/{machine}")
+
+
+def _get_binary_path(bin_dir=None):
+    bin_dir = bin_dir or _get_cloudflared_dir()
+    binary_name = "cloudflared.exe" if platform.system().lower() == "windows" else "cloudflared"
+    return os.path.join(bin_dir, binary_name)
+
+
+def _download_cloudflared():
+    asset = _get_platform_binary_name()
+    url = f"https://github.com/cloudflare/cloudflared/releases/latest/download/{asset}"
+
+    bin_dir = _get_cloudflared_dir()
+    os.makedirs(bin_dir, exist_ok=True)
+    target_path = _get_binary_path(bin_dir)
+
+    debug_log(f"Downloading cloudflared from {url}")
+    try:
+        with request.urlopen(url, timeout=30) as resp:
+            with open(target_path, "wb") as f:
+                shutil.copyfileobj(resp, f)
+    except urlerror.URLError as exc:
+        raise RuntimeError(f"Failed to download cloudflared: {exc}") from exc
+
+    st = os.stat(target_path)
+    os.chmod(target_path, st.st_mode | stat.S_IEXEC)
+    debug_log(f"Downloaded cloudflared to {target_path}")
+    return target_path
+
+
+def ensure_binary() -> str:
+    """Return a usable cloudflared binary path, downloading if necessary."""
+    env_path = os.environ.get("CLOUDFLARED_PATH")
+    if env_path and os.path.exists(env_path):
+        return env_path
+
+    local_candidate = _get_binary_path()
+    if os.path.exists(local_candidate):
+        return local_candidate
+
+    path_binary = shutil.which("cloudflared")
+    if path_binary:
+        return path_binary
+
+    return _download_cloudflared()
@@ -0,0 +1,97 @@
+"""Background cloudflared process output reader."""
+
+import asyncio
+import re
+import threading
+
+from ..constants import CLOUDFLARE_LOG_BUFFER_SIZE
+from ..logging import debug_log
+
+PUBLIC_URL_PATTERN = re.compile(
+    r"(https?://[\w.-]+\.(?:trycloudflare\.com|cloudflare\.dev))",
+    re.IGNORECASE,
+)
+class ProcessReader:
+    def __init__(self, log_file=None):
+        self._process = None
+        self._thread = None
+        self._loop = None
+        self._url_event = None
+        self._public_url = None
+        self._last_error = None
+        self._recent_logs = []
+        self._log_file = log_file
+
+    def set_log_file(self, log_file):
+        self._log_file = log_file
+
+    def _append_log(self, line):
+        if self._log_file:
+            try:
+                with open(self._log_file, "a", encoding="utf-8", errors="replace") as f:
+                    f.write(line + "\n")
+            except Exception as exc:  # pragma: no cover
+                debug_log(f"Failed to write tunnel log: {exc}")
+
+        self._recent_logs.append(line)
+        if len(self._recent_logs) > CLOUDFLARE_LOG_BUFFER_SIZE:
+            self._recent_logs = self._recent_logs[-CLOUDFLARE_LOG_BUFFER_SIZE:]
+
+    def _reader(self):
+        process = self._process
+        if process is None:
+            return
+
+        loop = self._loop
+        for raw_line in iter(process.stdout.readline, ""):
+            line = raw_line.strip()
+            if not line:
+                continue
+
+            self._append_log(line)
+            match = PUBLIC_URL_PATTERN.search(line)
+            if match and not self._public_url:
+                self._public_url = match.group(1).rstrip("/")
+                if self._url_event and loop:
+                    loop.call_soon_threadsafe(self._url_event.set)
+
+            if "error" in line.lower() and not self._last_error:
+                self._last_error = line
+
+        if self._url_event and loop:
+            if not self._last_error and not self._public_url:
+                self._last_error = "Cloudflare tunnel exited before becoming ready"
+            loop.call_soon_threadsafe(self._url_event.set)
+
+    def start(self, process, loop):
+        self._process = process
+        self._loop = loop
+        self._url_event = asyncio.Event()
+        self._public_url = None
+        self._last_error = None
+        self._recent_logs = []
+        self._thread = threading.Thread(target=self._reader, daemon=True)
+        self._thread.start()
+
+    async def wait_for_url(self, timeout):
+        if not self._url_event:
+            return None
+        await asyncio.wait_for(self._url_event.wait(), timeout=timeout)
+        return self._public_url
+
+    def stop(self):
+        if self._thread and self._thread.is_alive():
+            self._thread.join(timeout=1)
+        self._thread = None
+        self._process = None
+        self._loop = None
+        self._url_event = None
+
+    def get_url(self):
+        return self._public_url
+
+    def get_last_error(self):
+        return self._last_error
+
+    def get_recent_logs(self):
+        return list(self._recent_logs)
@@ -0,0 +1,81 @@
+"""Cloudflare tunnel state persistence helpers."""
+
+from ..config import load_config, save_config
+from ..network import normalize_host
+
+
+def _get_tunnel_config(cfg):
+    tunnel_cfg = cfg.get("tunnel", {})
+    if isinstance(tunnel_cfg, dict):
+        return tunnel_cfg
+    return {}
+
+
+def load_tunnel_state():
+    cfg = load_config()
+    tunnel_cfg = _get_tunnel_config(cfg)
+    master_cfg = cfg.get("master", {}) if isinstance(cfg.get("master", {}), dict) else {}
+    return {
+        "status": tunnel_cfg.get("status", "stopped"),
+        "public_url": tunnel_cfg.get("public_url") or None,
+        "pid": tunnel_cfg.get("pid"),
+        "log_file": tunnel_cfg.get("log_file"),
+        "previous_master_host": tunnel_cfg.get("previous_master_host"),
+        "master_host": master_cfg.get("host"),
+    }
+
+
+def persist_tunnel_state(
+    status=None,
+    public_url=None,
+    pid=None,
+    log_file=None,
+    previous_host=None,
+    master_host=None,
+):
+    cfg = load_config()
+    tunnel_cfg = _get_tunnel_config(cfg)
+
+    if status is not None:
+        tunnel_cfg["status"] = status
+    if public_url is not None:
+        tunnel_cfg["public_url"] = public_url
+    if pid is not None:
+        tunnel_cfg["pid"] = pid
+    if log_file is not None:
+        tunnel_cfg["log_file"] = log_file
+    if previous_host is not None:
+        tunnel_cfg["previous_master_host"] = previous_host
+    if master_host is not None:
+        cfg.setdefault("master", {})["host"] = master_host
+
+    cfg["tunnel"] = tunnel_cfg
+    save_config(cfg)
+
+
+def clear_tunnel_state(log_file=None, previous_host=None, master_host=None):
+    persist_tunnel_state(
+        status="stopped",
+        public_url="",
+        pid=None,
+        log_file=log_file,
+        previous_host=previous_host,
+        master_host=master_host,
+    )
+
+
+def resolve_restore_master_host(previous_master_host):
+    """Determine whether master host should be restored after tunnel stop."""
+    cfg = load_config()
+    tunnel_cfg = _get_tunnel_config(cfg)
+    active_url = tunnel_cfg.get("public_url")
+    current_master_host = (cfg.get("master") or {}).get("host")
+
+    if not active_url:
+        return None
+
+    active_host = normalize_host(active_url)
+    current_host = normalize_host(current_master_host)
+    if current_host == active_host:
+        return previous_master_host or ""
+    return None
@@ -0,0 +1,207 @@
+"""Cloudflare tunnel lifecycle manager."""
+
+import asyncio
+import os
+import shutil
+import signal
+import subprocess
+import time
+
+from ..constants import TUNNEL_START_TIMEOUT
+from ..logging import debug_log
+from ..network import get_server_port, normalize_host
+from ..process import is_process_alive, terminate_process
+from .binary import ensure_binary
+from .process_reader import ProcessReader
+from .state import clear_tunnel_state, load_tunnel_state, persist_tunnel_state, resolve_restore_master_host
+
+
+class CloudflareTunnelManager:
+    def __init__(self):
+        self.process = None
+        self.pid = None
+        self.public_url = None
+        self.last_error = None
+        self.log_file = None
+        self.status = "stopped"
+        self.previous_master_host = None
+
+        self._lock = asyncio.Lock()
+        self._reader = ProcessReader()
+        self.binary_path = None
+
+        self._restore_state()
+
+    @property
+    def base_dir(self):
+        return os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
+
+    def _restore_state(self):
+        state = load_tunnel_state()
+
+        self.public_url = state.get("public_url") or None
+        self.previous_master_host = state.get("previous_master_host")
+        self.log_file = state.get("log_file")
+        pid = state.get("pid")
+
+        if pid and is_process_alive(pid):
+            self.pid = pid
+            self.status = state.get("status") or "running"
+            debug_log(f"Detected existing cloudflared process (pid={pid})")
+        else:
+            clear_tunnel_state(log_file=self.log_file, previous_host=self.previous_master_host)
+            self.status = "stopped"
+            self.pid = None
+
+    async def start_tunnel(self):
+        async with self._lock:
+            if self.process and self.process.poll() is None:
+                return {
+                    "status": self.status,
+                    "public_url": self.public_url,
+                    "pid": self.process.pid,
+                    "log_file": self.log_file,
+                }
+
+            if self.pid and is_process_alive(self.pid):
+                debug_log(f"Stopping stale cloudflared pid {self.pid} before starting a new one")
+                await self.stop_tunnel()
+
+            binary = await asyncio.to_thread(ensure_binary)
+            self.binary_path = binary
+            port = get_server_port()
+            self.status = "starting"
+            self.last_error = None
+            self.public_url = None
+
+            state = load_tunnel_state()
+            master_host = state.get("master_host") or ""
+            if state.get("previous_master_host"):
+                self.previous_master_host = state.get("previous_master_host")
+            else:
+                self.previous_master_host = master_host
+
+            os.makedirs(os.path.join(self.base_dir, "logs"), exist_ok=True)
+            timestamp = time.strftime("%Y%m%d-%H%M%S")
+            self.log_file = os.path.join(self.base_dir, "logs", f"cloudflare-{timestamp}.log")
+
+            cmd = [
+                binary,
+                "tunnel",
+                "--no-autoupdate",
+                "--url",
+                f"http://127.0.0.1:{port}",
+            ]
+
+            debug_log(f"Starting cloudflared: {' '.join(cmd)}")
+            try:
+                self.process = subprocess.Popen(
+                    cmd,
+                    stdout=subprocess.PIPE,
+                    stderr=subprocess.STDOUT,
+                    text=True,
+                    bufsize=1,
+                )
+            except FileNotFoundError:
+                self.status = "error"
+                raise RuntimeError("cloudflared binary not found")
+            except Exception as exc:
+                self.status = "error"
+                raise RuntimeError(f"Failed to start cloudflared: {exc}") from exc
+
+            self.pid = self.process.pid
+            persist_tunnel_state(
+                status="starting",
+                pid=self.pid,
+                log_file=self.log_file,
+                previous_host=self.previous_master_host,
+            )
+
+            loop = asyncio.get_running_loop()
+            self._reader.set_log_file(self.log_file)
+            self._reader.start(self.process, loop)
+
+            try:
+                await self._reader.wait_for_url(timeout=TUNNEL_START_TIMEOUT)
+            except asyncio.TimeoutError:
+                self.last_error = "Timed out waiting for Cloudflare to assign a URL"
+                await self.stop_tunnel()
+                raise RuntimeError(self.last_error)
+
+            public_url = self._reader.get_url()
+            if not public_url:
+                self.last_error = self._reader.get_last_error() or "Cloudflare tunnel failed to start"
+                await self.stop_tunnel()
+                raise RuntimeError(self.last_error)
+
+            self.public_url = public_url
+            self.status = "running"
+            debug_log(f"Cloudflare tunnel ready at {self.public_url}")
+
+            persist_tunnel_state(
+                status="running",
+                public_url=self.public_url,
+                pid=self.pid,
+                log_file=self.log_file,
+                previous_host=self.previous_master_host or "",
+                master_host=normalize_host(self.public_url),
+            )
+            return {
+                "status": self.status,
+                "public_url": self.public_url,
+                "pid": self.pid,
+                "log_file": self.log_file,
+            }
+
+    async def stop_tunnel(self):
+        async with self._lock:
+            pid = self.process.pid if self.process else self.pid
+            if not pid:
+                clear_tunnel_state(log_file=self.log_file, previous_host=self.previous_master_host)
+                self.status = "stopped"
+                return {"status": "stopped"}
+
+            debug_log(f"Stopping cloudflared (pid={pid})")
+            if self.process:
+                terminate_process(self.process, timeout=5)
+            else:
+                try:
+                    os.kill(pid, signal.SIGTERM)
+                    time.sleep(0.5)
+                except Exception as exc:  # pragma: no cover
+                    debug_log(f"Error stopping cloudflared pid {pid}: {exc}")
+
+            restore_host = resolve_restore_master_host(self.previous_master_host)
+
+            self.status = "stopped"
+            self.public_url = None
+            self.pid = None
+            self.process = None
+            self.last_error = None
+            self._reader.stop()
+
+            clear_tunnel_state(
+                log_file=self.log_file,
+                previous_host=self.previous_master_host,
+                master_host=restore_host,
+            )
+            return {"status": "stopped"}
+
+    def get_status(self):
+        alive = False
+        pid = self.process.pid if self.process else self.pid
+        if pid:
+            alive = is_process_alive(pid)
+            if not alive and self.status == "running":
+                self.status = "stopped"
+
+        return {
+            "status": self.status,
+            "public_url": self.public_url,
+            "pid": pid,
+            "log_file": self.log_file,
+            "last_error": self.last_error or self._reader.get_last_error(),
+            "binary_path": self.binary_path or shutil.which("cloudflared"),
+            "recent_logs": self._reader.get_recent_logs()[-20:],
+            "previous_master_host": self.previous_master_host,
+        }
@@ -1,11 +1,23 @@
 """
 Configuration management for ComfyUI-Distributed.
 """
+import asyncio
 import os
 import json
+from contextlib import asynccontextmanager
 from .logging import log

+# Import defaults for timeout fallbacks
+from .constants import HEARTBEAT_TIMEOUT
+
 CONFIG_FILE = os.path.join(os.path.dirname(os.path.dirname(__file__)), "gpu_config.json")
+_config_cache = None
+_config_mtime = 0.0
+_config_lock = asyncio.Lock()
+
+
+def _config_path():
+    return CONFIG_FILE

 def get_default_config():
    """Returns the default configuration dictionary. Single source of truth."""
@@ -15,36 +27,140 @@ def get_default_config():
        "settings": {
            "debug": False,
            "auto_launch_workers": False,
-            "stop_workers_on_master_exit": True
+            "stop_workers_on_master_exit": True,
+            "master_delegate_only": False,
+            "websocket_orchestration": True,
+            "worker_probe_concurrency": 8,
+            "worker_prep_concurrency": 4,
+            "media_sync_concurrency": 2,
+            "media_sync_timeout_seconds": 120
+        },
+        "tunnel": {
+            "status": "stopped",
+            "public_url": "",
+            "pid": None,
+            "log_file": "",
+            "previous_master_host": ""
        }
    }

+def _merge_with_defaults(data, defaults):
+    """Recursively merge loaded config data with default keys."""
+    if not isinstance(data, dict):
+        return defaults
+
+    merged = {}
+    for key, default_value in defaults.items():
+        loaded_value = data.get(key, default_value)
+        if isinstance(default_value, dict) and isinstance(loaded_value, dict):
+            merged[key] = _merge_with_defaults(loaded_value, default_value)
+        else:
+            merged[key] = loaded_value
+
+    # Preserve unknown keys for forward compatibility.
+    for key, value in data.items():
+        if key not in merged:
+            merged[key] = value
+
+    return merged
+
+
+def invalidate_config_cache():
+    """Invalidate in-memory config cache so next load reads from disk."""
+    global _config_cache, _config_mtime
+    _config_cache = None
+    _config_mtime = 0.0
+
+
 def load_config():
    """Loads the config, falling back to defaults if the file is missing or invalid."""
-    if os.path.exists(CONFIG_FILE):
+    global _config_cache, _config_mtime
+    path = _config_path()
+
+    try:
+        mtime = os.path.getmtime(path)
+    except OSError:
+        if _config_cache is None:
+            _config_cache = get_default_config()
+        return _config_cache
+
+    if _config_cache is None or mtime != _config_mtime:
        try:
-            with open(CONFIG_FILE, 'r') as f:
-                return json.load(f)
+            with open(path, 'r', encoding='utf-8') as f:
+                loaded = json.load(f)
+            _config_cache = _merge_with_defaults(loaded, get_default_config())
        except Exception as e:
            log(f"Error loading config, using defaults: {e}")
-    return get_default_config()
+            _config_cache = get_default_config()
+        _config_mtime = mtime
+
+    return _config_cache

 def save_config(config):
    """Saves the configuration to file."""
+    tmp_path = f"{_config_path()}.tmp"
    try:
-        with open(CONFIG_FILE, 'w') as f:
+        with open(tmp_path, 'w', encoding='utf-8') as f:
            json.dump(config, f, indent=2)
+            f.flush()
+            os.fsync(f.fileno())
+        os.replace(tmp_path, _config_path())
+        invalidate_config_cache()
        return True
    except Exception as e:
+        try:
+            os.unlink(tmp_path)
+        except OSError:
+            pass
        log(f"Error saving config: {e}")
        return False

+
+@asynccontextmanager
+async def config_transaction():
+    """Acquire config lock, yield loaded config, and save if changed."""
+    async with _config_lock:
+        config = load_config()
+        original_snapshot = json.dumps(config, sort_keys=True)
+        yield config
+        updated_snapshot = json.dumps(config, sort_keys=True)
+        if updated_snapshot != original_snapshot:
+            if not save_config(config):
+                raise RuntimeError("Failed to save config")
+
 def ensure_config_exists():
    """Creates default config file if it doesn't exist. Used by __init__.py"""
-    if not os.path.exists(CONFIG_FILE):
+    if not os.path.exists(_config_path()):
        default_config = get_default_config()
        if save_config(default_config):
            from .logging import debug_log
            debug_log("Created default config file")
        else:
-            log("Could not create default config file")
+            log("Could not create default config file")
+
+def get_worker_timeout_seconds(default: int = HEARTBEAT_TIMEOUT) -> int:
+    """Return the unified worker timeout (seconds).
+
+    Priority:
+    1) UI-configured setting `settings.worker_timeout_seconds`
+    2) Fallback to provided `default` (defaults to HEARTBEAT_TIMEOUT which itself
+       can be overridden via the COMFYUI_HEARTBEAT_TIMEOUT env var)
+
+    This value should be used anywhere we consider a worker "timed out" from the
+    master's perspective (e.g., collector waits, upscaler result collection).
+    """
+    try:
+        cfg = load_config()
+        val = int(cfg.get('settings', {}).get('worker_timeout_seconds', default))
+        return max(1, val)
+    except Exception:
+        return max(1, int(default))
+
+
+def is_master_delegate_only() -> bool:
+    """Returns True when master should skip local workload and act as orchestrator only."""
+    try:
+        cfg = load_config()
+        return bool(cfg.get('settings', {}).get('master_delegate_only', False))
+    except Exception:
+        return False
@@ -13,6 +13,10 @@ PROCESS_TERMINATION_TIMEOUT = 5.0
 WORKER_CHECK_INTERVAL = 2.0
 STATUS_CHECK_INTERVAL = 5.0

+# Cloudflare tunnel
+TUNNEL_START_TIMEOUT = float(os.environ.get("TUNNEL_START_TIMEOUT", "25"))
+CLOUDFLARE_LOG_BUFFER_SIZE = 200
+
 # Network
 CHUNK_SIZE = 8192
 LOG_TAIL_BYTES = 65536  # 64KB
@@ -30,6 +34,7 @@ TILE_TRANSFER_TIMEOUT = 30.0
 PROCESS_WAIT_TIMEOUT = 3.0
 QUEUE_INIT_TIMEOUT = 5.0
 TILE_SEND_TIMEOUT = 60.0
+JOB_INIT_GRACE_PERIOD = 10.0

 # Memory operations  
 MEMORY_CLEAR_DELAY = 0.5
@@ -38,4 +43,26 @@ MEMORY_CLEAR_DELAY = 0.5
 MAX_BATCH = int(os.environ.get('COMFYUI_MAX_BATCH', '20'))  # Maximum items per batch to prevent timeouts/OOM (~100MB chunks for 512x512 PNGs)

 # Heartbeat monitoring
-HEARTBEAT_TIMEOUT = int(os.environ.get('COMFYUI_HEARTBEAT_TIMEOUT', '60'))  # Worker heartbeat timeout in seconds
+HEARTBEAT_INTERVAL = float(os.environ.get('COMFYUI_HEARTBEAT_INTERVAL', '10'))  # Heartbeat/check interval in seconds
+HEARTBEAT_TIMEOUT = int(os.environ.get('COMFYUI_HEARTBEAT_TIMEOUT', '60'))  # Worker heartbeat timeout in seconds (default 60s)
+
+# USDU result collection
+DYNAMIC_MODE_MAX_POLL_TIMEOUT = 10.0
+
+# Static mode job poll loop
+JOB_POLL_INTERVAL = 1.0
+JOB_POLL_MAX_ATTEMPTS = 20
+
+# Orchestration pipeline
+ORCHESTRATION_WORKER_PROBE_CONCURRENCY = int(
+    os.environ.get('COMFYUI_ORCHESTRATION_WORKER_PROBE_CONCURRENCY', '8')
+)
+ORCHESTRATION_WORKER_PREP_CONCURRENCY = int(
+    os.environ.get('COMFYUI_ORCHESTRATION_WORKER_PREP_CONCURRENCY', '4')
+)
+ORCHESTRATION_MEDIA_SYNC_CONCURRENCY = int(
+    os.environ.get('COMFYUI_ORCHESTRATION_MEDIA_SYNC_CONCURRENCY', '2')
+)
+ORCHESTRATION_MEDIA_SYNC_TIMEOUT = float(
+    os.environ.get('COMFYUI_ORCHESTRATION_MEDIA_SYNC_TIMEOUT', '120')
+)
@@ -0,0 +1,114 @@
+from contextlib import contextmanager
+
+import torch
+
+from .logging import debug_log
+from .usdu_utils import resize_region
+
+
+@contextmanager
+def crop_model_cond(model, crop_regions, init_size, canvas_size, tile_size, latent_crop=False):
+    """Clone model and crop compatible model patches for tile-local sampling."""
+    try:
+        patched_model = model.clone()
+    except Exception:
+        # Fallback to original model when clone/patch access is unavailable.
+        yield model
+        return
+
+    patches = (
+        patched_model
+        .model_options
+        .get("transformer_options", {})
+        .get("patches", {})
+    )
+    applied_croppers = {}
+    for _module, module_patches in patches.items():
+        for patch in module_patches:
+            if id(patch) in applied_croppers:
+                continue
+            if type(patch).__name__ not in ("DiffSynthCnetPatch", "ZImageControlPatch"):
+                continue
+            try:
+                cropper = ModelPatchCropper(patch).crop(crop_regions, canvas_size, latent_crop)
+                applied_croppers[id(patch)] = cropper
+            except Exception as exc:
+                debug_log(f"crop_model_cond: patch crop skipped for {type(patch).__name__}: {exc}")
+    try:
+        yield patched_model
+    finally:
+        for cropper in applied_croppers.values():
+            del cropper
+
+
+class ModelPatchCropper:
+    """Stateful crop helper that restores model patch tensors on cleanup."""
+
+    def __init__(self, patch):
+        self.patch = patch
+        self.original_state = {
+            "image": patch.image.clone() if isinstance(patch.image, torch.Tensor) else patch.image,
+            "encoded_image": patch.encoded_image.clone() if isinstance(patch.encoded_image, torch.Tensor) else patch.encoded_image,
+            "encoded_image_size": patch.encoded_image_size,
+        }
+        self.patch_class = type(patch).__name__
+        required_attrs = (
+            "image",
+            "model_patch",
+            "vae",
+            "strength",
+            "encoded_image",
+            "encoded_image_size",
+        )
+        missing = [attr for attr in required_attrs if not hasattr(patch, attr)]
+        if missing:
+            raise AttributeError(
+                f"{self.patch_class} missing required attrs: {', '.join(missing)}"
+            )
+
+    def __del__(self):
+        self.patch.image = self.original_state["image"]
+        self.patch.encoded_image = self.original_state["encoded_image"]
+        self.patch.encoded_image_size = self.original_state["encoded_image_size"]
+
+    def crop(self, crop_regions, canvas_size, latent_crop=True):
+        patch = self.patch
+
+        if not isinstance(crop_regions, list):
+            crop_regions = [crop_regions]
+
+        image_size = (patch.image.shape[2], patch.image.shape[1])  # (W,H)
+
+        cropped_images = []
+        for crop_region in crop_regions:
+            resized_crop = resize_region(crop_region, canvas_size, image_size)
+            x1, y1, x2, y2 = resized_crop
+            cropped_image = patch.image[:, y1:y2, x1:x2, :]
+            cropped_images.append(cropped_image)
+
+        concatenated_image = torch.cat(cropped_images, dim=0)
+        patch.image = concatenated_image
+        patch.encoded_image_size = (
+            concatenated_image.shape[1],
+            concatenated_image.shape[2],
+        )
+
+        if latent_crop:
+            downscale_ratio = patch.vae.spacial_compression_encode()
+            cropped_latents = []
+            for crop_region in crop_regions:
+                resized_crop = resize_region(crop_region, canvas_size, image_size)
+                x1, y1, x2, y2 = tuple(x // downscale_ratio for x in resized_crop)
+                cropped_latent = patch.encoded_image[:, :, y1:y2, x1:x2]
+                cropped_latents.append(cropped_latent)
+            patch.encoded_image = torch.cat(cropped_latents, dim=0)
+        else:
+            patch.__init__(
+                patch.model_patch,
+                patch.vae,
+                concatenated_image,
+                patch.strength,
+                inpaint_image=patch.inpaint_image,
+                mask=patch.mask,
+            )
+        return self
@@ -0,0 +1,42 @@
+"""Custom exceptions for ComfyUI-Distributed."""
+
+
+class DistributedError(Exception):
+    """Base exception for all ComfyUI-Distributed errors."""
+
+
+class WorkerError(DistributedError):
+    """Error related to a specific distributed worker."""
+
+    def __init__(self, message, worker_id=None):
+        super().__init__(message)
+        self.worker_id = worker_id
+
+
+class WorkerTimeoutError(WorkerError):
+    """Worker did not respond within the expected timeout."""
+
+
+class WorkerNotAvailableError(WorkerError):
+    """Worker is unreachable or not running."""
+
+
+class JobQueueError(DistributedError):
+    """Error in distributed job queue management."""
+
+
+class TileCollectionError(DistributedError):
+    """Error collecting processed tiles from workers."""
+
+
+class ProcessError(DistributedError):
+    """Error managing a worker subprocess."""
+
+    def __init__(self, message, pid=None, worker_id=None):
+        super().__init__(message)
+        self.pid = pid
+        self.worker_id = worker_id
+
+
+class TunnelError(DistributedError):
+    """Error managing the Cloudflare tunnel."""
@@ -3,20 +3,35 @@ Shared logging utilities for ComfyUI-Distributed.
 """
 import os
 import json
+import time

 # Config file is in parent directory
 CONFIG_FILE = os.path.join(os.path.dirname(os.path.dirname(__file__)), "gpu_config.json")

+_debug_cache: bool | None = None
+_debug_cache_time: float = 0.0
+_DEBUG_TTL: float = 5.0
+
 def is_debug_enabled():
    """Check if debug is enabled."""
+    global _debug_cache, _debug_cache_time
+
+    now = time.monotonic()
+    if _debug_cache is not None and (now - _debug_cache_time) < _DEBUG_TTL:
+        return _debug_cache
+
+    enabled = False
    if os.path.exists(CONFIG_FILE):
        try:
            with open(CONFIG_FILE, 'r') as f:
                config = json.load(f)
-                return config.get("settings", {}).get("debug", False)
-        except:
+                enabled = config.get("settings", {}).get("debug", False)
+        except (OSError, json.JSONDecodeError, ValueError):
            pass
-    return False
+
+    _debug_cache = enabled
+    _debug_cache_time = now
+    return enabled

 def debug_log(message):
    """Log debug messages only if debug is enabled in config."""
@@ -25,4 +40,4 @@ def debug_log(message):

 def log(message):
    """Always log important messages."""
-    print(f"[Distributed] {message}")
+    print(f"[Distributed] {message}")
@@ -1,7 +1,10 @@
 """
 Network and API utilities for ComfyUI-Distributed.
 """
+import asyncio
 import aiohttp
+import re
+import server
 from aiohttp import web
 from .logging import debug_log

@@ -11,6 +14,11 @@ _client_session = None
 async def get_client_session():
    """Get or create a shared aiohttp client session."""
    global _client_session
+    try:
+        asyncio.get_running_loop()
+    except RuntimeError as exc:
+        raise RuntimeError("get_client_session() requires an active asyncio event loop.") from exc
+
    if _client_session is None or _client_session.closed:
        connector = aiohttp.TCPConnector(limit=100, limit_per_host=30)
        # Don't set timeout here - set it per request
@@ -26,8 +34,14 @@ async def cleanup_client_session():

 async def handle_api_error(request, error, status=500):
    """Standardized error response handler."""
-    debug_log(f"API Error: {error}")
-    return web.json_response({"status": "error", "message": str(error)}, status=status)
+    if isinstance(error, list):
+        messages = [str(item) for item in error]
+        debug_log(f"API Error [{status}]: {messages}")
+        return web.json_response({"errors": messages}, status=status)
+
+    message = str(error)
+    debug_log(f"API Error [{status}]: {message}")
+    return web.json_response({"error": message}, status=status)

 def get_server_port():
    """Get the ComfyUI server port."""
@@ -39,3 +53,114 @@ def get_server_loop():
    import server
    return server.PromptServer.instance.loop

+
+def normalize_host(value):
+    if value is None:
+        return None
+    if not isinstance(value, str):
+        return value
+    host = value.strip()
+    if not host:
+        return host
+    host = re.sub(r"^https?://", "", host, flags=re.IGNORECASE)
+    return host.split("/")[0]
+
+
+def build_worker_url(worker, endpoint=""):
+    """Construct the worker base URL with optional endpoint."""
+    host = (worker.get("host") or "").strip()
+    port = int(worker.get("port", worker.get("listen_port", 8188)) or 8188)
+
+    if not host:
+        host = getattr(server.PromptServer.instance, "address", "127.0.0.1") or "127.0.0.1"
+
+    if host.startswith(("http://", "https://")):
+        base = host.rstrip("/")
+    else:
+        is_cloud = worker.get("type") == "cloud" or host.endswith(".proxy.runpod.net") or port == 443
+        scheme = "https" if is_cloud else "http"
+        default_port = 443 if scheme == "https" else 80
+        port_part = "" if port == default_port else f":{port}"
+        base = f"{scheme}://{host}{port_part}"
+
+    return f"{base}{endpoint}"
+
+
+async def probe_worker(worker_url: str, timeout: float = 5.0) -> dict | None:
+    """GET {worker_url}/prompt. Returns parsed JSON or None on any failure."""
+    base_url = (worker_url or "").strip().rstrip("/")
+    if not base_url:
+        return None
+    probe_url = base_url if base_url.endswith("/prompt") else f"{base_url}/prompt"
+    session = await get_client_session()
+    try:
+        async with session.get(
+            probe_url,
+            timeout=aiohttp.ClientTimeout(total=float(timeout)),
+        ) as response:
+            if response.status != 200:
+                debug_log(f"[Distributed] Worker probe non-200 status: {response.status} ({probe_url})")
+                return None
+            payload = await response.json()
+            if isinstance(payload, dict):
+                return payload
+            debug_log(f"[Distributed] Worker probe returned non-object JSON: {probe_url}")
+            return None
+    except asyncio.TimeoutError:
+        debug_log(f"[Distributed] Worker probe timed out: {probe_url}")
+        return None
+    except aiohttp.ClientConnectorError:
+        debug_log(f"[Distributed] Worker unreachable: {probe_url}")
+        return None
+    except Exception as exc:
+        debug_log(f"[Distributed] Worker probe error ({probe_url}): {exc}")
+        return None
+
+
+def build_master_url(config=None, prompt_server_instance=None):
+    """Build the best public URL workers should use to reach the master."""
+    if config is None:
+        from .config import load_config
+        config = load_config()
+
+    prompt_server_instance = prompt_server_instance or server.PromptServer.instance
+    master_cfg = (config or {}).get("master", {}) or {}
+    configured_host = (master_cfg.get("host") or "").strip()
+    configured_port = master_cfg.get("port")
+    default_port = getattr(prompt_server_instance, "port", 8188) or 8188
+    try:
+        port = int(configured_port or default_port)
+    except (TypeError, ValueError):
+        port = int(default_port)
+
+    def _needs_https(hostname):
+        hostname = hostname.lower()
+        https_domains = (
+            ".proxy.runpod.net",
+            ".ngrok-free.app",
+            ".ngrok-free.dev",
+            ".ngrok.io",
+            ".trycloudflare.com",
+            ".cloudflare.dev",
+        )
+        return any(hostname.endswith(suffix) for suffix in https_domains)
+
+    if configured_host:
+        if configured_host.startswith(("http://", "https://")):
+            return configured_host.rstrip("/")
+
+        host = configured_host
+        scheme = "https" if _needs_https(host) or port == 443 else "http"
+        default_port_for_scheme = 443 if scheme == "https" else 80
+        if configured_port is None and scheme == "https" and _needs_https(host):
+            port = default_port_for_scheme
+        port_part = "" if port == default_port_for_scheme else f":{port}"
+        return f"{scheme}://{host}{port_part}"
+
+    address = getattr(prompt_server_instance, "address", "127.0.0.1") or "127.0.0.1"
+    if address in ("0.0.0.0", "::"):
+        address = "127.0.0.1"
+    scheme = "https" if port == 443 else "http"
+    default_port_for_scheme = 443 if scheme == "https" else 80
+    port_part = "" if port == default_port_for_scheme else f":{port}"
+    return f"{scheme}://{address}{port_part}"
@@ -0,0 +1,13 @@
+from .logging import debug_log, log
+
+
+def trace_prefix(trace_execution_id: str) -> str:
+    return f"[Distributed][exec:{trace_execution_id}]"
+
+
+def trace_debug(trace_execution_id: str, message: str) -> None:
+    debug_log(f"{trace_prefix(trace_execution_id)} {message}")
+
+
+def trace_info(trace_execution_id: str, message: str) -> None:
+    log(f"{trace_prefix(trace_execution_id)} {message}")
@@ -1,212 +1,32 @@
-import asyncio
-import time
-import json
-import copy
-import os
-import io
-from aiohttp import web
-import server
-from PIL import Image
-import numpy as np
-import torch
+"""Backward-compatibility shim for USDU helpers.

