1614 lines
69 KiB
Python
1614 lines
69 KiB
Python
#---------------------------------------------------------------------------------------------------------------------------------------------------#
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# tinyterraNodes developed in 2023 by tinyterra https://github.com/TinyTerra #
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# for ComfyUI https://github.com/comfyanonymous/ComfyUI #
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#---------------------------------------------------------------------------------------------------------------------------------------------------#
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import os
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import sys
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import json
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import torch
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import comfy.sd
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import comfy.utils
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import numpy as np
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import folder_paths
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import comfy.samplers
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from torch import Tensor
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from pathlib import Path
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import comfy.model_management
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from nodes import common_ksampler
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from PIL.PngImagePlugin import PngInfo
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from PIL import Image, ImageDraw, ImageFont
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from comfy.sd import ModelPatcher, CLIP, VAE
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from comfy_extras.chainner_models import model_loading
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# Get absolute path's of the current parent directory, of the ComfyUI directory and add to sys.path list
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my_dir = Path(__file__).parent
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comfy_dir = Path(my_dir).parent.parent
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font_path = os.path.join(my_dir, 'arial.ttf')
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sitepkg = comfy_dir.parent / 'python_embeded' / 'Lib' / 'site-packages'
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script_path = comfy_dir.parent / 'python_embeded' / 'Scripts'
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sys.path.append(str(comfy_dir))
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sys.path.append(str(sitepkg))
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sys.path.append(str(script_path))
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MAX_RESOLUTION=8192
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# Tensor to PIL & PIL to Tensor (from WAS Suite)
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def tensor2pil(image: torch.Tensor) -> Image.Image:
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return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
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def pil2tensor(image: Image.Image) -> torch.Tensor:
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return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
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# Cache models in RAM
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loaded_objects = {
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"ckpt": [], # (ckpt_name, model)
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"clip": [], # (ckpt_name, clip)
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"bvae": [], # (ckpt_name, vae)
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"vae": [], # (vae_name, vae)
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"lora": [], # (lora_name, model_name, model_lora, clip_lora, strength_model, strength_clip)
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}
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def update_loaded_objects(prompt):
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global loaded_objects
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# Extract all Efficient Loader class type entries
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ttN_pipeLoader_entries = [entry for entry in prompt.values() if entry["class_type"] == "ttN pipeLoader"]
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# Collect all desired model, vae, and lora names
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desired_ckpt_names = set()
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desired_vae_names = set()
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desired_lora_names = set()
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for entry in ttN_pipeLoader_entries:
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desired_ckpt_names.add(entry["inputs"]["ckpt_name"])
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desired_vae_names.add(entry["inputs"]["vae_name"])
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desired_lora_names.add(entry["inputs"]["lora1_name"])
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desired_lora_names.add(entry["inputs"]["lora2_name"])
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desired_lora_names.add(entry["inputs"]["lora3_name"])
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# Check and clear unused ckpt, clip, and bvae entries
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for list_key in ["ckpt", "clip", "bvae"]:
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unused_indices = [i for i, entry in enumerate(loaded_objects[list_key]) if entry[0] not in desired_ckpt_names]
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for index in sorted(unused_indices, reverse=True):
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loaded_objects[list_key].pop(index)
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# Check and clear unused vae entries
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unused_vae_indices = [i for i, entry in enumerate(loaded_objects["vae"]) if entry[0] not in desired_vae_names]
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for index in sorted(unused_vae_indices, reverse=True):
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loaded_objects["vae"].pop(index)
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# Check and clear unused lora entries
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unused_lora_indices = [i for i, entry in enumerate(loaded_objects["lora"]) if entry[0] not in desired_lora_names]
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for index in sorted(unused_lora_indices, reverse=True):
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loaded_objects["lora"].pop(index)
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def load_checkpoint(ckpt_name,output_vae=True, output_clip=True):
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"""
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Searches for tuple index that contains ckpt_name in "ckpt" array of loaded_objects.
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If found, extracts the model, clip, and vae from the loaded_objects.
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If not found, loads the checkpoint, extracts the model, clip, and vae, and adds them to the loaded_objects.
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Returns the model, clip, and vae.
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"""
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global loaded_objects
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# Search for tuple index that contains ckpt_name in "ckpt" array of loaded_objects
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checkpoint_found = False
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for i, entry in enumerate(loaded_objects["ckpt"]):
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if entry[0] == ckpt_name:
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# Extract the second element of the tuple at 'i' in the "ckpt", "clip", "bvae" arrays
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model = loaded_objects["ckpt"][i][1]
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clip = loaded_objects["clip"][i][1]
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vae = loaded_objects["bvae"][i][1]
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checkpoint_found = True
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break
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# If not found, load ckpt
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if checkpoint_found == False:
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# Load Checkpoint
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ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
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out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True,
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embedding_directory=folder_paths.get_folder_paths("embeddings"))
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model = out[0]
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clip = out[1]
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vae = out[2]
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# Update loaded_objects[] array
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loaded_objects["ckpt"].append((ckpt_name, out[0]))
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loaded_objects["clip"].append((ckpt_name, out[1]))
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loaded_objects["bvae"].append((ckpt_name, out[2]))
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return model, clip, vae
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def load_vae(vae_name):
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"""
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Extracts the vae with a given name from the "vae" array in loaded_objects.
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If the vae is not found, creates a new VAE object with the given name and adds it to the "vae" array.
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"""
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global loaded_objects
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# Check if vae_name exists in "vae" array
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if any(entry[0] == vae_name for entry in loaded_objects["vae"]):
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# Extract the second tuple entry of the checkpoint
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vae = [entry[1] for entry in loaded_objects["vae"] if entry[0] == vae_name][0]
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else:
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vae_path = folder_paths.get_full_path("vae", vae_name)
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vae = comfy.sd.VAE(ckpt_path=vae_path)
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# Update loaded_objects[] array
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loaded_objects["vae"].append((vae_name, vae))
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return vae
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def load_lora(lora_name, model, clip, strength_model, strength_clip):
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"""
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Extracts the Lora model with a given name from the "lora" array in loaded_objects.
