efficiency-nodes-comfyui/tsc_utils.py

# Efficiency Nodes Utility functions

from torch import Tensor
import torch
from PIL import Image
import numpy as np

import os
import sys
import json
import folder_paths

# Get the absolute path of the parent directory of the current script
my_dir = os.path.dirname(os.path.abspath(__file__))

# Add the My directory path to the sys.path list
sys.path.append(my_dir)

# Construct the absolute path to the ComfyUI directory
comfy_dir = os.path.abspath(os.path.join(my_dir, '..', '..'))

# Add the ComfyUI directory path to the sys.path list
sys.path.append(comfy_dir)

# Import functions from ComfyUI
import comfy.sd

# Load my version of Comfy functions
from tsc_sd import *

# Cache for Efficiency Node models
loaded_objects = {
    "ckpt": [], # (ckpt_name, ckpt_model, clip, bvae, [id])
    "vae": [],  # (vae_name, vae, [id])
    "lora": []  # ([(lora_name, strength_model, strength_clip)], ckpt_name, lora_model, clip_lora, [id])
}

# Cache for Ksampler (Efficient) Outputs
last_helds: dict[str, list] = {
    "results": [],      # (results, id) # Preview Images, stored as a pil image list
    "latent": [],       # (latent, id)  # Latent outputs, stored as a latent tensor list
    "images": [],       # (images, id)  # Image outputs, stored as an image tensor list
    "vae_decode": [],   # (vae_decode, id) # Used to track wether to vae-decode or not
}

# Tensor to PIL (grabbed from WAS Suite)
def tensor2pil(image: torch.Tensor) -> Image.Image:
    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))

# Convert PIL to Tensor (grabbed from WAS Suite)
def pil2tensor(image: Image.Image) -> torch.Tensor:
    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)

def extract_node_info(prompt, id, indirect_key=None):
    # Convert ID to string
    id = str(id)
    node_id = None

    # If an indirect_key (like 'script') is provided, perform a two-step lookup
    if indirect_key:
        # Ensure the id exists in the prompt and has an 'inputs' entry with the indirect_key
        if id in prompt and 'inputs' in prompt[id] and indirect_key in prompt[id]['inputs']:
            # Extract the indirect_id
            indirect_id = prompt[id]['inputs'][indirect_key][0]

            # Ensure the indirect_id exists in the prompt
            if indirect_id in prompt:
                node_id = indirect_id
                return prompt[indirect_id].get('class_type', None), node_id

        # If indirect_key is not found within the prompt
        return None, None

    # If no indirect_key is provided, perform a direct lookup
    return prompt.get(id, {}).get('class_type', None), node_id

def extract_node_value(prompt, id, key):
    # If ID is in data, return its 'inputs' value for a given key. Otherwise, return None.
    return prompt.get(str(id), {}).get('inputs', {}).get(key, None)

def print_loaded_objects_entries(id=None, prompt=None, show_id=False):
    print("-" * 40)  # Print an empty line followed by a separator line
    if id is not None:
        id = str(id)  # Convert ID to string
    if prompt is not None and id is not None:
        node_name, _ = extract_node_info(prompt, id)
        if show_id:
            print(f"\033[36m{node_name} Models Cache: (node_id:{int(id)})\033[0m")
        else:
            print(f"\033[36m{node_name} Models Cache:\033[0m")
    elif id is None:
        print(f"\033[36mGlobal Models Cache:\033[0m")
    else:
        print(f"\033[36mModels Cache: \nnode_id:{int(id)}\033[0m")
    entries_found = False
    for key in ["ckpt", "vae", "lora"]:
        entries_with_id = loaded_objects[key] if id is None else [entry for entry in loaded_objects[key] if id in entry[-1]]
        if not entries_with_id:  # If no entries with the chosen ID, print None and skip this key
            continue
        entries_found = True
        print(f"{key.capitalize()}:")
        for i, entry in enumerate(entries_with_id, 1):  # Start numbering from 1
            if key == "lora":
                lora_models_info = ', '.join(f"{os.path.splitext(os.path.basename(name))[0]}({round(strength_model, 2)},{round(strength_clip, 2)})" for name, strength_model, strength_clip in entry[0])
                base_ckpt_name = os.path.splitext(os.path.basename(entry[1]))[0]  # Split logic for base_ckpt
                if id is None:
                    associated_ids = ', '.join(map(str, entry[-1]))  # Gather all associated ids
                    print(f"  [{i}] base_ckpt: {base_ckpt_name}, lora(mod,clip): {lora_models_info} (ids: {associated_ids})")
                else:
                    print(f"  [{i}] base_ckpt: {base_ckpt_name}, lora(mod,clip): {lora_models_info}")
            else:
                name_without_ext = os.path.splitext(os.path.basename(entry[0]))[0]
                if id is None:
                    associated_ids = ', '.join(map(str, entry[-1]))  # Gather all associated ids
                    print(f"  [{i}] {name_without_ext} (ids: {associated_ids})")
                else:
                    print(f"  [{i}] {name_without_ext}")
    if not entries_found:
        print("-")