-# Import from other utilities
-from .logging import debug_log, log
-from .network import handle_api_error, get_client_session
-from .image import tensor_to_pil
+Route handlers and job logic now live in:
+- upscale.job_store
+- upscale.job_timeout
+- upscale.payload_parsers
+- api.usdu_routes
+"""

-# Configure maximum payload size (50MB default, configurable via environment variable)
-MAX_PAYLOAD_SIZE = int(os.environ.get('COMFYUI_MAX_PAYLOAD_SIZE', str(50 * 1024 * 1024)))
+from ..upscale.conditioning import clone_conditioning, clone_control_chain
+from ..upscale.job_store import (
+    MAX_PAYLOAD_SIZE,
+    _cleanup_job,
+    _drain_results_queue,
+    _get_completed_count,
+    _init_job_queue,
+    _mark_task_completed,
+    ensure_tile_jobs_initialized,
+    init_dynamic_job,
+    init_static_job_batched,
+)
+from ..upscale.job_timeout import _check_and_requeue_timed_out_workers
+from ..upscale.payload_parsers import _parse_tiles_from_form
+from .logging import debug_log
+from .network import get_client_session

-# Import HEARTBEAT_TIMEOUT from constants
-from .constants import HEARTBEAT_TIMEOUT
-
-
-# Unified Job Data Structure Keys
-JOB_QUEUE = 'queue'
-JOB_MODE = 'mode'
-JOB_COMPLETED_TASKS = 'completed_tasks'
-JOB_WORKER_STATUS = 'worker_status'
-JOB_ASSIGNED_TO_WORKERS = 'assigned_to_workers'
-JOB_PENDING_TASKS = 'pending_tasks'
-JOB_BATCH_SIZE = 'batch_size'  # For dynamic
-JOB_NUM_TILES_PER_IMAGE = 'num_tiles_per_image'  # For static
-
-# Task Types
-TASK_TYPE_TILE = 'tile'
-TASK_TYPE_IMAGE = 'image'
-
-async def _init_job_queue(multi_job_id, mode, batch_size=None, num_tiles_per_image=None, all_indices=None, enabled_workers=None, task_assignments=None):
-    """Unified initialization for job queues in static and dynamic modes."""
-    prompt_server = ensure_tile_jobs_initialized()
-    async with prompt_server.distributed_tile_jobs_lock:
-        if multi_job_id in prompt_server.distributed_pending_tile_jobs:
-            debug_log(f"Queue already exists for {multi_job_id}")
-            return
-
-        job_data = {
-            JOB_QUEUE: asyncio.Queue(),
-            JOB_MODE: mode,
-            JOB_COMPLETED_TASKS: {},
-            JOB_WORKER_STATUS: {w: time.time() for w in enabled_workers or []},
-            JOB_ASSIGNED_TO_WORKERS: {w: [] for w in enabled_workers or []},
-            JOB_PENDING_TASKS: asyncio.Queue(),
-        }
-
-        if mode == 'dynamic':
-            job_data[JOB_BATCH_SIZE] = batch_size
-            pending_queue = job_data[JOB_PENDING_TASKS]
-            for i in all_indices or range(batch_size):
-                await pending_queue.put(i)
-            debug_log(f"Initialized dynamic queue with {batch_size} pending images")
-        elif mode == 'static':
-            job_data[JOB_NUM_TILES_PER_IMAGE] = num_tiles_per_image
-            # For static with dynamic distribution, populate all tile indices in pending queue
-            pending_queue = job_data[JOB_PENDING_TASKS]
-            total_tiles = batch_size * num_tiles_per_image
-            for i in range(total_tiles):
-                await pending_queue.put(i)
-            debug_log(f"Initialized static queue with {total_tiles} pending tiles for dynamic distribution")
-            
-            # Keep backward compatibility - if task assignments provided, still track them
-            if task_assignments and enabled_workers:
-                # task_assignments[0] is for master, 1+ are for workers
-                for i, worker_id in enumerate(enabled_workers):
-                    if i + 1 < len(task_assignments):
-                        job_data[JOB_ASSIGNED_TO_WORKERS][worker_id] = task_assignments[i + 1]
-                        debug_log(f"Worker {worker_id} pre-assigned {len(task_assignments[i + 1])} tasks (legacy mode)")
-        else:
-            raise ValueError(f"Unknown mode: {mode}")
-
-        prompt_server.distributed_pending_tile_jobs[multi_job_id] = job_data
-
-def _distribute_tasks(items: list, num_participants: int) -> list[list[any]]:
-    """Distribute a list of items among N participants (master + workers)."""
-    if num_participants <= 1:
-        return [items]
-
-    items_per_participant = len(items) // num_participants
-    remainder = len(items) % num_participants
-    assignments = []
-    start_idx = 0
-    for i in range(num_participants):
-        count = items_per_participant + (1 if i < remainder else 0)
-        end_idx = start_idx + count
-        assignments.append(items[start_idx:end_idx])
-        start_idx = end_idx
-    return assignments
-
-async def _get_next_task(multi_job_id):
-    """Get next task from pending queue (generalized for tiles/images)."""
-    prompt_server = ensure_tile_jobs_initialized()
-    async with prompt_server.distributed_tile_jobs_lock:
-        job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
-        if not job_data or JOB_PENDING_TASKS not in job_data:
-            return None
-        try:
-            task_id = await asyncio.wait_for(job_data[JOB_PENDING_TASKS].get(), timeout=1.0)
-            return task_id
-        except asyncio.TimeoutError:
-            return None
-
-async def _drain_results_queue(multi_job_id):
-    """Drain pending results from queue and update completed_tasks. Returns count drained."""
-    prompt_server = ensure_tile_jobs_initialized()
-    async with prompt_server.distributed_tile_jobs_lock:
-        job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
-        if not job_data or JOB_QUEUE not in job_data or JOB_COMPLETED_TASKS not in job_data:
-            return 0
-        q = job_data[JOB_QUEUE]
-        completed_tasks = job_data[JOB_COMPLETED_TASKS]
-
-        collected = 0
-        while not q.empty():
-            try:
-                result = await asyncio.wait_for(q.get(), timeout=0.1)
-                worker_id = result['worker_id']
-                is_last = result.get('is_last', False)
-                
-                if 'image_idx' in result and 'image' in result:
-                    task_id = result['image_idx']
-                    if task_id not in completed_tasks:
-                        completed_tasks[task_id] = result['image']
-                        collected += 1
-                elif 'tiles' in result:
-                    for tile_data in result['tiles']:
-                        task_id = tile_data.get('global_idx', tile_data['tile_idx'])
-                        if task_id not in completed_tasks:
-                            completed_tasks[task_id] = tile_data
-                            collected += 1
-                elif 'tensor' in result and 'tile_idx' in result:  # Single tile backward compat
-                    task_id = result.get('global_idx', result['tile_idx'])
-                    if task_id not in completed_tasks:
-                        completed_tasks[task_id] = {
-                            'tensor': result['tensor'],
-                            'tile_idx': result['tile_idx'],
-                            'x': result['x'],
-                            'y': result['y'],
-                            'extracted_width': result['extracted_width'],
-                            'extracted_height': result['extracted_height'],
-                            'padding': result['padding'],
-                            'batch_idx': result.get('batch_idx', 0),
-                            'global_idx': task_id
-                        }
-                        collected += 1
-
-                if is_last:
-                    # Track worker completion
-                    if worker_id in job_data[JOB_WORKER_STATUS]:
-                        del job_data[JOB_WORKER_STATUS][worker_id]
-            except asyncio.TimeoutError:
-                break
-
-        return collected
-
-async def _check_and_requeue_timed_out_workers(multi_job_id, total_tasks):
-    """Check timed out workers and requeue their tasks. Returns requeued count."""
-    prompt_server = ensure_tile_jobs_initialized()
-    async with prompt_server.distributed_tile_jobs_lock:
-        job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
-        if not job_data:
-            return 0
-
-        current_time = time.time()
-        requeued_count = 0
-        completed_tasks = job_data.get(JOB_COMPLETED_TASKS, {})
-
-        for worker, last_heartbeat in list(job_data.get(JOB_WORKER_STATUS, {}).items()):
-            if current_time - last_heartbeat > HEARTBEAT_TIMEOUT:
-                log(f"Worker {worker} timed out")
-                for task_id in job_data.get(JOB_ASSIGNED_TO_WORKERS, {}).get(worker, []):
-                    if task_id not in completed_tasks:
-                        await job_data[JOB_PENDING_TASKS].put(task_id)
-                        requeued_count += 1
-                if JOB_WORKER_STATUS in job_data:
-                    del job_data[JOB_WORKER_STATUS][worker]
-                if JOB_ASSIGNED_TO_WORKERS in job_data:
-                    job_data[JOB_ASSIGNED_TO_WORKERS][worker] = []
-
-        return requeued_count
-
-async def _get_completed_count(multi_job_id):
-    """Get count of completed tasks."""
-    prompt_server = ensure_tile_jobs_initialized()
-    async with prompt_server.distributed_tile_jobs_lock:
-        job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
-        if job_data and JOB_COMPLETED_TASKS in job_data:
-            return len(job_data[JOB_COMPLETED_TASKS])
-        return 0
-
-async def _mark_task_completed(multi_job_id, task_id, result):
-    """Mark a task as completed."""
-    prompt_server = ensure_tile_jobs_initialized()
-    async with prompt_server.distributed_tile_jobs_lock:
-        job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
-        if job_data and JOB_COMPLETED_TASKS in job_data:
-            job_data[JOB_COMPLETED_TASKS][task_id] = result

 async def _send_heartbeat_to_master(multi_job_id, master_url, worker_id):
-    """Send heartbeat to master."""
+    """Send heartbeat to master from worker-side processing loops."""
    try:
        data = {'multi_job_id': multi_job_id, 'worker_id': str(worker_id)}
        session = await get_client_session()
@@ -216,606 +36,20 @@ async def _send_heartbeat_to_master(multi_job_id, master_url, worker_id):
    except Exception as e:
        debug_log(f"Heartbeat failed: {e}")

-async def _cleanup_job(multi_job_id):
-    """Cleanup the job data."""
-    prompt_server = ensure_tile_jobs_initialized()
-    async with prompt_server.distributed_tile_jobs_lock:
-        if multi_job_id in prompt_server.distributed_pending_tile_jobs:
-            del prompt_server.distributed_pending_tile_jobs[multi_job_id]
-            debug_log(f"Cleaned up job {multi_job_id}")
-
-# API Endpoints (generalized)
-
-@server.PromptServer.instance.routes.post("/distributed/request_task")
-async def request_task_endpoint(request):
-    try:
-        data = await request.json()
-        worker_id = data.get('worker_id')
-        multi_job_id = data.get('multi_job_id')
-        
-        if not worker_id or not multi_job_id:
-            return await handle_api_error(request, "Missing worker_id or multi_job_id", 400)
-        
-        prompt_server = ensure_tile_jobs_initialized()
-        async with prompt_server.distributed_tile_jobs_lock:
-            if multi_job_id in prompt_server.distributed_pending_tile_jobs:
-                job_data = prompt_server.distributed_pending_tile_jobs[multi_job_id]
-                mode = job_data.get(JOB_MODE)
-                pending_queue = job_data.get(JOB_PENDING_TASKS)
-                if pending_queue:
-                    try:
-                        task_id = await asyncio.wait_for(pending_queue.get(), timeout=0.1)
-                        if JOB_ASSIGNED_TO_WORKERS in job_data and worker_id in job_data[JOB_ASSIGNED_TO_WORKERS]:
-                            job_data[JOB_ASSIGNED_TO_WORKERS][worker_id].append(task_id)
-                        if JOB_WORKER_STATUS in job_data:
-                            job_data[JOB_WORKER_STATUS][worker_id] = time.time()
-                        remaining = pending_queue.qsize()
-                        debug_log(f"Assigned task {task_id} to worker {worker_id} in {mode} mode")
-                        return web.json_response({"task_id": task_id, "estimated_remaining": remaining, "mode": mode})
-                    except asyncio.TimeoutError:
-                        return web.json_response({"task_id": None})
-                else:
-                    return await handle_api_error(request, "No pending tasks", 400)
-            else:
-                return await handle_api_error(request, "Job not found", 404)
-    except Exception as e:
-        return await handle_api_error(request, e, 500)
-
-
-@server.PromptServer.instance.routes.post("/distributed/heartbeat")
-async def heartbeat_endpoint(request):
-    try:
-        data = await request.json()
-        worker_id = data.get('worker_id')
-        multi_job_id = data.get('multi_job_id')
-        
-        if not worker_id or not multi_job_id:
-            return await handle_api_error(request, "Missing worker_id or multi_job_id", 400)
-        
-        prompt_server = ensure_tile_jobs_initialized()
-        async with prompt_server.distributed_tile_jobs_lock:
-            if multi_job_id in prompt_server.distributed_pending_tile_jobs:
-                job_data = prompt_server.distributed_pending_tile_jobs[multi_job_id]
-                if JOB_WORKER_STATUS in job_data:
-                    job_data[JOB_WORKER_STATUS][worker_id] = time.time()
-                    debug_log(f"Heartbeat from worker {worker_id}")
-                    return web.json_response({"status": "success"})
-                else:
-                    return await handle_api_error(request, "Worker status tracking not available", 400)
-            else:
-                return await handle_api_error(request, "Job not found", 404)
-    except Exception as e:
-        return await handle_api_error(request, e, 500)
-
-@server.PromptServer.instance.routes.post("/distributed/submit_tiles")
-async def submit_tiles_endpoint(request):
-    """Endpoint for workers to submit processed tiles in static mode."""
-    try:
-        content_length = request.headers.get('content-length')
-        if content_length and int(content_length) > MAX_PAYLOAD_SIZE:
-            return await handle_api_error(request, f"Payload too large: {content_length} bytes", 413)
-        
-        data = await request.post()
-        multi_job_id = data.get('multi_job_id')
-        worker_id = data.get('worker_id')
-        is_last = data.get('is_last', 'False').lower() == 'true'
-        
-        if multi_job_id is None or worker_id is None:
-            return await handle_api_error(request, "Missing multi_job_id or worker_id", 400)
-
-        prompt_server = ensure_tile_jobs_initialized()
-        
-        batch_size = int(data.get('batch_size', 0))
-        tiles = []
-        
-        # Handle completion signal
-        if batch_size == 0 and is_last:
-            async with prompt_server.distributed_tile_jobs_lock:
-                if multi_job_id in prompt_server.distributed_pending_tile_jobs:
-                    job_data = prompt_server.distributed_pending_tile_jobs[multi_job_id]
-                    if JOB_MODE in job_data and job_data[JOB_MODE] != 'static':
-                        return await handle_api_error(request, "Mode mismatch: expected static mode", 400)
-                    if JOB_QUEUE in job_data:
-                        await job_data[JOB_QUEUE].put({
-                            'worker_id': worker_id,
-                            'is_last': True,
-                            'tiles': []
-                        })
-                        debug_log(f"Received completion signal from worker {worker_id}")
-                        return web.json_response({"status": "success"})
-        
-        # Handle batch tiles with metadata
-        if batch_size > 0:
-            padding = int(data.get('padding', 32))
-            metadata_field = data.get('tiles_metadata')
-            if metadata_field:
-                if hasattr(metadata_field, 'file'):
-                    metadata_str = metadata_field.file.read().decode('utf-8')
-                elif isinstance(metadata_field, (bytes, bytearray)):
-                    metadata_str = metadata_field.decode('utf-8')
-                else:
-                    metadata_str = str(metadata_field)
-                
-                metadata = json.loads(metadata_str)
-                if len(metadata) != batch_size:
-                    return await handle_api_error(request, "Metadata length mismatch", 400)
-                
-                tile_data_list = []
-                
-                for i in range(batch_size):
-                    tile_field = data.get(f'tile_{i}')
-                    if tile_field is None:
-                        continue
-                    
-                    img_data = tile_field.file.read()
-                    img = Image.open(io.BytesIO(img_data)).convert("RGB")
-                    img_np = np.array(img).astype(np.float32) / 255.0
-                    tensor = torch.from_numpy(img_np)[None,]
-                    
-                    if i < len(metadata):
-                        tile_meta = metadata[i]
-                        tile_idx = tile_meta.get('tile_idx', i)
-                        tile_info = {
-                            'tensor': tensor,
-                            'tile_idx': tile_idx,
-                            'x': tile_meta['x'],
-                            'y': tile_meta['y'],
-                            'extracted_width': tile_meta['extracted_width'],
-                            'extracted_height': tile_meta['extracted_height'],
-                            'padding': padding,
-                            'batch_idx': tile_meta.get('batch_idx', 0),
-                            'global_idx': tile_meta.get('global_idx', tile_idx)
-                        }
-                        tile_data_list.append(tile_info)
-                    
-                tile_data_list.sort(key=lambda x: x['tile_idx'])
-                tiles.extend(tile_data_list)
-            else:
-                # Legacy format
-                for i in range(batch_size):
-                    tile_field = data.get(f'tile_{i}')
-                    if tile_field is None:
-                        continue
-                    
-                    tile_idx = int(data.get(f'tile_{i}_idx', i))
-                    x = int(data.get(f'tile_{i}_x', 0))
-                    y = int(data.get(f'tile_{i}_y', 0))
-                    extracted_width = int(data.get(f'tile_{i}_width', 512))
-                    extracted_height = int(data.get(f'tile_{i}_height', 512))
-                    
-                    img_data = tile_field.file.read()
-                    img = Image.open(io.BytesIO(img_data)).convert("RGB")
-                    img_np = np.array(img).astype(np.float32) / 255.0
-                    tensor = torch.from_numpy(img_np)[None,]
-                    
-                    tiles.append({
-                        'tensor': tensor,
-                        'tile_idx': tile_idx,
-                        'x': x,
-                        'y': y,
-                        'extracted_width': extracted_width,
-                        'extracted_height': extracted_height,
-                        'padding': padding
-                    })
-        else:
-            # Single tile legacy
-            image_file = data.get('image')
-            if not image_file:
-                return await handle_api_error(request, "Missing image data", 400)
-                
-            tile_idx = int(data.get('tile_idx', 0))
-            x = int(data.get('x', 0))
-            y = int(data.get('y', 0))
-            extracted_width = int(data.get('extracted_width', 512))
-            extracted_height = int(data.get('extracted_height', 512))
-            padding = int(data.get('padding', 32))
-            
-            img_data = image_file.file.read()
-            img = Image.open(io.BytesIO(img_data)).convert("RGB")
-            img_np = np.array(img).astype(np.float32) / 255.0
-            tensor = torch.from_numpy(img_np)[None,]
-            
-            tiles = [{
-                'tensor': tensor,
-                'tile_idx': tile_idx,
-                'x': x,
-                'y': y,
-                'extracted_width': extracted_width,
-                'extracted_height': extracted_height,
-                'padding': padding,
-                'batch_idx': 0,
-                'global_idx': tile_idx
-            }]
-
-        # Submit tiles to queue
-        async with prompt_server.distributed_tile_jobs_lock:
-            if multi_job_id in prompt_server.distributed_pending_tile_jobs:
-                job_data = prompt_server.distributed_pending_tile_jobs[multi_job_id]
-                if JOB_MODE in job_data and job_data[JOB_MODE] != 'static':
-                    return await handle_api_error(request, "Mode mismatch: expected static mode", 400)
-                
-                q = job_data[JOB_QUEUE]
-                if batch_size > 0 or len(tiles) > 0:
-                    await q.put({
-                        'worker_id': worker_id,
-                        'tiles': tiles,
-                        'is_last': is_last
-                    })
-                else:
-                    await q.put({
-                        'worker_id': worker_id,
-                        'is_last': True,
-                        'tiles': []
-                    })
-                    
-                return web.json_response({"status": "success"})
-            else:
-                return await handle_api_error(request, "Job not found", 404)
-    except Exception as e:
-        return await handle_api_error(request, e, 500)
-
-@server.PromptServer.instance.routes.post("/distributed/submit_image")
-async def submit_image_endpoint(request):
-    """Endpoint for workers to submit processed images in dynamic mode."""
-    try:
-        content_length = request.headers.get('content-length')
-        if content_length and int(content_length) > MAX_PAYLOAD_SIZE:
-            return await handle_api_error(request, f"Payload too large: {content_length} bytes", 413)
-        
-        data = await request.post()
-        multi_job_id = data.get('multi_job_id')
-        worker_id = data.get('worker_id')
-        is_last = data.get('is_last', 'False').lower() == 'true'
-        
-        if multi_job_id is None or worker_id is None:
-            return await handle_api_error(request, "Missing multi_job_id or worker_id", 400)
-
-        prompt_server = ensure_tile_jobs_initialized()
-        
-        # Handle image submission
-        if 'full_image' in data and 'image_idx' in data:
-            image_idx = int(data.get('image_idx'))
-            img_data = data['full_image'].file.read()
-            img = Image.open(io.BytesIO(img_data)).convert("RGB")
-            
-            debug_log(f"Received full image {image_idx} from worker {worker_id}")
-            
-            async with prompt_server.distributed_tile_jobs_lock:
-                if multi_job_id in prompt_server.distributed_pending_tile_jobs:
-                    job_data = prompt_server.distributed_pending_tile_jobs[multi_job_id]
-                    if JOB_MODE in job_data and job_data[JOB_MODE] != 'dynamic':
-                        return await handle_api_error(request, "Mode mismatch: expected dynamic mode", 400)
-                    if JOB_QUEUE in job_data:
-                        await job_data[JOB_QUEUE].put({
-                            'worker_id': worker_id,
-                            'image_idx': image_idx,
-                            'image': img,
-                            'is_last': is_last
-                        })
-                        return web.json_response({"status": "success"})
-        
-        # Handle completion signal (no image data)
-        elif is_last:
-            async with prompt_server.distributed_tile_jobs_lock:
-                if multi_job_id in prompt_server.distributed_pending_tile_jobs:
-                    job_data = prompt_server.distributed_pending_tile_jobs[multi_job_id]
-                    if JOB_MODE in job_data and job_data[JOB_MODE] != 'dynamic':
-                        return await handle_api_error(request, "Mode mismatch: expected dynamic mode", 400)
-                    if JOB_QUEUE in job_data:
-                        await job_data[JOB_QUEUE].put({
-                            'worker_id': worker_id,
-                            'is_last': True,
-                            'tiles': []  # For compatibility
-                        })
-                        debug_log(f"Received completion signal from worker {worker_id}")
-                        return web.json_response({"status": "success"})
-        else:
-            return await handle_api_error(request, "Missing image data or invalid request", 400)
-            
-        return await handle_api_error(request, "Job not found", 404)
-    except Exception as e:
-        return await handle_api_error(request, e, 500)
-
-# Keep legacy endpoint for backward compatibility
-@server.PromptServer.instance.routes.post("/distributed/tile_complete")
-async def tile_complete_endpoint(request):
-    try:
-        content_length = request.headers.get('content-length')
-        if content_length and int(content_length) > MAX_PAYLOAD_SIZE:
-            return await handle_api_error(request, f"Payload too large: {content_length} bytes", 413)
-        
-        data = await request.post()
-        multi_job_id = data.get('multi_job_id')
-        worker_id = data.get('worker_id')
-        is_last = data.get('is_last', 'False').lower() == 'true'
-        
-        if multi_job_id is None or worker_id is None:
-            return await handle_api_error(request, "Missing multi_job_id or worker_id", 400)
-
-        prompt_server = ensure_tile_jobs_initialized()
-        
-        if 'full_image' in data and 'image_idx' in data:
-            image_idx = int(data.get('image_idx'))
-            img_data = data['full_image'].file.read()
-            img = Image.open(io.BytesIO(img_data)).convert("RGB")
-            
-            debug_log(f"Received full image {image_idx} from worker {worker_id}")
-            
-            async with prompt_server.distributed_tile_jobs_lock:
-                if multi_job_id in prompt_server.distributed_pending_tile_jobs:
-                    job_data = prompt_server.distributed_pending_tile_jobs[multi_job_id]
-                    if JOB_MODE in job_data and job_data[JOB_MODE] != 'dynamic':
-                        return await handle_api_error(request, "Mode mismatch for image submission", 400)
-                    if JOB_QUEUE in job_data:
-                        await job_data[JOB_QUEUE].put({
-                            'worker_id': worker_id,
-                            'image_idx': image_idx,
-                            'image': img,
-                            'is_last': is_last
-                        })
-                        return web.json_response({"status": "success"})
-        
-        batch_size = int(data.get('batch_size', 0))
-        tiles = []
-        
-        if batch_size == 0 and is_last:
-            async with prompt_server.distributed_tile_jobs_lock:
-                if multi_job_id in prompt_server.distributed_pending_tile_jobs:
-                    job_data = prompt_server.distributed_pending_tile_jobs[multi_job_id]
-                    if JOB_QUEUE in job_data:
-                        await job_data[JOB_QUEUE].put({
-                            'worker_id': worker_id,
-                            'is_last': True,
-                            'tiles': []
-                        })
-                        debug_log(f"Received completion signal from worker {worker_id}")
-                        return web.json_response({"status": "success"})
-        
-        if batch_size > 0:
-            padding = int(data.get('padding', 32))
-            metadata_field = data.get('tiles_metadata')
-            if metadata_field:
-                if hasattr(metadata_field, 'file'):
-                    metadata_str = metadata_field.file.read().decode('utf-8')
-                elif isinstance(metadata_field, (bytes, bytearray)):
-                    metadata_str = metadata_field.decode('utf-8')
-                else:
-                    metadata_str = str(metadata_field)
-                
-                metadata = json.loads(metadata_str)
-                if len(metadata) != batch_size:
-                    return await handle_api_error(request, "Metadata length mismatch", 400)
-                
-                tile_data_list = []
-                
-                for i in range(batch_size):
-                    tile_field = data.get(f'tile_{i}')
-                    if tile_field is None:
-                        continue
-                    
-                    img_data = tile_field.file.read()
-                    img = Image.open(io.BytesIO(img_data)).convert("RGB")
-                    img_np = np.array(img).astype(np.float32) / 255.0
-                    tensor = torch.from_numpy(img_np)[None,]
-                    
-                    if i < len(metadata):
-                        tile_meta = metadata[i]
-                        tile_idx = tile_meta.get('tile_idx', i)
-                        tile_info = {
-                            'tensor': tensor,
-                            'tile_idx': tile_idx,
-                            'x': tile_meta['x'],
-                            'y': tile_meta['y'],
-                            'extracted_width': tile_meta['extracted_width'],
-                            'extracted_height': tile_meta['extracted_height'],
-                            'padding': padding,
-                            'batch_idx': tile_meta.get('batch_idx', 0),
-                            'global_idx': tile_meta.get('global_idx', tile_idx)
-                        }
-                        tile_data_list.append(tile_info)
-                    
-                tile_data_list.sort(key=lambda x: x['tile_idx'])
-                tiles.extend(tile_data_list)
-            else:
-                # Legacy
-                for i in range(batch_size):
-                    tile_field = data.get(f'tile_{i}')
-                    if tile_field is None:
-                        continue
-                    
-                    tile_idx = int(data.get(f'tile_{i}_idx', i))
-                    x = int(data.get(f'tile_{i}_x', 0))
-                    y = int(data.get(f'tile_{i}_y', 0))
-                    extracted_width = int(data.get(f'tile_{i}_width', 512))
-                    extracted_height = int(data.get(f'tile_{i}_height', 512))
-                    
-                    img_data = tile_field.file.read()
-                    img = Image.open(io.BytesIO(img_data)).convert("RGB")
-                    img_np = np.array(img).astype(np.float32) / 255.0
-                    tensor = torch.from_numpy(img_np)[None,]
-                    
-                    tiles.append({
-                        'tensor': tensor,
-                        'tile_idx': tile_idx,
-                        'x': x,
-                        'y': y,
-                        'extracted_width': extracted_width,
-                        'extracted_height': extracted_height,
-                        'padding': padding
-                    })
-        else:
-            # Single tile legacy
-            image_file = data.get('image')
-            if not image_file:
-                return await handle_api_error(request, "Missing image data", 400)
-                
-            tile_idx = int(data.get('tile_idx', 0))
-            x = int(data.get('x', 0))
-            y = int(data.get('y', 0))
-            extracted_width = int(data.get('extracted_width', 512))
-            extracted_height = int(data.get('extracted_height', 512))
-            padding = int(data.get('padding', 32))
-            
-            img_data = image_file.file.read()
-            img = Image.open(io.BytesIO(img_data)).convert("RGB")
-            img_np = np.array(img).astype(np.float32) / 255.0
-            tensor = torch.from_numpy(img_np)[None,]
-            
-            tiles = [{
-                'tensor': tensor,
-                'tile_idx': tile_idx,
-                'x': x,
-                'y': y,
-                'extracted_width': extracted_width,
-                'extracted_height': extracted_height,
-                'padding': padding,
-                'batch_idx': 0,
-                'global_idx': tile_idx
-            }]
-
-        async with prompt_server.distributed_tile_jobs_lock:
-            if multi_job_id in prompt_server.distributed_pending_tile_jobs:
-                job_data = prompt_server.distributed_pending_tile_jobs[multi_job_id]
-                if JOB_MODE in job_data and job_data[JOB_MODE] != 'static':
-                    return await handle_api_error(request, "Mode mismatch for tile submission", 400)
-                
-                q = job_data[JOB_QUEUE]
-                if batch_size > 0 or len(tiles) > 0:
-                    await q.put({
-                        'worker_id': worker_id,
-                        'tiles': tiles,
-                        'is_last': is_last
-                    })
-                else:
-                    await q.put({
-                        'worker_id': worker_id,
-                        'is_last': True,
-                        'tiles': []
-                    })
-                    
-                return web.json_response({"status": "success"})
-            else:
-                return await handle_api_error(request, "Job not found", 404)
-    except Exception as e:
-        return await handle_api_error(request, e, 500)
-
-
-
-# Helper functions for shallow copying conditioning without duplicating models
-def clone_control_chain(control, clone_hint=True):
-    """Shallow copy the ControlNet chain, optionally cloning hints but sharing models."""
-    if control is None:
-        return None
-    new_control = copy.copy(control)  # Shallow copy (shares model)
-    if clone_hint and hasattr(control, 'cond_hint_original'):
-        new_control.cond_hint_original = control.cond_hint_original.clone()
-    if hasattr(control, 'previous_controlnet'):
-        new_control.previous_controlnet = clone_control_chain(control.previous_controlnet, clone_hint)
-    return new_control
-
-def clone_conditioning(cond_list, clone_hints=True):
-    """Clone conditioning without duplicating ControlNet models."""
-    new_cond = []
-    for emb, cond_dict in cond_list:
-        new_emb = emb.clone() if emb is not None else None
-        new_dict = cond_dict.copy()
-        if 'control' in new_dict:
-            new_dict['control'] = clone_control_chain(new_dict['control'], clone_hints)
-        if 'mask' in new_dict:
-            new_dict['mask'] = new_dict['mask'].clone()
-        # Handle other potential fields if needed
-        if 'pooled_output' in new_dict:
-            new_dict['pooled_output'] = new_dict['pooled_output'].clone()
-        if 'area' in new_dict:
-            new_dict['area'] = new_dict['area'][:]  # Shallow copy list/tuple
-        new_cond.append([new_emb, new_dict])
-    return new_cond
-
-# Helper function to ensure persistent state is initialized
-def ensure_tile_jobs_initialized():
-    """Ensure tile job storage is initialized on the server instance."""
-    prompt_server = server.PromptServer.instance
-    if not hasattr(prompt_server, 'distributed_pending_tile_jobs'):
-        debug_log("Initializing persistent tile job queue on server instance.")
-        prompt_server.distributed_pending_tile_jobs = {}
-        prompt_server.distributed_tile_jobs_lock = asyncio.Lock()
-    else:
-        # Clean up any legacy queue structures that don't have the 'mode' field
-        # (Should be rare after fixes, but keep for safety)
-        to_remove = [job_id for job_id, job_data in prompt_server.distributed_pending_tile_jobs.items()
-                     if not isinstance(job_data, dict) or 'mode' not in job_data]
-        for job_id in to_remove:
-            debug_log(f"Removing legacy queue structure for job {job_id}")
-            del prompt_server.distributed_pending_tile_jobs[job_id]
-    return prompt_server
-
-# API Endpoint for tile completion
-@server.PromptServer.instance.routes.post("/distributed/request_image")
-async def request_image_endpoint(request):
-    """Endpoint for workers to request tasks (images in dynamic mode, tiles in static mode)."""
-    try:
-        data = await request.json()
-        worker_id = data.get('worker_id')
-        multi_job_id = data.get('multi_job_id')
-        
-        if not worker_id or not multi_job_id:
-            return await handle_api_error(request, "Missing worker_id or multi_job_id", 400)
-        
-        prompt_server = ensure_tile_jobs_initialized()
-        async with prompt_server.distributed_tile_jobs_lock:
-            if multi_job_id in prompt_server.distributed_pending_tile_jobs:
-                job_data = prompt_server.distributed_pending_tile_jobs[multi_job_id]
-                if not isinstance(job_data, dict) or 'mode' not in job_data:
-                    return await handle_api_error(request, "Invalid job data structure", 500)
-                
-                mode = job_data['mode']
-                
-                # Handle both dynamic and static modes
-                if mode == 'dynamic' and 'pending_images' in job_data:
-                    pending_queue = job_data['pending_images']
-                elif mode == 'static' and JOB_PENDING_TASKS in job_data:
-                    pending_queue = job_data[JOB_PENDING_TASKS]
-                else:
-                    return await handle_api_error(request, f"Invalid {mode} mode configuration", 400)
-                
-                try:
-                    task_idx = await asyncio.wait_for(pending_queue.get(), timeout=0.1)
-                    # Track assigned task
-                    if 'assigned_to_workers' in job_data and worker_id in job_data['assigned_to_workers']:
-                        job_data['assigned_to_workers'][worker_id].append(task_idx)
-                    # Update worker heartbeat
-                    if 'worker_status' in job_data:
-                        job_data['worker_status'][worker_id] = time.time()
-                    # Get estimated remaining count
-                    remaining = pending_queue.qsize()  # Approximate
-                    
-                    # Return appropriate response based on mode
-                    if mode == 'dynamic':
-                        debug_log(f"UltimateSDUpscale API - Assigned image {task_idx} to worker {worker_id}")
-                        return web.json_response({"image_idx": task_idx, "estimated_remaining": remaining})
-                    else:  # static
-                        debug_log(f"UltimateSDUpscale API - Assigned tile {task_idx} to worker {worker_id}")
-                        return web.json_response({"tile_idx": task_idx, "estimated_remaining": remaining})
-                except asyncio.TimeoutError:
-                    if mode == 'dynamic':
-                        return web.json_response({"image_idx": None})
-                    else:
-                        return web.json_response({"tile_idx": None})
-            else:
-                return await handle_api_error(request, "Job not found", 404)
-    except Exception as e:
-        return await handle_api_error(request, e, 500)
-
-@server.PromptServer.instance.routes.get("/distributed/job_status")
-async def job_status_endpoint(request):
-    """Endpoint to check if a job is ready."""
-    multi_job_id = request.query.get('multi_job_id')
-    if not multi_job_id:
-        return web.json_response({"ready": False})
-    prompt_server = ensure_tile_jobs_initialized()
-    async with prompt_server.distributed_tile_jobs_lock:
-        job_data = prompt_server.distributed_pending_tile_jobs.get(multi_job_id)
-        ready = bool(job_data and isinstance(job_data, dict) and 'queue' in job_data)
-        return web.json_response({"ready": ready})
-