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If the Lora model is not found or the strength values change or the original model has changed, creates a new Lora object with the given name and adds it to the "lora" array.
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"""
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global loaded_objects
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# Get the model_name (ckpt_name) from the first entry in loaded_objects
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model_name = loaded_objects["ckpt"][0][0] if loaded_objects["ckpt"] else None
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# Check if lora_name exists in "lora" array
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existing_lora = [entry for entry in loaded_objects["lora"] if entry[0] == lora_name]
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if existing_lora:
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lora_name, stored_model_name, model_lora, clip_lora, stored_strength_model, stored_strength_clip = existing_lora[0]
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# Check if the model_name, strength_model, and strength_clip are the same
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if model_name == stored_model_name and strength_model == stored_strength_model and strength_clip == stored_strength_clip:
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# Check if the model has not changed in the loaded_objects
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existing_model = [entry for entry in loaded_objects["ckpt"] if entry[0] == model_name]
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if existing_model and existing_model[0][1] == model:
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return model_lora, clip_lora
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# If Lora model not found or strength values changed or model changed, generate new Lora models
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lora_path = folder_paths.get_full_path("loras", lora_name)
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model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora_path, strength_model, strength_clip)
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# Remove existing Lora model if it exists
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if existing_lora:
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loaded_objects["lora"].remove(existing_lora[0])
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# Update loaded_objects[] array
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loaded_objects["lora"].append((lora_name, model_name, model_lora, clip_lora, strength_model, strength_clip))
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return model_lora, clip_lora
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#---------------------------------------------------------------ttN Pipe Loader START---------------------------------------------------------------#
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# ttN Pipe Loader (Modifed from TSC Efficient Loader and Advanced clip text encode)
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from .adv_encode import advanced_encode
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class ttN_TSC_pipeLoader:
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@classmethod
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def INPUT_TYPES(cls):
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return {"required": {
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"ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
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"vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),),
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"clip_skip": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
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"lora1_name": (["None"] + folder_paths.get_filename_list("loras"),),
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"lora1_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lora1_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lora2_name": (["None"] + folder_paths.get_filename_list("loras"),),
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"lora2_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lora2_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lora3_name": (["None"] + folder_paths.get_filename_list("loras"),),
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"lora3_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lora3_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"positive": ("STRING", {"default": "Positive","multiline": True}),
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"positive_token_normalization": (["none", "mean", "length", "length+mean"],),
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"positive_weight_interpretation": (["comfy", "A1111", "compel", "comfy++"],),
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"negative": ("STRING", {"default": "Negative", "multiline": True}),
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"negative_token_normalization": (["none", "mean", "length", "length+mean"],),
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"negative_weight_interpretation": (["comfy", "A1111", "compel", "comfy++"],),
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"empty_latent_width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
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"empty_latent_height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
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"batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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},
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"hidden": {"prompt": "PROMPT"}}
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RETURN_TYPES = ("PIPE_LINE" ,"MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "CLIP", "INT",)
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RETURN_NAMES = ("pipe","model", "positive", "negative", "latent", "vae", "clip", "seed",)
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FUNCTION = "adv_pipeloader"
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CATEGORY = "ttN/pipe"
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def adv_pipeloader(self, ckpt_name, vae_name, clip_skip,
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lora1_name, lora1_model_strength, lora1_clip_strength,
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lora2_name, lora2_model_strength, lora2_clip_strength,
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lora3_name, lora3_model_strength, lora3_clip_strength,
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positive, positive_token_normalization, positive_weight_interpretation,
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negative, negative_token_normalization, negative_weight_interpretation,
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empty_latent_width, empty_latent_height, batch_size, seed, prompt=None):
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model: ModelPatcher | None = None
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clip: CLIP | None = None
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vae: VAE | None = None
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# Create Empty Latent
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latent = torch.zeros([batch_size, 4, empty_latent_height // 8, empty_latent_width // 8]).cpu()
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samples = {"samples":latent}
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# Clean models from loaded_objects
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update_loaded_objects(prompt)
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# Load models
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model, clip, vae = load_checkpoint(ckpt_name)
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# note: load_lora only works properly (as of now) when ckpt dictionary is only 1 entry long!