# This function cleans global variables associated with nodes that are no longer detected on UI
def globals_cleanup(prompt):
    global loaded_objects
    global last_helds

    # Step 1: Clean up last_helds
    for key in list(last_helds.keys()):
        original_length = len(last_helds[key])
        last_helds[key] = [(value, id) for value, id in last_helds[key] if str(id) in prompt.keys()]
        ###if original_length != len(last_helds[key]):
            ###print(f'Updated {key} in last_helds: {last_helds[key]}')

    # Step 2: Clean up loaded_objects
    for key in list(loaded_objects.keys()):
        for i, tup in enumerate(list(loaded_objects[key])):
            # Remove ids from id array in each tuple that don't exist in prompt
            id_array = [id for id in tup[-1] if str(id) in prompt.keys()]
            if len(id_array) != len(tup[-1]):
                if id_array:
                    loaded_objects[key][i] = tup[:-1] + (id_array,)
                    ###print(f'Updated tuple at index {i} in {key} in loaded_objects: {loaded_objects[key][i]}')
                else:
                    # If id array becomes empty, delete the corresponding tuple
                    loaded_objects[key].remove(tup)
                    ###print(f'Deleted tuple at index {i} in {key} in loaded_objects because its id array became empty.')

def load_checkpoint(ckpt_name, id, output_vae=True, cache=None, cache_overwrite=False):
    """
    Searches for tuple index that contains ckpt_name in "ckpt" array of loaded_objects.
    If found, extracts the model, clip, and vae from the loaded_objects.
    If not found, loads the checkpoint, extracts the model, clip, and vae.
    The id parameter represents the node ID and is used for caching models for the XY Plot node.
    If the cache limit is reached for a specific id, clears the cache and returns the loaded model, clip, and vae without adding a new entry.
    If there is cache space, adds the id to the ids list if it's not already there.
    If there is cache space and the checkpoint was not found in loaded_objects, adds a new entry to loaded_objects.

    Parameters:
    - ckpt_name: name of the checkpoint to load.
    - id: an identifier for caching models for specific nodes.
    - output_vae: boolean, if True loads the VAE too.
    - cache (optional): an integer that specifies how many checkpoint entries with a given id can exist in loaded_objects. Defaults to None.
    """
    global loaded_objects

    for entry in loaded_objects["ckpt"]:
        if entry[0] == ckpt_name:
            _, model, clip, vae, ids = entry
            cache_full = cache and len([entry for entry in loaded_objects["ckpt"] if id in entry[-1]]) >= cache

            if cache_full:
                clear_cache(id, cache, "ckpt")
            elif id not in ids:
                ids.append(id)

            return model, clip, vae

    ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
    out = load_checkpoint_guess_config_tsc(ckpt_path, output_vae, output_clip=True,
                                                embedding_directory=folder_paths.get_folder_paths("embeddings"))
    model = out[0]
    clip = out[1]
    vae = out[2]  # bvae

    if cache:
        if len([entry for entry in loaded_objects["ckpt"] if id in entry[-1]]) < cache:
            loaded_objects["ckpt"].append((ckpt_name, model, clip, vae, [id]))
        else:
            clear_cache(id, cache, "ckpt")
            if cache_overwrite:
                # Find the first entry with the id, remove the id from the entry's id list
                for e in loaded_objects["ckpt"]:
                    if id in e[-1]:
                        e[-1].remove(id)
                        # If the id list becomes empty, remove the entry from the "ckpt" list
                        if not e[-1]:
                            loaded_objects["ckpt"].remove(e)
                        break
                loaded_objects["ckpt"].append((ckpt_name, model, clip, vae, [id]))

    return model, clip, vae

def get_bvae_by_ckpt_name(ckpt_name):
    for ckpt in loaded_objects["ckpt"]:
        if ckpt[0] == ckpt_name:
            return ckpt[3]  # return 'bvae' variable
    return None  # return None if no match is found

def load_vae(vae_name, id, cache=None, cache_overwrite=False):
    """
    Extracts the vae with a given name from the "vae" array in loaded_objects.
    If the vae is not found, creates a new VAE object with the given name and adds it to the "vae" array.
    Also stores the id parameter, which is used for caching models specifically for nodes with the given ID.
    If the cache limit is reached for a specific id, returns the loaded vae without adding id or making a new entry in loaded_objects.
    If there is cache space, and the id is not in the ids list, adds the id to the ids list.
    If there is cache space, and the vae was not found in loaded_objects, adds a new entry to the loaded_objects.