+__all__ = [
+    "MAX_PAYLOAD_SIZE",
+    "_check_and_requeue_timed_out_workers",
+    "_cleanup_job",
+    "_drain_results_queue",
+    "_get_completed_count",
+    "_init_job_queue",
+    "_mark_task_completed",
+    "_parse_tiles_from_form",
+    "_send_heartbeat_to_master",
+    "clone_conditioning",
+    "clone_control_chain",
+    "ensure_tile_jobs_initialized",
+    "init_dynamic_job",
+    "init_static_job_batched",
+]
@@ -4,7 +4,6 @@ import torch
 import torch.nn.functional as F
 from torchvision.transforms import GaussianBlur
 import math
-import os

 if (not hasattr(Image, 'Resampling')):  # For older versions of Pillow
    Image.Resampling = Image
@@ -442,6 +441,67 @@ def crop_mask(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad

    cond_dict["mask"] = torch.cat(masks, dim=0)  # (B, H, W)

+# Added Flux-Kontext Support crop_reference_latents by TBG ETUR
+def crop_reference_latents(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad):
+    """
+    1. Resize each latent to `canvas_size` in latent units.
+    2. Crop the rectangle `region` (pixel coordinates).
+    3. Down-sample the crop to latent-space `tile_size`.
+    Expects a list of BCHW tensors under "reference_latents".
+    """
+
+    latents = cond_dict.get("reference_latents")
+    if not isinstance(latents, list):
+        return  # nothing to do
+
+    k = 8  # down-sample factor from pixel space → latent space (SD-type models)
+
+    W_can_px, H_can_px = canvas_size
+    # canvas size expressed in latent units
+    W_can_lat, H_can_lat = W_can_px // k, H_can_px // k
+
+    W_tile_px, H_tile_px = tile_size
+    W_tile_lat, H_tile_lat = max(1, W_tile_px // k), max(1, H_tile_px // k)
+
+    x1_px, y1_px, x2_px, y2_px = region
+
+    new_latents = []
+    for t in latents:  # (B,C,H_lat_in,W_lat_in) or (B,C,1,H_lat_in,W_lat_in)
+        has_5d = False
+        if t.ndim == 5:
+            has_5d = True
+            t = t.squeeze(2)
+        if t.ndim != 4:
+            raise ValueError(f"expected BCHW or BC1HW, got {t.shape}")
+
+        # 1. Resize to canvas resolution in latent units only if needed
+        if t.shape[-2:] != (H_can_lat, W_can_lat):
+            t = F.interpolate(t,
+                              size=(H_can_lat, W_can_lat),
+                              mode="bilinear",
+                              align_corners=False)
+
+        # 2. Convert pixel crop → latent slice
+        w0_lat = int(round(x1_px / k))
+        w1_lat = int(round(x2_px / k))
+        h0_lat = int(round(y1_px / k))
+        h1_lat = int(round(y2_px / k))
+
+        cropped = t[:, :, h0_lat:h1_lat, w0_lat:w1_lat]  # view
+
+        # 3. Down-sample to latent-tile size
+        cropped = F.interpolate(cropped,
+                                size=(H_tile_lat, W_tile_lat),
+                                mode="bilinear",
+                                align_corners=False)
+
+        if has_5d:
+            cropped = cropped.unsqueeze(2)
+        new_latents.append(cropped)
+
+    cond_dict["reference_latents"] = new_latents
+
+

 def crop_cond(cond, region, init_size, canvas_size, tile_size, w_pad=0, h_pad=0):
    cropped = []
@@ -452,5 +512,6 @@ def crop_cond(cond, region, init_size, canvas_size, tile_size, w_pad=0, h_pad=0)
        crop_gligen(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad)
        crop_area(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad)
        crop_mask(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad)
+        crop_reference_latents(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad)
        cropped.append(n)
-    return cropped
+    return cropped
@@ -0,0 +1,8 @@
+import { defineConfig } from "vitest/config";
+
+export default defineConfig({
+  test: {
+    include: ["web/tests/**/*.test.js"],
+    environment: "node",
+  },
+});
@@ -1,27 +1,43 @@
 import { TIMEOUTS } from './constants.js';
+import { normalizeWorkerUrl } from './urlUtils.js';

 export function createApiClient(baseUrl) {
-    const request = async (endpoint, options = {}, retries = TIMEOUTS.MAX_RETRIES) => {
+    const normalizedBaseUrl = normalizeWorkerUrl(baseUrl);
+
+    const request = async (
+        endpoint,
+        options = {},
+        { retries = TIMEOUTS.MAX_RETRIES, retry = true } = {},
+    ) => {
+        const maxAttempts = retry ? retries : 1;
        let lastError;
        let delay = TIMEOUTS.RETRY_DELAY; // Initial delay for exponential backoff

-        for (let attempt = 0; attempt < retries; attempt++) {
+        for (let attempt = 0; attempt < maxAttempts; attempt++) {
            try {
-                const response = await fetch(`${baseUrl}${endpoint}`, {
-                    headers: { 'Content-Type': 'application/json' },
-                    ...options
+                const headers = {
+                    'Content-Type': 'application/json',
+                    ...(options.headers || {}),
+                };
+                const response = await fetch(`${normalizedBaseUrl}${endpoint}`, {
+                    ...options,
+                    headers,
                });
                
                if (!response.ok) {
-                    const error = await response.json().catch(() => ({ message: 'Request failed' }));
-                    throw new Error(error.message || `HTTP ${response.status}`);
+                    const error = await response.json().catch(() => ({}));
+                    const message = error.message
+                        || error.error
+                        || (Array.isArray(error.errors) ? error.errors.join('; ') : null)
+                        || `HTTP ${response.status}`;
+                    throw new Error(message);
                }
                
                return await response.json();
            } catch (error) {
                lastError = error;
-                console.log(`API Error (attempt ${attempt + 1}/${retries}): ${endpoint} - ${error.message}`);
-                if (attempt < retries - 1) {
+                console.log(`API Error (attempt ${attempt + 1}/${maxAttempts}): ${endpoint} - ${error.message}`);
+                if (attempt < maxAttempts - 1) {
                    await new Promise(resolve => setTimeout(resolve, delay));
                    delay *= 2; // Exponential backoff
                }
@@ -29,6 +45,39 @@ export function createApiClient(baseUrl) {
        }
        throw lastError;
    };
+
+    const requestUrl = async (
+        url,
+        options = {},
+        { retries = TIMEOUTS.MAX_RETRIES, retry = true } = {},
+    ) => {
+        const maxAttempts = retry ? retries : 1;
+        let lastError;
+        let delay = TIMEOUTS.RETRY_DELAY;
+
+        for (let attempt = 0; attempt < maxAttempts; attempt++) {
+            try {
+                const response = await fetch(url, options);
+                if (!response.ok) {
+                    const error = await response.json().catch(() => ({}));
+                    const message = error.message
+                        || error.error
+                        || (Array.isArray(error.errors) ? error.errors.join('; ') : null)
+                        || `HTTP ${response.status}`;
+                    throw new Error(message);
+                }
+                return await response.json();
+            } catch (error) {
+                lastError = error;
+                console.log(`API Error (attempt ${attempt + 1}/${maxAttempts}): ${url} - ${error.message}`);
+                if (attempt < maxAttempts - 1) {
+                    await new Promise(resolve => setTimeout(resolve, delay));
+                    delay *= 2;
+                }
+            }
+        }
+        throw lastError;
+    };
    
    return {
        // Config endpoints
@@ -40,28 +89,28 @@ export function createApiClient(baseUrl) {
            return request('/distributed/config/update_worker', {
                method: 'POST',
                body: JSON.stringify({ worker_id: workerId, ...data })
-            });
+            }, { retry: false });
        },
        
        async deleteWorker(workerId) {
            return request('/distributed/config/delete_worker', {
                method: 'POST',
                body: JSON.stringify({ worker_id: workerId })
-            });
+            }, { retry: false });
        },
        
        async updateSetting(key, value) {
            return request('/distributed/config/update_setting', {
                method: 'POST',
                body: JSON.stringify({ key, value })
-            });
+            }, { retry: false });
        },
        
        async updateMaster(data) {
            return request('/distributed/config/update_master', {
                method: 'POST',
                body: JSON.stringify(data)
-            });
+            }, { retry: false });
        },
        
        // Worker management endpoints
@@ -69,14 +118,14 @@ export function createApiClient(baseUrl) {
            return request('/distributed/launch_worker', {
                method: 'POST',
                body: JSON.stringify({ worker_id: workerId })
-            });
+            }, { retry: false });
        },
        
        async stopWorker(workerId) {
            return request('/distributed/stop_worker', {
                method: 'POST',
                body: JSON.stringify({ worker_id: workerId })
-            });
+            }, { retry: false });
        },
        
        async getManagedWorkers() {
@@ -86,28 +135,68 @@ export function createApiClient(baseUrl) {
        async getWorkerLog(workerId, lines = 1000) {
            return request(`/distributed/worker_log/${workerId}?lines=${lines}`);
        },
+
+        async getRemoteWorkerLog(workerId, lines = 300) {
+            return request(`/distributed/remote_worker_log/${workerId}?lines=${lines}`);
+        },
        
        async clearLaunchingFlag(workerId) {
            return request('/distributed/worker/clear_launching', {
                method: 'POST',
                body: JSON.stringify({ worker_id: workerId })
-            });
+            }, { retry: false });
        },
        
-        // Job preparation
-        async prepareJob(multiJobId) {
-            return request('/distributed/prepare_job', {
+        async queueDistributed(payload) {
+            return request('/distributed/queue', {
                method: 'POST',
-                body: JSON.stringify({ multi_job_id: multiJobId })
-            });
+                headers: {
+                    ...(payload?.trace_execution_id
+                        ? { 'X-Idempotency-Key': payload.trace_execution_id }
+                        : {}),
+                },
+                body: JSON.stringify(payload)
+            }, { retry: false });
        },
-        
-        // Image loading
-        async loadImage(imagePath) {
-            return request('/distributed/load_image', {
+
+        async probeWorker(workerUrl, timeoutMs = TIMEOUTS.STATUS_CHECK, signal = null) {
+            const normalizedWorkerUrl = normalizeWorkerUrl(workerUrl);
+            const controller = new AbortController();
+            const timeoutId = setTimeout(() => controller.abort(), timeoutMs);
+            const effectiveSignal = signal
+                ? AbortSignal.any([controller.signal, signal])
+                : controller.signal;
+            try {
+                const response = await fetch(`${normalizedWorkerUrl}/prompt`, {
+                    method: 'GET',
+                    mode: 'cors',
+                    cache: 'no-store',
+                    signal: effectiveSignal,
+                });
+
+                if (!response.ok) {
+                    return { ok: false, status: response.status, queueRemaining: null };
+                }
+
+                const data = await response.json().catch(() => ({}));
+                return {
+                    ok: true,
+                    status: response.status,
+                    queueRemaining: data.exec_info?.queue_remaining || 0,
+                };
+            } finally {
+                clearTimeout(timeoutId);
+            }
+        },
+
+        async dispatchToWorker(workerUrl, promptPayload) {
+            const normalizedWorkerUrl = normalizeWorkerUrl(workerUrl);
+            return requestUrl(`${normalizedWorkerUrl}/prompt`, {
                method: 'POST',
-                body: JSON.stringify({ image_path: imagePath })
-            });
+                headers: { 'Content-Type': 'application/json' },
+                mode: 'cors',
+                body: JSON.stringify(promptPayload),
+            }, { retry: false });
        },
        
        // Network info
@@ -141,6 +230,25 @@ export function createApiClient(baseUrl) {
            return Promise.allSettled(
                urls.map(url => this.checkStatus(url))
            );
+        },
+
+        // Cloudflare tunnel management
+        async startTunnel() {
+            return request('/distributed/tunnel/start', {
+                method: 'POST',
+                body: JSON.stringify({})
+            }, { retry: false });
+        },
+
+        async stopTunnel() {
+            return request('/distributed/tunnel/stop', {
+                method: 'POST',
+                body: JSON.stringify({})
+            }, { retry: false });
+        },
+
+        async getTunnelStatus() {
+            return request('/distributed/tunnel/status');
        }
    };
-}
+}
@@ -17,6 +17,7 @@ export const BUTTON_STYLES = {
    launch: "background-color: #4a7c4a;",
    stop: "background-color: #7c4a4a;",
    log: "background-color: #685434;",
+    working: "background-color: #666;",
    clearMemory: "background-color: #555; padding: 6px 14px;",
    interrupt: "background-color: #555; padding: 6px 14px;",
 };
@@ -62,6 +63,22 @@ export const PULSE_ANIMATION_CSS = `
        animation: pulse 1.2s ease-in-out infinite;
        transform-origin: center;
    }
+
+    .worker-status--online {
+        background: var(--status-online, #3ca03c) !important;
+    }
+
+    .worker-status--offline {
+        background: var(--status-offline, #c04c4c) !important;
+    }
+
+    .worker-status--unknown {
+        background: var(--status-unknown, #888) !important;
+    }
+
+    .worker-status--processing {
+        background: var(--status-processing, #f0ad4e) !important;
+    }
    
    /* Button hover effects */
    .distributed-button:hover:not(:disabled) {
@@ -89,7 +106,26 @@ export const PULSE_ANIMATION_CSS = `
    .worker-settings.expanded {
        max-height: 500px;
        opacity: 1;
-        padding: 12px 0;
+        padding: 12px;
+        margin-top: 8px;
+        margin-bottom: 8px;
+    }
+
+    /* Cloudflare tunnel spinner */
+    @keyframes tunnel-spin {
+        from { transform: rotate(0deg); }
+        to { transform: rotate(360deg); }
+    }
+    .tunnel-spinner {
+        width: 14px;
+        height: 14px;
+        border: 2px solid rgba(255, 255, 255, 0.35);
+        border-top-color: #fff;
+        border-radius: 50%;
+        display: inline-block;
+        animation: tunnel-spin 0.9s linear infinite;
+        margin-right: 8px;
+        vertical-align: middle;
    }
 `;

@@ -97,20 +133,21 @@ export const UI_STYLES = {
    statusDot: "display: inline-block; width: 10px; height: 10px; border-radius: 50%; margin-right: 10px;",
    controlsDiv: "padding: 0 12px 12px 12px; display: flex; gap: 6px;",
    formGroup: "display: flex; flex-direction: column; gap: 5px;",
-    formLabel: "font-size: 12px; color: #ccc; font-weight: 500;",
-    formInput: "padding: 6px 10px; background: #2a2a2a; border: 1px solid #444; color: white; font-size: 12px; border-radius: 4px; transition: border-color 0.2s;",
+    formLabel: "font-size: 12px; color: var(--dist-label-text, #ccc); font-weight: 500;",
+    formInput:
+        "padding: 6px 10px; color: var(--dist-input-text, white); background: var(--dist-input-bg, transparent); font-size: 12px; transition: border-color 0.2s;",
    
    // Card styles
-    cardBase: "margin-bottom: 12px; border-radius: 6px; overflow: hidden; display: flex;",
-    workerCard: "margin-bottom: 12px; border-radius: 6px; overflow: hidden; display: flex; background: #2a2a2a;",
-    cardBlueprint: "border: 2px dashed #555; cursor: pointer; transition: all 0.2s ease; background: rgba(255, 255, 255, 0.02);",
-    cardAdd: "border: 1px dashed #444; cursor: pointer; transition: all 0.2s ease; background: transparent;",
-    
+    cardBase: "margin-bottom: 12px; overflow: hidden; display: flex;",
+    workerCard: "margin-bottom: 12px; overflow: hidden; display: flex;",
+    cardBlueprint: "cursor: pointer; transition: all 0.2s ease;",
+    cardAdd: "cursor: pointer; transition: all 0.2s ease;",
+
    // Column styles
    columnBase: "display: flex; align-items: center; justify-content: center;",
-    checkboxColumn: "flex: 0 0 44px; display: flex; align-items: center; justify-content: center; border-right: 1px solid #3a3a3a; cursor: default; background: rgba(0,0,0,0.1);",
+    checkboxColumn: "flex: 0 0 44px; display: flex; align-items: center; justify-content: center; cursor: default;",
    contentColumn: "flex: 1; display: flex; flex-direction: column; transition: background-color 0.2s ease;",
-    iconColumn: "width: 44px; flex-shrink: 0; font-size: 20px; color: #666;",
+    iconColumn: "width: 44px; flex-shrink: 0; font-size: 20px; color: var(--dist-placeholder-add-color, #666);",
    
    // Row and content styles
    infoRow: "display: flex; align-items: center; padding: 12px; cursor: pointer; min-height: 64px;",
@@ -120,14 +157,16 @@ export const UI_STYLES = {
    buttonGroup: "display: flex; gap: 4px; margin-top: 10px;",
    settingsForm: "display: flex; flex-direction: column; gap: 10px;",
    checkboxGroup: "display: flex; align-items: center; gap: 8px; margin: 5px 0;",
-    formLabelClickable: "font-size: 12px; color: #ccc; cursor: pointer;",
+    formLabelClickable: "font-size: 12px; color: var(--dist-label-text, #ccc); cursor: pointer;",
    settingsToggle: "display: flex; align-items: center; gap: 6px; padding: 4px 0; cursor: pointer; user-select: none;",
    controlsWrapper: "display: flex; gap: 6px; align-items: stretch; width: 100%;",
    
    // Existing styles
-    settingsArrow: "font-size: 12px; color: #888; transition: all 0.2s ease; margin-left: auto; padding: 4px;",
-    infoBox: "background-color: #333; color: #999; padding: 5px 14px; border-radius: 4px; font-size: 11px; text-align: center; flex: 1; font-weight: 500;",
-    workerSettings: "margin: 0 12px; padding: 0 12px; background: #1e1e1e; border-radius: 4px; border: 1px solid #2a2a2a;"
+    settingsArrow:
+        "font-size: 12px; color: var(--dist-settings-arrow, #888); transition: all 0.2s ease; margin-left: auto; padding: 4px;",
+    infoBox:
+        "color: var(--dist-info-box-text, #999); padding: 5px 14px; font-size: 11px; text-align: center; flex: 1; font-weight: 500;",
+    workerSettings: "margin: 0 12px; padding: 0 12px;"
 };