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if lora1_name != "None":
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model, clip = load_lora(lora1_name, model, clip, lora1_model_strength, lora1_clip_strength)
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if lora2_name != "None":
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model, clip = load_lora(lora2_name, model, clip, lora2_model_strength, lora2_clip_strength)
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if lora3_name != "None":
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model, clip = load_lora(lora3_name, model, clip, lora3_model_strength, lora3_clip_strength)
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# Check for custom VAE
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if vae_name != "Baked VAE":
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vae = load_vae(vae_name)
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# CLIP skip
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if not clip:
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raise Exception("No CLIP found")
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clip = clip.clone()
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clip.clip_layer(clip_skip)
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positive_embeddings_final = advanced_encode(clip, positive, positive_token_normalization, positive_weight_interpretation, w_max=1.0)
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negative_embeddings_final = advanced_encode(clip, negative, negative_token_normalization, negative_weight_interpretation, w_max=1.0)
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image = pil2tensor(Image.new('RGB', (1, 1), (0, 0, 0)))
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pipe = (model, [[positive_embeddings_final, {}]], [[negative_embeddings_final, {}]], samples, vae, clip, image, seed)
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return (pipe, model, [[positive_embeddings_final, {}]], [[negative_embeddings_final, {}]], samples, vae, clip, seed)
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#---------------------------------------------------------------ttN Pipe Loader END-----------------------------------------------------------------#
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#Functions for upscaling
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def enforce_mul_of_64(d):
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if d<=7:
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d = 8
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leftover = d % 8 # 8 is the number of pixels per byte
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if leftover != 0: # if the number of pixels is not a multiple of 8
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if (leftover < 4): # if the number of pixels is less than 4
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d -= leftover # remove the leftover pixels
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else: # if the number of pixels is more than 4
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d += 8 - leftover # add the leftover pixels
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return d
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def upscale(samples, upscale_method, factor, crop):
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samples = samples[0]
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s = samples.copy()
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x = samples["samples"].shape[3]
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y = samples["samples"].shape[2]
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new_x = int(x * factor)
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new_y = int(y * factor)
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if (new_x > MAX_RESOLUTION):
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new_x = MAX_RESOLUTION
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if (new_y > MAX_RESOLUTION):
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new_y = MAX_RESOLUTION
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#print(f'{PACKAGE_NAME}:upscale from ({x*8},{y*8}) to ({new_x*8},{new_y*8})')
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s["samples"] = comfy.utils.common_upscale(
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samples["samples"], enforce_mul_of_64(
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new_x), enforce_mul_of_64(new_y), upscale_method, crop
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)
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return (s,)
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def get_save_image_path(filename_prefix, output_dir, image_width=0, image_height=0, output_folder="Default"):
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def map_filename(filename):
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prefix_len = len(os.path.basename(filename_prefix))
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prefix = filename[:prefix_len + 1]
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try:
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digits = int(filename[prefix_len + 1:].split('_')[0])
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except:
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digits = 0
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return (digits, prefix)
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def compute_vars(input, image_width, image_height):
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input = input.replace("%width%", str(image_width))
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input = input.replace("%height%", str(image_height))
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return input
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filename_prefix = compute_vars(filename_prefix, image_width, image_height)
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subfolder = os.path.dirname(os.path.normpath(filename_prefix))
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filename = os.path.basename(os.path.normpath(filename_prefix))
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if os.path.isdir(output_folder):
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full_output_folder = output_folder
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else:
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full_output_folder = os.path.join(output_dir, subfolder)
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try:
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counter = max(filter(lambda a: a[1][:-1] == filename and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1
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except ValueError:
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counter = 1
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except FileNotFoundError:
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os.makedirs(full_output_folder, exist_ok=True)
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counter = 1
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return full_output_folder, filename, counter, subfolder, filename_prefix
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def save_images(self, images, preview_prefix, save_prefix, image_output, prompt=None, extra_pnginfo=None, output_folder="Default"):
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if image_output in ("Save", "Hide/Save"):
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output_dir = folder_paths.get_output_directory()
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filename_prefix = save_prefix
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type = "output"
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elif image_output in ("Preview", "Hide"):
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output_dir = folder_paths.get_temp_directory()
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filename_prefix = preview_prefix
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type = "temp"
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full_output_folder, filename, counter, subfolder, filename_prefix = get_save_image_path(filename_prefix, output_dir, images[0].shape[1], images[0].shape[0], output_folder)
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results = list()
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for image in images:
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i = 255. * image.cpu().numpy()
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img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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metadata = PngInfo()
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if prompt is not None:
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metadata.add_text("prompt", json.dumps(prompt))
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if extra_pnginfo is not None:
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for x in extra_pnginfo:
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metadata.add_text(x, json.dumps(extra_pnginfo[x]))
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file = f"{filename}_{counter:05}_.png"
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img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4)
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results.append({
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"filename": file,
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"subfolder": subfolder,
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"type": type
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})
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counter += 1
