ComfyMCP Studio

Instructions

This tool uses depth maps to guide the generation, ensuring consistent camera angles
like top-down, side view, front view, etc.

Args:
    prompt: Description of the asset to generate (e.g., "a wooden barrel")
    view_type: Camera angle - "topdown", "side", "front", "3/4"
    shape: Object shape hint - "flat", "sphere", "cylinder", "box"
    preset: Style preset to use (default: topdown_prop)
    controlnet_model: ControlNet model (default: diffusers_xl_depth_full.safetensors)
    control_strength: How strongly to follow viewpoint (0.0-1.0, default: 0.8)
    width: Output width in pixels
    height: Output height in pixels
    seed: Random seed for reproducibility
    save_to_file: Whether to save the image to disk

Returns:
    JSON with base64 image data and metadata

Note:
    Requires ControlNet models installed in ComfyUI. Common depth models:
    - diffusers_xl_depth_full.safetensors (SDXL)
    - control_v11f1p_sd15_depth.pth (SD1.5)

Implementation Reference

• server/main.py:602-742 (handler)

  Primary handler for the 'generate_with_viewpoint' MCP tool. Decorated with @mcp.tool() for automatic registration and schema inference. Implements viewpoint-controlled asset generation using ControlNet depth maps.

  @mcp.tool()
  async def generate_with_viewpoint(
      prompt: str,
      view_type: str = "topdown",
      shape: str = "flat",
      preset: str = "topdown_prop",
      controlnet_model: str = "diffusers_xl_depth_full.safetensors",
      control_strength: float = 0.95,
      width: int = 1024,
      height: int = 1024,
      seed: Optional[int] = None,
      save_to_file: bool = False
  ) -> str:
      """Generate a game asset with precise camera viewpoint control using ControlNet.
      
      This tool uses depth maps to guide the generation, ensuring consistent camera angles
      like top-down, side view, front view, etc.
      
      Args:
          prompt: Description of the asset to generate (e.g., "a wooden barrel")
          view_type: Camera angle - "topdown", "side", "front", "3/4"
          shape: Object shape hint - "flat", "sphere", "cylinder", "box"
          preset: Style preset to use (default: topdown_prop)
          controlnet_model: ControlNet model (default: diffusers_xl_depth_full.safetensors)
          control_strength: How strongly to follow viewpoint (0.0-1.0, default: 0.8)
          width: Output width in pixels
          height: Output height in pixels
          seed: Random seed for reproducibility
          save_to_file: Whether to save the image to disk
      
      Returns:
          JSON with base64 image data and metadata
      
      Note:
          Requires ControlNet models installed in ComfyUI. Common depth models:
          - diffusers_xl_depth_full.safetensors (SDXL)
          - control_v11f1p_sd15_depth.pth (SD1.5)
      """
      preset_config = get_preset(preset)
      
      # Build full prompt with preset
      full_prompt = f"{preset_config.prompt_prefix}{prompt}{preset_config.prompt_suffix}"
      full_negative = preset_config.negative_prompt
  
      img_width = width
      img_height = height
  
      render_width = img_width
      render_height = img_height
      should_downscale = (img_width < preset_config.default_width) or (img_height < preset_config.default_height)
      if should_downscale:
          scale = max(preset_config.default_width / max(1, img_width), preset_config.default_height / max(1, img_height))
          render_width = int(round(img_width * scale))
          render_height = int(round(img_height * scale))
  
      # Clamp render dimensions to match backend constraints (SDXL-safe)
      render_width = max(512, min(2048, (render_width // 8) * 8))
      render_height = max(512, min(2048, (render_height // 8) * 8))
      
      # Create depth map for the specified viewpoint
      depth_map = create_depth_map(render_width, render_height, view_type=view_type, shape=shape)
      
      try:
          # Generate with timeout to prevent hanging
          image_bytes = await asyncio.wait_for(
              backend.generate_with_controlnet(
                  prompt=full_prompt,
                  control_image=depth_map,
                  controlnet_model=controlnet_model,
                  control_strength=control_strength,
                  negative_prompt=full_negative,
                  width=render_width,
                  height=render_height,
                  seed=seed,
                  steps=preset_config.steps,
                  cfg_scale=preset_config.cfg_scale,
                  sampler=preset_config.sampler,
                  scheduler=preset_config.scheduler
              ),
              timeout=300.0  # 5 minute timeout
          )
      except asyncio.TimeoutError:
          return json.dumps({
              "success": False,
              "error": "Generation timed out after 5 minutes",
              "backend": backend.get_name(),
              "backend_type": BACKEND_TYPE
          }, indent=2)
      except NotImplementedError as e:
          return json.dumps({
              "success": False,
              "error": str(e),
              "hint": "ControlNet requires ComfyUI backend with ControlNet models installed",
              "backend": backend.get_name(),
              "backend_type": BACKEND_TYPE
          }, indent=2)
      except Exception as e:
          return json.dumps({
              "success": False,
              "error": str(e),
              "hint": "Check if ControlNet model exists in ComfyUI/models/controlnet/",
              "backend": backend.get_name()
          }, indent=2)
      