 export const TIMEOUTS = {
@@ -150,4 +189,51 @@ export const TIMEOUTS = {
    // Background tasks
    LOG_REFRESH: 2000, // log auto-refresh interval
    IMAGE_CACHE_CLEAR: 30000 // delay before clearing image cache
-};
+};
+
+export const ENDPOINTS = {
+    // ComfyUI core
+    PROMPT: '/prompt',
+    INTERRUPT: '/interrupt',
+    UPLOAD_IMAGE: '/upload/image',
+    SYSTEM_INFO: '/system_stats',
+
+    // Distributed API
+    CONFIG: '/distributed/config',
+    UPDATE_WORKER: '/distributed/config/update_worker',
+    DELETE_WORKER: '/distributed/config/delete_worker',
+    UPDATE_SETTING: '/distributed/config/update_setting',
+    UPDATE_MASTER: '/distributed/config/update_master',
+    LAUNCH_WORKER: '/distributed/launch_worker',
+    STOP_WORKER: '/distributed/stop_worker',
+    MANAGED_WORKERS: '/distributed/managed_workers',
+    WORKER_LOG: '/distributed/worker_log',
+    REMOTE_WORKER_LOG: '/distributed/remote_worker_log',
+    LOCAL_LOG: '/distributed/local_log',
+    CLEAR_LAUNCHING: '/distributed/worker/clear_launching',
+    PREPARE_JOB: '/distributed/prepare_job',
+    LOAD_IMAGE: '/distributed/load_image',
+    NETWORK_INFO: '/distributed/network_info',
+    CHECK_FILE: '/distributed/check_file',
+    CLEAR_MEMORY: '/distributed/clear_memory',
+    SYSTEM_INFO_DIST: '/distributed/system_info',
+    TUNNEL_START: '/distributed/tunnel/start',
+    TUNNEL_STOP: '/distributed/tunnel/stop',
+    TUNNEL_STATUS: '/distributed/tunnel/status',
+};
+
+export const NODE_CLASSES = {
+    DISTRIBUTED_COLLECTOR: 'DistributedCollector',
+    DISTRIBUTED_SEED: 'DistributedSeed',
+    DISTRIBUTED_EMPTY_IMAGE: 'DistributedEmptyImage',
+    UPSCALE_DISTRIBUTED: 'UltimateSDUpscaleDistributed',
+    PREVIEW_IMAGE: 'PreviewImage',
+};
+
+export function generateUUID() {
+    if (crypto.randomUUID) return crypto.randomUUID();
+    return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, c => {
+        const r = Math.random() * 16 | 0;
+        return (c === 'x' ? r : (r & 0x3 | 0x8)).toString(16);
+    });
+}
@@ -0,0 +1,416 @@
+:root {
+    --btn-stop: #7c4a4a;
+    --btn-launch: #4a7c4a;
+    --btn-log: #685434;
+    --btn-working: #666;
+    --btn-success: #3a6a3a;
+    --btn-error: #6a3a3a;
+    --tunnel-enable: #665533;
+    --tunnel-disable: #7c4a4a;
+    --master-badge-fallback-bg: #243024;
+    --master-badge-fallback-text: #6bd06b;
+    --master-badge-fallback-border: #335533;
+    --master-badge-delegate-bg: #3a3a3a;
+    --master-badge-delegate-text: #ffcc66;
+    --dist-divider: #444;
+    --dist-muted-text: #888;
+    --dist-label-text: #ccc;
+    --dist-settings-arrow: #888;
+    --dist-settings-arrow-hover: #fff;
+    --dist-card-bg: #2a2a2a;
+    --dist-card-title: #f4f5f7;
+    --dist-card-subtext: #a9afb9;
+    --dist-card-placeholder-title: #aaa;
+    --dist-left-col-border: #3a3a3a;
+    --dist-left-col-bg: rgba(0, 0, 0, 0.1);
+    --dist-info-box-bg: #333;
+    --dist-info-box-text: #999;
+    --dist-input-bg: #2a2a2a;
+    --dist-input-border: #444;
+    --dist-input-text: #fff;
+    --dist-settings-bg: #1e1e1e;
+    --dist-settings-border: #2a2a2a;
+    --dist-hover-bg: #333;
+    --dist-placeholder-blueprint-border: #555;
+    --dist-placeholder-blueprint-hover-border: #777;
+    --dist-placeholder-add-border: #444;
+    --dist-placeholder-add-hover-border: #666;
+    --dist-placeholder-blueprint-bg: rgba(255, 255, 255, 0.02);
+    --dist-placeholder-blueprint-hover-bg: rgba(255, 255, 255, 0.05);
+    --dist-placeholder-add-hover-bg: rgba(255, 255, 255, 0.02);
+    --dist-placeholder-blueprint-color: #777;
+    --dist-placeholder-blueprint-hover-color: #999;
+    --dist-placeholder-add-color: #555;
+    --dist-placeholder-add-hover-color: #888;
+    --dist-log-modal-bg: #1e1e1e;
+    --dist-log-modal-border: #444;
+    --dist-log-modal-header-border: #444;
+    --dist-log-modal-title: #fff;
+    --dist-log-modal-label: #ccc;
+    --dist-log-modal-body-bg: #0d0d0d;
+    --dist-log-modal-body-text: #ddd;
+    --dist-log-modal-status: #888;
+}
+
+.distributed-panel--light {
+    --dist-divider: #c7ced9;
+    --dist-muted-text: #5b6472;
+    --dist-label-text: #2f3a4a;
+    --dist-settings-arrow: #6b7483;
+    --dist-settings-arrow-hover: #253040;
+    --dist-card-bg: #f6f8fb;
+    --dist-card-title: #1e293b;
+    --dist-card-subtext: #4b5565;
+    --dist-card-placeholder-title: #3f4a5a;
+    --dist-left-col-border: #d8deea;
+    --dist-left-col-bg: #eef2f8;
+    --dist-info-box-bg: #e8edf5;
+    --dist-info-box-text: #4f5b6c;
+    --dist-input-bg: #ffffff;
+    --dist-input-border: #b8c2d3;
+    --dist-input-text: #1f2937;
+    --dist-settings-bg: #eef2f8;
+    --dist-settings-border: #d5dcea;
+    --dist-hover-bg: #e6ebf3;
+    --dist-placeholder-blueprint-border: #aeb8c9;
+    --dist-placeholder-blueprint-hover-border: #8e9ab0;
+    --dist-placeholder-add-border: #b7c1d3;
+    --dist-placeholder-add-hover-border: #919eb4;
+    --dist-placeholder-blueprint-bg: rgba(80, 100, 140, 0.05);
+    --dist-placeholder-blueprint-hover-bg: rgba(80, 100, 140, 0.08);
+    --dist-placeholder-add-hover-bg: rgba(80, 100, 140, 0.05);
+    --dist-placeholder-blueprint-color: #5f6c82;
+    --dist-placeholder-blueprint-hover-color: #47556e;
+    --dist-placeholder-add-color: #6a7588;
+    --dist-placeholder-add-hover-color: #4c586f;
+    --master-badge-delegate-bg: #efe7ce;
+    --master-badge-delegate-text: #6a4f00;
+    --master-badge-fallback-bg: #dff1df;
+    --master-badge-fallback-text: #1f5f1f;
+    --master-badge-fallback-border: #9fc79f;
+    --dist-log-modal-bg: #f9fbff;
+    --dist-log-modal-border: #bcc7db;
+    --dist-log-modal-header-border: #c7d1e3;
+    --dist-log-modal-title: #1f2937;
+    --dist-log-modal-label: #4a5568;
+    --dist-log-modal-body-bg: #f2f5fb;
+    --dist-log-modal-body-text: #1f2937;
+    --dist-log-modal-status: #5c6779;
+}
+
+.btn--stop {
+    background-color: var(--btn-stop) !important;
+}
+
+.btn--launch {
+    background-color: var(--btn-launch) !important;
+}
+
+.btn--log {
+    background-color: var(--btn-log) !important;
+}
+
+.btn--working {
+    background-color: var(--btn-working) !important;
+}
+
+.btn--success {
+    background-color: var(--btn-success) !important;
+}
+
+.btn--error {
+    background-color: var(--btn-error) !important;
+}
+
+.master-info-badge--fallback {
+    background-color: var(--master-badge-fallback-bg) !important;
+    color: var(--master-badge-fallback-text) !important;
+    border: 1px solid var(--master-badge-fallback-border) !important;
+}
+
+.master-info-badge--delegate {
+    background-color: var(--master-badge-delegate-bg) !important;
+    color: var(--master-badge-delegate-text) !important;
+}
+
+.entity-card-content--hovered {
+    background-color: var(--dist-hover-bg) !important;
+}
+
+.placeholder-card--blueprint {
+    border-color: var(--dist-placeholder-blueprint-border) !important;
+    background-color: var(--dist-placeholder-blueprint-bg) !important;
+}
+
+.placeholder-card--blueprint.is-hovered {
+    border-color: var(--dist-placeholder-blueprint-hover-border) !important;
+    background-color: var(--dist-placeholder-blueprint-hover-bg) !important;
+}
+
+.placeholder-card--add {
+    border-color: var(--dist-placeholder-add-border) !important;
+    background-color: transparent !important;
+}
+
+.placeholder-card--add.is-hovered {
+    border-color: var(--dist-placeholder-add-hover-border) !important;
+    background-color: var(--dist-placeholder-add-hover-bg) !important;
+}
+
+.placeholder-column--blueprint {
+    color: var(--dist-placeholder-blueprint-color) !important;
+    border-right-color: var(--dist-placeholder-blueprint-border) !important;
+}
+
+.placeholder-column--blueprint.is-hovered {
+    color: var(--dist-placeholder-blueprint-hover-color) !important;
+}
+
+.placeholder-column--add {
+    color: var(--dist-placeholder-add-color) !important;
+    border-color: var(--dist-placeholder-add-border) !important;
+    border-right-color: var(--dist-placeholder-add-border) !important;
+}
+
+.placeholder-column--add.is-hovered {
+    color: var(--dist-placeholder-add-hover-color) !important;
+    border-color: var(--dist-placeholder-add-hover-border) !important;
+}
+
+.tunnel-button--enable {
+    background-color: var(--tunnel-enable) !important;
+}
+
+.tunnel-button--disable {
+    background-color: var(--tunnel-disable) !important;
+}
+
+.tunnel-status--enable {
+    background-color: var(--tunnel-enable) !important;
+}
+
+.tunnel-status--disable {
+    background-color: var(--tunnel-disable) !important;
+}
+
+/* ---- Themeable card/column/input defaults (Classic) ---- */
+
+.dist-card {
+    background: var(--dist-card-bg);
+    color: var(--dist-card-title);
+    border-radius: 6px;
+}
+
+.dist-card--blueprint {
+    border: 2px dashed var(--dist-placeholder-blueprint-border);
+    background: var(--dist-placeholder-blueprint-bg);
+}
+
+.dist-card--add {
+    border: 1px dashed var(--dist-placeholder-add-border);
+    background: transparent;
+}
+
+.dist-card__left-col {
+    border-right: 1px solid var(--dist-left-col-border);
+    background: var(--dist-left-col-bg);
+}
+
+.dist-info-box {
+    background-color: var(--dist-info-box-bg);
+    color: var(--dist-info-box-text);
+    border-radius: 4px;
+}
+
+.dist-form-input {
+    background: var(--dist-input-bg);
+    border: 1px solid var(--dist-input-border);
+    color: var(--dist-input-text);
+    border-radius: 4px;
+}
+
+.worker-settings {
+    background: var(--dist-settings-bg);
+    border: 1px solid var(--dist-settings-border);
+    border-radius: 4px;
+}
+
+.dist-worker-info__title {
+    color: var(--dist-card-title) !important;
+    font-size: 1.03em;
+    font-weight: 700;
+}
+
+.dist-worker-info__meta {
+    color: var(--dist-card-subtext) !important;
+    font-size: 0.88em;
+}
+
+.dist-worker-info__fallback {
+    color: var(--master-badge-fallback-text) !important;
+    font-size: 0.86em;
+    font-weight: 600;
+}
+
+/* ---- Nodes 2.0 Theme ---- */
+
+.distributed-panel--nodes2 .dist-card {
+    background: var(--p-surface-800, #1e1e1e);
+    border: 1px solid var(--p-surface-700, #2c2c2c);
+    border-radius: 8px;
+    box-shadow: 0 1px 3px rgba(0, 0, 0, 0.3);
+}
+
+.distributed-panel--nodes2 .dist-card--blueprint {
+    border: 2px dashed var(--p-surface-600, #404040);
+    background: rgba(255, 255, 255, 0.015);
+    box-shadow: none;
+}
+
+.distributed-panel--nodes2 .dist-card--add {
+    border: 1px dashed var(--p-surface-600, #404040);
+    background: transparent;
+    box-shadow: none;
+}
+
+.distributed-panel--nodes2 .dist-card__left-col {
+    border-right-color: var(--p-surface-700, #2c2c2c);
+    background: var(--p-surface-900, #131313);
+}
+
+.distributed-panel--nodes2 .dist-info-box {
+    background-color: var(--p-surface-700, #2c2c2c);
+    color: var(--p-text-muted-color, #9e9e9e);
+    border-radius: 6px;
+}
+
+.distributed-panel--nodes2 .dist-form-input {
+    background: var(--p-surface-800, #1e1e1e);
+    border-color: var(--p-surface-600, #404040);
+    border-radius: 6px;
+    color: var(--p-text-color, #ffffff);
+}
+
+.distributed-panel--nodes2 .worker-settings {
+    background: var(--p-surface-900, #131313);
+    border-color: var(--p-surface-700, #2c2c2c);
+    border-radius: 6px;
+}
+
+.distributed-panel--nodes2 .entity-card-content--hovered {
+    background-color: var(--p-surface-700, #2c2c2c) !important;
+}
+
+.distributed-panel--nodes2 .placeholder-card--blueprint {
+    border-color: var(--p-surface-600, #404040) !important;
+}
+
+.distributed-panel--nodes2 .placeholder-card--blueprint.is-hovered {
+    border-color: var(--p-surface-500, #555555) !important;
+}
+
+.distributed-panel--nodes2 .placeholder-card--add {
+    border-color: var(--p-surface-600, #404040) !important;
+}
+
+.distributed-panel--nodes2 .placeholder-card--add.is-hovered {
+    border-color: var(--p-surface-500, #555555) !important;
+}
+
+/* ---- End Nodes 2.0 Theme ---- */
+
+.is-hidden {
+    display: none !important;
+}
+
+.settings-arrow--expanded {
+    transform: rotate(90deg) !important;
+}
+
+.log-modal {
+    position: fixed;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    background: rgba(0, 0, 0, 0.8);
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    z-index: 10000;
+}
+
+.log-modal__content {
+    background: var(--dist-log-modal-bg);
+    border-radius: 8px;
+    width: 90%;
+    max-width: 1200px;
+    height: 80%;
+    display: flex;
+    flex-direction: column;
+    border: 1px solid var(--dist-log-modal-border);
+}
+
+.log-modal__header {
+    padding: 15px 20px;
+    border-bottom: 1px solid var(--dist-log-modal-header-border);
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+}
+
+.log-modal__title {
+    margin: 0;
+    color: var(--dist-log-modal-title);
+}
+
+.log-modal__header-buttons {
+    display: flex;
+    gap: 20px;
+    align-items: center;
+}
+
+.log-modal__refresh {
+    display: flex;
+    align-items: center;
+    gap: 4px;
+}
+
+.log-modal__refresh-input {
+    cursor: pointer;
+}
+
+.log-modal__refresh-label {
+    font-size: 12px;
+    color: var(--dist-log-modal-label);
+    cursor: pointer;
+    white-space: nowrap;
+}
+
+.log-modal__close {
+    background-color: #c04c4c !important;
+    padding: 5px 10px !important;
+    font-size: 14px !important;
+    font-weight: bold !important;
+    border-radius: 6px !important;
+}
+
+.log-modal__body {
+    flex: 1;
+    overflow: auto;
+    padding: 15px;
+    font-family: 'Consolas', 'Monaco', 'Courier New', monospace;
+    font-size: 12px;
+    line-height: 1.4;
+    color: var(--dist-log-modal-body-text);
+    background: var(--dist-log-modal-body-bg);
+    white-space: pre-wrap;
+    word-wrap: break-word;
+}
+
+.log-modal__status {
+    padding: 10px 20px;
+    border-top: 1px solid var(--dist-log-modal-header-border);
+    font-size: 11px;
+    color: var(--dist-log-modal-status);
+}
@@ -0,0 +1,481 @@
+import { app } from "/scripts/app.js";
+import { ENDPOINTS } from "./constants.js";
+
+const NODE_CLASS = "DistributedValue";
+const CONVERTED_WIDGET = "converted-widget";
+const DYNAMIC_DEFAULT_WIDGET = "_dv_default";
+const DYNAMIC_WORKER_WIDGET_PREFIX = "_dv_worker_";
+const WORKERS_CHANGED_EVENT = "distributed:workers-changed";
+
+const trackedNodes = new Set();
+let workersChangedListenerAttached = false;
+
+function filterEnabledWorkers(workers) {
+    if (!Array.isArray(workers)) return [];
+    return workers.filter((worker) => Boolean(worker?.enabled));
+}
+
+async function fetchWorkers() {
+    try {
+        const resp = await fetch(ENDPOINTS.CONFIG);
+        if (!resp.ok) return [];
+        const config = await resp.json();
+        return filterEnabledWorkers(config.workers);
+    } catch {
+        return [];
+    }
+}
+
+function getRawDefaultWidget(node) {
+    return node.widgets?.find((w) => w.name === "default_value");
+}
+
+function getRawWorkerValuesWidget(node) {
+    return node.widgets?.find((w) => w.name === "worker_values");
+}
+
+function getDynamicDefaultWidget(node) {
+    return node.widgets?.find((w) => w.name === DYNAMIC_DEFAULT_WIDGET);
+}
+
+function getDynamicWorkerWidgets(node) {
+    return (node.widgets || []).filter((w) => w.name.startsWith(DYNAMIC_WORKER_WIDGET_PREFIX));
+}
+
+function hideWidgetForGood(node, widget, suffix = "") {
+    if (!widget) return;
+    if (typeof widget.type === "string" && widget.type.startsWith(CONVERTED_WIDGET)) return;
+
+    widget.origType = widget.type;
+    widget.origComputeSize = widget.computeSize;
+    widget.origSerializeValue = widget.serializeValue;
+    widget.computeSize = () => [0, -4];
+    widget.type = `${CONVERTED_WIDGET}${suffix}`;
+
+    // Hide any attached DOM element (multiline widgets).
+    if (widget.element) widget.element.style.display = "none";
+    if (widget.inputEl) widget.inputEl.style.display = "none";
+
+    if (widget.linkedWidgets) {
+        for (const linked of widget.linkedWidgets) {
+            hideWidgetForGood(node, linked, `:${widget.name}`);
+        }
+    }
+}
+
+function hideRawWidgets(node) {
+    hideWidgetForGood(node, getRawDefaultWidget(node), ":default_value");
+    hideWidgetForGood(node, getRawWorkerValuesWidget(node), ":worker_values");
+}
+
+function removeDynamicDefaultWidget(node) {
+    const idx = node.widgets?.findIndex((w) => w.name === DYNAMIC_DEFAULT_WIDGET);
+    if (idx != null && idx >= 0) {
+        node.widgets.splice(idx, 1);
+    }
+}
+
+function removeDynamicWorkerWidgets(node) {
+    if (!node.widgets) return;
+    for (let i = node.widgets.length - 1; i >= 0; i--) {
+        if (node.widgets[i].name.startsWith(DYNAMIC_WORKER_WIDGET_PREFIX)) {
+            node.widgets.splice(i, 1);
+        }
+    }
+}
+
+function readWorkerStore(node) {
+    const raw = getRawWorkerValuesWidget(node);
+    if (!raw) return {};
+    try {
+        const parsed = JSON.parse(raw.value || "{}");
+        return typeof parsed === "object" && parsed !== null ? parsed : {};
+    } catch {
+        return {};
+    }
+}
+
+function writeWorkerStore(node, store) {
+    const raw = getRawWorkerValuesWidget(node);
+    if (!raw) return;
+    raw.value = JSON.stringify(store);
+}
+
+function normalizeComboOptions(options) {
+    if (!options) return null;
+    if (Array.isArray(options)) return options;
+    if (Array.isArray(options.values)) return options.values;
+    return null;
+}
+
+function resolveGraphLink(graph, linkId) {
+    const links = graph.links || graph._links;
+    if (!links) return null;
+    const link = links[linkId] ?? (typeof links.get === "function" ? links.get(linkId) : null);
+    if (!link) return null;
+    if (Array.isArray(link)) {
+        return {
+            target_id: link[2],
+            target_slot: link[3],
+        };
+    }
+    return link;
+}
+
+function detectTargetType(node) {
+    const out = node.outputs?.[0];
+    const linkIds = out?.links || [];
+    if (!linkIds.length) {
+        return { connected: false, type: "STRING", options: null };
+    }
+
+    const graph = node.graph || app.graph;
+    if (!graph) {
+        return { connected: false, type: "STRING", options: null };
+    }
+
+    const link = resolveGraphLink(graph, linkIds[0]);
+    if (!link) {
+        return { connected: false, type: "STRING", options: null };
+    }
+
+    const targetNode = graph.getNodeById(link.target_id);
+    if (!targetNode) {
+        return { connected: false, type: "STRING", options: null };
+    }
+
+    const targetInputName = targetNode.inputs?.[link.target_slot]?.name;
+    if (!targetInputName) {
+        return { connected: false, type: "STRING", options: null };
+    }
+
+    const targetWidget = targetNode.widgets?.find((w) => w.name === targetInputName);
+    if (targetWidget) {
+        if (targetWidget.type === "combo") {
+            const comboOptions = normalizeComboOptions(targetWidget.options);
+            return { connected: true, type: "COMBO", options: comboOptions };
+        }
+        if (targetWidget.type === "number") {
+            const step = targetWidget.options?.step;
+            const precision = targetWidget.options?.precision;
+            const isInt = Number.isInteger(step) && (precision === 0 || precision == null);
+            return { connected: true, type: isInt ? "INT" : "FLOAT", options: null };
+        }
+    }
+
+    const nodeDef = targetNode.constructor?.nodeData;
+    const inputDef = nodeDef?.input?.required?.[targetInputName] || nodeDef?.input?.optional?.[targetInputName];
+    if (inputDef) {
+        const defType = inputDef[0];
+        if (Array.isArray(defType)) {
+            return { connected: true, type: "COMBO", options: defType };
+        }
+        if (defType === "INT") return { connected: true, type: "INT", options: null };
+        if (defType === "FLOAT") return { connected: true, type: "FLOAT", options: null };
+    }
+
+    return { connected: true, type: "STRING", options: null };
+}
+
+function normalizeNumber(value, fallback) {
+    const parsed = Number(value);
+    return Number.isFinite(parsed) ? parsed : fallback;
+}
+
+function getDefaultInitialValue(node, inputType, comboOptions) {
+    const rawDefault = getRawDefaultWidget(node);
+    const current = rawDefault?.value;
+
+    if (inputType === "INT") {
+        return Math.trunc(normalizeNumber(current, 0));
+    }
+    if (inputType === "FLOAT") {
+        return normalizeNumber(current, 0);
+    }
+    if (inputType === "COMBO" && Array.isArray(comboOptions) && comboOptions.length) {
+        const currentText = current == null ? "" : String(current);
+        return comboOptions.includes(currentText) ? currentText : comboOptions[0];
+    }
+    return current == null ? "" : String(current);
+}
+
+function setRawDefaultValue(node, value) {
+    const rawDefault = getRawDefaultWidget(node);
+    if (!rawDefault) return;
+    rawDefault.value = value;
+}
+
+function serializeWorkerStoreFromWidgets(node, inputType, comboOptions) {
+    const nextStore = { _type: inputType };
+    if (inputType === "COMBO" && Array.isArray(comboOptions)) {
+        nextStore._options = comboOptions;
+    }
+    const valuesByWorkerId = {};
+
+    for (const widget of getDynamicWorkerWidgets(node)) {
+        const key = widget.name.slice(DYNAMIC_WORKER_WIDGET_PREFIX.length);
+        if (widget.value !== "" && widget.value !== null && widget.value !== undefined) {
+            const value = String(widget.value);
+            nextStore[key] = value;
+            if (widget._dvWorkerId) {
+                valuesByWorkerId[widget._dvWorkerId] = value;
+            }
+        }
+    }
+    if (Object.keys(valuesByWorkerId).length) {
+        nextStore._by_worker_id = valuesByWorkerId;
+    }
+
+    writeWorkerStore(node, nextStore);
+}
+
+function updateWorkerStoreTypeMetadata(node, inputType, comboOptions) {
+    const store = readWorkerStore(node);
+    store._type = inputType;
+    if (inputType === "COMBO" && Array.isArray(comboOptions)) {
+        store._options = comboOptions;
+    } else {
+        delete store._options;
+    }
+    writeWorkerStore(node, store);
+}
+
+function createDynamicDefaultWidget(node, inputType, comboOptions) {
+    removeDynamicDefaultWidget(node);
+    const initial = getDefaultInitialValue(node, inputType, comboOptions);
+    let widget;
+
+    if (inputType === "COMBO" && Array.isArray(comboOptions) && comboOptions.length) {
+        widget = node.addWidget(
+            "combo",
+            DYNAMIC_DEFAULT_WIDGET,
+            initial,
+            (value) => {
+                widget.value = value;
+                setRawDefaultValue(node, String(value));
+            },
+            { values: comboOptions }
+        );
+    } else if (inputType === "INT") {
+        widget = node.addWidget(
+            "number",
+            DYNAMIC_DEFAULT_WIDGET,
+            initial,
+            (value) => {
+                widget.value = Math.trunc(normalizeNumber(value, 0));
+                setRawDefaultValue(node, widget.value);
+            },
+            { min: -Infinity, max: Infinity, step: 1, precision: 0 }
+        );
+    } else if (inputType === "FLOAT") {
+        widget = node.addWidget(
+            "number",
+            DYNAMIC_DEFAULT_WIDGET,
+            initial,
+            (value) => {
+                widget.value = normalizeNumber(value, 0);
+                setRawDefaultValue(node, widget.value);
+            },
+            { min: -Infinity, max: Infinity, step: 0.1, precision: 3 }
+        );
+    } else {
+        widget = node.addWidget(
+            "string",
+            DYNAMIC_DEFAULT_WIDGET,
+            initial,
+            (value) => {
+                widget.value = value ?? "";
+                setRawDefaultValue(node, widget.value);
+            },
+            {}
+        );
+    }
+
+    widget.label = "default_value";
+}
+
+function getWorkerInitialValue(store, key, workerId, inputType, comboOptions) {
+    const byWorkerId = store?._by_worker_id;
+    const saved = (byWorkerId && workerId && byWorkerId[workerId] != null)
+        ? byWorkerId[workerId]
+        : store[key];
+    if (saved == null) {
+        if (inputType === "INT" || inputType === "FLOAT") return 0;
+        if (inputType === "COMBO" && Array.isArray(comboOptions) && comboOptions.length) {
+            return comboOptions[0];
+        }
+        return "";
+    }
+
+    if (inputType === "INT") return Math.trunc(normalizeNumber(saved, 0));
+    if (inputType === "FLOAT") return normalizeNumber(saved, 0);
+    if (inputType === "COMBO" && Array.isArray(comboOptions) && comboOptions.length) {
+        const savedText = String(saved);
+        return comboOptions.includes(savedText) ? savedText : comboOptions[0];
+    }
+    return String(saved);
+}
+
+function createWorkerWidgets(node, workers, inputType, comboOptions) {
+    removeDynamicWorkerWidgets(node);
+    const store = readWorkerStore(node);
+
+    for (let i = 0; i < workers.length; i++) {
+        const key = String(i + 1);
+        const worker = workers[i];
+        const label = worker.name || worker.id || `Worker ${key}`;
+        const widgetName = `${DYNAMIC_WORKER_WIDGET_PREFIX}${key}`;
+        const initial = getWorkerInitialValue(store, key, worker.id, inputType, comboOptions);
+        let widget;
+
+        if (inputType === "COMBO" && Array.isArray(comboOptions) && comboOptions.length) {
+            widget = node.addWidget(
+                "combo",
+                widgetName,
+                initial,
+                (value) => {
+                    widget.value = value;
+                    serializeWorkerStoreFromWidgets(node, inputType, comboOptions);
+                },
+                { values: comboOptions }
+            );
+        } else if (inputType === "INT") {
+            widget = node.addWidget(
+                "number",
+                widgetName,
+                initial,
+                (value) => {
+                    widget.value = Math.trunc(normalizeNumber(value, 0));
+                    serializeWorkerStoreFromWidgets(node, inputType, comboOptions);
+                },
+                { min: -Infinity, max: Infinity, step: 1, precision: 0 }
+            );
+        } else if (inputType === "FLOAT") {
+            widget = node.addWidget(
+                "number",
+                widgetName,
+                initial,
+                (value) => {
+                    widget.value = normalizeNumber(value, 0);
+                    serializeWorkerStoreFromWidgets(node, inputType, comboOptions);
+                },
+                { min: -Infinity, max: Infinity, step: 0.1, precision: 3 }
+            );
+        } else {
+            widget = node.addWidget(
+                "string",
+                widgetName,
+                initial,
+                (value) => {
+                    widget.value = value ?? "";
+                    serializeWorkerStoreFromWidgets(node, inputType, comboOptions);
+                },
+                {}
+            );
+        }
+
+        widget.label = label;
+        widget._dvWorkerId = worker.id;
+    }
+
+    serializeWorkerStoreFromWidgets(node, inputType, comboOptions);
+}
+
+function rebuildWidgets(node) {
+    hideRawWidgets(node);
+    const workers = node._dvWorkers || [];
+    const store = readWorkerStore(node);
+    const detected = detectTargetType(node);
+    const disconnected = !detected.connected;
+    const inputType = disconnected ? "STRING" : detected.type;
+    const comboOptions = disconnected ? null : detected.options;
+
+    if (disconnected) {
+        // Reset disconnected node back to the neutral default state.
+        setRawDefaultValue(node, "");
+        writeWorkerStore(node, { _type: "STRING" });
+    }
+
+    createDynamicDefaultWidget(node, inputType, comboOptions);
+    if (workers.length > 0) {
+        createWorkerWidgets(node, workers, inputType, comboOptions);
+    } else {
+        removeDynamicWorkerWidgets(node);
+        updateWorkerStoreTypeMetadata(node, inputType, comboOptions);
+    }
+
+    const size = node.computeSize();
+    size[0] = Math.max(size[0], node.size?.[0] || 0);
+    node.setSize(size);
+    if (node.setDirtyCanvas) node.setDirtyCanvas(true, true);
+}
+
+function refreshNodeWorkers(node, workers) {
+    if (!node || !node.graph) return;
+    node._dvWorkers = workers;
+    rebuildWidgets(node);
+}
+
+async function refreshTrackedNodes(workers = null) {
+    const nextWorkers = workers || (await fetchWorkers());
+    for (const node of trackedNodes) {
+        refreshNodeWorkers(node, nextWorkers);
+    }
+}
+
+function attachWorkersChangedListener() {
+    if (workersChangedListenerAttached) return;
+    if (typeof window === "undefined" || typeof window.addEventListener !== "function") return;
+
+    window.addEventListener(WORKERS_CHANGED_EVENT, (event) => {
+        const changedWorkers = filterEnabledWorkers(event?.detail?.workers);
+        if (changedWorkers.length > 0 || Array.isArray(event?.detail?.workers)) {
+            void refreshTrackedNodes(changedWorkers);
+            return;
+        }
+        void refreshTrackedNodes();
+    });
+
+    workersChangedListenerAttached = true;
+}
+
+app.registerExtension({
+    name: "Distributed.DistributedValue",
+    async nodeCreated(node) {
+        if (node.comfyClass !== NODE_CLASS) return;
+
+        try {
+            attachWorkersChangedListener();
+            trackedNodes.add(node);
+            node._dvWorkers = await fetchWorkers();
+            rebuildWidgets(node);
+
+            const originalOnConnectionsChange = node.onConnectionsChange;
+            node.onConnectionsChange = function (type, index, connected, linkInfo, ioSlot) {
+                if (originalOnConnectionsChange) {
+                    originalOnConnectionsChange.call(this, type, index, connected, linkInfo, ioSlot);
+                }
+                if (type === 2 && index === 0) {
+                    setTimeout(() => rebuildWidgets(this), 20);
+                }
+            };
+
+            const originalConfigure = node.configure;
+            node.configure = function (data) {
+                const result = originalConfigure ? originalConfigure.call(this, data) : undefined;
+                setTimeout(() => rebuildWidgets(this), 20);
+                return result;
+            };
+
+            const originalOnRemoved = node.onRemoved;
+            node.onRemoved = function () {
+                trackedNodes.delete(this);
+                if (originalOnRemoved) {
+                    return originalOnRemoved.call(this);
+                }
+            };
+        } catch (error) {
+            console.error("Error in DistributedValue extension:", error);
+        }
+    },
+});
@@ -1,60 +1,20 @@
 import { api } from "../../scripts/api.js";
-import { findNodesByClass, findImageReferences, hasUpstreamNode, pruneWorkflowForWorker, getCachedWorkerSystemInfo } from './workerUtils.js';
-import { TIMEOUTS } from './constants.js';
-
-/**
- * Convert paths in the API prompt to match the target platform's separator
- * @param {Object} apiPrompt - The workflow API prompt
- * @param {string} targetSeparator - The target path separator ('\\' or '/')
- * @returns {Object} The converted API prompt
- */
-function convertPathsForPlatform(apiPrompt, targetSeparator) {
-    // Validate target separator
-    if (!targetSeparator || (targetSeparator !== '/' && targetSeparator !== '\\')) {
-        console.warn('[Distributed] Invalid target separator:', targetSeparator, '- skipping path conversion');
-        return apiPrompt;
-    }
-    
-    // Regex to identify likely file paths with extensions
-    const isLikelyFilename = (value) => {
-        return value.match(/\.(ckpt|safetensors|pt|pth|bin|yaml|json|png|jpg|jpeg|webp|gif|bmp|latent|txt|vae|lora|embedding)(\s*\[\w+\])?$/i);
-    };
-    
-    function convert(obj) {
-        if (typeof obj === 'string') {
-            // Only convert strings that look like file paths
-            if ((obj.includes('\\') || obj.includes('/')) && isLikelyFilename(obj)) {
-                // Replace any path separator with the target one
-                return obj.replace(/[\\\/]/g, targetSeparator);
-            }
-            return obj;
-        } else if (Array.isArray(obj)) {
-            return obj.map(convert);
-        } else if (typeof obj === 'object' && obj !== null) {
-            const newObj = {};
-            for (const [key, value] of Object.entries(obj)) {
-                newObj[key] = convert(value);
-            }
-            return newObj;
-        }
-        return obj;
-    }
-    
-    return convert(apiPrompt);
-}
+import { applyProbeResultToWorkerDot, findNodesByClass } from './workerUtils.js';
+import { TIMEOUTS, NODE_CLASSES, generateUUID } from './constants.js';
+import { checkAllWorkerStatuses, getWorkerUrl } from './workerLifecycle.js';

 export function setupInterceptor(extension) {
    api.queuePrompt = async (number, prompt) => {
        if (extension.isEnabled) {
-            const hasCollector = findNodesByClass(prompt.output, "DistributedCollector").length > 0;
-            const hasDistUpscale = findNodesByClass(prompt.output, "UltimateSDUpscaleDistributed").length > 0;
-            
+            const hasCollector = findNodesByClass(prompt.output, NODE_CLASSES.DISTRIBUTED_COLLECTOR).length > 0;
+            const hasDistUpscale = findNodesByClass(prompt.output, NODE_CLASSES.UPSCALE_DISTRIBUTED).length > 0;
+
            if (hasCollector || hasDistUpscale) {
                const result = await executeParallelDistributed(extension, prompt);
                // Immediate status check for instant feedback
-                extension.checkAllWorkerStatuses();
+                checkAllWorkerStatuses(extension);
                // Another check after a short delay to catch state changes
-                setTimeout(() => extension.checkAllWorkerStatuses(), TIMEOUTS.POST_ACTION_DELAY);
+                setTimeout(() => checkAllWorkerStatuses(extension), TIMEOUTS.POST_ACTION_DELAY);
                return result;
            }
        }
@@ -63,9 +23,10 @@ export function setupInterceptor(extension) {
 }

 export async function executeParallelDistributed(extension, promptWrapper) {
+    const traceExecutionId = `exec_${Date.now()}_${generateUUID().slice(0, 6)}`;
    try {
-        const executionPrefix = "exec_" + Date.now(); // Unique ID for this specific execution
        const enabledWorkers = extension.enabledWorkers;
+        extension.log(`[exec:${traceExecutionId}] Starting distributed execution`, "debug");
        
        // Pre-flight health check on all enabled workers
        const activeWorkers = await performPreflightCheck(extension, enabledWorkers);
@@ -82,11 +43,11 @@ export async function executeParallelDistributed(extension, promptWrapper) {
        }
        
        extension.log(`Pre-flight check: ${activeWorkers.length} of ${enabledWorkers.length} workers are active`, "debug");
-        
+
        // Check if master host might be unreachable by workers (cloudflare tunnel down)
        const masterHost = extension.config?.master?.host || '';
        const isCloudflareHost = /\.(trycloudflare\.com|cloudflare\.dev)$/i.test(masterHost);
-        
+
        if (isCloudflareHost && activeWorkers.length > 0) {
            // Try to verify if the cloudflare tunnel is actually up
            try {
@@ -97,444 +58,50 @@ export async function executeParallelDistributed(extension, promptWrapper) {
                    cache: 'no-cache',
                    signal: AbortSignal.timeout(3000) // 3 second timeout
                });
-                
+
                if (!response.ok) {
                    throw new Error('Master not reachable');
                }
            } catch (error) {
                // Cloudflare tunnel appears to be down
                extension.log(`Master host ${masterHost} is not reachable - cloudflare tunnel may be down`, "error");
-                
+
                if (extension.ui?.showCloudflareWarning) {
                    extension.ui.showCloudflareWarning(extension, masterHost);
                }
-                
+
                // Stop execution - workers won't be able to send results back
                extension.log("Blocking execution - workers cannot reach master at cloudflare domain", "error");
                return null; // This will prevent the workflow from running
            }
        }
-        
-        // Find all distributed nodes in the workflow
-        const collectorNodes = findNodesByClass(promptWrapper.output, "DistributedCollector");
-        const upscaleNodes = findNodesByClass(promptWrapper.output, "UltimateSDUpscaleDistributed");
-        const allDistributedNodes = [...collectorNodes, ...upscaleNodes];
-        
-        // Map original node IDs to truly unique job IDs for this specific run
-        const job_id_map = new Map(allDistributedNodes.map(node => [node.id, `${executionPrefix}_${node.id}`]));
-        
-        // Prepare a separate job queue on the backend for each unique job ID
-        const preparePromises = Array.from(job_id_map.values()).map(uniqueId => prepareDistributedJob(extension, uniqueId));
-        await Promise.all(preparePromises);