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return results
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#---------------------------------------------------------------ttN Pipe KSampler START-------------------------------------------------------------#
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last_helds: dict[str, list] = {
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"results": [],
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"samples": [],
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"images": [],
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"vae_decode": []
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}
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# ttN pipeKSampler (Modified from TSC KSampler (Advanced), Upscale from QualityOfLifeSuite_Omar92)
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class ttN_TSC_pipeKSampler:
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empty_image = pil2tensor(Image.new('RGBA', (1, 1), (0, 0, 0, 0)))
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upscale_methods = ["None", "nearest-exact", "bilinear", "area"]
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crop_methods = ["disabled", "center"]
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {"required":
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{"pipe": ("PIPE_LINE",),
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"lora_name": (["None"] + folder_paths.get_filename_list("loras"),),
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"lora_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lora_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"upscale_method": (cls.upscale_methods,),
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"factor": ("FLOAT", {"default": 2, "min": 0.0, "max": 10.0, "step": 0.25}),
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"crop": (cls.crop_methods,),
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"sampler_state": (["Sample", "Hold", "Script"], ),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"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": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
|
|
"image_output": (["Disabled", "Hide", "Preview", "Save", "Hide/Save"],),
|
|
"save_prefix": ("STRING", {"default": "ComfyUI"})
|
|
},
|
|
"optional":
|
|
{"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
"optional_model": ("MODEL",),
|
|
"optional_positive": ("CONDITIONING",),
|
|
"optional_negative": ("CONDITIONING",),
|
|
"optional_latent": ("LATENT",),
|
|
"optional_vae": ("VAE",),
|
|
"optional_clip": ("CLIP",),
|
|
"script": ("SCRIPT",),
|
|
},
|
|
"hidden":
|
|
{"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID",},
|
|
}
|
|
|
|
RETURN_TYPES = ("PIPE_LINE", "MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "CLIP", "IMAGE", "INT",)
|
|
RETURN_NAMES = ("pipe", "model", "positive", "negative", "latent","vae", "clip", "image", "seed", )
|
|
OUTPUT_NODE = True
|
|
FUNCTION = "sample"
|
|
CATEGORY = "ttN/pipe"
|
|
|
|
def sample(self, pipe, lora_name, lora_model_strength, lora_clip_strength, sampler_state, steps, cfg, sampler_name, scheduler, image_output, save_prefix, denoise=1.0,
|
|
optional_model=None, optional_positive=None, optional_negative=None, optional_latent=None, optional_vae=None, optional_clip=None, seed=None, script=None, upscale_method=None, factor=None, crop=None, prompt=None, extra_pnginfo=None, my_unique_id=None,):
|
|
|
|
global last_helds
|
|
|
|
model, positive, negative, samples, vae, clip, images, pipe_seed = pipe
|
|
|
|
#Optional overrides
|
|
model = optional_model if optional_model is not None else model
|
|
positive = optional_positive if optional_positive is not None else positive
|
|
negative = optional_negative if optional_negative is not None else negative
|
|
samples = optional_latent if optional_latent is not None else samples
|
|
vae = optional_vae if optional_vae is not None else vae
|
|
clip = optional_clip if optional_clip is not None else clip
|
|
|
|
seed = pipe_seed if seed in (None, 'undefined') else seed
|
|
|
|
#load Lora
|
|
if lora_name not in (None, "None"):
|
|
model, clip = load_lora(lora_name, model, clip, lora_model_strength, lora_clip_strength)
|
|
|
|
def get_value_by_id(key: str, my_unique_id):
|
|
for value, id_ in last_helds[key]:
|
|
if id_ == my_unique_id:
|
|
return value
|
|
return None
|
|
|
|
def update_value_by_id(key: str, my_unique_id, new_value):
|
|
for i, (value, id_) in enumerate(last_helds[key]):
|
|
if id_ == my_unique_id:
|
|
last_helds[key][i] = (new_value, id_)
|
|
return True
|
|
|
|
last_helds[key].append((new_value, my_unique_id))
|
|
return True
|
|
|
|
def handle_upscale(samples, upscale_method, factor, crop):
|
|
if upscale_method != "None":
|
|
samples = upscale(samples, upscale_method, factor, crop)[0]
|
|
return samples
|
|
|
|
def init_state(my_unique_id, key, default):
|
|
value = get_value_by_id(key, my_unique_id)
|
|
if value is not None:
|
|
return value
|
|
return default
|
|
|
|
|
|
def process_sample_state(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, samples, denoise, vae, clip, images, image_output, preview_prefix, save_prefix, prompt, extra_pnginfo, my_unique_id):
|
|
|
|
samples = common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, samples, denoise=denoise)
|
|
update_value_by_id("samples", my_unique_id, samples)
|
|
|
|
new_pipe = (model, positive, negative, samples, vae, clip, images, seed)
|
|
|
|
if image_output == "Disabled":
|
|
update_value_by_id("vae_decode", my_unique_id, True)
|
|
return (new_pipe, model, positive, negative, samples, vae, clip, images, seed,)
|
|
|
|
latent = samples[0]["samples"]
|
|
images = vae.decode(latent).cpu()
|
|
update_value_by_id("images", my_unique_id, images)
|
|
update_value_by_id("vae_decode", my_unique_id, False)
|
|
|
|
results = save_images(self, images, preview_prefix, save_prefix, image_output, prompt, extra_pnginfo)
|
|
update_value_by_id("results", my_unique_id, results)
|
|
|
|
list(new_pipe)[6] = images
|
|
tuple(new_pipe)
|
|
|
|
if image_output in ("Hide", "Hide/Save"):
|
|
return (new_pipe, model, positive, negative, samples, vae, clip, images, seed,)
|
|
|
|
return {"ui": {"images": results}, "result": (new_pipe, model, positive, negative, samples, vae, clip, images, seed,)}
|
|
|
|
def process_hold_state(self, model, positive, negative, vae, clip, seed, image_output, preview_prefix, save_prefix, prompt, extra_pnginfo, my_unique_id, samples, images):
|
|
print(f'\033[32mpipeKSampler[{my_unique_id}]:\033[0mHeld')
|
|
|
|
last_samples = init_state(my_unique_id, "samples", (samples,))
|
|
|
|
last_images = init_state(my_unique_id, "images", images)
|
|
|
|
last_results = init_state(my_unique_id, "results", list())
|
|
|
|
new_pipe = (model, positive, negative, last_samples, vae, clip, last_images, seed,)
|
|
if image_output == "Disabled":
|
|
return (new_pipe, model, positive, negative, last_samples, vae, clip, last_images, seed,)
|
|
|
|
latent = last_samples[0]["samples"]
|
|
if get_value_by_id("vae_decode", my_unique_id) == True:
|
|
images = vae.decode(latent).cpu()
|
|
list(new_pipe)[6] = images
|
|
tuple(new_pipe)
|
|
|
|
update_value_by_id("images", my_unique_id, images)
|
|
update_value_by_id("vae_decode", my_unique_id, False)
|
|
|
|
results = save_images(self, images, preview_prefix, save_prefix, image_output, prompt, extra_pnginfo)
|
|
update_value_by_id("results", my_unique_id, results)
|
|
else:
|
|
images = last_images
|
|
results = last_results
|
|
|
|
if image_output in ("Hide", "Hide/Save"):
|
|
return (new_pipe, model, positive, negative, last_samples, vae, clip, images, seed,)
|
|
|
|
return {"ui": {"images": results}, "result": (new_pipe, model, positive, negative, last_samples, vae, clip, images, seed,)}
|
|
|
|
def process_script_state(self, script, samples, images, vae, my_unique_id, seed, model, positive, negative, clip, preview_prefix, save_prefix, image_output, prompt, extra_pnginfo, scheduler, steps, cfg, sampler_name, denoise):
|
|
|
|
last_samples = init_state(my_unique_id, "samples", (samples,))
|
|
last_images = init_state(my_unique_id, "images", images)
|
|
|
|
new_pipe = (model, positive, negative, samples, vae, clip, last_images, seed,)
|
|
# If no script input connected, set X_type and Y_type to "Nothing"
|
|
if script is None:
|
|
X_type = "Nothing"
|
|
Y_type = "Nothing"
|
|
else:
|
|
# Unpack script Tuple (X_type, X_value, Y_type, Y_value, grid_spacing, latent_id)
|
|
X_type, X_value, Y_type, Y_value, grid_spacing, latent_id = script
|
|
|
|
if (X_type == "Nothing" and Y_type == "Nothing"):
|
|
print('\033[31mpipeKSampler[{}] Error:\033[0m No valid script entry detected'.format(my_unique_id))
|
|
return {"ui": {"images": list()},
|
|
"result": (new_pipe, model, positive, negative, last_samples, vae, clip, last_images, seed)}
|
|
|
|
if vae == (None,):
|
|
print('\033[31mpipeKSampler[{}] Error:\033[0m VAE must be connected to use Script mode.'.format(my_unique_id))
|
|
return {"ui": {"images": list()},
|
|
"result": (new_pipe, model, positive, negative, last_samples, vae, clip, last_images, seed)}
|
|
|
|
# Extract the 'samples' tensor from the dictionary
|
|
latent_image_tensor = samples['samples']
|
|
|
|
# Split the tensor into individual image tensors
|
|
image_tensors = torch.split(latent_image_tensor, 1, dim=0)
|
|
|
|
# Create a list of dictionaries containing the individual image tensors
|
|
latent_list = [{'samples': image} for image in image_tensors]
|
|
|
|
# Set latent only to the first latent of batch
|
|
if latent_id >= len(latent_list):
|
|
print(
|
|
f'\033[31mpipeKSampler[{my_unique_id}] Warning:\033[0m '
|
|
f'The selected latent_id ({latent_id}) is out of range.\n'
|
|
f'Automatically setting the latent_id to the last image in the list (index: {len(latent_list) - 1}).')
