CR Cycle Models (Legacy)

Documentation

• Class name: CR Cycle Models
• Category: 🧩 Comfyroll Studio/🎥 Animation/💀 Legacy
• Output node: False

This node is designed to cycle through different models sequentially or maintain a single model based on the specified mode. It facilitates dynamic model switching during animation sequences, enhancing the versatility and creativity of the animation process.

Input types

Required

• mode
  • Specifies the cycling mode, such as 'Off' for no cycling or 'Sequential' for cycling through models based on the current frame and interval settings.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• model
  • The initial model to be used before any cycling occurs. It serves as the default model when the cycling mode is set to 'Off'.
  • Comfy dtype: MODEL
  • Python dtype: torch.nn.Module
• clip
  • The initial CLIP model to be used alongside the main model, providing additional capabilities or enhancements.
  • Comfy dtype: CLIP
  • Python dtype: torch.nn.Module
• model_list
  • A list of models to cycle through, each defined by its parameters including model alias and checkpoint name.
  • Comfy dtype: MODEL_LIST
  • Python dtype: List[Tuple[str, str]]
• frame_interval
  • The interval between frames at which the model cycling occurs, determining the frequency of model switches.
  • Comfy dtype: INT
  • Python dtype: int
• loops
  • The number of times the model list is repeated, extending the sequence of models available for cycling.
  • Comfy dtype: INT
  • Python dtype: int
• current_frame
  • The current frame number in the animation sequence, used to determine the active model based on the cycling mode and frame interval.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• MODEL
  • Comfy dtype: MODEL
  • The current model after cycling, which may be different from the initial model depending on the cycling mode and frame progress.
  • Python dtype: torch.nn.Module
• CLIP
  • Comfy dtype: CLIP
  • The current CLIP model after cycling, which may be different from the initial CLIP model depending on the cycling mode and frame progress.
  • Python dtype: torch.nn.Module
• VAE
  • Comfy dtype: VAE
  • The current VAE model after cycling, which may be different from the initial VAE model depending on the cycling mode and frame progress.
  • Python dtype: torch.nn.Module
• show_help
  • Comfy dtype: STRING
  • A help URL providing additional information about the cycling process.
  • Python dtype: str

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class CR_CycleModels:

    @classmethod
    def INPUT_TYPES(s):

        modes = ["Off", "Sequential"]

        return {"required": {"mode": (modes,),
                             "model": ("MODEL",),
                             "clip": ("CLIP",),
                             "model_list": ("MODEL_LIST",),
                             "frame_interval": ("INT", {"default": 30, "min": 0, "max": 999, "step": 1,}),        
                             "loops": ("INT", {"default": 1, "min": 1, "max": 1000}),
                             "current_frame": ("INT", {"default": 0.0, "min": 0.0, "max": 9999.0, "step": 1.0,}),
                },
        }

    RETURN_TYPES = ("MODEL", "CLIP", "VAE", "STRING", )
    RETURN_NAMES = ("MODEL", "CLIP", "VAE", "show_help", )
    FUNCTION = "cycle_models"
    CATEGORY = icons.get("Comfyroll/Animation/Legacy")

    def cycle_models(self, mode, model, clip, model_list, frame_interval, loops, current_frame,):
        show_help = "https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes/wiki/Cycler-Nodes#cr-cycle-models"

        # Initialize the list
        model_params = list()

        # Extend lora_params with the lora_list items
        if model_list:
            for _ in range(loops):
                model_params.extend(model_list)
            #print(f"[Debug] CR Cycle Models:{model_params}")

        if mode == "Off":
            return (model, clip, show_help, )               

        elif mode == "Sequential":
            if current_frame == 0:
                return (model, clip, show_help, ) 
            else:    
                # Calculate the index of the current model based on the current_frame and frame_interval
                current_model_index = (current_frame // frame_interval) % len(model_params)
                #print(f"[Debug] CR Cycle Models:{current_model_index}")

                # Get the parameters of the current model
                current_model_params = model_params[current_model_index]
                model_alias, ckpt_name = current_model_params
                print(f"[Info] CR Cycle Models: Current model is {ckpt_name}")

                # Load the current model
                ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
                out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, 
                embedding_directory=folder_paths.get_folder_paths("embeddings"))
                return (out, show_help, )
        #else:
        #    return (model, clip)