-        const jobs = [];
-        // Use only active workers
-        const participants = ['master', ...activeWorkers.map(w => w.id)];
-
-        for (const participantId of participants) {
-            const options = { 
-                enabled_worker_ids: activeWorkers.map(w => w.id), 
-                workflow: promptWrapper.workflow,
-                job_id_map: job_id_map // Pass the map of unique IDs
-            };
-            
-            const jobApiPrompt = await prepareApiPromptForParticipant(
-                extension, promptWrapper.output, participantId, options
-            );
-            
-            if (participantId === 'master') {
-                jobs.push({ type: 'master', promptWrapper: { ...promptWrapper, output: jobApiPrompt } });
-            } else {
-                const worker = activeWorkers.find(w => w.id === participantId);
-                if (worker) {
-                    const job = {
-                        type: 'worker',
-                        worker,
-                        prompt: jobApiPrompt,
-                        workflow: promptWrapper.workflow
-                    };
-                    
-                    // Add image references if found for remote workers
-                    if (options._imageReferences) {
-                        job.imageReferences = options._imageReferences;
-                    }
-                    
-                    jobs.push(job);
-                }
-            }
-        }
-        
-        const result = await executeJobs(extension, jobs);
-        return result;
-    } catch (error) {
-        extension.log("Parallel execution failed: " + error.message, "error");
-        throw error;
-    }
-}
-
-export async function prepareApiPromptForParticipant(extension, baseApiPrompt, participantId, options = {}) {
-    let jobApiPrompt = JSON.parse(JSON.stringify(baseApiPrompt));
-    const isMaster = participantId === 'master';
-    
-    // Find all distributed nodes once (before pruning)
-    const collectorNodes = findNodesByClass(jobApiPrompt, "DistributedCollector");
-    const upscaleNodes = findNodesByClass(jobApiPrompt, "UltimateSDUpscaleDistributed");
-    const allDistributedNodes = [...collectorNodes, ...upscaleNodes];
-    
-    // For workers, handle platform-specific path conversion
-    if (!isMaster) {
-        const workerInfo = extension.config.workers.find(w => w.id === participantId);
-        
-        if (workerInfo && workerInfo.host) {
-            // Remote or cloud worker - needs path translation
-            try {
-                const workerUrl = extension.getWorkerUrl(workerInfo);
-                const systemInfo = await getCachedWorkerSystemInfo(workerUrl);
-                const targetSeparator = systemInfo?.platform?.path_separator;
-                
-                if (targetSeparator) {
-                    // Convert paths to match worker's platform
-                    jobApiPrompt = convertPathsForPlatform(jobApiPrompt, targetSeparator);
-                    extension.log(`Converted paths for ${systemInfo.platform.system} worker ${participantId} (separator: '${targetSeparator}')`, "debug");
-                } else {
-                    extension.log(`No path separator found for worker ${participantId}, skipping path conversion`, "debug");
-                }
-            } catch (e) {
-                extension.log(`Failed to get system info for worker ${participantId}: ${e.message}`, "warn");
-                // Continue without path conversion
-            }
-        }
-        
-        // Prune the workflow to only include distributed node dependencies
-        if (allDistributedNodes.length > 0) {
-            jobApiPrompt = pruneWorkflowForWorker(extension, jobApiPrompt, allDistributedNodes);
-        }
-    }
-    
-    // Handle image references for remote workers
-    if (!isMaster && options.enabled_worker_ids) {
-        // Check if this is a remote worker
-        const workerId = participantId;
-        const workerInfo = extension.config.workers.find(w => w.id === workerId);
-        const isRemote = workerInfo && workerInfo.host;
-        
-        if (isRemote) {
-            // Find all image/video references in the pruned workflow
-            const imageReferences = findImageReferences(extension, jobApiPrompt);
-            if (imageReferences.size > 0) {
-                extension.log(`Found ${imageReferences.size} media references (images/videos) for remote worker ${workerId}`, "debug");
-                // Store image references for later processing
-                options._imageReferences = imageReferences;
-            }
-        }
-    }
-    
-    // Handle Distributed seed nodes
-    const distributorNodes = findNodesByClass(jobApiPrompt, "DistributedSeed");
-    if (distributorNodes.length > 0) {
-        extension.log(`Found ${distributorNodes.length} seed node(s)`, "debug");
-    }
-    
-    for (const seedNode of distributorNodes) {
-        const { inputs } = jobApiPrompt[seedNode.id];
-        inputs.is_worker = !isMaster;
-        if (!isMaster) {
-            const workerIndex = options.enabled_worker_ids.indexOf(participantId);
-            inputs.worker_id = `worker_${workerIndex}`;
-            extension.log(`Set seed node ${seedNode.id} for worker ${workerIndex}`, "debug");
-        }
-    }
-    
-    // Handle Distributed collector nodes (already found above)
-    for (const collector of collectorNodes) {
-        const { inputs } = jobApiPrompt[collector.id];
-        
-        // Check if this collector is downstream from a distributed upscaler
-        const hasUpstreamDistributedUpscaler = hasUpstreamNode(
-            jobApiPrompt, 
-            collector.id, 
-            'UltimateSDUpscaleDistributed'
-        );
-        
-        if (hasUpstreamDistributedUpscaler) {
-            // Set pass_through mode for this collector
-            inputs.pass_through = true;
-            extension.log(`Collector ${collector.id} set to pass-through mode (downstream from distributed upscaler)`, "debug");
-        } else {
-            // Normal collector behavior
-            // Get the unique job ID from the map created for this execution
-            const uniqueJobId = options.job_id_map ? options.job_id_map.get(collector.id) : collector.id;
-            
-            // Use the truly unique ID for this execution
-            inputs.multi_job_id = uniqueJobId;
-            inputs.is_worker = !isMaster;
-            if (isMaster) {
-                inputs.enabled_worker_ids = JSON.stringify(options.enabled_worker_ids || []);
-            } else {
-                inputs.master_url = extension.getMasterUrl();
-                // Also make the worker_job_id unique to prevent potential caching issues
-                inputs.worker_job_id = `${uniqueJobId}_worker_${participantId}`;
-                inputs.worker_id = participantId;
-            }
-        }
-    }
-    
-    // Handle Ultimate SD Upscale Distributed nodes
-    for (const upscaleNode of upscaleNodes) {
-        const { inputs } = jobApiPrompt[upscaleNode.id];
-        
-        // Get the unique job ID from the map
-        const uniqueJobId = options.job_id_map ? options.job_id_map.get(upscaleNode.id) : upscaleNode.id;
-        
-        inputs.multi_job_id = uniqueJobId;
-        inputs.is_worker = !isMaster;
-        
-        if (isMaster) {
-            inputs.enabled_worker_ids = JSON.stringify(options.enabled_worker_ids || []);
-        } else {
-            inputs.master_url = extension.getMasterUrl();
-            inputs.worker_id = participantId;
-            // Workers also need the enabled_worker_ids to calculate tile distribution
-            inputs.enabled_worker_ids = JSON.stringify(options.enabled_worker_ids || []);
-        }
-    }
-    
-    return jobApiPrompt;
-}
-
-export async function prepareDistributedJob(extension, multi_job_id) {
-    try {
-        await extension.api.prepareJob(multi_job_id);
-    } catch (error) {
-        extension.log("Error preparing job: " + error.message, "error");
-        throw error;
-    }
-}
-
-export async function executeJobs(extension, jobs) {
-    let masterPromptId = null;
-    
-    // Pre-load all unique images before dispatching to workers
-    const allImageReferences = new Map();
-    for (const job of jobs) {
-        if (job.type === 'worker' && job.imageReferences) {
-            for (const [filename, info] of job.imageReferences) {
-                allImageReferences.set(filename, info);
-            }
-        }
-    }
-    
-    if (allImageReferences.size > 0) {
-        extension.log(`Pre-loading ${allImageReferences.size} unique media file(s) for all workers`, "debug");
-        await loadImagesForWorker(extension, allImageReferences);
-    }
-    
-    // Now dispatch jobs in parallel
-    const promises = jobs.map(job => {
-        if (job.type === 'master') {
-            return extension.originalQueuePrompt(0, job.promptWrapper).then(result => {
-                masterPromptId = result;
-                return result;
-            });
-        } else {
-            return dispatchToWorker(extension, job.worker, job.prompt, job.workflow, job.imageReferences);
-        }
-    });
-    await Promise.all(promises);
-    
-    // Trigger immediate status check for instant feedback
-    extension.checkAllWorkerStatuses();
-    
-    return masterPromptId || { "prompt_id": "distributed-job-dispatched" };
-}
-
-async function dispatchToWorker(extension, worker, prompt, workflow, imageReferences) {
-    const workerUrl = extension.getWorkerUrl(worker);
-    
-    // Debug logging - always log to console for debugging
-    extension.log(`[Distributed] === Dispatching to ${worker.name} (${worker.id}) ===`, "debug");
-    extension.log('[Distributed] Worker URL: ' + workerUrl, "debug");
-    
-    // Handle image uploads for remote workers
-    if (imageReferences && imageReferences.size > 0) {
-        // Check if this is a local worker (same host as master)
-        const isLocalWorker = workerUrl.includes('127.0.0.1') || workerUrl.includes('localhost');
-        
-        if (isLocalWorker) {
-            extension.log(`[Distributed] Skipping image processing for local worker ${worker.name} (shares filesystem with master)`, "debug");
-        } else {
-            extension.log(`[Distributed] Processing ${imageReferences.size} image(s) for remote worker`, "debug");
-            
-            try {
-                // Load images from master
-                const images = await loadImagesForWorker(extension, imageReferences);
-                
-                // Upload images to worker
-                if (images.length > 0) {
-                    await uploadImagesToWorker(extension, workerUrl, images);
-                    extension.log(`[Distributed] Successfully uploaded ${images.length} image(s) to worker`, "debug");
-                }
-            } catch (error) {
-                extension.log(`Failed to process images for worker ${worker.name}: ${error.message}`, "error");
-                // Continue with workflow execution even if image upload fails
-            }
-        }
-    }
-    
-    const promptToSend = {
-        prompt,
-        extra_data: { extra_pnginfo: { workflow } },
-        client_id: api.clientId
-    };
-    
-    extension.log('[Distributed] Prompt data: ' + JSON.stringify(promptToSend), "debug");
-    
-    try {
-        await fetch(`${workerUrl}/prompt`, { 
-            method: 'POST', 
-            headers: { 'Content-Type': 'application/json' }, 
-            mode: 'cors',
-            body: JSON.stringify(promptToSend) 
+        const queueResponse = await extension.api.queueDistributed({
+            prompt: promptWrapper.output,
+            workflow: promptWrapper.workflow,
+            enabled_worker_ids: activeWorkers.map((worker) => worker.id),
+            workers: activeWorkers.map((worker) => ({ id: worker.id })),
+            client_id: api.clientId,
+            delegate_master: Boolean(extension.config?.settings?.master_delegate_only),
+            auto_prepare: true,
+            trace_execution_id: traceExecutionId,
        });
-    } catch (e) {
-        extension.log(`Failed to connect to worker ${worker.name} at ${workerUrl}: ${e.message}`, "error");
-    }
-}
-
-export async function loadImagesForWorker(extension, imageReferences) {
-    const images = [];
-    
-    // Use a cache to avoid loading the same image multiple times
-    if (!extension._imageCache) {
-        extension._imageCache = new Map();
-    }
-    
-    for (const [filename, info] of imageReferences) {
-        try {
-            // Check cache first
-            if (extension._imageCache.has(filename)) {
-                images.push(extension._imageCache.get(filename));
-                extension.log(`Using cached image: ${filename}`, "debug");
-                continue;
-            }
-            
-            // Limit cache size
-            if (extension._imageCache.size >= 10) {
-                const oldestKey = extension._imageCache.keys().next().value;
-                extension._imageCache.delete(oldestKey);
-                extension.log(`Evicted oldest cache entry: ${oldestKey} (cache limit reached)`, "debug");
-            }
-            
-            // Load image from master's filesystem via API
-            try {
-                const data = await extension.api.loadImage(filename);
-                const imageData = {
-                    name: filename,
-                    image: data.image_data,
-                    hash: data.hash  // Include hash from the response
-                };
-                images.push(imageData);
-                
-                // Cache the image for future use
-                extension._imageCache.set(filename, imageData);
-                extension.log(`Loaded and cached image: ${filename}`, "debug");
-            } catch (loadError) {
-                extension.log(`Failed to load image ${filename}: ${loadError.message}`, "error");
-                throw loadError;
-            }
-        } catch (error) {
-            extension.log(`Error loading image ${filename}: ${error.message}`, "error");
+        if (queueResponse?.prompt_id) {
+            extension.log(
+                `[exec:${traceExecutionId}] Distributed queue accepted by backend (prompt_id=${queueResponse.prompt_id}, workers=${queueResponse.worker_count ?? activeWorkers.length})`,
+                "debug"
+            );
+            return queueResponse;
        }
-    }
-    
-    // Clear cache after a reasonable time to avoid memory issues
-    setTimeout(() => {
-        if (extension._imageCache && extension._imageCache.size > 0) {
-            extension.log(`Clearing image cache (${extension._imageCache.size} images)`, "debug");
-            extension._imageCache.clear();
-        }
-    }, TIMEOUTS.IMAGE_CACHE_CLEAR); // Clear after 30 seconds
-    
-    return images;
-}
-
-export async function uploadImagesToWorker(extension, workerUrl, images) {
-    // Upload images to worker's ComfyUI instance
-    for (const imageData of images) {
-        // Check if file already exists with matching hash
-        if (imageData.hash) {
-            try {
-                const checkResponse = await fetch(`${workerUrl}/distributed/check_file`, {
-                    method: 'POST',
-                    headers: { 'Content-Type': 'application/json' },
-                    mode: 'cors',
-                    body: JSON.stringify({ 
-                        filename: imageData.name, 
-                        hash: imageData.hash 
-                    })
-                });
-                
-                if (checkResponse.ok) {
-                    const result = await checkResponse.json();
-                    if (result.exists && result.hash_matches) {
-                        extension.log(`File ${imageData.name} already exists on worker with matching hash, skipping upload`, "debug");
-                        continue;
-                    }
-                }
-            } catch (error) {
-                // If check fails, proceed with upload
-                extension.log(`Failed to check file existence for ${imageData.name}: ${error.message}`, "debug");
-            }
-        }
-        
-        const formData = new FormData();
-        
-        // Detect MIME type from base64 header (supports both image and video)
-        const mimeMatch = imageData.image.match(/^data:((?:image|video)\/\w+);base64,/);
-        const mimeType = mimeMatch ? mimeMatch[1] : 'image/png'; // Default to PNG if not detected
-        
-        // Convert base64 to blob
-        const base64Data = imageData.image.replace(/^data:(?:image|video)\/\w+;base64,/, '');
-        const byteCharacters = atob(base64Data);
-        const byteNumbers = new Array(byteCharacters.length);
-        for (let i = 0; i < byteCharacters.length; i++) {
-            byteNumbers[i] = byteCharacters.charCodeAt(i);
-        }
-        const byteArray = new Uint8Array(byteNumbers);
-        const blob = new Blob([byteArray], { type: mimeType });
-        
-        // Use original filename without heavy cleaning
-        let cleanName = imageData.name;
-        let subfolder = '';
-        
-        // Extract subfolder if present
-        if (cleanName.includes('/')) {
-            const parts = cleanName.split('/');
-            subfolder = parts.slice(0, -1).join('/');
-            cleanName = parts[parts.length - 1];
-        }
-        
-        formData.append('image', blob, cleanName);
-        formData.append('type', 'input');
-        formData.append('subfolder', subfolder);
-        formData.append('overwrite', 'true');
-        
-        try {
-            const response = await fetch(`${workerUrl}/upload/image`, {
-                method: 'POST',
-                mode: 'cors',
-                body: formData
-            });
-            
-            if (!response.ok) {
-                throw new Error(`Upload failed: ${response.statusText}`);
-            }
-            
-            extension.log(`Uploaded image to worker: ${imageData.name} -> ${subfolder}/${cleanName}`, "debug");
-        } catch (error) {
-            extension.log(`Failed to upload ${imageData.name}: ${error.message}`, "error");
-            // Continue with other images
+        throw new Error(
+            `[exec:${traceExecutionId}] Backend did not return a prompt_id for distributed queue.`
+        );
+    } catch (error) {
+        extension.log(`[exec:${traceExecutionId}] Distributed execution failed: ${error.message}`, "error");
+        if (extension.ui?.showToast) {
+            extension.ui.showToast(extension.app, "error", "Distributed Failed", error.message, 5000);
        }
+        return null;
    }
 }

@@ -545,25 +112,25 @@ export async function performPreflightCheck(extension, workers) {
    const startTime = Date.now();
    
    const checkPromises = workers.map(async (worker) => {
-        const url = extension.getWorkerUrl(worker, '/prompt');
+        const workerUrl = getWorkerUrl(extension, worker);
        
-        extension.log(`Pre-flight checking ${worker.name} at: ${url}`, "debug");
+        extension.log(`Pre-flight checking ${worker.name} at: ${workerUrl}`, "debug");
        
        try {
-            const response = await fetch(url, {
-                method: 'GET',
-                mode: 'cors',
-                signal: AbortSignal.timeout(TIMEOUTS.STATUS_CHECK)
-            });
-            
-            if (response.ok) {
+            const probeResult = await extension.api.probeWorker(workerUrl, TIMEOUTS.STATUS_CHECK);
+
+            if (probeResult.ok) {
                extension.log(`Worker ${worker.name} is active`, "debug");
                return { worker, active: true };
            } else {
-                extension.log(`Worker ${worker.name} returned ${response.status}`, "debug");
+                extension.log(`Worker ${worker.name} returned ${probeResult.status}`, "debug");
                return { worker, active: false };
            }
        } catch (error) {
+            if (error?.name === 'AbortError') {
+                extension.log(`Worker ${worker.name} pre-flight check timed out; assuming active`, "debug");
+                return { worker, active: true, uncertain: true };
+            }
            extension.log(`Worker ${worker.name} is offline or unreachable: ${error.message}`, "debug");
            return { worker, active: false };
        }
@@ -577,14 +144,8 @@ export async function performPreflightCheck(extension, workers) {
    
    // Update UI status indicators for inactive workers
    results.filter(r => !r.active).forEach(r => {
-        const statusDot = document.getElementById(`status-${r.worker.id}`);
-        if (statusDot) {
-            // Remove pulsing animation once status is determined
-            statusDot.classList.remove('status-pulsing');
-            statusDot.style.backgroundColor = "#c04c4c"; // Red for offline
-            statusDot.title = "Offline - Cannot connect";
-        }
+        applyProbeResultToWorkerDot(r.worker.id, { ok: false });
    });
    
    return activeWorkers;
-}
+}
@@ -1,86 +1,88 @@
 import { app } from "/scripts/app.js";

+// Configuration for each batch divider node type
+const BATCH_DIVIDER_NODES = {
+    "ImageBatchDivider": { outputPrefix: "batch_", outputType: "IMAGE" },
+    "AudioBatchDivider": { outputPrefix: "audio_", outputType: "AUDIO" }
+};
+
 app.registerExtension({
-    name: "Distributed.ImageBatchDivider",
+    name: "Distributed.BatchDividers",
    async nodeCreated(node) {
-        if (node.comfyClass === "ImageBatchDivider") {
-            try {
-                const updateOutputs = () => {
-                    if (!node.widgets) return;
-                    
-                    const divideByWidget = node.widgets.find(w => w.name === "divide_by");
-                    if (!divideByWidget) return;
-                    
-                    const divideBy = parseInt(divideByWidget.value, 10) || 1;
-                    const totalOutputs = divideBy;  // Direct divide by value
-                    
-                    // Ensure outputs array exists
-                    if (!node.outputs) node.outputs = [];
-                    
-                    // Remove excess outputs
-                    while (node.outputs.length > totalOutputs) {
-                        node.removeOutput(node.outputs.length - 1);
-                    }
-                    
-                    // Add missing outputs
-                    while (node.outputs.length < totalOutputs) {
-                        const outputIndex = node.outputs.length + 1;
-                        node.addOutput(`batch_${outputIndex}`, "IMAGE");
-                    }
-                    
-                    if (node.setDirty) node.setDirty(true);  // Refresh canvas
-                };
-                
-                // Initial update with delay to allow workflow loading
-                setTimeout(updateOutputs, 200);
-                
-                // Find the widget and set up responsive handlers
+        const config = BATCH_DIVIDER_NODES[node.comfyClass];
+        if (!config) return;
+
+        try {
+            const updateOutputs = () => {
+                if (!node.widgets) return;
+
                const divideByWidget = node.widgets.find(w => w.name === "divide_by");
-                if (divideByWidget) {
-                    // Override callback for immediate trigger on value set
-                    const originalCallback = divideByWidget.callback;
-                    divideByWidget.callback = (value) => {
-                        updateOutputs();
-                        if (originalCallback) originalCallback.call(divideByWidget, value);  // Preserve 'this' context
-                    };
-                    
-                    // Add event listener for real-time input changes (e.g., typing/dragging)
-                    if (divideByWidget.inputEl) {
-                        divideByWidget.inputEl.addEventListener('input', updateOutputs);
-                    }
-                    
-                    // Lightweight MutationObserver as fallback (observe attributes on widget element if available)
-                    const observer = new MutationObserver(updateOutputs);
-                    if (divideByWidget.element) {
-                        observer.observe(divideByWidget.element, { attributes: true, childList: true, subtree: true });
-                    }
-                    
-                    // Store cleanup function
-                    node._batchDividerCleanup = () => {
-                        observer.disconnect();
-                        if (divideByWidget.inputEl) {
-                            divideByWidget.inputEl.removeEventListener('input', updateOutputs);
-                        }
-                        divideByWidget.callback = originalCallback;  // Restore original
-                    };
+                if (!divideByWidget) return;
+
+                const divideBy = parseInt(divideByWidget.value, 10) || 1;
+                const totalOutputs = divideBy;
+
+                // Ensure outputs array exists
+                if (!node.outputs) node.outputs = [];
+
+                // Remove excess outputs
+                while (node.outputs.length > totalOutputs) {
+                    node.removeOutput(node.outputs.length - 1);
                }
-                
-                // Add post-configure hook for reliable workflow loading
-                const originalConfigure = node.configure;
-                node.configure = function(data) {
-                    const result = originalConfigure ? originalConfigure.call(this, data) : undefined;
-                    updateOutputs();  // Re-run after config load
-                    return result;
+
+                // Add missing outputs
+                while (node.outputs.length < totalOutputs) {
+                    const outputIndex = node.outputs.length + 1;
+                    node.addOutput(`${config.outputPrefix}${outputIndex}`, config.outputType);
+                }
+
+                if (node.setDirty) node.setDirty(true);
+            };
+
+            // Initial update with delay to allow workflow loading
+            setTimeout(updateOutputs, 200);
+
+            // Find the widget and set up responsive handlers
+            const divideByWidget = node.widgets.find(w => w.name === "divide_by");
+            if (divideByWidget) {
+                const originalCallback = divideByWidget.callback;
+                divideByWidget.callback = (value) => {
+                    updateOutputs();
+                    if (originalCallback) originalCallback.call(divideByWidget, value);
+                };
+
+                if (divideByWidget.inputEl) {
+                    divideByWidget.inputEl.addEventListener('input', updateOutputs);
+                }
+
+                const observer = new MutationObserver(updateOutputs);
+                if (divideByWidget.element) {
+                    observer.observe(divideByWidget.element, { attributes: true, childList: true, subtree: true });
+                }
+
+                node._batchDividerCleanup = () => {
+                    observer.disconnect();
+                    if (divideByWidget.inputEl) {
+                        divideByWidget.inputEl.removeEventListener('input', updateOutputs);
+                    }
+                    divideByWidget.callback = originalCallback;
                };
-            } catch (error) {
-                console.error("Error in ImageBatchDivider extension:", error);
            }
+
+            const originalConfigure = node.configure;
+            node.configure = function(data) {
+                const result = originalConfigure ? originalConfigure.call(this, data) : undefined;
+                updateOutputs();
+                return result;
+            };
+        } catch (error) {
+            console.error(`Error in ${node.comfyClass} extension:`, error);
        }
    },
-    
+
    nodeBeforeRemove(node) {
-        if (node.comfyClass === "ImageBatchDivider" && node._batchDividerCleanup) {
+        if (BATCH_DIVIDER_NODES[node.comfyClass] && node._batchDividerCleanup) {
            node._batchDividerCleanup();
        }
    }
-});
+});
@@ -0,0 +1,147 @@
+import { generateUUID } from './constants.js';
+
+export async function detectMasterIP(extension) {
+    try {
+        const isRunpod = window.location.hostname.endsWith('.proxy.runpod.net');
+        if (isRunpod) {
+            extension.log("Detected Runpod environment", "info");
+        }
+
+        const data = await extension.api.getNetworkInfo();
+        extension.log("Network info: " + JSON.stringify(data), "debug");
+
+        if (data.cuda_device !== null && data.cuda_device !== undefined) {
+            extension.masterCudaDevice = data.cuda_device;
+
+            if (!extension.config.master) {
+                extension.config.master = {};
+            }
+            if (extension.config.master.cuda_device === undefined || extension.config.master.cuda_device !== data.cuda_device) {
+                extension.config.master.cuda_device = data.cuda_device;
+                try {
+                    await extension.api.updateMaster({ cuda_device: data.cuda_device });
+                    extension.log(`Stored master CUDA device: ${data.cuda_device}`, "debug");
+                } catch (error) {
+                    extension.log(`Error storing master CUDA device: ${error.message}`, "error");
+                }
+            }
+
+            extension.ui.updateMasterDisplay(extension);
+        }
+
+        if (data.cuda_device_count > 0) {
+            extension.cudaDeviceCount = data.cuda_device_count;
+            extension.log(`Detected ${extension.cudaDeviceCount} CUDA devices`, "info");
+
+            const shouldAutoPopulate =
+                !extension.config.settings.has_auto_populated_workers &&
+                (!extension.config.workers || extension.config.workers.length === 0);
+
+            extension.log(`Auto-population check: has_populated=${extension.config.settings.has_auto_populated_workers}, workers=${extension.config.workers ? extension.config.workers.length : 'null'}, should_populate=${shouldAutoPopulate}`, "debug");
+
+            if (shouldAutoPopulate) {
+                extension.log(`Auto-populating workers based on ${extension.cudaDeviceCount} CUDA devices (excluding master on CUDA ${extension.masterCudaDevice})`, "info");
+
+                const newWorkers = [];
+                let workerNum = 1;
+                let portOffset = 0;
+
+                for (let i = 0; i < extension.cudaDeviceCount; i++) {
+                    if (i === extension.masterCudaDevice) {
+                        extension.log(`Skipping CUDA ${i} (used by master)`, "debug");
+                        continue;
+                    }
+
+                    const worker = {
+                        id: generateUUID(),
+                        name: `Worker ${workerNum}`,
+                        host: isRunpod ? null : "localhost",
+                        port: 8189 + portOffset,
+                        cuda_device: i,
+                        enabled: true,
+                        extra_args: isRunpod ? "--listen" : "",
+                    };
+                    newWorkers.push(worker);
+                    workerNum += 1;
+                    portOffset += 1;
+                }
+
+                if (newWorkers.length > 0) {
+                    extension.log(`Auto-populating ${newWorkers.length} workers`, "info");
+
+                    extension.config.workers = newWorkers;
+                    extension.config.settings.has_auto_populated_workers = true;
+
+                    for (const worker of newWorkers) {
+                        try {
+                            await extension.api.updateWorker(worker.id, worker);
+                        } catch (error) {
+                            extension.log(`Error saving worker ${worker.name}: ${error.message}`, "error");
+                        }
+                    }
+
+                    try {
+                        await extension.api.updateSetting('has_auto_populated_workers', true);
+                    } catch (error) {
+                        extension.log(`Error saving auto-population flag: ${error.message}`, "error");
+                    }
+
+                    extension.log(`Auto-populated ${newWorkers.length} workers and saved config`, "info");
+
+                    if (extension.app.extensionManager?.toast) {
+                        extension.app.extensionManager.toast.add({
+                            severity: "success",
+                            summary: "Workers Auto-populated",
+                            detail: `Automatically created ${newWorkers.length} workers based on detected CUDA devices`,
+                            life: 5000,
+                        });
+                    }
+
+                    await extension.loadConfig();
+                } else {
+                    extension.log("No additional CUDA devices available for workers (all used by master)", "debug");
+                }
+            }
+        }
+
+        if (extension.config?.master?.host) {
+            extension.log(`Master host already configured: ${extension.config.master.host}`, "debug");
+            return;
+        }
+
+        if (isRunpod) {
+            const runpodHost = window.location.hostname;
+            extension.log(`Setting Runpod master host: ${runpodHost}`, "info");
+
+            await extension.api.updateMaster({ host: runpodHost });
+
+            if (!extension.config.master) {
+                extension.config.master = {};
+            }
+            extension.config.master.host = runpodHost;
+
+            if (extension.app.extensionManager?.toast) {
+                extension.app.extensionManager.toast.add({
+                    severity: "info",
+                    summary: "Runpod Auto-Configuration",
+                    detail: `Master host set to ${runpodHost} with --listen flag for workers`,
+                    life: 5000,
+                });
+            }
+            return;
+        }
+
+        if (data.recommended_ip && data.recommended_ip !== '127.0.0.1') {
+            extension.log(`Auto-detected master IP: ${data.recommended_ip}`, "info");
+
+            await extension.api.updateMaster({ host: data.recommended_ip });
+
+            if (!extension.config.master) {
+                extension.config.master = {};
+            }
+            extension.config.master.host = data.recommended_ip;
+        }
+    } catch (error) {
+        extension.log("Error detecting master IP: " + error.message, "error");
+    }
+}
@@ -0,0 +1,31 @@
+import { BUTTON_STYLES } from "../constants.js";
+
+export function renderActionsSection(extension) {
+    const actionsSection = document.createElement("div");
+    actionsSection.style.cssText =
+        "padding-top: 10px; margin-bottom: 15px; border-top: 1px solid var(--dist-divider, #444);";
+
+    const buttonRow = document.createElement("div");
+    buttonRow.style.cssText = "display: flex; gap: 8px;";
+
+    const clearMemButton = extension.ui.createButtonHelper(
+        "Clear Worker VRAM",
+        (event) => extension._handleClearMemory(event.target),
+        BUTTON_STYLES.clearMemory
+    );
+    clearMemButton.title = "Clear VRAM on all enabled worker GPUs (not master)";
+    clearMemButton.style.cssText = BUTTON_STYLES.base + " flex: 1;" + BUTTON_STYLES.clearMemory;
+
+    const interruptButton = extension.ui.createButtonHelper(
+        "Interrupt Workers",
+        (event) => extension._handleInterruptWorkers(event.target),
+        BUTTON_STYLES.interrupt
+    );
+    interruptButton.title = "Cancel/interrupt execution on all enabled worker GPUs";
+    interruptButton.style.cssText = BUTTON_STYLES.base + " flex: 1;" + BUTTON_STYLES.interrupt;
+
+    buttonRow.appendChild(clearMemButton);
+    buttonRow.appendChild(interruptButton);
+    actionsSection.appendChild(buttonRow);
+    return actionsSection;
+}
@@ -0,0 +1,122 @@
+import { createCheckboxSetting, createNumberSetting } from "../ui/buttonHelpers.js";
+
+export function renderSettingsSection(extension) {
+    const settingsSection = document.createElement("div");
+    settingsSection.style.cssText = "border-top: 1px solid var(--dist-divider, #444); margin-bottom: 10px;";
+
+    const settingsToggleArea = document.createElement("div");
+    settingsToggleArea.style.cssText = "padding: 16.5px 0; cursor: pointer; user-select: none;";
+
+    const settingsHeader = document.createElement("div");
+    settingsHeader.style.cssText = "display: flex; align-items: center; justify-content: space-between;";
+
+    const workerSettingsTitle = document.createElement("h4");
+    workerSettingsTitle.textContent = "Settings";
+    workerSettingsTitle.style.cssText = "margin: 0; font-size: 14px;";
+
+    const workerSettingsToggle = document.createElement("span");
+    workerSettingsToggle.textContent = "▶";
+    workerSettingsToggle.style.cssText =
+        "font-size: 12px; color: var(--dist-settings-arrow, #888); transition: all 0.2s ease;";
+
+    settingsHeader.appendChild(workerSettingsTitle);
+    settingsHeader.appendChild(workerSettingsToggle);
+    settingsToggleArea.appendChild(settingsHeader);
+
+    settingsToggleArea.onmouseover = () => {
+        workerSettingsToggle.style.color = "var(--dist-settings-arrow-hover, #fff)";
+    };
+    settingsToggleArea.onmouseout = () => {
+        workerSettingsToggle.style.color = "var(--dist-settings-arrow, #888)";
+    };
+
+    const settingsSeparator = document.createElement("div");
+    settingsSeparator.style.cssText = "border-bottom: 1px solid var(--dist-divider, #444); margin: 0;";
+
+    const settingsContent = document.createElement("div");
+    settingsContent.style.cssText =
+        "max-height: 0; overflow: hidden; opacity: 0; transition: max-height 0.3s ease, opacity 0.3s ease;";
+
+    const settingsDiv = document.createElement("div");
+    settingsDiv.style.cssText =
+        "display: grid; grid-template-columns: 1fr auto; row-gap: 10px; column-gap: 10px; padding-top: 10px; align-items: center;";
+
+    let settingsExpanded = false;
+    settingsToggleArea.onclick = () => {
+        settingsExpanded = !settingsExpanded;
+        if (settingsExpanded) {
+            settingsContent.style.maxHeight = "200px";
+            settingsContent.style.opacity = "1";
+            workerSettingsToggle.style.transform = "rotate(90deg)";
+            settingsSeparator.style.display = "none";
+        } else {
+            settingsContent.style.maxHeight = "0";
+            settingsContent.style.opacity = "0";
+            workerSettingsToggle.style.transform = "rotate(0deg)";
+            settingsSeparator.style.display = "block";
+        }
+    };
+
+    const generalLabel = document.createElement("div");
+    generalLabel.textContent = "GENERAL";
+    generalLabel.style.cssText =
+        "grid-column: 1 / span 2; font-size: 11px; color: var(--dist-muted-text, #888); letter-spacing: 0.06em; padding-top: 2px;";
+
+    const timeoutsLabel = document.createElement("div");
+    timeoutsLabel.textContent = "TIMEOUTS";
+    timeoutsLabel.style.cssText =
+        "grid-column: 1 / span 2; font-size: 11px; color: var(--dist-muted-text, #888); letter-spacing: 0.06em; padding-top: 4px;";
+
+    settingsDiv.appendChild(generalLabel);
+    settingsDiv.appendChild(
+        createCheckboxSetting(
+            "setting-debug",
+            "Debug Mode",
+            "Enable verbose logging in the browser console.",
+            extension.config?.settings?.debug || false,
+            (event) => extension._updateSetting("debug", event.target.checked)
+        )
+    );
+    settingsDiv.appendChild(
+        createCheckboxSetting(
+            "setting-auto-launch",
+            "Auto-launch Local Workers on Startup",
+            "Start local worker processes automatically when the master starts.",
+            extension.config?.settings?.auto_launch_workers || false,
+            (event) => extension._updateSetting("auto_launch_workers", event.target.checked)
+        )
+    );
+    settingsDiv.appendChild(
+        createCheckboxSetting(
+            "setting-stop-on-exit",
+            "Stop Local Workers on Master Exit",
+            "Stop local worker processes automatically when the master exits.",
+            extension.config?.settings?.stop_workers_on_master_exit !== false,
+            (event) => extension._updateSetting("stop_workers_on_master_exit", event.target.checked)
+        )
+    );
+    settingsDiv.appendChild(timeoutsLabel);
+    settingsDiv.appendChild(
+        createNumberSetting(
+            "setting-worker-timeout",
+            "Worker Timeout",
+            "Seconds without a heartbeat before a worker is considered timed out. Default 60.",
+            extension.config?.settings?.worker_timeout_seconds ?? 60,
+            10,
+            1,
+            (event) => {
+                const value = parseInt(event.target.value, 10);
+                if (!Number.isFinite(value) || value <= 0) {
+                    return;
+                }
+                extension._updateSetting("worker_timeout_seconds", value);
+            }
+        )
+    );
+
+    settingsContent.appendChild(settingsDiv);
+    settingsSection.appendChild(settingsToggleArea);
+    settingsSection.appendChild(settingsSeparator);
+    settingsSection.appendChild(settingsContent);
+    return settingsSection;
+}
@@ -0,0 +1,36 @@
+import { addNewWorker } from "../workerSettings.js";
+
+export function renderWorkersSection(extension) {
+    const workersSection = document.createElement("div");
+    workersSection.style.cssText = "flex: 1; overflow-y: auto; margin-bottom: 15px;";
+
+    const workersList = document.createElement("div");
+    const workers = extension.config?.workers || [];
+
+    if (workers.length === 0) {
+        const blueprintDiv = extension.ui.renderEntityCard(
+            "blueprint",
+            { onClick: () => addNewWorker(extension) },
+            extension
+        );
+        workersList.appendChild(blueprintDiv);
+    }
+
+    workers.forEach((worker) => {
+        const workerCard = extension.ui.renderEntityCard("worker", worker, extension);
+        workersList.appendChild(workerCard);
+    });
+
+    workersSection.appendChild(workersList);
+
+    if (workers.length > 0) {
+        const addWorkerDiv = extension.ui.renderEntityCard(
+            "add",
+            { onClick: () => addNewWorker(extension) },
+            extension
+        );
+        workersSection.appendChild(addWorkerDiv);
+    }
+
+    return workersSection;
+}
@@ -1,34 +1,63 @@
-import { BUTTON_STYLES } from './constants.js';
+import { STATUS_COLORS } from './constants.js';
+import { checkAllWorkerStatuses, loadManagedWorkers, updateWorkerControls } from './workerLifecycle.js';
+import { renderActionsSection } from './sidebar/actionsSection.js';
+import { renderSettingsSection } from './sidebar/settingsSection.js';
+import { renderWorkersSection } from './sidebar/workersSection.js';
+
+export function updateWorkerCard(extension, workerId, newStatus = {}) {
+    const card = document.querySelector(`[data-worker-id="${workerId}"]`);
+    if (!card) {
+        return false;
+    }
+
+    const worker = extension.config?.workers?.find((w) => w.id === workerId);
+    if (!worker) {
+        return false;
+    }
+
+    const workerState = extension.state.getWorker(workerId);
+    const isLaunching = Boolean(workerState?.launching);
+
+    if (isLaunching && !newStatus.online) {
+        extension.ui.updateStatusDot(workerId, STATUS_COLORS.PROCESSING_YELLOW, "Launching...", true);
+    } else if (newStatus.online && newStatus.processing) {
+        const queue = newStatus.queueCount || 0;
+        extension.ui.updateStatusDot(workerId, STATUS_COLORS.PROCESSING_YELLOW, `Online - Processing (${queue} in queue)`, false);
+    } else if (newStatus.online) {
+        extension.ui.updateStatusDot(workerId, STATUS_COLORS.ONLINE_GREEN, "Online - Idle", false);
+    } else if (worker.enabled) {
+        extension.ui.updateStatusDot(workerId, STATUS_COLORS.OFFLINE_RED, "Offline - Cannot connect", false);
+    }
+
+    updateWorkerControls(extension, workerId);
+    return true;
+}