|
|
latent_id = len(latent_list) - 1
|
|
|
|
latent_image = latent_list[latent_id]
|
|
|
|
# Define X/Y_values for "Seeds++ Batch"
|
|
if X_type == "Seeds++ Batch":
|
|
X_value = [latent_image for _ in range(X_value[0])]
|
|
if Y_type == "Seeds++ Batch":
|
|
Y_value = [latent_image for _ in range(Y_value[0])]
|
|
|
|
# Define X/Y_values for "Latent Batch"
|
|
if X_type == "Latent Batch":
|
|
X_value = latent_list
|
|
if Y_type == "Latent Batch":
|
|
Y_value = latent_list
|
|
|
|
# Embedd information into "Scheduler" X/Y_values for text label
|
|
if X_type == "Scheduler" and Y_type != "Sampler":
|
|
# X_value second list value of each array entry = None
|
|
for i in range(len(X_value)):
|
|
if len(X_value[i]) == 2:
|
|
X_value[i][1] = None
|
|
else:
|
|
X_value[i] = [X_value[i], None]
|
|
if Y_type == "Scheduler" and X_type != "Sampler":
|
|
# Y_value second list value of each array entry = None
|
|
for i in range(len(Y_value)):
|
|
if len(Y_value[i]) == 2:
|
|
Y_value[i][1] = None
|
|
else:
|
|
Y_value[i] = [Y_value[i], None]
|
|
|
|
def define_variable(var_type, var, seed, steps, cfg,sampler_name, scheduler, latent_image, denoise,
|
|
vae_name, var_label, num_label):
|
|
|
|
# If var_type is "Seeds++ Batch", update var and seed, and generate labels
|
|
if var_type == "Latent Batch":
|
|
latent_image = var
|
|
text = f"{len(var_label)}"
|
|
# If var_type is "Seeds++ Batch", update var and seed, and generate labels
|
|
elif var_type == "Seeds++ Batch":
|
|
text = f"seed: {seed}"
|
|
# If var_type is "Steps", update steps and generate labels
|
|
elif var_type == "Steps":
|
|
steps = var
|
|
text = f"Steps: {steps}"
|
|
# If var_type is "CFG Scale", update cfg and generate labels
|
|
elif var_type == "CFG Scale":
|
|
cfg = var
|
|
text = f"CFG Scale: {cfg}"
|
|
# If var_type is "Sampler", update sampler_name, scheduler, and generate labels
|
|
elif var_type == "Sampler":
|
|
sampler_name = var[0]
|
|
if var[1] == "":
|
|
text = f"{sampler_name}"
|
|
else:
|
|
if var[1] != None:
|
|
scheduler[0] = var[1]
|
|
else:
|
|
scheduler[0] = scheduler[1]
|
|
text = f"{sampler_name} ({scheduler[0]})"
|
|
text = text.replace("ancestral", "a").replace("uniform", "u")
|
|
# If var_type is "Scheduler", update scheduler and generate labels
|
|
elif var_type == "Scheduler":
|
|
scheduler[0] = var[0]
|
|
if len(var) == 2:
|
|
text = f"{sampler_name} ({var[0]})"
|
|
else:
|
|
text = f"{var}"
|
|
text = text.replace("ancestral", "a").replace("uniform", "u")
|
|
# If var_type is "Denoise", update denoise and generate labels
|
|
elif var_type == "Denoise":
|
|
denoise = var
|
|
text = f"Denoise: {denoise}"
|
|
# For any other var_type, set text to "?"
|
|
elif var_type == "VAE":
|
|
vae_name = var
|
|
text = f"VAE: {vae_name}"
|
|
# For any other var_type, set text to ""
|
|
else:
|
|
text = ""
|
|
|
|
def truncate_texts(texts, num_label):
|
|
min_length = min([len(text) for text in texts])
|
|
truncate_length = min(min_length, 24)
|
|
|
|
if truncate_length < 16:
|
|
truncate_length = 16
|
|
|
|
truncated_texts = []
|
|
for text in texts:
|
|
if len(text) > truncate_length:
|
|
text = text[:truncate_length] + "..."
