ComfyMCP Studio

Instructions

This tool takes a reference character image and generates variations with different
poses while maintaining the same character identity (outfit, colors, style).

Args:
    reference_image_base64: Base64 encoded reference character image (front/idle view)
    description: Character description (e.g., "a knight in silver armor")
    poses: List of poses to generate (e.g., ["walking", "attacking", "jumping"])
    denoise: How much to change from reference (0.2=very similar, 0.5=more different). Default 0.35
    seed: Random seed for reproducibility (same seed = same variations)
    preset: Style preset to use (default: character)
    pose_denoise_boost: Additional denoise for action poses (default: 0.25)
    save_to_file: Whether to save images to disk

Returns:
    JSON with base64 images for each pose, all maintaining character identity

Example workflow:
    1. First generate a base character with generate_character(description, poses=["idle"])
    2. Take the best result's image_base64 as reference_image_base64
    3. Call this tool with poses=["walking", "running", "attacking"]

Implementation Reference

• server/main.py:481-599 (handler)

  The handler function decorated with @mcp.tool(), which registers and implements the generate_character_animations tool. It generates consistent character animations from a reference image using img2img with pose-specific prompts and denoising.

  @mcp.tool()
  async def generate_character_animations(
      reference_image_base64: str,
      description: str,
      poses: List[str],
      denoise: float = 0.35,
      seed: Optional[int] = None,
      preset: str = "character",
      pose_denoise_boost: float = 0.25,
      save_to_file: bool = False
  ) -> str:
      """Generate multiple character poses with consistent identity using img2img.
      
      This tool takes a reference character image and generates variations with different
      poses while maintaining the same character identity (outfit, colors, style).
      
      Args:
          reference_image_base64: Base64 encoded reference character image (front/idle view)
          description: Character description (e.g., "a knight in silver armor")
          poses: List of poses to generate (e.g., ["walking", "attacking", "jumping"])
          denoise: How much to change from reference (0.2=very similar, 0.5=more different). Default 0.35
          seed: Random seed for reproducibility (same seed = same variations)
          preset: Style preset to use (default: character)
          pose_denoise_boost: Additional denoise for action poses (default: 0.25)
          save_to_file: Whether to save images to disk
      
      Returns:
          JSON with base64 images for each pose, all maintaining character identity
      
      Example workflow:
          1. First generate a base character with generate_character(description, poses=["idle"])
          2. Take the best result's image_base64 as reference_image_base64
          3. Call this tool with poses=["walking", "running", "attacking"]
      """
      preset_config = get_preset(preset)
      reference_bytes = base64.b64decode(reference_image_base64)
      
      # Use fixed seed for all poses if provided (better consistency)
      if seed is None:
          import random
          seed = random.randint(0, 2**32 - 1)
      
      animations = []
      for i, pose in enumerate(poses):
          import hashlib
          pose_key = pose.strip().lower()
   
          # Stronger pose templates to prevent img2img from copying the reference pose.
          # (img2img without ControlNet can only change pose to a limited extent, so we need to be explicit.)
          pose_hint_map = {
              "idle": "idle stance, neutral pose",
              "walk": "walking pose, one leg forward, arms swinging",
              "walking": "walking pose, one leg forward, arms swinging",
              "run": "running pose, dynamic motion, leaning forward",
              "running": "running pose, dynamic motion, leaning forward",
              "attack": "attacking pose, dynamic action, weapon swing",
              "attacking": "attacking pose, dynamic action, weapon swing",
              "jump": "jumping pose, mid-air, dynamic",
              "jumping": "jumping pose, mid-air, dynamic",
          }
          pose_hint = next((v for k, v in pose_hint_map.items() if k in pose_key), "dynamic pose")
          pose_hash = int.from_bytes(hashlib.sha256(pose_key.encode("utf-8")).digest()[:4], "little")
          pose_seed = (seed + (pose_hash % 1000003) + (i * 10000019)) % (2**32 - 1)
   
          pose_is_action = any(k in pose_key for k in ["walk", "run", "attack", "hit", "slash", "jump", "kick", "punch", "cast", "shoot", "dash"])
          pose_denoise = denoise
          if pose_is_action:
              pose_denoise = min(0.75, denoise + pose_denoise_boost)
          pose_denoise = max(0.2, min(0.75, pose_denoise))
   
          prompt = f"{description}, {pose_hint}, {pose} pose, full body, same character identity, same outfit, same colors, consistent character sheet"
          full_prompt = f"{preset_config.prompt_prefix}{prompt}{preset_config.prompt_suffix}"
          full_negative = f"{preset_config.negative_prompt}, same pose as reference, idle pose, standing straight, front view, identical composition"
           
          try:
              image_bytes = await backend.generate_img2img(
                  reference_image=reference_bytes,
                  prompt=full_prompt,
                  negative_prompt=full_negative,
                  denoise=pose_denoise,
                  seed=pose_seed,
                  steps=preset_config.steps,
                  cfg_scale=preset_config.cfg_scale,
                  sampler=preset_config.sampler,
                  scheduler=preset_config.scheduler
              )
          except NotImplementedError as e:
              return json.dumps({
                  "success": False,
                  "error": str(e),
                  "backend": backend.get_name(),
                  "backend_type": BACKEND_TYPE
              }, indent=2)
          
          anim_data = {
              "index": i,
              "pose": pose,
              "image_base64": image_to_base64(image_bytes),
              "denoise": pose_denoise,
              "seed": pose_seed
          }
          
          if save_to_file:
              output_dir = ensure_directory(OUTPUT_DIR / "characters" / "animations")
              fname = generate_filename(prefix=f"anim_{pose}")
              file_path = output_dir / fname
              file_path.write_bytes(image_bytes)
              anim_data["file_path"] = str(file_path)
          
          animations.append(anim_data)
      
      return json.dumps({
          "success": True,
          "description": description,
          "denoise": denoise,
          "base_seed": seed,
          "count": len(animations),
          "animations": animations
      }, indent=2)