 export async function renderSidebarContent(extension, el) {
    // Panel is being opened/rendered
    extension.log("Panel opened", "debug");
-    
    if (!el) {
        extension.log("No element provided to renderSidebarContent", "debug");
        return;
    }
-    
    // Prevent infinite recursion
    if (extension._isRendering) {
        extension.log("Already rendering, skipping", "debug");
        return;
    }
    extension._isRendering = true;
-    
    try {
        // Store reference to the panel element
        extension.panelElement = el;
-        
        // Show loading indicator
        el.innerHTML = '';
        const loadingDiv = document.createElement("div");
-        loadingDiv.style.cssText = "display: flex; align-items: center; justify-content: center; height: calc(100vh - 100px); color: #888;";
-        loadingDiv.innerHTML = `<svg width="24" height="24" viewBox="0 0 24 24" style="color: #888;">
+        loadingDiv.style.cssText =
+            "display: flex; align-items: center; justify-content: center; height: calc(100vh - 100px); color: var(--dist-muted-text, #888);";
+        loadingDiv.innerHTML = `<svg width="24" height="24" viewBox="0 0 24 24" style="color: var(--dist-muted-text, #888);">
            <circle cx="12" cy="12" r="10" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-dasharray="40 40"/>
        </svg>`;
        el.appendChild(loadingDiv);
-        
        // Add rotation animation
        const style = document.createElement('style');
        style.textContent = `
@@ -39,239 +68,52 @@ export async function renderSidebarContent(extension, el) {
        `;
        document.head.appendChild(style);
        loadingDiv.querySelector('svg').style.animation = 'rotate 1s linear infinite';
-        
        // Preload data outside render
-        await extension.loadConfig();
-        await extension.loadManagedWorkers();
-        
+        await Promise.all([extension.loadConfig(), loadManagedWorkers(extension), extension.refreshTunnelStatus()]);
+        extension.tunnelElements = {};
        el.innerHTML = '';
-        
        // Create toolbar header to match ComfyUI style
        const toolbar = document.createElement("div");
        toolbar.className = "p-toolbar p-component border-x-0 border-t-0 rounded-none px-2 py-1 min-h-8";
-        toolbar.style.cssText = "border-bottom: 1px solid #444; background: transparent; display: flex; align-items: center;";
-        
+        toolbar.style.cssText =
+            "border-bottom: 1px solid var(--dist-divider, #444); background: transparent; display: flex; align-items: center;";
        const toolbarStart = document.createElement("div");
        toolbarStart.className = "p-toolbar-start";
        toolbarStart.style.cssText = "display: flex; align-items: center;";
-        
        const titleSpan = document.createElement("span");
        titleSpan.className = "text-xs 2xl:text-sm truncate";
        titleSpan.textContent = "COMFYUI DISTRIBUTED";
        titleSpan.title = "ComfyUI Distributed";
-        
        toolbarStart.appendChild(titleSpan);
        toolbar.appendChild(toolbarStart);
-        
        const toolbarCenter = document.createElement("div");
        toolbarCenter.className = "p-toolbar-center";
        toolbar.appendChild(toolbarCenter);
-        
        const toolbarEnd = document.createElement("div");
        toolbarEnd.className = "p-toolbar-end";
        toolbar.appendChild(toolbarEnd);
-        
        el.appendChild(toolbar);
-        
        // Main container with adjusted padding
        const container = document.createElement("div");
        container.style.cssText = "padding: 15px; display: flex; flex-direction: column; height: calc(100% - 32px);";
-        
        // Detect master info on panel open (in case CUDA info wasn't available at startup)
        extension.log(`Panel opened. CUDA device count: ${extension.cudaDeviceCount}, Workers: ${extension.config?.workers?.length || 0}`, "debug");
        if (!extension.cudaDeviceCount) {
            await extension.detectMasterIP();
        }
-        
-        
        // Now render with guaranteed up-to-date config
        // Master Node Section
        const masterDiv = extension.ui.renderEntityCard('master', extension.config?.master, extension);
        container.appendChild(masterDiv);
-        
-        // Workers Section (no heading)
-        const gpuSection = document.createElement("div");
-        gpuSection.style.cssText = "flex: 1; overflow-y: auto; margin-bottom: 15px;";
-        
-        const gpuList = document.createElement("div");
-        const workers = extension.config?.workers || [];
-        
-        // If no workers exist, show a full blueprint placeholder first
-        if (workers.length === 0) {
-            const blueprintDiv = extension.ui.renderEntityCard('blueprint', { onClick: () => extension.addNewWorker() }, extension);
-            gpuList.appendChild(blueprintDiv);
-        }
-        
-        // Show existing workers
-        workers.forEach(worker => {
-            const gpuDiv = extension.ui.renderEntityCard('worker', worker, extension);
-            gpuList.appendChild(gpuDiv);
-        });
-        gpuSection.appendChild(gpuList);
-        
-        // Only show the minimal "Add Worker" box if there are existing workers
-        if (workers.length > 0) {
-            const addWorkerDiv = extension.ui.renderEntityCard('add', { onClick: () => extension.addNewWorker() }, extension);
-            gpuSection.appendChild(addWorkerDiv);
-        }
-        
-        container.appendChild(gpuSection);
-        
-        const actionsSection = document.createElement("div");
-        actionsSection.style.cssText = "padding-top: 10px; margin-bottom: 15px; border-top: 1px solid #444;";
-        
-        // Create a row for both buttons
-        const buttonRow = document.createElement("div");
-        buttonRow.style.cssText = "display: flex; gap: 8px;";
-        
-        const clearMemButton = extension.ui.createButtonHelper(
-            "Clear Worker VRAM",
-            (e) => extension._handleClearMemory(e.target),
-            BUTTON_STYLES.clearMemory
-        );
-        clearMemButton.title = "Clear VRAM on all enabled worker GPUs (not master)";
-        clearMemButton.style.cssText = BUTTON_STYLES.base + " flex: 1;" + BUTTON_STYLES.clearMemory;
-        
-        const interruptButton = extension.ui.createButtonHelper(
-            "Interrupt Workers",
-            (e) => extension._handleInterruptWorkers(e.target),
-            BUTTON_STYLES.interrupt
-        );
-        interruptButton.title = "Cancel/interrupt execution on all enabled worker GPUs";
-        interruptButton.style.cssText = BUTTON_STYLES.base + " flex: 1;" + BUTTON_STYLES.interrupt;
-        
-        buttonRow.appendChild(clearMemButton);
-        buttonRow.appendChild(interruptButton);
-        actionsSection.appendChild(buttonRow);
-        
-        container.appendChild(actionsSection);
-        
-        // Settings section
-        const settingsSection = document.createElement("div");
-        settingsSection.style.cssText = "border-top: 1px solid #444; padding-top: 10px; margin-bottom: 10px;";
-        
-        // Settings header with toggle
-        const settingsHeader = document.createElement("div");
-        settingsHeader.style.cssText = "display: flex; align-items: center; justify-content: space-between; cursor: pointer; user-select: none;";
-        
-        const workerSettingsTitle = document.createElement("h4");
-        workerSettingsTitle.textContent = "Settings";
-        workerSettingsTitle.style.cssText = "margin: 0; font-size: 14px;";
-        
-        const workerSettingsToggle = document.createElement("span");
-        workerSettingsToggle.textContent = "▶"; // Right arrow when collapsed
-        workerSettingsToggle.style.cssText = "font-size: 12px; color: #888; transition: all 0.2s ease;";
-        
-        settingsHeader.appendChild(workerSettingsTitle);
-        settingsHeader.appendChild(workerSettingsToggle);
-        
-        // Hover effect for header
-        settingsHeader.onmouseover = () => {
-            workerSettingsToggle.style.color = "#fff";
-        };
-        settingsHeader.onmouseout = () => {
-            workerSettingsToggle.style.color = "#888";
-        };
-        
-        // Collapsible settings content
-        const settingsContent = document.createElement("div");
-        settingsContent.style.cssText = "max-height: 0; overflow: hidden; opacity: 0; transition: max-height 0.3s ease, opacity 0.3s ease;";
-        
-        const settingsDiv = document.createElement("div");
-        settingsDiv.style.cssText = "display: flex; flex-direction: column; gap: 8px; padding-top: 10px;";
-        
-        // Toggle functionality
-        let settingsExpanded = false;
-        settingsHeader.onclick = () => {
-            settingsExpanded = !settingsExpanded;
-            if (settingsExpanded) {
-                settingsContent.style.maxHeight = "200px";
-                settingsContent.style.opacity = "1";
-                workerSettingsToggle.style.transform = "rotate(90deg)";
-            } else {
-                settingsContent.style.maxHeight = "0";
-                settingsContent.style.opacity = "0";
-                workerSettingsToggle.style.transform = "rotate(0deg)";
-            }
-        };
-        
-        // Debug mode setting
-        const debugGroup = document.createElement("div");
-        debugGroup.style.cssText = "display: flex; align-items: center; gap: 8px;";
-        
-        const debugCheckbox = document.createElement("input");
-        debugCheckbox.type = "checkbox";
-        debugCheckbox.id = "setting-debug";
-        debugCheckbox.checked = extension.config?.settings?.debug || false;
-        debugCheckbox.onchange = (e) => extension._updateSetting('debug', e.target.checked);
-        
-        const debugLabel = document.createElement("label");
-        debugLabel.htmlFor = "setting-debug";
-        debugLabel.textContent = "Debug Mode";
-        debugLabel.style.cssText = "font-size: 12px; color: #ccc; cursor: pointer;";
-        
-        debugGroup.appendChild(debugCheckbox);
-        debugGroup.appendChild(debugLabel);
-        
-        // Auto-launch workers setting
-        const autoLaunchGroup = document.createElement("div");
-        autoLaunchGroup.style.cssText = "display: flex; align-items: center; gap: 8px;";
-        
-        const autoLaunchCheckbox = document.createElement("input");
-        autoLaunchCheckbox.type = "checkbox";
-        autoLaunchCheckbox.id = "setting-auto-launch";
-        autoLaunchCheckbox.checked = extension.config?.settings?.auto_launch_workers || false;
-        autoLaunchCheckbox.onchange = (e) => extension._updateSetting('auto_launch_workers', e.target.checked);
-        
-        const autoLaunchLabel = document.createElement("label");
-        autoLaunchLabel.htmlFor = "setting-auto-launch";
-        autoLaunchLabel.textContent = "Auto-launch Local Workers on Startup";
-        autoLaunchLabel.style.cssText = "font-size: 12px; color: #ccc; cursor: pointer;";
-        
-        autoLaunchGroup.appendChild(autoLaunchCheckbox);
-        autoLaunchGroup.appendChild(autoLaunchLabel);
-        
-        // Stop workers on exit setting
-        const stopOnExitGroup = document.createElement("div");
-        stopOnExitGroup.style.cssText = "display: flex; align-items: center; gap: 8px;";
-        
-        const stopOnExitCheckbox = document.createElement("input");
-        stopOnExitCheckbox.type = "checkbox";
-        stopOnExitCheckbox.id = "setting-stop-on-exit";
-        stopOnExitCheckbox.checked = extension.config?.settings?.stop_workers_on_master_exit !== false; // Default true
-        stopOnExitCheckbox.onchange = (e) => extension._updateSetting('stop_workers_on_master_exit', e.target.checked);
-        
-        const stopOnExitLabel = document.createElement("label");
-        stopOnExitLabel.htmlFor = "setting-stop-on-exit";
-        stopOnExitLabel.textContent = "Stop Local Workers on Master Exit";
-        stopOnExitLabel.style.cssText = "font-size: 12px; color: #ccc; cursor: pointer;";
-        
-        stopOnExitGroup.appendChild(stopOnExitCheckbox);
-        stopOnExitGroup.appendChild(stopOnExitLabel);
-        
-        settingsDiv.appendChild(debugGroup);
-        settingsDiv.appendChild(autoLaunchGroup);
-        settingsDiv.appendChild(stopOnExitGroup);
-        settingsContent.appendChild(settingsDiv);
-        
-        settingsSection.appendChild(settingsHeader);
-        settingsSection.appendChild(settingsContent);
-        container.appendChild(settingsSection);
-
-        const summarySection = document.createElement("div");
-        summarySection.style.cssText = "border-top: 1px solid #444; padding-top: 10px;";
-        const summary = document.createElement("div");
-        summary.id = "distributed-summary";
-        summary.style.cssText = "font-size: 11px; color: #888;";
-        summarySection.appendChild(summary);
-        container.appendChild(summarySection);
+        container.appendChild(renderWorkersSection(extension));
+        container.appendChild(renderActionsSection(extension));
+        container.appendChild(renderSettingsSection(extension));
        el.appendChild(container);
-        extension.updateSummary();
-        
+        extension._applyThemeToneClass?.();
        // Start checking worker statuses immediately in parallel
-        setTimeout(() => extension.checkAllWorkerStatuses(), 0);
+        setTimeout(() => checkAllWorkerStatuses(extension), 0);
    } finally {
        // Always reset the rendering flag
        extension._isRendering = false;
    }
-}
+}
@@ -0,0 +1,15 @@
+import { describe, expect, it } from "vitest";
+
+import { buildWorkerWebSocketUrl } from "../urlUtils.js";
+
+
+describe("execution decision helpers", () => {
+    it("buildWorkerWebSocketUrl converts http/https to ws/wss", () => {
+        expect(buildWorkerWebSocketUrl("http://worker.local:8188")).toBe(
+            "ws://worker.local:8188/distributed/worker_ws"
+        );
+        expect(buildWorkerWebSocketUrl("https://worker.example.com")).toBe(
+            "wss://worker.example.com/distributed/worker_ws"
+        );
+    });
+});
@@ -0,0 +1,242 @@
+import { afterEach, beforeEach, describe, expect, it } from "vitest";
+
+import {
+    buildWorkerUrl,
+    buildWorkerWebSocketUrl,
+    getMasterUrl,
+    normalizeWorkerUrl,
+    parseHostInput,
+} from "../urlUtils.js";
+
+
+// ---------------------------------------------------------------------------
+// normalizeWorkerUrl
+// ---------------------------------------------------------------------------
+
+describe("normalizeWorkerUrl", () => {
+    it("returns empty string for empty input", () => {
+        expect(normalizeWorkerUrl("")).toBe("");
+    });
+
+    it("returns empty string for null input", () => {
+        expect(normalizeWorkerUrl(null)).toBe("");
+    });
+
+    it("returns empty string for non-string input", () => {
+        expect(normalizeWorkerUrl(42)).toBe("");
+    });
+
+    it("preserves https protocol", () => {
+        expect(normalizeWorkerUrl("https://example.com")).toBe("https://example.com");
+    });
+
+    it("strips trailing slash", () => {
+        expect(normalizeWorkerUrl("http://example.com/")).toBe("http://example.com");
+    });
+
+    it("prepends http when protocol is missing", () => {
+        const result = normalizeWorkerUrl("worker.local:8188");
+        expect(result).toMatch(/^http:\/\//);
+    });
+
+    it("trims leading and trailing whitespace", () => {
+        expect(normalizeWorkerUrl("  http://example.com  ")).toBe("http://example.com");
+    });
+});
+
+
+// ---------------------------------------------------------------------------
+// parseHostInput
+// ---------------------------------------------------------------------------
+
+describe("parseHostInput", () => {
+    it("returns empty host and null port for null", () => {
+        expect(parseHostInput(null)).toEqual({ host: "", port: null });
+    });
+
+    it("returns empty host and null port for empty string", () => {
+        expect(parseHostInput("")).toEqual({ host: "", port: null });
+    });
+
+    it("parses hostname without port", () => {
+        const result = parseHostInput("worker.example.com");
+        expect(result.host).toBe("worker.example.com");
+        expect(result.port).toBeNull();
+    });
+
+    it("parses hostname with port", () => {
+        const result = parseHostInput("worker.example.com:9000");
+        expect(result.host).toBe("worker.example.com");
+        expect(result.port).toBe(9000);
+    });
+
+    it("strips http:// protocol prefix", () => {
+        const result = parseHostInput("http://worker.example.com:8188");
+        expect(result.host).toBe("worker.example.com");
+        expect(result.port).toBe(8188);
+    });
+
+    it("strips https:// protocol prefix", () => {
+        const result = parseHostInput("https://worker.example.com");
+        expect(result.host).toBe("worker.example.com");
+        expect(result.port).toBeNull();
+    });
+
+    it("ignores path after host:port", () => {
+        const result = parseHostInput("worker.example.com:8188/some/path");
+        expect(result.host).toBe("worker.example.com");
+        expect(result.port).toBe(8188);
+    });
+});
+
+
+// ---------------------------------------------------------------------------
+// buildWorkerWebSocketUrl
+// ---------------------------------------------------------------------------
+
+describe("buildWorkerWebSocketUrl", () => {
+    it("converts http to ws", () => {
+        expect(buildWorkerWebSocketUrl("http://worker.local:8188")).toBe(
+            "ws://worker.local:8188/distributed/worker_ws"
+        );
+    });
+
+    it("converts https to wss", () => {
+        expect(buildWorkerWebSocketUrl("https://worker.example.com")).toBe(
+            "wss://worker.example.com/distributed/worker_ws"
+        );
+    });
+
+    it("always appends /distributed/worker_ws", () => {
+        const url = buildWorkerWebSocketUrl("http://worker.local:8188");
+        expect(url.endsWith("/distributed/worker_ws")).toBe(true);
+    });
+});
+
+
+// ---------------------------------------------------------------------------
+// buildWorkerUrl (requires window.location stub)
+// ---------------------------------------------------------------------------
+
+describe("buildWorkerUrl", () => {
+    let originalWindow;
+
+    beforeEach(() => {
+        originalWindow = globalThis.window;
+        globalThis.window = {
+            location: {
+                hostname: "127.0.0.1",
+                protocol: "http:",
+                port: "8188",
+                origin: "http://127.0.0.1:8188",
+            },
+        };
+    });
+
+    afterEach(() => {
+        globalThis.window = originalWindow;
+    });
+
+    it("builds local worker URL using window hostname when no host set", () => {
+        const worker = { id: "w1", port: 8189 };
+        expect(buildWorkerUrl(worker, "/prompt")).toBe("http://127.0.0.1:8189/prompt");
+    });
+
+    it("builds remote worker URL using explicit host", () => {
+        const worker = { id: "w2", host: "worker.example.com", port: 9000 };
+        expect(buildWorkerUrl(worker, "/prompt")).toBe("http://worker.example.com:9000/prompt");
+    });
+
+    it("builds cloud worker URL with https when type=cloud", () => {
+        const worker = { id: "w3", host: "cloud.example.com", port: 443, type: "cloud" };
+        expect(buildWorkerUrl(worker, "/prompt")).toBe("https://cloud.example.com/prompt");
+    });
+
+    it("uses https for port 443 even without type=cloud", () => {
+        const worker = { id: "w4", host: "worker.example.com", port: 443 };
+        const result = buildWorkerUrl(worker, "");
+        expect(result.startsWith("https://")).toBe(true);
+    });
+
+    it("rewrites runpod proxy hostname for local port", () => {
+        globalThis.window = {
+            location: {
+                hostname: "podabc.proxy.runpod.net",
+                protocol: "https:",
+                port: "",
+                origin: "https://podabc.proxy.runpod.net",
+            },
+        };
+        const worker = { id: "w5", port: 8189 };
+        expect(buildWorkerUrl(worker, "/prompt")).toBe(
+            "https://podabc-8189.proxy.runpod.net/prompt"
+        );
+    });
+
+    it("returns URL without trailing slash when no endpoint given", () => {
+        const worker = { id: "w6", host: "worker.example.com", port: 8188 };
+        const result = buildWorkerUrl(worker, "");
+        expect(result.endsWith("/")).toBe(false);
+    });
+});
+
+
+// ---------------------------------------------------------------------------
+// getMasterUrl
+// ---------------------------------------------------------------------------
+
+describe("getMasterUrl", () => {
+    const _loc = (hostname, protocol = "http:", port = "8188") => ({
+        hostname,
+        protocol,
+        port,
+        origin: `${protocol}//${hostname}${port ? `:${port}` : ""}`,
+    });
+
+    it("returns origin when master host not configured and hostname is non-localhost", () => {
+        const loc = _loc("192.168.1.10");
+        const result = getMasterUrl({}, loc);
+        expect(result).toBe(loc.origin);
+    });
+
+    it("returns origin for localhost when master host not configured", () => {
+        const loc = _loc("127.0.0.1");
+        const result = getMasterUrl({}, loc);
+        expect(result).toBe(loc.origin);
+    });
+
+    it("uses configured master host as-is when it includes http://", () => {
+        const config = { master: { host: "http://master.example.com" } };
+        const result = getMasterUrl(config, _loc("127.0.0.1"));
+        expect(result).toBe("http://master.example.com");
+    });
+
+    it("uses configured master host as-is when it includes https://", () => {
+        const config = { master: { host: "https://secure.master.com" } };
+        const result = getMasterUrl(config, _loc("127.0.0.1"));
+        expect(result).toBe("https://secure.master.com");
+    });
+
+    it("defaults to https for domain-name master hosts", () => {
+        const config = { master: { host: "master.example.com" } };
+        const result = getMasterUrl(config, _loc("127.0.0.1"));
+        expect(result).toBe("https://master.example.com");
+    });
+
+    it("does not force https for IP-address master hosts", () => {
+        const config = { master: { host: "192.168.1.100" } };
+        const result = getMasterUrl(config, _loc("127.0.0.1"));
+        expect(result.startsWith("https://")).toBe(false);
+    });
+
+    it("does not force https for localhost master host", () => {
+        const config = { master: { host: "localhost" } };
+        const result = getMasterUrl(config, _loc("127.0.0.1"));
+        expect(result.startsWith("https://")).toBe(false);
+    });
+
+    it("accepts null log parameter without throwing", () => {
+        const loc = _loc("127.0.0.1");
+        expect(() => getMasterUrl({}, loc, null)).not.toThrow();
+    });
+});
@@ -0,0 +1,52 @@
+import { afterEach, beforeEach, describe, expect, it } from "vitest";
+
+import { getWorkerUrl } from "../workerLifecycle.js";
+
+
+describe("workerLifecycle URL construction", () => {
+    let originalWindow;
+
+    beforeEach(() => {
+        originalWindow = globalThis.window;
+        globalThis.window = {
+            location: {
+                hostname: "127.0.0.1",
+                protocol: "http:",
+                port: "8190",
+                origin: "http://127.0.0.1:8190",
+            },
+        };
+    });
+
+    afterEach(() => {
+        globalThis.window = originalWindow;
+    });
+
+    it("builds local worker URL with explicit local port", () => {
+        const worker = { id: "w1", port: 8189, type: "local" };
+        expect(getWorkerUrl({}, worker, "/prompt")).toBe("http://127.0.0.1:8189/prompt");
+    });
+
+    it("builds remote worker URL with host:port", () => {
+        const worker = { id: "w2", host: "worker.example.com", port: 9000, type: "remote" };
+        expect(getWorkerUrl({}, worker, "/prompt")).toBe("http://worker.example.com:9000/prompt");
+    });
+
+    it("builds cloud worker URL as https", () => {
+        const worker = { id: "w3", host: "cloud.example.com", port: 443, type: "cloud" };
+        expect(getWorkerUrl({}, worker, "/prompt")).toBe("https://cloud.example.com/prompt");
+    });
+
+    it("rewrites runpod proxy hostname for local worker ports", () => {
+        globalThis.window = {
+            location: {
+                hostname: "podabc.proxy.runpod.net",
+                protocol: "https:",
+                port: "",
+                origin: "https://podabc.proxy.runpod.net",
+            },
+        };
+        const worker = { id: "w4", port: 8189, type: "local" };
+        expect(getWorkerUrl({}, worker, "/prompt")).toBe("https://podabc-8189.proxy.runpod.net/prompt");
+    });
+});
@@ -0,0 +1,122 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from "vitest";
+
+import { addNewWorker, isRemoteWorker } from "../workerSettings.js";
+
+
+describe("workerSettings remote classification", () => {
+    let originalWindow;
+
+    beforeEach(() => {
+        originalWindow = globalThis.window;
+        globalThis.window = {
+            location: {
+                hostname: "127.0.0.1",
+            },
+        };
+    });
+
+    afterEach(() => {
+        globalThis.window = originalWindow;
+    });
+
+    it("treats explicit local worker type as local even with non-local host", () => {
+        const worker = { type: "local", host: "192.168.1.50" };
+        expect(isRemoteWorker({}, worker)).toBe(false);
+    });
+
+    it("treats explicit remote worker type as remote", () => {
+        const worker = { type: "remote", host: "127.0.0.1" };
+        expect(isRemoteWorker({}, worker)).toBe(true);
+    });
+
+    it("treats cloud worker type as remote", () => {
+        const worker = { type: "cloud", host: "worker.example.com" };
+        expect(isRemoteWorker({}, worker)).toBe(true);
+    });
+
+    it("falls back to host heuristic for legacy workers", () => {
+        expect(isRemoteWorker({}, { host: "127.0.0.1" })).toBe(false);
+        expect(isRemoteWorker({}, { host: "worker.example.com" })).toBe(true);
+    });
+});
+
+describe("addNewWorker GPU availability guard", () => {
+    it("falls back to a disabled remote worker when no local CUDA device is available", async () => {
+        const toastAdd = vi.fn();
+        const updateWorker = vi.fn().mockResolvedValue({});
+        const stateUpdateWorker = vi.fn();
+        const extension = {
+            cudaDeviceCount: 1,
+            masterCudaDevice: 0,
+            panelElement: null,
+            config: {
+                workers: [],
+                master: { cuda_device: 0 },
+            },
+            api: { updateWorker },
+            state: { updateWorker: stateUpdateWorker, setWorkerExpanded: vi.fn() },
+            app: { extensionManager: { toast: { add: toastAdd } } },
+        };
+
+        await addNewWorker(extension);
+
+        expect(updateWorker).toHaveBeenCalledTimes(1);
+        expect(updateWorker.mock.calls[0][1]).toEqual(
+            expect.objectContaining({
+                type: "remote",
+                enabled: false,
+                cuda_device: null,
+                host: "",
+            })
+        );
+        expect(extension.config.workers).toHaveLength(1);
+        expect(extension.config.workers[0]).toEqual(
+            expect.objectContaining({
+                type: "remote",
+                enabled: false,
+                cuda_device: null,
+                host: "",
+            })
+        );
+        expect(stateUpdateWorker).toHaveBeenCalledWith(
+            expect.any(String),
+            expect.objectContaining({ enabled: false })
+        );
+        expect(toastAdd).toHaveBeenCalledWith(
+            expect.objectContaining({
+                severity: "warn",
+                summary: "Remote Worker Added",
+            })
+        );
+    });
+
+    it("assigns the first free local CUDA device when adding a worker", async () => {
+        const toastAdd = vi.fn();
+        const updateWorker = vi.fn().mockResolvedValue({});
+        const stateUpdateWorker = vi.fn();
+        const setWorkerExpanded = vi.fn();
+        const extension = {
+            cudaDeviceCount: 3,
+            masterCudaDevice: 0,
+            panelElement: null,
+            config: {
+                workers: [
+                    { id: "w-existing", type: "local", port: 8189, cuda_device: 1, enabled: true },
+                ],
+                master: { cuda_device: 0 },
+            },
+            api: { updateWorker },
+            state: { updateWorker: stateUpdateWorker, setWorkerExpanded },
+            app: { extensionManager: { toast: { add: toastAdd } } },
+        };
+
+        await addNewWorker(extension);
+
+        expect(updateWorker).toHaveBeenCalledTimes(1);
+        expect(updateWorker.mock.calls[0][1]).toEqual(
+            expect.objectContaining({
+                cuda_device: 2,
+            })
+        );
+    });
+});
@@ -0,0 +1,136 @@
+export function updateTunnelUIElements(extension, isRunning, isStarting) {
+    void isRunning;
+    void isStarting;
+
+    const elements = extension.tunnelElements || {};
+    const status = (extension.tunnelStatus?.status || "stopped").toLowerCase();
+    const tunnelButtonColorClasses = ["tunnel-button--enable", "tunnel-button--disable"];
+    const tunnelStatusColorClasses = ["tunnel-status--enable", "tunnel-status--disable"];
+
+    if (elements.button) {
+        elements.button.disabled = status === "starting" || status === "stopping";
+        elements.button.classList.remove(...tunnelButtonColorClasses);
+
+        if (status === "starting") {
+            elements.button.innerHTML = `<span class="tunnel-spinner"></span> Starting...`;
+            elements.button.classList.add("tunnel-button--enable");
+        } else if (status === "stopping") {
+            elements.button.innerHTML = `<span class="tunnel-spinner"></span> Stopping...`;
+            elements.button.classList.add("tunnel-button--disable");
+        } else if (status === "running") {
+            elements.button.textContent = "Disable Cloudflare Tunnel";
+            elements.button.classList.add("tunnel-button--disable");
+        } else if (status === "error") {
+            elements.button.textContent = "Retry Cloudflare Tunnel";
+            elements.button.classList.add("tunnel-button--disable");
+        } else {
+            elements.button.textContent = "Enable Cloudflare Tunnel";
+            elements.button.classList.add("tunnel-button--enable");
+        }
+    }
+
+    if (elements.status) {
+        elements.status.textContent = status.toUpperCase();
+        elements.status.classList.remove(...tunnelStatusColorClasses);
+        if (status === "running" || status === "error" || status === "stopping") {
+            elements.status.classList.add("tunnel-status--disable");
+        } else {
+            elements.status.classList.add("tunnel-status--enable");
+        }
+    }
+
+    if (elements.url) {
+        const url = extension.tunnelStatus?.public_url;
+        if (url) {
+            elements.url.innerHTML = `<a href="${url}" target="_blank" style="color: #eee; text-decoration: none;">${url}</a>`;
+        } else {
+            elements.url.textContent = status === "starting" ? "Requesting public URL..." : "No tunnel active";
+        }
+    }
+
+    if (elements.copyBtn) {
+        const hasUrl = Boolean(extension.tunnelStatus?.public_url);
+        elements.copyBtn.disabled = !hasUrl;
+        elements.copyBtn.style.opacity = hasUrl ? "1" : "0.5";
+    }
+}
+
+export async function refreshTunnelStatus(extension) {
+    try {
+        const data = await extension.api.getTunnelStatus();
+        extension.tunnelStatus = data.tunnel || { status: "stopped" };
+        if (data.master_host !== undefined) {
+            extension._applyMasterHost(data.master_host);
+        }
+        return extension.tunnelStatus;
+    } catch (error) {
+        extension.tunnelStatus = { status: "error", last_error: error.message };
+        extension.log("Failed to fetch tunnel status: " + error.message, "error");
+        return extension.tunnelStatus;
+    } finally {
+        updateTunnelUIElements(extension);
+    }
+}
+
+export async function handleTunnelToggle(extension, button) {
+    const currentStatus = (extension.tunnelStatus?.status || "stopped").toLowerCase();
+    if (currentStatus === "starting" || currentStatus === "stopping") {
+        return;
+    }
+
+    const setStatus = (status) => {
+        extension.tunnelStatus = { ...(extension.tunnelStatus || {}), status };
+        updateTunnelUIElements(extension);
+    };
+
+    if (currentStatus === "running") {
+        setStatus("stopping");
+        try {
+            if (button) {
+                button.innerHTML = `<span class="tunnel-spinner"></span> Stopping...`;
+                button.disabled = true;
+            }
+            const data = await extension.api.stopTunnel();
+            extension.tunnelStatus = data.tunnel || { status: "stopped" };
+            if (data.master_host !== undefined) {
+                extension._applyMasterHost(data.master_host);
+            }
+            updateTunnelUIElements(extension);
+            extension.ui.showToast(extension.app, "info", "Cloudflare Tunnel Disabled", "Master address restored", 4000);
+        } catch (error) {
+            extension.tunnelStatus = { status: "error", last_error: error.message };
+            updateTunnelUIElements(extension);
+            extension.ui.showToast(extension.app, "error", "Failed to stop tunnel", error.message, 5000);
+        } finally {
+            if (button) {
+                button.disabled = false;
+            }
+        }
+        return;
+    }
+
+    // Start tunnel
+    setStatus("starting");
+    if (button) {
+        button.innerHTML = `<span class="tunnel-spinner"></span> Starting...`;
+        button.disabled = true;
+    }
+    try {
+        const data = await extension.api.startTunnel();
+        extension.tunnelStatus = data.tunnel || { status: "running" };
+        if (data.master_host !== undefined) {
+            extension._applyMasterHost(data.master_host);
+        }
+        updateTunnelUIElements(extension);
+        const url = data.tunnel?.public_url || data.master_host;
+        extension.ui.showToast(extension.app, "success", "Cloudflare Tunnel Ready", url || "Public URL created", 4500);
+    } catch (error) {
+        extension.tunnelStatus = { status: "error", last_error: error.message };
+        updateTunnelUIElements(extension);
+        extension.ui.showToast(extension.app, "error", "Failed to start tunnel", error.message, 5000);
+    } finally {
+        if (button) {
+            button.disabled = false;
+        }
+    }
+}
@@ -1,30 +1,39 @@
 import { BUTTON_STYLES, UI_STYLES, STATUS_COLORS, UI_COLORS, TIMEOUTS } from './constants.js';
+import { createButtonHelper as createButtonHelperFn } from './ui/buttonHelpers.js';
+import { showCloudflareWarning as showCloudflareWarningFn } from './ui/cloudflareWarning.js';
+import { createWorkerSettingsForm as createWorkerSettingsFormFn } from './ui/settingsForm.js';
+import { renderEntityCard as renderEntityCardFn } from './ui/entityCard.js';
+import { createLogModal } from './ui/logModal.js';
+import { launchWorker, stopWorker, updateWorkerControls, viewWorkerLog } from './workerLifecycle.js';
+import { isRemoteWorker } from './workerSettings.js';