      if should_downscale:
          resample = Image.Resampling.NEAREST if preset.startswith("pixel") else Image.Resampling.LANCZOS
          image_bytes = resize_image(image_bytes, img_width, img_height, resample=resample)
  
      image_b64 = image_to_base64(image_bytes)
      result = {
          "success": True,
          "backend": backend.get_name(),
          "width": img_width,
          "height": img_height,
          "view_type": view_type,
          "shape": shape,
          "control_strength": control_strength,
          "preset": preset,
          "prompt": full_prompt,
          "hash": hash_image(image_bytes)
      }
      
      # ControlNet images are always saved to file to ensure reliable MCP response
      # (large base64 payloads can cause MCP stdio transport issues)
      output_dir = ensure_directory(OUTPUT_DIR / "controlnet")
      fname = generate_filename(prefix=f"cn_{view_type}", suffix=shape)
      file_path = output_dir / fname
      file_path.write_bytes(image_bytes)
      result["file_path"] = str(file_path)
      
      depth_path = output_dir / f"depth_{fname}"
      depth_path.write_bytes(depth_map)
      result["depth_map_path"] = str(depth_path)
      
      # Never include base64 for ControlNet - always use file_path
      # This prevents MCP stdio blocking and ensures agent receives response
      result["image_base64_omitted"] = True
      result["image_base64_omitted_reason"] = "controlnet_always_saves_to_file"
      
      return json.dumps(result, indent=2)

• server/main.py:744-813 (helper)

  Convenience wrapper tool 'generate_topdown_asset' that invokes 'generate_with_viewpoint' with top-down specific parameters and asset type adjustments.

  @mcp.tool()
  async def generate_topdown_asset(
      prompt: str,
      asset_type: str = "prop",
      size: int = 512,
      control_strength: float = 0.65,
      seed: Optional[int] = None,
      save_to_file: bool = True
  ) -> str:
      """Simplified tool to generate top-down 2D game assets with guaranteed viewpoint.
      
      This is a convenience wrapper around generate_with_viewpoint specifically for
      top-down games (RPG, strategy, etc.).
      
      Args:
          prompt: Description of the asset (e.g., "wooden treasure chest", "stone well")
          asset_type: Type of asset - "prop", "character", "creature", "tile", "effect"
          size: Output size in pixels (square)
          control_strength: How strictly to enforce top-down view (0.5-1.0)
          seed: Random seed for reproducibility
          save_to_file: Whether to save the image to disk (default: True for reliability)
      
      Returns:
          JSON with file_path to generated image
      """
      # Map asset type to preset and shape
      preset_map = {
          "prop": ("topdown_prop", "box"),
          "character": ("topdown_character", "humanoid"),
          "creature": ("topdown_creature", "humanoid"),
          "tile": ("topdown_tile", "flat"),
          "effect": ("effect", "sphere"),
      }
      
      preset, shape = preset_map.get(asset_type, ("topdown_prop", "flat"))
  
      effective_strength = control_strength
      effective_prompt = prompt
      if asset_type == "character":
          effective_strength = min(control_strength, 0.70)
          effective_prompt = (
              f"{prompt}, single character, one body, one head, full body, "
              f"no visible face, no eyes, no mouth, helmet top view, "
              f"no duplicated weapons, no duplicated armor, no floating parts, no separate objects"
          )
      elif asset_type == "creature":
          effective_strength = min(control_strength, 0.70)
          effective_prompt = (
              f"{prompt}, single creature, one body, full body, "
              f"no duplicated limbs, no floating parts, no separate objects"
          )
      elif asset_type == "prop":
          # Stardew Valley style - allow more artistic freedom
          effective_strength = min(control_strength, 0.60)
          effective_prompt = f"{prompt}, single object"
      elif asset_type == "effect":
          effective_prompt = f"{prompt}, centered effect, radial glow"
      
      # Use the viewpoint tool
      return await generate_with_viewpoint(
          prompt=effective_prompt,
          view_type="topdown",
          shape=shape,
          preset=preset,
          control_strength=effective_strength,
          width=size,
          height=size,
          seed=seed,
          save_to_file=save_to_file
      )