|
|
truncated_texts.append(text)
|
|
|
|
return truncated_texts
|
|
|
|
# Add the generated text to var_label if it's not full
|
|
if len(var_label) < num_label:
|
|
var_label.append(text)
|
|
|
|
# If var_type VAE , truncate entries in the var_label list when it's full
|
|
if len(var_label) == num_label and var_type == "VAE":
|
|
var_label = truncate_texts(var_label, num_label)
|
|
|
|
# Return the modified variables
|
|
return steps, cfg,sampler_name, scheduler, latent_image, denoise, vae_name, var_label
|
|
|
|
# Define a helper function to help process X and Y values
|
|
def process_values(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise,
|
|
vae,vae_name, latent_new=[], max_width=0, max_height=0, image_list=[], size_list=[]):
|
|
|
|
# Sample
|
|
samples = common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative,
|
|
latent_image, denoise=denoise)
|
|
|
|
# Decode images and store
|
|
latent = samples[0]["samples"]
|
|
|
|
# Add the latent tensor to the tensors list
|
|
latent_new.append(latent)
|
|
|
|
# Load custom vae if available
|
|
if vae_name is not None:
|
|
vae = load_vae(vae_name)
|
|
|
|
# Decode the image
|
|
image = vae.decode(latent).cpu()
|
|
|
|
# Convert the image from tensor to PIL Image and add it to the list
|
|
pil_image = tensor2pil(image)
|
|
image_list.append(pil_image)
|
|
size_list.append(pil_image.size)
|
|
|
|
# Save the original image
|
|
save_images(self, image, preview_prefix, save_prefix, image_output, prompt, extra_pnginfo)
|
|
|
|
# Update max dimensions
|
|
max_width = max(max_width, pil_image.width)
|
|
max_height = max(max_height, pil_image.height)
|
|
|
|
# Return the touched variables
|
|
return image_list, size_list, max_width, max_height, latent_new
|
|
|
|
# Initiate Plot label text variables X/Y_label
|
|
X_label = []
|
|
Y_label = []
|
|
|
|
# Seed_updated for "Seeds++ Batch" incremental seeds
|
|
seed_updated = seed
|
|
|
|
# Store the KSamplers original scheduler inside the same scheduler variable
|
|
scheduler = [scheduler, scheduler]
|
|
|
|
# By default set vae_name to None
|
|
vae_name = None
|
|
|
|
# Fill Plot Rows (X)
|
|
for X_index, X in enumerate(X_value):
|
|
# Seed control based on loop index during Batch
|
|
if X_type == "Seeds++ Batch":
|
|
# Update seed based on the inner loop index
|
|
seed_updated = seed + X_index
|
|
|
|
# Define X parameters and generate labels
|
|
steps, cfg, sampler_name, scheduler, latent_image, denoise, vae_name, X_label = \
|
|
define_variable(X_type, X, seed_updated, steps, cfg, sampler_name, scheduler, latent_image,
|
|
denoise, vae_name, X_label, len(X_value))
|
|
|
|
if Y_type != "Nothing":
|
|
# Seed control based on loop index during Batch
|
|
for Y_index, Y in enumerate(Y_value):
|
|
if Y_type == "Seeds++ Batch":
|
|
# Update seed based on the inner loop index
|
|
seed_updated = seed + Y_index
|
|
|
|
# Define Y parameters and generate labels
|
|
steps, cfg, sampler_name, scheduler, latent_image, denoise, vae_name, Y_label = \
|
|
define_variable(Y_type, Y, seed_updated, steps, cfg, sampler_name, scheduler, latent_image,
|
|
denoise, vae_name, Y_label, len(Y_value))
|
|
|
|
# Generate images
|
|
image_list, size_list, max_width, max_height, latent_new = \
|
|
process_values(model, seed_updated, steps, cfg, sampler_name, scheduler[0],
|
|
positive, negative, latent_image, denoise, vae, vae_name)
|
|
else:
|
|
# Generate images
|
|
image_list, size_list, max_width, max_height, latent_new = \
|
|
process_values(model, seed_updated, steps, cfg, sampler_name, scheduler[0],
|
|
positive, negative, latent_image, denoise, vae, vae_name)
|
|
|
|
|
|
def adjusted_font_size(text, initial_font_size, max_width):
|
|
font = ImageFont.truetype(str(Path(font_path)), initial_font_size)
|
|
text_width, _ = font.getsize(text)
|
|
|
|
if text_width > (max_width * 0.9):
|
|
scaling_factor = 0.9 # A value less than 1 to shrink the font size more aggressively
|
|
new_font_size = int(initial_font_size * (max_width / text_width) * scaling_factor)
|
|
else:
|
|
new_font_size = initial_font_size
|
|
|
|
return new_font_size
|
|
|
|
# Disable vae decode on next Hold
|
|
update_value_by_id("vae_decode", my_unique_id, False)
|
|
|
|
# Extract plot dimensions
|
|
num_rows = max(len(Y_value) if Y_value is not None else 0, 1)
|
|
num_cols = max(len(X_value) if X_value is not None else 0, 1)
|
|
|
|
def rearrange_tensors(latent, num_cols, num_rows):
|
|
new_latent = []
|
|
for i in range(num_rows):
|
|
for j in range(num_cols):
|
|
index = j * num_rows + i
|
|
new_latent.append(latent[index])
|
|
return new_latent
|
|
|
|
# Rearrange latent array to match preview image grid
|
|
latent_new = rearrange_tensors(latent_new, num_cols, num_rows)
|
|
|
|
# Concatenate the tensors along the first dimension (dim=0)
|
|
latent_new = torch.cat(latent_new, dim=0)
|
|
samples_new = {"samples": latent_new}
|
|
|
|
# Store latent_new as last latent
|
|
update_value_by_id("samples", my_unique_id, samples_new)
|
|
|
|
# Calculate the dimensions of the white background image
|
|
border_size = max_width // 15
|
|
|
|
# Modify the background width and x_offset initialization based on Y_type
|
|
if Y_type == "Nothing":
|
|
bg_width = num_cols * max_width + (num_cols - 1) * grid_spacing
|
|
x_offset_initial = 0