    Parameters:
    - vae_name: name of the VAE to load.
    - id (optional): an identifier for caching models for specific nodes. Defaults to None.
    - cache (optional): an integer that specifies how many vae entries with a given id can exist in loaded_objects. Defaults to None.
    """
    global loaded_objects

    for i, entry in enumerate(loaded_objects["vae"]):
        if entry[0] == vae_name:
            vae, ids = entry[1], entry[2]
            if id not in ids:
                if cache and len([entry for entry in loaded_objects["vae"] if id in entry[-1]]) >= cache:
                    return vae
                ids.append(id)
            if cache:
                clear_cache(id, cache, "vae")
            return vae

    vae_path = folder_paths.get_full_path("vae", vae_name)
    vae = comfy.sd.VAE(ckpt_path=vae_path)

    if cache:
        if len([entry for entry in loaded_objects["vae"] if id in entry[-1]]) < cache:
            loaded_objects["vae"].append((vae_name, vae, [id]))
        else:
            clear_cache(id, cache, "vae")
            if cache_overwrite:
                # Find the first entry with the id, remove the id from the entry's id list
                for e in loaded_objects["vae"]:
                    if id in e[-1]:
                        e[-1].remove(id)
                        # If the id list becomes empty, remove the entry from the "vae" list
                        if not e[-1]:
                            loaded_objects["vae"].remove(e)
                        break
                loaded_objects["vae"].append((vae_name, vae, [id]))

    return vae

def load_lora(lora_params, ckpt_name, id, cache=None, ckpt_cache=None, cache_overwrite=False):
    """
    Extracts the Lora model with a given name from the "lora" array in loaded_objects.
    If the Lora model is not found or strength values changed or model changed, creates a new Lora object with the given name and adds it to the "lora" array.
    Also stores the id parameter, which is used for caching models specifically for nodes with the given ID.
    If the cache limit is reached for a specific id, clears the cache and returns the loaded Lora model and clip without adding a new entry.
    If there is cache space, adds the id to the ids list if it's not already there.
    If there is cache space and the Lora model was not found in loaded_objects, adds a new entry to loaded_objects.

    Parameters:
    - lora_params: A list of tuples, where each tuple contains lora_name, strength_model, strength_clip.
    - ckpt_name: name of the checkpoint from which the Lora model is created.
    - id: an identifier for caching models for specific nodes.
    - cache (optional): an integer that specifies how many Lora entries with a given id can exist in loaded_objects. Defaults to None.
    """
    global loaded_objects

    for entry in loaded_objects["lora"]:
        # Convert to sets and compare
        if set(entry[0]) == set(lora_params) and entry[1] == ckpt_name:

            _, _, lora_model, lora_clip, ids = entry
            cache_full = cache and len([entry for entry in loaded_objects["lora"] if id in entry[-1]]) >= cache

            if cache_full:
                clear_cache(id, cache, "lora")
            elif id not in ids:
                ids.append(id)

            # Additional cache handling for 'ckpt' just like in 'load_checkpoint' function
            for ckpt_entry in loaded_objects["ckpt"]:
                if ckpt_entry[0] == ckpt_name:
                    _, _, _, _, ckpt_ids = ckpt_entry
                    ckpt_cache_full = ckpt_cache and len(
                        [ckpt_entry for ckpt_entry in loaded_objects["ckpt"] if id in ckpt_entry[-1]]) >= ckpt_cache

                    if ckpt_cache_full:
                        clear_cache(id, ckpt_cache, "ckpt")
                    elif id not in ckpt_ids:
                        ckpt_ids.append(id)

            return lora_model, lora_clip

    def recursive_load_lora(lora_params, ckpt, clip, id, ckpt_cache, cache_overwrite, folder_paths):
        if len(lora_params) == 0:
            return ckpt, clip

        lora_name, strength_model, strength_clip = lora_params[0]
        lora_path = folder_paths.get_full_path("loras", lora_name)
        lora_model, lora_clip = load_lora_for_models_tsc(ckpt, clip, lora_path, strength_model, strength_clip)

        # Call the function again with the new lora_model and lora_clip and the remaining tuples
        return recursive_load_lora(lora_params[1:], lora_model, lora_clip, id, ckpt_cache, cache_overwrite, folder_paths)