 const cardConfigs = {
    master: {
        checkbox: { 
-            enabled: false, 
-            checked: true, 
-            disabled: true, 
-            opacity: 0.6, 
-            title: "Master node is always enabled" 
+            enabled: true,
+            masterToggle: true,
+            title: "Toggle master participation in workloads"
        },
        statusDot: { 
-            color: STATUS_COLORS.ONLINE_GREEN,
-            title: 'Online',
            id: 'master-status',
+            initialColor: (_, extension) => extension.isMasterParticipating() ? STATUS_COLORS.ONLINE_GREEN : STATUS_COLORS.DISABLED_GRAY,
+            initialTitle: (_, extension) => extension.isMasterParticipating() ? 'Master participating' : 'Master orchestrator only',
            dynamic: true
        },
        infoText: (data, extension) => {
            const cudaDevice = extension.config?.master?.cuda_device ?? extension.masterCudaDevice;
            const cudaInfo = cudaDevice !== undefined ? `CUDA ${cudaDevice} • ` : '';
            const port = window.location.port || (window.location.protocol === 'https:' ? '443' : '80');
-            return `<strong id="master-name-display">${data?.name || extension.config?.master?.name || "Master"}</strong><br><small style="color: ${UI_COLORS.MUTED_TEXT};"><span id="master-cuda-info">${cudaInfo}Port ${port}</span></small>`;
+            const participationEnabled = extension.isMasterParticipationEnabled();
+            const fallbackActive = extension.isMasterFallbackActive();
+            let delegateBadge = '';
+            if (!participationEnabled && fallbackActive) {
+                delegateBadge = `<br><span class="dist-worker-info__fallback">Fallback active • Master executing</span>`;
+            }
+            return `<span class="dist-worker-info__title" id="master-name-display">${data?.name || extension.config?.master?.name || "Master"}</span><br><span class="dist-worker-info__meta"><span id="master-cuda-info">${cudaInfo}Port ${port}</span></span>${delegateBadge}`;
        },
        controls: { 
-            type: 'info', 
-            text: 'Master', 
-            style: "background-color: #333; color: #999;" 
+            type: 'master'
        },
        settings: { 
            formType: 'master', 
@@ -44,21 +53,23 @@ const cardConfigs = {
            dynamic: true,
            initialColor: (data) => data.enabled ? STATUS_COLORS.OFFLINE_RED : STATUS_COLORS.DISABLED_GRAY,
            initialTitle: (data) => data.enabled ? "Checking status..." : "Disabled",
-            pulsing: (data) => data.enabled,
            id: (data) => `status-${data.id}`
        },
        infoText: (data, extension) => {
-            const isRemote = extension.isRemoteWorker(data);
+            const isRemote = isRemoteWorker(extension, data);
            const isCloud = data.type === 'cloud';
            
            if (isCloud) {
                // For cloud workers, don't show port (it's always 443)
-                return `<strong>${data.name}</strong><br><small style="color: ${UI_COLORS.MUTED_TEXT};">${data.host}</small>`;
+                return `<span class="dist-worker-info__title">${data.name}</span><br><span class="dist-worker-info__meta">${data.host}</span>`;
            } else if (isRemote) {
-                return `<strong>${data.name}</strong><br><small style="color: ${UI_COLORS.MUTED_TEXT};">${data.host}:${data.port}</small>`;
+                const hostLabel = data.host
+                    ? `${data.host}:${data.port}`
+                    : `Unconfigured remote worker • Port ${data.port}`;
+                return `<span class="dist-worker-info__title">${data.name}</span><br><span class="dist-worker-info__meta">${hostLabel}</span>`;
            } else {
                const cudaInfo = data.cuda_device !== undefined ? `CUDA ${data.cuda_device} • ` : '';
-                return `<strong>${data.name}</strong><br><small style="color: ${UI_COLORS.MUTED_TEXT};">${cudaInfo}Port ${data.port}</small>`;
+                return `<span class="dist-worker-info__title">${data.name}</span><br><span class="dist-worker-info__meta">${cudaInfo}Port ${data.port}</span>`;
            }
        },
        controls: { 
@@ -190,6 +201,7 @@ export class DistributedUI {
        input.id = id;
        input.value = value;
        input.placeholder = placeholder;
+        input.classList.add('dist-form-input');
        input.style.cssText = this.styles.formInput;
        
        group.appendChild(labelEl);
@@ -200,6 +212,7 @@ export class DistributedUI {

    createInfoBox(text) {
        const box = document.createElement("div");
+        box.classList.add('dist-info-box');
        box.style.cssText = this.styles.infoBox;
        box.textContent = text;
        return box;
@@ -212,18 +225,21 @@ export class DistributedUI {

    createCard(type = 'worker', options = {}) {
        const card = document.createElement("div");
-        
+        card.classList.add('dist-card');
+
        switch(type) {
            case 'master':
            case 'worker':
                card.style.cssText = this.styles.workerCard;
                break;
            case 'blueprint':
+                card.classList.add('dist-card--blueprint');
                card.style.cssText = this.styles.cardBase + this.styles.cardBlueprint;
                if (options.onClick) card.onclick = options.onClick;
                if (options.title) card.title = options.title;
                break;
            case 'add':
+                card.classList.add('dist-card--add');
                card.style.cssText = this.styles.cardBase + this.styles.cardAdd;
                if (options.onClick) card.onclick = options.onClick;
                if (options.title) card.title = options.title;
@@ -242,9 +258,10 @@ export class DistributedUI {

    createCardColumn(type = 'checkbox', options = {}) {
        const column = document.createElement("div");
-        
+
        switch(type) {
            case 'checkbox':
+                column.classList.add('dist-card__left-col');
                column.style.cssText = this.styles.checkboxColumn;
                if (options.title) column.title = options.title;
                break;
@@ -313,7 +330,7 @@ export class DistributedUI {


    createButtonHelper(text, onClick, style) {
-        return this.createButton(text, onClick, style);
+        return createButtonHelperFn(this, text, onClick, style);
    }

    updateMasterDisplay(extension) {
@@ -345,482 +362,65 @@ export class DistributedUI {
    }

    showCloudflareWarning(extension, masterHost) {
-        // Remove any existing banner first
-        const existingBanner = document.getElementById('cloudflare-warning-banner');
-        if (existingBanner) {
-            existingBanner.remove();
-        }
-
-        // Create warning banner
-        const banner = document.createElement('div');
-        banner.id = 'cloudflare-warning-banner';
-        banner.style.cssText = `
-            position: fixed;
-            top: 0;
-            left: 0;
-            right: 0;
-            background: #ff9800;
-            color: #333;
-            padding: 8px 16px;
-            text-align: center;
-            z-index: 10000;
-            display: flex;
-            align-items: center;
-            justify-content: center;
-            gap: 16px;
-            box-shadow: 0 2px 5px rgba(0,0,0,0.2);
-            font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif;
-        `;
-        
-        const messageSpan = document.createElement('span');
-        messageSpan.innerHTML = `Connection issue: Master address <strong>${masterHost}</strong> is not reachable. The cloudflare tunnel may be offline.`;
-        messageSpan.style.fontSize = '13px';
-        
-        const resetButton = document.createElement('button');
-        resetButton.textContent = 'Reset Master Address';
-        resetButton.style.cssText = `
-            background: #333;
-            color: white;
-            border: none;
-            padding: 6px 14px;
-            border-radius: 4px;
-            cursor: pointer;
-            font-weight: 500;
-            font-size: 13px;
-            transition: background 0.2s;
-        `;
-        resetButton.onmouseover = () => resetButton.style.background = '#555';
-        resetButton.onmouseout = () => resetButton.style.background = '#333';
-        
-        const dismissButton = document.createElement('button');
-        dismissButton.textContent = 'Dismiss';
-        dismissButton.style.cssText = `
-            background: transparent;
-            color: #333;
-            border: 1px solid #333;
-            padding: 6px 14px;
-            border-radius: 4px;
-            cursor: pointer;
-            font-size: 13px;
-            transition: opacity 0.2s;
-        `;
-        dismissButton.onmouseover = () => dismissButton.style.opacity = '0.7';
-        dismissButton.onmouseout = () => dismissButton.style.opacity = '1';
-        
-        // Add click handlers
-        resetButton.onclick = async () => {
-            resetButton.disabled = true;
-            resetButton.textContent = 'Resetting...';
-            
-            try {
-                // Save with empty host - this will trigger auto-detection
-                await extension.api.updateMaster({ 
-                    name: extension.config?.master?.name || "Master",
-                    host: "" 
-                });
-                
-                // Clear the local config host so detectMasterIP() doesn't skip
-                if (extension.config?.master) {
-                    extension.config.master.host = "";
-                }
-                
-                // The API call above doesn't trigger auto-detection, so we need to do it
-                await extension.detectMasterIP();
-                
-                // Reload config to get the new detected IP
-                await extension.loadConfig();
-                
-                // Log the new master URL for debugging
-                const newMasterUrl = extension.getMasterUrl();
-                extension.log(`Master host reset. New URL: ${newMasterUrl}`, "info");
-                
-                // Update UI if sidebar is open
-                if (extension.panelElement) {
-                    const hostInput = document.getElementById('master-host');
-                    if (hostInput) {
-                        hostInput.value = extension.config?.master?.host || "";
-                    }
-                }
-                
-                // Show success message with the actual URL that will be used
-                extension.app.extensionManager.toast.add({
-                    severity: "success",
-                    summary: "Master Host Reset",
-                    detail: `New address: ${newMasterUrl}`,
-                    life: 4000
-                });
-                
-                banner.remove();
-            } catch (error) {
-                resetButton.disabled = false;
-                resetButton.textContent = 'Reset Master Host';
-                extension.log(`Failed to reset master host: ${error.message}`, "error");
-            }
-        };
-        
-        dismissButton.onclick = () => banner.remove();
-        
-        // Assemble banner
-        banner.appendChild(messageSpan);
-        banner.appendChild(resetButton);
-        banner.appendChild(dismissButton);
-        
-        // Add to page
-        document.body.prepend(banner);
-        
-        // Auto-dismiss after 30 seconds
-        setTimeout(() => {
-            if (document.getElementById('cloudflare-warning-banner')) {
-                banner.style.transition = 'opacity 0.5s';
-                banner.style.opacity = '0';
-                setTimeout(() => banner.remove(), 500);
-            }
-        }, 30000);
+        return showCloudflareWarningFn(extension, masterHost);
    }

    updateStatusDot(workerId, color, title, pulsing = false) {
        const statusDot = document.getElementById(`status-${workerId}`);
        if (!statusDot) return;
-        
-        statusDot.style.backgroundColor = color;
+
+        const statusClasses = [
+            "worker-status--online",
+            "worker-status--offline",
+            "worker-status--unknown",
+            "worker-status--processing",
+        ];
+        statusDot.classList.remove(...statusClasses);
+
+        const colorClassMap = {
+            [STATUS_COLORS.ONLINE_GREEN]: "worker-status--online",
+            [STATUS_COLORS.OFFLINE_RED]: "worker-status--offline",
+            [STATUS_COLORS.DISABLED_GRAY]: "worker-status--unknown",
+            [STATUS_COLORS.PROCESSING_YELLOW]: "worker-status--processing",
+        };
+
+        const statusClass = colorClassMap[color] || "worker-status--unknown";
+        statusDot.classList.add(statusClass);
+        statusDot.style.backgroundColor = "";
        statusDot.title = title;
        statusDot.classList.toggle('status-pulsing', pulsing);
    }

-    showLogModal(extension, workerId, logData) {
-        // Remove any existing modal
-        const existingModal = document.getElementById('distributed-log-modal');
-        if (existingModal) {
-            existingModal.remove();
+    showLogModal(extension, workerId, logData, fetchLog = null) {
+        if (this._logModal) {
+            this._logModal.unmount();
+            this._logModal = null;
        }
-        
+
        const worker = extension.config.workers.find(w => w.id === workerId);
        const workerName = worker?.name || `Worker ${workerId}`;
-        
-        // Create modal container
-        const modal = document.createElement('div');
-        modal.id = 'distributed-log-modal';
-        modal.style.cssText = `
-            position: fixed;
-            top: 0;
-            left: 0;
-            width: 100%;
-            height: 100%;
-            background: rgba(0, 0, 0, 0.8);
-            display: flex;
-            align-items: center;
-            justify-content: center;
-            z-index: 10000;
-        `;
-        
-        // Create modal content
-        const content = document.createElement('div');
-        content.style.cssText = `
-            background: #1e1e1e;
-            border-radius: 8px;
-            width: 90%;
-            max-width: 1200px;
-            height: 80%;
-            display: flex;
-            flex-direction: column;
-            border: 1px solid #444;
-        `;
-        
-        // Header
-        const header = document.createElement('div');
-        header.style.cssText = `
-            padding: 15px 20px;
-            border-bottom: 1px solid #444;
-            display: flex;
-            justify-content: space-between;
-            align-items: center;
-        `;
-        
-        const title = document.createElement('h3');
-        title.textContent = `${workerName} - Log Viewer`;
-        title.style.cssText = 'margin: 0; color: #fff;';
-        
-        const headerButtons = document.createElement('div');
-        headerButtons.style.cssText = 'display: flex; gap: 20px; align-items: center;';
-        
-        // Auto-refresh container
-        const refreshContainer = document.createElement('div');
-        refreshContainer.style.cssText = 'display: flex; align-items: center; gap: 4px;';
-        
-        // Auto-refresh checkbox
-        const refreshCheckbox = document.createElement('input');
-        refreshCheckbox.type = 'checkbox';
-        refreshCheckbox.id = 'log-auto-refresh';
-        refreshCheckbox.checked = true; // Enabled by default
-        refreshCheckbox.style.cssText = 'cursor: pointer;';
-        refreshCheckbox.onchange = (e) => {
-            if (e.target.checked) {
-                extension.startLogAutoRefresh(workerId);
-            } else {
-                extension.stopLogAutoRefresh();
-            }
-        };
-        
-        const refreshLabel = document.createElement('label');
-        refreshLabel.htmlFor = 'log-auto-refresh';
-        refreshLabel.style.cssText = 'font-size: 12px; color: #ccc; cursor: pointer; white-space: nowrap;';
-        refreshLabel.textContent = 'Auto-refresh';
-        
-        // Add checkbox and label to container
-        refreshContainer.appendChild(refreshCheckbox);
-        refreshContainer.appendChild(refreshLabel);
-        
-        // Close button
-        const closeBtn = this.createButton('✕', 
-            () => {
-                extension.stopLogAutoRefresh();
-                modal.remove();
-            }, 
-            'background-color: #c04c4c;');
-        closeBtn.style.cssText += ' padding: 5px 10px; font-size: 14px; font-weight: bold;';
-        
-        headerButtons.appendChild(refreshContainer);
-        headerButtons.appendChild(closeBtn);
-        
-        header.appendChild(title);
-        header.appendChild(headerButtons);
-        
-        // Log content area
-        const logContainer = document.createElement('div');
-        logContainer.style.cssText = `
-            flex: 1;
-            overflow: auto;
-            padding: 15px;
-            font-family: 'Consolas', 'Monaco', 'Courier New', monospace;
-            font-size: 12px;
-            line-height: 1.4;
-            color: #ddd;
-            background: #0d0d0d;
-            white-space: pre-wrap;
-            word-wrap: break-word;
-        `;
-        logContainer.id = 'distributed-log-content';
-        logContainer.textContent = logData.content;
-        
-        // Auto-scroll to bottom
-        setTimeout(() => {
-            logContainer.scrollTop = logContainer.scrollHeight;
-        }, 0);
-        
-        // Status bar
-        const statusBar = document.createElement('div');
-        statusBar.style.cssText = `
-            padding: 10px 20px;
-            border-top: 1px solid #444;
-            font-size: 11px;
-            color: #888;
-        `;
-        statusBar.textContent = `Log file: ${logData.log_file}`;
-        if (logData.truncated) {
-            statusBar.textContent += ` (showing last ${logData.lines_shown} lines of ${this.formatFileSize(logData.file_size)})`;
-        }
-        
-        // Assemble modal
-        content.appendChild(header);
-        content.appendChild(logContainer);
-        content.appendChild(statusBar);
-        modal.appendChild(content);
-        
-        // Close on background click
-        modal.onclick = (e) => {
-            if (e.target === modal) {
-                extension.stopLogAutoRefresh();
-                modal.remove();
-            }
-        };
-        
-        // Close on Escape key
-        const handleEscape = (e) => {
-            if (e.key === 'Escape') {
-                extension.stopLogAutoRefresh();
-                modal.remove();
-                document.removeEventListener('keydown', handleEscape);
-            }
-        };
-        document.addEventListener('keydown', handleEscape);
-        
-        document.body.appendChild(modal);
-        
-        // Start auto-refresh
-        extension.startLogAutoRefresh(workerId);
-    }

-    formatFileSize(bytes) {
-        if (bytes < 1024) return bytes + ' B';
-        if (bytes < 1024 * 1024) return (bytes / 1024).toFixed(1) + ' KB';
-        return (bytes / (1024 * 1024)).toFixed(1) + ' MB';
+        const modal = createLogModal();
+        this._logModal = modal;
+        const themeClass =
+            extension.panelElement?.classList.contains("distributed-panel--light")
+                ? "distributed-panel--light"
+                : "";
+        modal.mount(document.body, {
+            workerName,
+            logData,
+            fetchLog: fetchLog || (async () => extension.api.getWorkerLog(workerId, 1000)),
+            themeClass,
+            onClose: () => {
+                if (this._logModal === modal) {
+                    this._logModal = null;
+                }
+            },
+        });
    }

    createWorkerSettingsForm(extension, worker) {
-        const form = document.createElement("div");
-        form.style.cssText = "display: flex; flex-direction: column; gap: 8px;";
-        
-        // Name field
-        const nameGroup = this.createFormGroup("Name:", worker.name, `name-${worker.id}`);
-        form.appendChild(nameGroup.group);
-        
-        // Worker type dropdown
-        const typeGroup = document.createElement("div");
-        typeGroup.style.cssText = "display: flex; flex-direction: column; gap: 4px; margin: 5px 0;";
-        
-        const typeLabel = document.createElement("label");
-        typeLabel.htmlFor = `worker-type-${worker.id}`;
-        typeLabel.textContent = "Worker Type:";
-        typeLabel.style.cssText = "font-size: 12px; color: #ccc;";
-        
-        const typeSelect = document.createElement("select");
-        typeSelect.id = `worker-type-${worker.id}`;
-        typeSelect.style.cssText = "padding: 4px 8px; background: #333; color: #fff; border: 1px solid #555; border-radius: 4px; font-size: 12px;";
-        
-        // Create options
-        const localOption = document.createElement("option");
-        localOption.value = "local";
-        localOption.textContent = "Local";
-        
-        const remoteOption = document.createElement("option");
-        remoteOption.value = "remote";
-        remoteOption.textContent = "Remote";
-        
-        const cloudOption = document.createElement("option");
-        cloudOption.value = "cloud";
-        cloudOption.textContent = "Cloud";
-        
-        typeSelect.appendChild(localOption);
-        typeSelect.appendChild(remoteOption);
-        typeSelect.appendChild(cloudOption);
-        
-        // Create powered by Runpod text (initially hidden)
-        const runpodText = document.createElement("a");
-        runpodText.id = `runpod-text-${worker.id}`;
-        runpodText.href = "https://get.runpod.io/0bw29uf3ug0p";
-        runpodText.target = "_blank";
-        runpodText.textContent = "Deploy Cloud Worker with Runpod";
-        runpodText.style.cssText = "font-size: 12px; color: #4a90e2; text-decoration: none; margin-top: 4px; display: none; cursor: pointer;";
-        
-        // Store the onchange function to be assigned later
-        const createOnChangeHandler = () => {
-            return (e) => {
-                const workerType = e.target.value;
-                // Show/hide relevant fields
-                const hostGroup = document.getElementById(`host-group-${worker.id}`);
-                const hostInput = document.getElementById(`host-${worker.id}`);
-                const portGroup = document.getElementById(`port-group-${worker.id}`);
-                const portInput = document.getElementById(`port-${worker.id}`);
-                const cudaGroup = document.getElementById(`cuda-group-${worker.id}`);
-                const argsGroup = document.getElementById(`args-group-${worker.id}`);
-                const runpodTextElem = document.getElementById(`runpod-text-${worker.id}`);
-                
-                // Check if elements exist before accessing them
-                if (!hostGroup || !portGroup || !cudaGroup || !argsGroup || !runpodTextElem || !hostInput || !portInput) {
-                    return; // Elements not ready yet
-                }
-                
-                if (workerType === "local") {
-                    hostGroup.style.display = "none";
-                    portGroup.style.display = "flex";
-                    cudaGroup.style.display = "flex";
-                    argsGroup.style.display = "flex";
-                    runpodTextElem.style.display = "none";
-                } else if (workerType === "remote") {
-                    hostGroup.style.display = "flex";
-                    portGroup.style.display = "flex";
-                    cudaGroup.style.display = "none";
-                    argsGroup.style.display = "none";
-                    runpodTextElem.style.display = "none";
-                    // Update placeholder for remote workers
-                    hostInput.placeholder = "e.g., 192.168.1.100";
-                    // If switching to remote and host is localhost, clear it
-                    if (hostInput.value === "localhost" || hostInput.value === "127.0.0.1") {
-                        hostInput.value = "";
-                    }
-                } else if (workerType === "cloud") {
-                    hostGroup.style.display = "flex";
-                    portGroup.style.display = "flex"; // Keep port visible for cloud workers
-                    cudaGroup.style.display = "none";
-                    argsGroup.style.display = "none";
-                    runpodTextElem.style.display = "block";
-                    // Update placeholder for cloud workers
-                    hostInput.placeholder = "e.g., your-cloud-worker.trycloudflare.com";
-                    // Set port to 443 for cloud workers
-                    portInput.value = "443";
-                    // If switching to cloud and host is localhost, clear it
-                    if (hostInput.value === "localhost" || hostInput.value === "127.0.0.1") {
-                        hostInput.value = "";
-                    }
-                }
-            };
-        };
-        
-        typeGroup.appendChild(typeLabel);
-        typeGroup.appendChild(typeSelect);
-        typeGroup.appendChild(runpodText);
-        form.appendChild(typeGroup);
-        
-        // Host field (only for remote workers)
-        const hostGroup = this.createFormGroup("Host:", worker.host || "", `host-${worker.id}`, "text", "e.g., 192.168.1.100");
-        hostGroup.group.id = `host-group-${worker.id}`;
-        hostGroup.group.style.display = (extension.isRemoteWorker(worker) || worker.type === "cloud") ? "flex" : "none";
-        form.appendChild(hostGroup.group);
-        
-        // Port field
-        const portGroup = this.createFormGroup("Port:", worker.port, `port-${worker.id}`, "number");
-        portGroup.group.id = `port-group-${worker.id}`;
-        form.appendChild(portGroup.group);
-        
-        // CUDA Device field (only for local workers)
-        const cudaGroup = this.createFormGroup("CUDA Device:", worker.cuda_device || 0, `cuda-${worker.id}`, "number");
-        cudaGroup.group.id = `cuda-group-${worker.id}`;
-        cudaGroup.group.style.display = (extension.isRemoteWorker(worker) || worker.type === "cloud") ? "none" : "flex";
-        form.appendChild(cudaGroup.group);
-        
-        // Extra Args field (only for local workers)
-        const argsGroup = this.createFormGroup("Extra Args:", worker.extra_args || "", `args-${worker.id}`);
-        argsGroup.group.id = `args-group-${worker.id}`;
-        argsGroup.group.style.display = (extension.isRemoteWorker(worker) || worker.type === "cloud") ? "none" : "flex";
-        form.appendChild(argsGroup.group);
-        
-        // Buttons
-        const saveBtn = this.createButton("Save", 
-            () => extension.saveWorkerSettings(worker.id),
-            "background-color: #4a7c4a;");
-        saveBtn.style.cssText = BUTTON_STYLES.base + BUTTON_STYLES.success;
-        
-        const cancelBtn = this.createButton("Cancel", 
-            () => extension.cancelWorkerSettings(worker.id),
-            "background-color: #555;");
-        cancelBtn.style.cssText = BUTTON_STYLES.base + BUTTON_STYLES.cancel;
-        
-        const deleteBtn = this.createButton("Delete", 
-            () => extension.deleteWorker(worker.id),
-            "background-color: #7c4a4a;");
-        deleteBtn.style.cssText = BUTTON_STYLES.base + BUTTON_STYLES.error + BUTTON_STYLES.marginLeftAuto;
-        
-        const buttonGroup = this.createButtonGroup([saveBtn, cancelBtn, deleteBtn], " margin-top: 8px;");
-        form.appendChild(buttonGroup);
-        
-        // Assign the onchange handler now that all elements are created
-        typeSelect.onchange = createOnChangeHandler();
-        
-        // Set initial value and trigger state after all DOM elements are created
-        if (worker.type === "cloud") {
-            typeSelect.value = "cloud";
-            // Show Runpod text immediately for cloud workers
-            runpodText.style.display = "block";
-        } else if (extension.isRemoteWorker(worker)) {
-            typeSelect.value = "remote";
-        } else {
-            typeSelect.value = "local";
-        }
-        
-        // Trigger initial state now that all elements exist
-        typeSelect.dispatchEvent(new Event('change'));
-        
-        return form;
+        return createWorkerSettingsFormFn(this, extension, worker);
    }

    createSettingsToggle() {
@@ -833,7 +433,8 @@ export class DistributedUI {
        
        const settingsToggle = document.createElement("span");
        settingsToggle.textContent = "▶"; // Right arrow when collapsed
-        settingsToggle.style.cssText = "font-size: 12px; color: #888; transition: all 0.2s ease;";
+        settingsToggle.style.cssText =
+            "font-size: 12px; color: var(--dist-settings-arrow, #888); transition: all 0.2s ease;";
        
        settingsRow.appendChild(settingsToggle);
        settingsRow.appendChild(settingsTitle);
@@ -868,13 +469,41 @@ export class DistributedUI {
            if (config?.opacity) checkbox.style.opacity = config.opacity;
            if (config?.title) column.title = config.title;
            