|
|
else:
|
|
bg_width = num_cols * max_width + (num_cols - 1) * grid_spacing + 3 * border_size
|
|
x_offset_initial = border_size * 3
|
|
|
|
# Modify the background height based on X_type
|
|
if X_type == "Nothing":
|
|
bg_height = num_rows * max_height + (num_rows - 1) * grid_spacing
|
|
y_offset = 0
|
|
else:
|
|
bg_height = num_rows * max_height + (num_rows - 1) * grid_spacing + 3 * border_size
|
|
y_offset = border_size * 3
|
|
|
|
# Create the white background image
|
|
background = Image.new('RGBA', (int(bg_width), int(bg_height)), color=(255, 255, 255, 255))
|
|
|
|
for row in range(num_rows):
|
|
|
|
# Initialize the X_offset
|
|
x_offset = x_offset_initial
|
|
|
|
for col in range(num_cols):
|
|
# Calculate the index for image_list
|
|
index = col * num_rows + row
|
|
img = image_list[index]
|
|
|
|
# Paste the image
|
|
background.paste(img, (x_offset, y_offset))
|
|
|
|
if row == 0 and X_type != "Nothing":
|
|
# Assign text
|
|
text = X_label[col]
|
|
|
|
# Add the corresponding X_value as a label above the image
|
|
initial_font_size = int(48 * img.width / 512)
|
|
font_size = adjusted_font_size(text, initial_font_size, img.width)
|
|
label_height = int(font_size*1.5)
|
|
|
|
# Create a white background label image
|
|
label_bg = Image.new('RGBA', (img.width, label_height), color=(255, 255, 255, 0))
|
|
d = ImageDraw.Draw(label_bg)
|
|
|
|
# Create the font object
|
|
font = ImageFont.truetype(str(Path(font_path)), font_size)
|
|
|
|
# Calculate the text size and the starting position
|
|
text_width, text_height = d.textsize(text, font=font)
|
|
text_x = (img.width - text_width) // 2
|
|
text_y = (label_height - text_height) // 2
|
|
|
|
# Add the text to the label image
|
|
d.text((text_x, text_y), text, fill='black', font=font)
|
|
|
|
# Calculate the available space between the top of the background and the top of the image
|
|
available_space = y_offset - label_height
|
|
|
|
# Calculate the new Y position for the label image
|
|
label_y = available_space // 2
|
|
|
|
# Paste the label image above the image on the background using alpha_composite()
|
|
background.alpha_composite(label_bg, (x_offset, label_y))
|
|
|
|
if col == 0 and Y_type != "Nothing":
|
|
# Assign text
|
|
text = Y_label[row]
|
|
|
|
# Add the corresponding Y_value as a label to the left of the image
|
|
initial_font_size = int(48 * img.height / 512)
|
|
font_size = adjusted_font_size(text, initial_font_size, img.height)
|
|
|
|
# Create a white background label image
|
|
label_bg = Image.new('RGBA', (img.height, font_size), color=(255, 255, 255, 0))
|
|
d = ImageDraw.Draw(label_bg)
|
|
|
|
# Create the font object
|
|
font = ImageFont.truetype(str(Path(font_path)), font_size)
|
|
|
|
# Calculate the text size and the starting position
|
|
text_width, text_height = d.textsize(text, font=font)
|
|
text_x = (img.height - text_width) // 2
|
|
text_y = (font_size - text_height) // 2
|
|
|
|
# Add the text to the label image
|
|
d.text((text_x, text_y), text, fill='black', font=font)
|
|
|
|
# Rotate the label_bg 90 degrees counter-clockwise
|
|
if Y_type != "Latent Batch":
|
|
label_bg = label_bg.rotate(90, expand=True)
|
|
|
|
# Calculate the available space between the left of the background and the left of the image
|
|
available_space = x_offset - label_bg.width
|
|
|
|
# Calculate the new X position for the label image
|
|
label_x = available_space // 2
|
|
|
|
# Calculate the Y position for the label image
|
|
label_y = y_offset + (img.height - label_bg.height) // 2
|
|
|
|
# Paste the label image to the left of the image on the background using alpha_composite()
|
|
background.alpha_composite(label_bg, (label_x, label_y))
|
|
|
|
# Update the x_offset
|
|
x_offset += img.width + grid_spacing
|
|
|
|
# Update the y_offset
|
|
y_offset += img.height + grid_spacing
|
|
|
|
images = pil2tensor(background)
|
|
update_value_by_id("images", my_unique_id, images)
|
|
|
|
# Generate image results and store
|
|
results = save_images(self, images, preview_prefix, save_prefix, image_output, prompt, extra_pnginfo)
|
|
update_value_by_id("results", my_unique_id, results)
|
|
|
|
# Clean loaded_objects
|
|
update_loaded_objects(prompt)
|
|
|
|
new_pipe = (model, positive, negative, latent, vae, clip, images, seed,)
|
|
|
|
if image_output in ("Hide", "Hide/Save"):
|
|
return (new_pipe, model, positive, negative, {"samples": latent_new}, vae, clip, images, seed)
|
|
|
|
# Output image results to ui and node outputs
|
|
return {"ui": {"images": results}, "result": (new_pipe, model, positive, negative, {"samples": latent_new}, vae, clip, images, seed)}
|
|
|
|
|
|
samples = handle_upscale(samples, upscale_method, factor, crop)
|
|
update_loaded_objects(prompt)
|
|
my_unique_id = int(my_unique_id)
|
|
|
|
if vae == (None,):
|
|
print(f'\033[32mpipeKSampler[{my_unique_id}] Warning:\033[0m No vae input detected, preview and output image disabled.\n')
|
|
image_output = "Disabled"
|
|
|
|
latent: Tensor | None = None
|
|
preview_prefix = f"KSpipe_{my_unique_id:02d}"
|
|
|
|
if sampler_state == "Sample":
|
|
return process_sample_state(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, samples, denoise, vae, clip, images, image_output, preview_prefix, save_prefix, prompt, extra_pnginfo, my_unique_id)