    # Unpack lora parameters from the first element of the list for now
    lora_name, strength_model, strength_clip = lora_params[0]
    ckpt, clip, _ = load_checkpoint(ckpt_name, id, cache=ckpt_cache, cache_overwrite=cache_overwrite)

    lora_model, lora_clip = recursive_load_lora(lora_params, ckpt, clip, id, ckpt_cache, cache_overwrite, folder_paths)

    if cache:
        if len([entry for entry in loaded_objects["lora"] if id in entry[-1]]) < cache:
            loaded_objects["lora"].append((lora_params, ckpt_name, lora_model, lora_clip, [id]))
        else:
            clear_cache(id, cache, "lora")
            if cache_overwrite:
                # Find the first entry with the id, remove the id from the entry's id list
                for e in loaded_objects["lora"]:
                    if id in e[-1]:
                        e[-1].remove(id)
                        # If the id list becomes empty, remove the entry from the "lora" list
                        if not e[-1]:
                            loaded_objects["lora"].remove(e)
                        break
                loaded_objects["lora"].append((lora_params, ckpt_name, lora_model, lora_clip, [id]))

    return lora_model, lora_clip

def clear_cache(id, cache, dict_name):
    """
    Clear the cache for a specific id in a specific dictionary (either "ckpt" or "vae").
    If the cache limit is reached for a specific id, deletes the id from the oldest entry.
    If the id array of the entry becomes empty, deletes the entry.
    """
    # Get all entries associated with the id_element
    id_associated_entries = [entry for entry in loaded_objects[dict_name] if id in entry[-1]]
    while len(id_associated_entries) > cache:
        # Identify an older entry (but not necessarily the oldest) containing id
        older_entry = id_associated_entries[0]
        # Remove the id_element from the older entry
        older_entry[-1].remove(id)
        # If the id array of the older entry becomes empty after this, delete the entry
        if not older_entry[-1]:
            loaded_objects[dict_name].remove(older_entry)
        # Update the id_associated_entries
        id_associated_entries = [entry for entry in loaded_objects[dict_name] if id in entry[-1]]

def clear_cache_by_exception(node_id, vae_dict=None, ckpt_dict=None, lora_dict=None):
    global loaded_objects

    dict_mapping = {
        "vae_dict": "vae",
        "ckpt_dict": "ckpt",
        "lora_dict": "lora"
    }

    for arg_name, arg_val in {"vae_dict": vae_dict, "ckpt_dict": ckpt_dict, "lora_dict": lora_dict}.items():
        if arg_val is None:
            continue

        dict_name = dict_mapping[arg_name]

        for tuple_idx, tuple_item in enumerate(loaded_objects[dict_name].copy()):
            if arg_name == "lora_dict":
                # Iterate over the tuples (lora_params, ckpt_name) in arg_val
                for lora_params, ckpt_name in arg_val:
                    # Compare lists of tuples considering order inside tuples, but not order of tuples
                    if set(lora_params) == set(tuple_item[0]) and ckpt_name == tuple_item[1]:
                        break
                else:  # If no match was found in lora_dict, remove the tuple from loaded_objects
                    if node_id in tuple_item[-1]:
                        tuple_item[-1].remove(node_id)
                        if not tuple_item[-1]:
                            loaded_objects[dict_name].remove(tuple_item)
                    continue
            elif tuple_item[0] not in arg_val:  # Only remove the tuple if it's not in arg_val
                if node_id in tuple_item[-1]:
                    tuple_item[-1].remove(node_id)
                    if not tuple_item[-1]:
                        loaded_objects[dict_name].remove(tuple_item)


# Retrieve the cache number from 'node_settings' json file
def get_cache_numbers(node_name):
    # Get the directory path of the current file
    my_dir = os.path.dirname(os.path.abspath(__file__))
    # Construct the file path for node_settings.json
    settings_file = os.path.join(my_dir, 'node_settings.json')
    # Load the settings from the JSON file
    with open(settings_file, 'r') as file:
        node_settings = json.load(file)
    # Retrieve the cache numbers for the given node
    model_cache_settings = node_settings.get(node_name, {}).get('model_cache', {})
    vae_cache = int(model_cache_settings.get('vae', 1))
    ckpt_cache = int(model_cache_settings.get('ckpt', 1))
    lora_cache = int(model_cache_settings.get('lora', 1))
    return vae_cache, ckpt_cache, lora_cache

def print_last_helds(id=None):
    print("\n" + "-" * 40)  # Print an empty line followed by a separator line
    if id is not None:
        id = str(id)  # Convert ID to string
        print(f"Node-specific Last Helds (node_id:{int(id)})")
    else:
        print(f"Global Last Helds:")
    for key in ["results", "latent", "images", "vae_decode"]:
        entries_with_id = last_helds[key] if id is None else [entry for entry in last_helds[key] if id == entry[-1]]
        if not entries_with_id:  # If no entries with the chosen ID, print None and skip this key
            continue
        print(f"{key.capitalize()}:")
        for i, entry in enumerate(entries_with_id, 1):  # Start numbering from 1
            if isinstance(entry[0], bool):  # Special handling for boolean types
                output = entry[0]
            else:
                output = len(entry[0])
            if id is None:
                print(f"  [{i}] Output: {output} (id: {entry[-1]})")
            else:
                print(f"  [{i}] Output: {output}")
    print("-" * 40)  # Print a separator line
    print("\n")  # Print an empty line