-            if (config?.enabled && !config?.disabled && data?.id) {
+            const isMasterToggle = config?.masterToggle && typeof extension.isMasterParticipating === 'function';
+            if (isMasterToggle) {
+                const participationEnabled = extension.isMasterParticipationEnabled();
+                const fallbackActive = extension.isMasterFallbackActive();
+                const buildTitle = (enabled, fallback) => {
+                    if (enabled) {
+                        return "Master participating • Click to switch to orchestrator-only";
+                    }
+                    if (fallback) {
+                        return "No workers selected • Master fallback execution active";
+                    }
+                    return "Master orchestrator-only • Click to re-enable participation";
+                };
+
+                checkbox.checked = participationEnabled;
+                checkbox.style.pointerEvents = "none";
+                column.style.cursor = "pointer";
+                column.title = buildTitle(participationEnabled, fallbackActive);
+                column.onclick = async (event) => {
+                    if (event) {
+                        event.stopPropagation();
+                        event.preventDefault();
+                    }
+                    const nextState = !extension.isMasterParticipationEnabled();
+                    const nextFallback = !nextState && extension.enabledWorkers.length === 0;
+                    checkbox.checked = nextState;
+                    column.title = buildTitle(nextState, nextFallback);
+                    await extension.updateMasterParticipation(nextState);
+                };
+            } else if (config?.enabled && !config?.disabled && data?.id) {
                checkbox.style.pointerEvents = "none";
                column.style.cursor = "pointer";
                column.onclick = async () => {
                    checkbox.checked = !checkbox.checked;
                    await extension.updateWorkerEnabled(data.id, checkbox.checked);
-                    extension.updateSummary();
                };
            }
            
@@ -890,10 +519,10 @@ export class DistributedUI {
        let id = config.id;
        
        if (typeof config.initialColor === 'function') {
-            color = config.initialColor(data);
+            color = config.initialColor(data, extension);
        }
        if (typeof config.initialTitle === 'function') {
-            title = config.initialTitle(data);
+            title = config.initialTitle(data, extension);
        }
        if (typeof config.id === 'function') {
            id = config.id(data);
@@ -940,32 +569,61 @@ export class DistributedUI {
        const controlsWrapper = document.createElement("div");
        controlsWrapper.style.cssText = this.styles.controlsWrapper;
        
-        if (config.dynamic && data) {
+        if (config.type === 'master') {
+            const participationEnabled = extension.isMasterParticipationEnabled();
+            const fallbackActive = extension.isMasterFallbackActive();
+            let message;
+            const badge = document.createElement("div");
+            badge.classList.add("dist-info-box", "master-info-badge");
+            badge.style.cssText = this.styles.infoBox;
+            if (fallbackActive) {
+                message = "No workers selected. Master fallback execution active.";
+                badge.textContent = message;
+                badge.classList.add("master-info-badge--fallback");
+            } else if (!participationEnabled) {
+                message = "Master disabled: running as orchestrator only";
+                badge.textContent = message;
+                badge.classList.add("master-info-badge--delegate");
+            } else {
+                message = "Master participating in workflows";
+                badge.textContent = message;
+            }
+            controlsWrapper.appendChild(badge);
+        } else if (config.dynamic && data) {
            if (isRemote) {
                const isCloud = data.type === 'cloud';
                const workerTypeText = isCloud ? "Cloud worker" : "Remote worker";
-                const remoteInfo = this.createButton(workerTypeText, null, BUTTON_STYLES.info);
-                remoteInfo.style.cssText = BUTTON_STYLES.base + BUTTON_STYLES.workerControl + BUTTON_STYLES.info + " color: #999; cursor: default;";
-                remoteInfo.disabled = true;
-                controlsWrapper.appendChild(remoteInfo);
+                const workerTypeBadge = this.createInfoBox(workerTypeText);
+                workerTypeBadge.title = "Worker is externally hosted";
+                controlsWrapper.appendChild(workerTypeBadge);
+
+                const logBtn = this.createButton('View Log', () => viewWorkerLog(extension, data.id, true));
+                logBtn.id = `log-${data.id}`;
+                logBtn.style.cssText = BUTTON_STYLES.base + BUTTON_STYLES.workerControl;
+                logBtn.classList.add("btn--log");
+                logBtn.title = "View remote worker log";
+                controlsWrapper.appendChild(logBtn);
            } else {
                const controls = this.createWorkerControls(data.id, {
-                    launch: () => extension.launchWorker(data.id),
-                    stop: () => extension.stopWorker(data.id),
-                    viewLog: () => extension.viewWorkerLog(data.id)
+                    launch: () => launchWorker(extension, data.id),
+                    stop: () => stopWorker(extension, data.id),
+                    viewLog: () => viewWorkerLog(extension, data.id)
                });
                
                const launchBtn = controls.querySelector(`#launch-${data.id}`);
                const stopBtn = controls.querySelector(`#stop-${data.id}`);
                const logBtn = controls.querySelector(`#log-${data.id}`);
                
-                launchBtn.style.cssText = BUTTON_STYLES.base + BUTTON_STYLES.workerControl + BUTTON_STYLES.launch;
+                launchBtn.style.cssText = BUTTON_STYLES.base + BUTTON_STYLES.workerControl;
+                launchBtn.classList.add("btn--launch");
                launchBtn.title = "Launch worker (runs in background with logging)";
                
-                stopBtn.style.cssText = BUTTON_STYLES.base + BUTTON_STYLES.workerControl + BUTTON_STYLES.stop + BUTTON_STYLES.hidden;
+                stopBtn.style.cssText = BUTTON_STYLES.base + BUTTON_STYLES.workerControl + BUTTON_STYLES.hidden;
+                stopBtn.classList.add("btn--stop");
                stopBtn.title = "Stop worker";
                
-                logBtn.style.cssText = BUTTON_STYLES.base + BUTTON_STYLES.workerControl + BUTTON_STYLES.log + BUTTON_STYLES.hidden;
+                logBtn.style.cssText = BUTTON_STYLES.base + BUTTON_STYLES.workerControl + BUTTON_STYLES.hidden;
+                logBtn.classList.add("btn--log");
                
                while (controls.firstChild) {
                    controlsWrapper.appendChild(controls.firstChild);
@@ -1029,6 +687,15 @@ export class DistributedUI {
        
        const hostResult = this.createFormGroup("Host:", extension.config?.master?.host || "", "master-host", "text", "Auto-detect if empty");
        settingsForm.appendChild(hostResult.group);
+
+        // Cloudflare tunnel toggle (simple button inside master settings)
+        const tunnelBtn = this.createButton("Enable Cloudflare Tunnel", (e) => extension.handleTunnelToggle(e.target));
+        tunnelBtn.id = "cloudflare-tunnel-button";
+        tunnelBtn.style.cssText = BUTTON_STYLES.base + " margin: 4px 0 -5px 0;";
+        tunnelBtn.classList.add("tunnel-button", "tunnel-button--enable");
+        settingsForm.appendChild(tunnelBtn);
+        extension.tunnelElements = { button: tunnelBtn };
+        extension.updateTunnelUIElements();
        
        const saveBtn = this.createButton("Save", async () => {
            const nameInput = document.getElementById('master-name');
@@ -1091,146 +758,23 @@ export class DistributedUI {
    }

    addPlaceholderHover(card, leftColumn, entityType) {
+        const cardTypeClass = entityType === 'blueprint' ? 'placeholder-card--blueprint' : 'placeholder-card--add';
+        const columnTypeClass = entityType === 'blueprint' ? 'placeholder-column--blueprint' : 'placeholder-column--add';
+        card.classList.add('placeholder-card', cardTypeClass);
+        leftColumn.classList.add('placeholder-column', columnTypeClass);
+
        card.onmouseover = () => {
-            if (entityType === 'blueprint') {
-                card.style.borderColor = "#777";
-                card.style.backgroundColor = "rgba(255, 255, 255, 0.05)";
-                leftColumn.style.color = "#999";
-            } else {
-                card.style.borderColor = "#666";
-                card.style.backgroundColor = "rgba(255, 255, 255, 0.02)";
-                leftColumn.style.color = "#888";
-                leftColumn.style.borderColor = "#666";
-            }
+            card.classList.add('is-hovered');
+            leftColumn.classList.add('is-hovered');
        };
        
        card.onmouseout = () => {
-            if (entityType === 'blueprint') {
-                card.style.borderColor = "#555";
-                card.style.backgroundColor = "rgba(255, 255, 255, 0.02)";
-                leftColumn.style.color = "#777";
-            } else {
-                card.style.borderColor = "#444";
-                card.style.backgroundColor = "transparent";
-                leftColumn.style.color = "#555";
-                leftColumn.style.borderColor = "#444";
-            }
+            card.classList.remove('is-hovered');
+            leftColumn.classList.remove('is-hovered');
        };
    }

    renderEntityCard(entityType, data, extension) {
-        const config = cardConfigs[entityType] || {};
-        const isPlaceholder = entityType === 'blueprint' || entityType === 'add';
-        const isWorker = entityType === 'worker';
-        const isMaster = entityType === 'master';
-        const isRemote = isWorker && extension.isRemoteWorker(data);
-
-        const cardOptions = { 
-            onClick: isPlaceholder ? data?.onClick : null 
-        };
-        if (isPlaceholder) {
-            cardOptions.title = entityType === 'blueprint' ? "Click to add your first worker" : "Click to add a new worker";
-        }
-        const card = this.createCard(entityType, cardOptions);
-
-        const leftColumn = this.createCheckboxOrIconColumn(config.checkbox, data, extension);
-        card.appendChild(leftColumn);
-
-        const rightColumn = this.createCardColumn('content');
-
-        const infoRow = this.createInfoRow();
-        if (config.infoRowPadding) {
-            infoRow.style.padding = config.infoRowPadding;
-        }
-        if (config.minHeight === 'auto') {
-            infoRow.style.minHeight = 'auto';
-        } else if (config.minHeight) {
-            infoRow.style.minHeight = config.minHeight;
-        }
-        if (config.expand) {
-            infoRow.title = "Click to expand settings";
-            infoRow.onclick = () => {
-                if (isMaster) {
-                    const masterSettingsExpanded = !extension.masterSettingsExpanded;
-                    extension.masterSettingsExpanded = masterSettingsExpanded;
-                    const masterSettingsDiv = document.getElementById("master-settings");
-                    const arrow = infoRow.querySelector('.settings-arrow');
-                    if (masterSettingsExpanded) {
-                        masterSettingsDiv.classList.add("expanded");
-                        masterSettingsDiv.style.padding = "12px";
-                        masterSettingsDiv.style.marginTop = "8px";
-                        masterSettingsDiv.style.marginBottom = "8px";
-                        arrow.style.transform = "rotate(90deg)";
-                    } else {
-                        masterSettingsDiv.classList.remove("expanded");
-                        masterSettingsDiv.style.padding = "0 12px";
-                        masterSettingsDiv.style.marginTop = "0";
-                        masterSettingsDiv.style.marginBottom = "0";
-                        arrow.style.transform = "rotate(0deg)";
-                    }
-                } else {
-                    extension.toggleWorkerExpanded(data.id);
-                }
-            };
-        }
-
-        const workerContent = this.createWorkerContent();
-        if (entityType === 'add') {
-            workerContent.style.alignItems = "center";
-        }
-
-        const statusDot = this.createStatusDotHelper(config.statusDot, data, extension);
-        workerContent.appendChild(statusDot);
-
-        const infoSpan = document.createElement("span");
-        infoSpan.innerHTML = config.infoText(data, extension);
-        workerContent.appendChild(infoSpan);
-
-        infoRow.appendChild(workerContent);
-
-        let settingsArrow;
-        if (config.expand) {
-            const expandedId = config.settings?.expandedId || (isMaster ? 'master' : data?.id);
-            settingsArrow = this.createSettingsToggleHelper(expandedId, extension);
-            if (isMaster && !extension.masterSettingsExpanded) {
-                settingsArrow.style.transform = "rotate(0deg)";
-            }
-            infoRow.appendChild(settingsArrow);
-        }
-
-        rightColumn.appendChild(infoRow);
-
-        if (config.hover === true) {
-            rightColumn.onmouseover = () => {
-                rightColumn.style.backgroundColor = "#333";
-                if (settingsArrow) settingsArrow.style.color = "#fff";
-            };
-            rightColumn.onmouseout = () => {
-                rightColumn.style.backgroundColor = "transparent";
-                if (settingsArrow) settingsArrow.style.color = "#888";
-            };
-        }
-
-        const controlsDiv = this.createControlsSection(config.controls, data, extension, isRemote);
-        if (controlsDiv) {
-            rightColumn.appendChild(controlsDiv);
-        }
-
-        if (config.settings) {
-            const settingsDiv = this.createSettingsSection(config.settings, data, extension);
-            rightColumn.appendChild(settingsDiv);
-        }
-
-        card.appendChild(rightColumn);
-
-        if (config.hover === 'placeholder') {
-            this.addPlaceholderHover(card, leftColumn, entityType);
-        }
-
-        if (isWorker && !isRemote) {
-            extension.updateWorkerControls(data.id);
-        }
-
-        return card;
+        return renderEntityCardFn(this, cardConfigs, entityType, data, extension);
    }
-}
+}
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Robert Wojciechowski	071d1c1d93	Update pyproject.toml	2026-02-28 04:53:20 +00:00
Robert Wojciechowski	ff7bfb5ef5	Update docs for load balance, logs, and Distributed Value	2026-02-28 04:53:20 +00:00
Robert Wojciechowski	d361d6e418	Order collector outputs by enabled worker list	2026-02-28 04:53:20 +00:00
Robert Wojciechowski	16c3f6455b	Fix collector progress accounting and value reset on disconnect	2026-02-28 04:53:20 +00:00
Robert Wojciechowski	2c2110504a	Update DistributedValue to track enabled workers	2026-02-28 04:53:20 +00:00
Robert Wojciechowski	6ce739e03a	Make DistributedValue default widget type-aware	2026-02-28 04:53:20 +00:00
Robert Wojciechowski	c297b34b94	Add DistributedValue node with per-worker overrides	2026-02-28 04:53:20 +00:00
Robert Wojciechowski	2cf3d717d1	Refine worker UI theme behavior and remote fallback add flow	2026-02-28 04:53:20 +00:00
Robert Wojciechowski	243086391d	Add remote worker log viewing and local log proxy endpoints	2026-02-28 04:53:20 +00:00
Robert Wojciechowski	a4b354d56a	Add tooltip for collector load_balance toggle	2026-02-28 04:53:20 +00:00
Robert Wojciechowski	b95675d1ff	Move load balancing to collector/orchestrator and remove queue node	2026-02-28 04:53:20 +00:00
Robert Wojciechowski	e4c025e06e	USDU parity: progressive tiles, deterministic blend, crop/seed/model fixes	2026-02-28 04:53:20 +00:00
Robert Wojciechowski	487f5b9c85	refactor: centralize audio payload codec and harden worker dispatch compatibility	2026-02-28 04:53:20 +00:00
Robert Wojciechowski	0dae73ec7d	fix: rewrite synced media inputs to worker-local paths	2026-02-28 04:53:20 +00:00
Robert Wojciechowski	df606af80e	fix: sync LoadVideo 'file' media refs for remote workers	2026-02-28 04:53:20 +00:00
Robert Wojciechowski	9058cf061d	fix: sync audio media refs for worker prompt validation	2026-02-28 04:53:20 +00:00
Robert Wojciechowski	04169ac651	fix: correct divider chunking and improve distributed validation/audio transport	2026-02-28 04:53:20 +00:00
Robert Wojciechowski	e4c945e536	chore: commit all pending distributed changes	2026-02-28 04:53:20 +00:00
Robert Wojciechowski	26c00daacf	chore: ignore AGENTS.md	2026-02-28 04:53:20 +00:00
Robert Wojciechowski	6de2050b3f	fix(tests): restore config error contract and stub compatibility	2026-02-28 04:53:20 +00:00
Robert Wojciechowski	2565f5aece	feat: adapt panel styling to Nodes 2.0 (Modern Node Design) Detects the Comfy.VueNodes.Enabled setting via addEventListener and toggles a `distributed-panel--nodes2` class on the panel root element. CSS visual properties (backgrounds, borders, border-radius) are migrated from inline JS styles to CSS classes, enabling clean theme overrides for Nodes 2.0 using PrimeVue surface variables (--p-surface-, --p-text-).	2026-02-28 04:52:59 +00:00
Robert Wojciechowski	d8095cddb6	fix(ui): restore worker panel states and spacing behavior	2026-02-24 21:44:33 +00:00
Robert Wojciechowski	9c42f889ac	chore: commit remaining workspace updates	2026-02-24 01:40:38 +00:00
Robert Wojciechowski	c9676345e7	fix: use centralized tile state init in distributed startup	2026-02-24 01:39:56 +00:00
Robert Wojciechowski	5eacd17aeb	refactor: implement REFACTOR_PLAN_2 across distributed stack	2026-02-24 01:38:41 +00:00
Robert Wojciechowski	f0fe702f80	Commit all current refactor changes	2026-02-23 23:47:23 +00:00
Robert Wojciechowski	7fc4f05abd	Refactor USDU job state to typed dataclasses	2026-02-23 23:46:23 +00:00
Robert Wojciechowski	0f384f51d6	Fix config guard and parallel worker probing	2026-02-23 11:17:48 +00:00
Robert Wojciechowski	7f24cb0c7a	Refactor distributed orchestration and frontend modules	2026-02-23 11:11:40 +00:00
Robert Wojciechowski	e950335577	Refactor worker monitor placement and remove legacy init patch	2026-02-23 05:23:25 +00:00
Robert Wojciechowski	c1010dcd3c	refactor(web): split main.js into focused modules and shared constants	2026-02-23 04:38:04 +00:00
Robert Wojciechowski	f89a0cc711	refactor: decompose collector, queue overrides, and worker network info	2026-02-23 02:44:55 +00:00
Robert Wojciechowski	5bd2a60ab3	Harden exception handling and add distributed exception types	2026-02-23 02:27:06 +00:00
Robert Wojciechowski	d8fc6751f2	Refactor distributed upscale node into mixin modules	2026-02-23 02:12:13 +00:00
Robert Wojciechowski	30e55dc7df	Refactor distributed module into packages and optimize utilities	2026-02-22 23:52:58 +00:00
Robert Wojciechowski	4911ead630	Update README.md	2026-01-25 18:26:38 +11:00
Robert Wojciechowski	a00be15304	Update README.md	2026-01-25 18:23:06 +11:00
Robert Wojciechowski	09cc65662b	Update pyproject.toml	2026-01-25 17:41:44 +11:00
robertvoy	56083ec0b1	Merge branch 'main' of https://github.com/robertvoy/ComfyUI-Distributed	2026-01-25 17:37:34 +11:00
robertvoy	d691ba841f	Feat: Audio Batch Divider	2026-01-25 17:36:55 +11:00
Robert Wojciechowski	453b02a2b0	Update pyproject.toml	2026-01-25 08:04:39 +11:00
Robert Wojciechowski	5414338bb7	Merge pull request #69 from robertvoy/feature/cloudflare Feature/cloudflare	2026-01-25 08:04:04 +11:00
robertvoy	b6d4756392	collector supports audio	2026-01-24 18:32:24 +11:00
robertvoy	044c54b780	merged fork	2026-01-24 14:19:22 +11:00
robertvoy	b768e7fc8a	https handling in host field	2025-12-30 12:11:12 +11:00
robertvoy	9805049268	DistributedModelName	2025-12-30 11:17:44 +11:00
robertvoy	399ed7c7d1	feat: initial setup for cloudflare integration	2025-12-29 09:38:03 +11:00
Robert Wojciechowski	64ba4b75db	Update pyproject.toml	2025-12-28 10:55:48 +11:00
Robert Wojciechowski	d353d118d7	Update README.md	2025-12-28 10:54:16 +11:00
robertvoy	5dd10cef39	organise docs	2025-12-28 10:48:54 +11:00
Robert Wojciechowski	8eba225f5e	Merge pull request #61 from umanets/feature/distributed-queue-api Add public POST /distributed/queue API endpoint + docs	2025-12-28 10:36:59 +11:00
Igor Umanets	9bc2cfc43e	Update README-API.md with links to video walkthrough and examples repository	2025-12-26 16:42:18 +01:00
Igor Umanets	370b48438b	Add instructions for retrieving enabled worker IDs in API documentation	2025-12-26 15:12:05 +01:00
Igor Umanets	71c7a99e1e	Add API documentation for distributed queue	2025-12-26 14:05:15 +01:00
Igor Umanets	def7c0df1d	Add distributed queue API orchestration	2025-12-26 10:47:34 +01:00
Robert Wojciechowski	5f5320b34e	Update pyproject.toml	2025-10-28 08:01:06 +11:00
Robert Wojciechowski	5a33da828c	Merge pull request #50 from IT-BillDeng/feature/disable-master Feature/disable master	2025-10-28 08:00:30 +11:00
IT-BillDeng	ef8e0542c8	Document master participation, orchestrator-only, and fallback modes in the worker setup guide	2025-10-27 03:03:17 +08:00
IT-BillDeng	89b7812fb4	Improve master participation logic with fallback handling and UI messaging	2025-10-27 03:00:57 +08:00
IT-BillDeng	f13b953a82	Enable UI toggle for master participation with live status feedback and delegate-mode orchestration.	2025-10-27 02:29:51 +08:00
Robert Wojciechowski	1e3e094cdb	Update README.md	2025-10-25 09:40:53 +11:00
Robert Wojciechowski	7bcf9290ba	Update video-upscaler-runpod-preset.md	2025-09-15 12:18:44 +10:00
Robert Wojciechowski	6aec567071	Update README.md	2025-09-15 12:01:27 +10:00
Robert Wojciechowski	2fec61d567	Update video-upscaler-runpod-preset.md	2025-09-15 11:53:49 +10:00
Robert Wojciechowski	528cd2e895	Update pyproject.toml	2025-09-15 11:51:51 +10:00
Robert Wojciechowski	0572e084e3	Update video-upscaler-runpod-preset.md	2025-09-15 11:51:28 +10:00
Robert Wojciechowski	bc9bba0c76	Update video-upscaler-runpod-preset.md	2025-09-15 11:50:33 +10:00
Robert Wojciechowski	701c6de9eb	Update README.md	2025-09-15 11:44:08 +10:00
Robert Wojciechowski	c846aac0e6	Update video-upscaler-runpod-preset.md	2025-09-15 11:41:32 +10:00
Robert Wojciechowski	0af3df071d	Create video-upscaler-runpod-preset.md	2025-09-15 11:32:29 +10:00
robertvoy	f7c7b960c7	change upscaler in video upscaler workflow	2025-09-14 09:54:10 +10:00
robertvoy	c45b50830e	Merge branch 'main' of https://github.com/robertvoy/ComfyUI-Distributed	2025-09-13 14:42:41 +10:00
robertvoy	213ac38227	Add new /prompt liveness probe (USDU + collector). when worker timeout value in exceeded, the master will probe the workers.	2025-09-13 14:42:33 +10:00
robertvoy	9ae354a788	upscaler workflow change	2025-09-13 12:21:26 +10:00
Robert Wojciechowski	c1928ebd09	Update README.md	2025-09-13 10:41:52 +10:00
Robert Wojciechowski	98a63938c8	Update README.md	2025-09-13 10:40:03 +10:00
Robert Wojciechowski	27b73fd280	Update README.md	2025-09-13 10:38:55 +10:00
robertvoy	48c8792af2	video upscaler workflow	2025-09-13 10:34:06 +10:00
robertvoy	4b8836cd03	Unify worker timeout via UI across collector/upscaler (90s); add interruptible sliced waits + cleanup to collector	2025-09-13 10:26:03 +10:00
robertvoy	b69fc40fbd	updated video upscaler workflow	2025-09-12 09:58:53 +10:00
Robert Wojciechowski	e5eb2c8b86	Delete workflows/distributed-upscale-batch.json	2025-09-12 09:49:48 +10:00
Robert Wojciechowski	b90c3e42ac	Update README.md	2025-09-12 09:49:27 +10:00
robertvoy	187b38d4f5	worker timeout in settings, fixed requeue video tiles	2025-09-11 16:40:19 +10:00
robertvoy	39fb4db486	warning for non-WAN compatible frame count and WanVideo Closest Frame Count	2025-09-11 14:26:53 +10:00
robertvoy	763e238120	fixed: if batch =! (4*n)+1	2025-09-11 12:51:13 +10:00
robertvoy	857aa80174	optimisations	2025-09-10 10:34:00 +10:00
robertvoy	287e4c05fa	speed optimisation	2025-09-10 10:15:35 +10:00
robertvoy	58f26a106f	clean up	2025-09-10 09:38:40 +10:00
robertvoy	138cac3eb7	remove static distribution	2025-09-09 19:52:34 +10:00
robertvoy	0e07868b98	initial	2025-09-09 19:18:57 +10:00
Robert Wojciechowski	a8be7c7ed9	Update pyproject.toml	2025-08-25 13:38:01 +10:00
Robert Wojciechowski	c9ca7f5390	Merge pull request #32 from doubletwisted/fix/pasted-paths Update executionUtils.js	2025-08-25 13:37:34 +10:00
Dominik Bargiel	c860df0aa7	Update executionUtils.js Previous fix caused problems with loading models that are in subfolders like: "'FLUX.1\\Shakker-Labs-ControlNet-Union-Pro-2.0.safetensors" Now images and wideos are treated separetlly.	2025-08-24 19:46:40 +02:00
Robert Wojciechowski	05662eb7eb	Update pyproject.toml	2025-08-16 10:49:56 +10:00
Robert Wojciechowski	8441c92d95	Merge pull request #29 from doubletwisted/fix/pasted-paths fix: normalize pasted image paths and subfolder extraction	2025-08-16 10:49:27 +10:00
doubletwisted	4b513d49c3	fix: normalize pasted image paths and subfolder extraction (Windows-safe, upstream-only)	2025-08-15 21:45:19 +02:00
Robert Wojciechowski	f91d2d579d	Update pyproject.toml	2025-08-13 10:00:36 +10:00
Robert Wojciechowski	1ff863fb5d	Add files via upload	2025-08-13 10:00:03 +10:00
Robert Wojciechowski	74bba258da	Update worker-setup-guides.md	2025-08-09 12:40:53 +10:00
Robert Wojciechowski	bb559e8ce0	Merge pull request #27 from ComfyNodePRs/update-publish-yaml Update Github Action for Publishing to Comfy Registry	2025-08-06 06:53:17 +10:00
Robert Wojciechowski	c032de224b	Update README.md	2025-08-05 07:58:40 +10:00
Robert Wojciechowski	68f8627b0b	Update README.md	2025-08-04 11:09:36 +10:00
Robert Wojciechowski	0e3c04fba7	Update README.md	2025-08-04 11:02:55 +10:00
robertvoy	e076cf3455	Merge branch 'main' of https://github.com/robertvoy/ComfyUI-Distributed	2025-08-03 18:38:06 +10:00
robertvoy	5f3d358f61	wan 2.2 workflow	2025-08-03 18:38:00 +10:00
Robert Wojciechowski	1a6bff3ab1	Update pyproject.toml	2025-08-01 15:15:35 +10:00
Robert Wojciechowski	d7aec0b0fe	Update pyproject.toml	2025-08-01 15:13:11 +10:00
Robert Wojciechowski	7cd014fc0e	Update worker-setup-guides.md	2025-07-31 18:58:44 +10:00
Robert Wojciechowski	51e9a03dd5	Update README.md	2025-07-31 17:55:41 +10:00
Robert Wojciechowski	421ca2e023	Update worker-setup-guides.md	2025-07-31 17:52:45 +10:00
robertvoy	ae3201accb	Merge branch 'main' of https://github.com/robertvoy/ComfyUI-Distributed	2025-07-31 13:46:58 +10:00
Robert Wojciechowski	ae1d1a7f89	Update pyproject.toml	2025-07-31 13:40:36 +10:00
robertvoy	407e68816d	Add index preservation to fix distributed batch ordering	2025-07-31 13:37:59 +10:00
Robert Wojciechowski	e4b0fe11fd	Update pyproject.toml	2025-07-31 13:06:23 +10:00
Robert Wojciechowski	960c44b095	Update README.md	2025-07-31 12:39:15 +10:00
Robert Wojciechowski	35925c1e1b	Update worker-setup-guides.md	2025-07-31 12:36:08 +10:00
Robert Wojciechowski	feb56e234b	Update worker-setup-guides.md	2025-07-31 12:30:50 +10:00
Robert Wojciechowski	f5d7307aef	Update README.md	2025-07-31 12:30:28 +10:00
Robert Wojciechowski	386f7ae2d4	Update worker-setup-guides.md	2025-07-31 12:27:15 +10:00
Robert Wojciechowski	a161213a8f	Update worker-setup-guides.md	2025-07-31 09:57:08 +10:00
Robert Wojciechowski	a884527ad3	Update README.md	2025-07-31 09:56:22 +10:00
Robert Wojciechowski	53be3e0c18	Update README.md	2025-07-31 09:31:51 +10:00
Robert Wojciechowski	8f87d1ac29	Update README.md	2025-07-31 08:01:05 +10:00
robertvoy	b54f2be19c	Merge branch 'main' of https://github.com/robertvoy/ComfyUI-Distributed	2025-07-30 13:23:09 +10:00
robertvoy	1e9ba2383d	increased worker timeout for static distribution	2025-07-30 13:23:00 +10:00
Robert Wojciechowski	33fbe4cb62	Update README.md	2025-07-30 12:58:57 +10:00
Robert Wojciechowski	5882a1064e	Update worker-setup-guides.md	2025-07-30 11:31:06 +10:00
Robert Wojciechowski	b646f0b41c	Update worker-setup-guides.md	2025-07-30 10:38:32 +10:00
Robert Wojciechowski	3c988daad1	Update worker-setup-guides.md	2025-07-30 10:05:39 +10:00
Robert Wojciechowski	500a5b38b2	Update worker-setup-guides.md	2025-07-30 09:40:31 +10:00
Robert Wojciechowski	a49c7a8ed5	Update worker-setup-guides.md	2025-07-30 07:30:25 +10:00
Robert Wojciechowski	8c41749ea6	Update worker-setup-guides.md	2025-07-30 07:29:02 +10:00
Robert Wojciechowski	394a2d8a9a	Update worker-setup-guides.md	2025-07-29 16:48:58 +10:00
Robert Wojciechowski	e13ea47a93	Update worker-setup-guides.md	2025-07-29 16:41:11 +10:00
Robert Wojciechowski	81e644e661	Update ui.js	2025-07-29 16:35:42 +10:00
Robert Wojciechowski	b13c78ad71	Update README.md	2025-07-29 16:31:16 +10:00
Robert Wojciechowski	520393b150	Update worker-setup-guides.md	2025-07-29 16:20:17 +10:00
Robert Wojciechowski	8c029e5702	Update worker-setup-guides.md	2025-07-29 16:12:43 +10:00
Robert Wojciechowski	0391605c80	Update model-download-script.md	2025-07-29 16:09:33 +10:00
Robert Wojciechowski	784aa3a06b	Update model-download-script.md	2025-07-29 16:05:35 +10:00
Robert Wojciechowski	f589a723fe	Update README.md	2025-07-29 15:59:30 +10:00
Robert Wojciechowski	d247746e22	Update worker-setup-guides.md	2025-07-29 15:58:26 +10:00
Robert Wojciechowski	0fe8485c6c	Update README.md	2025-07-29 15:28:06 +10:00
Robert Wojciechowski	ccb00a7142	Update README.md	2025-07-29 14:01:52 +10:00
Robert Wojciechowski	0acf557ba5	Update model-download-script.md	2025-07-29 13:53:03 +10:00
Robert Wojciechowski	9926253c8b	Update model-download-script.md	2025-07-29 13:52:12 +10:00
Robert Wojciechowski	285710bb28	Update README.md	2025-07-29 13:51:31 +10:00
Robert Wojciechowski	a58ca1fc1d	Update worker-setup-guides.md	2025-07-29 13:50:22 +10:00
Robert Wojciechowski	f5a1e59d65	Update worker-setup-guides.md	2025-07-29 13:48:56 +10:00
Robert Wojciechowski	ff02c88224	Update README.md	2025-07-29 13:45:49 +10:00
Robert Wojciechowski	b7c7c033d5	Update pyproject.toml	2025-07-29 13:40:53 +10:00
Robert Wojciechowski	f7efde4eb5	Update README.md	2025-07-29 13:40:18 +10:00
robertvoy	f4f7e48d89	Merge branch 'feature/cloud-workers'	2025-07-29 13:37:53 +10:00
robertvoy	0a480c143a	readme	2025-07-29 13:36:49 +10:00
Robert Wojciechowski	fb46d32e39	Update README.md	2025-07-14 07:05:00 +10:00
Robert Wojciechowski	acf166fe6c	Update README.md	2025-07-11 09:54:12 +10:00
Robert Wojciechowski	e5a2ab8fc5	Update pyproject.toml	2025-07-11 07:23:14 +10:00
snomiao	26aaa1f1c8	chore(publish): update GitHub Actions workflow for node publishing - Add permissions for issue writing - Restrict job execution to specific repository owner - Update action version for node publishing to v1	2025-07-10 00:44:29 +00:00