|
|
|
|
elif sampler_state == "Hold":
|
|
return process_hold_state(self, model, positive, negative, vae, clip, seed, image_output, preview_prefix, save_prefix, prompt, extra_pnginfo, my_unique_id, samples, images)
|
|
|
|
elif sampler_state == "Script":
|
|
return process_script_state(self, script, samples, images, vae, my_unique_id, seed, model, positive, negative, clip, preview_prefix, save_prefix, image_output, prompt, extra_pnginfo, scheduler, steps, cfg, sampler_name, denoise)
|
|
|
|
|
|
|
|
|
|
#---------------------------------------------------------------ttN Pipe KSampler END---------------------------------------------------------------#
|
|
#---------------------------------------------------------------ttN/pipe START----------------------------------------------------------------------#
|
|
class ttN_pipe_IN:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"model": ("MODEL",),
|
|
},
|
|
"optional": {
|
|
"pos": ("CONDITIONING",),
|
|
"neg": ("CONDITIONING",),
|
|
"latent": ("LATENT",),
|
|
"vae": ("VAE",),
|
|
"clip": ("CLIP",),
|
|
"image": ("IMAGE",),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("PIPE_LINE", )
|
|
RETURN_NAMES = ("pipe", )
|
|
FUNCTION = "flush"
|
|
|
|
CATEGORY = "ttN/pipe"
|
|
|
|
def flush(self, model, pos=0, neg=0, latent=0, vae=0, clip=0, image=0, seed=0):
|
|
pipe_line = (model, pos, neg, latent, vae, clip, image, seed, )
|
|
return (pipe_line, )
|
|
|
|
class ttN_pipe_OUT:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"pipe": ("PIPE_LINE",),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "CLIP", "IMAGE", "INT", "PIPE_LINE",)
|
|
RETURN_NAMES = ("model", "pos", "neg", "latent", "vae", "clip", "image", "seed", "pipe")
|
|
FUNCTION = "flush"
|
|
|
|
CATEGORY = "ttN/pipe"
|
|
|
|
def flush(self, pipe):
|
|
model, pos, neg, latent, vae, clip, image, seed = pipe
|
|
return model, pos, neg, latent, vae, clip, image, seed, pipe
|
|
|
|
class ttN_pipe_EDIT:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {"pipe": ("PIPE_LINE",)},
|
|
"optional": {
|
|
"model": ("MODEL",),
|
|
"pos": ("CONDITIONING",),
|
|
"neg": ("CONDITIONING",),
|
|
"latent": ("LATENT",),
|
|
"vae": ("VAE",),
|
|
"clip": ("CLIP",),
|
|
"image": ("IMAGE",),
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "forceInput": True}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("PIPE_LINE", )
|
|
RETURN_NAMES = ("pipe", )
|
|
FUNCTION = "flush"
|
|
|
|
CATEGORY = "ttN/pipe"
|
|
|
|
def flush(self, pipe, model=None, pos=None, neg=None, latent=None, vae=None, clip=None, image=None, seed=None):
|
|
new_model, new_pos, new_neg, new_latent, new_vae, new_clip, new_image, new_seed = pipe
|
|
|
|
if model is not None:
|
|
new_model = model
|
|
|
|
if pos is not None:
|
|
new_pos = pos
|
|
|
|
if neg is not None:
|
|
new_neg = neg
|
|
|
|
if latent is not None:
|
|
new_latent = latent
|
|
|
|
if vae is not None:
|
|
new_vae = vae
|
|
|
|
if clip is not None:
|
|
new_clip = clip
|
|
|
|
if image is not None:
|
|
new_image = image
|
|
|
|
if seed is not None:
|
|
new_seed = seed
|
|
|
|
pipe = new_model, new_pos, new_neg, new_latent, new_vae, new_clip, new_image, new_seed
|
|
|
|
return (pipe, )
|
|
|
|
class ttN_pipe_2BASIC:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"pipe": ("PIPE_LINE",),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("BASIC_PIPE", "PIPE_LINE",)
|
|
RETURN_NAMES = ("basic_pipe", "pipe",)
|
|
FUNCTION = "flush"
|
|
|
|
CATEGORY = "ttN/pipe"
|
|
|
|
def flush(self, pipe):
|
|
model, pos, neg, _, vae, clip, _, _ = pipe
|
|
basic_pipe = (model, clip, vae, pos, neg)
|
|
return (basic_pipe, pipe, )
|
|
|
|
class ttN_pipe_2DETAILER:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {"pipe": ("PIPE_LINE",),
|
|
"bbox_detector": ("BBOX_DETECTOR", ), },
|
|
"optional": {"sam_model_opt": ("SAM_MODEL", ), },
|
|
}
|
|
|
|
RETURN_TYPES = ("DETAILER_PIPE", "PIPE_LINE" )
|
|
RETURN_NAMES = ("detailer_pipe", "pipe")
|
|
FUNCTION = "flush"
|
|
|
|
CATEGORY = "ttN/pipe"
|
|
|
|
def flush(self, pipe, bbox_detector, sam_model_opt=None):
|
|
model, positive, negative, _, vae, _, _, _ = pipe
|
|
detailer_pipe = model, vae, positive, negative, bbox_detector, sam_model_opt
|
|
return (detailer_pipe, pipe, )
|
|
#---------------------------------------------------------------ttN/pipe END------------------------------------------------------------------------#
|
|
|
|
|
|
|
|
#---------------------------------------------------------------ttN/text START----------------------------------------------------------------------#
|
|
class ttN_text:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"text": ("STRING", {"default": "", "multiline": True}),
|
|
}}
|
|
|
|
RETURN_TYPES = ("STRING",)
|
|
RETURN_NAMES = ("text",)
|
|
FUNCTION = "conmeow"
|
|
|
|
CATEGORY = "ttN/text"
|
|
|
|
@staticmethod
|
|
def conmeow(text):
|
|
return text,
|
|
|
|
class ttN_textDebug:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"print_to_console": ([False, True],),
|
|
"text": ("STRING", {"default": '', "multiline": True, "forceInput": True}),
|
|
},
|
|
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID",},
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING",)
|
|
RETURN_NAMES = ("text",)
|
|
FUNCTION = "write"
|
|
OUTPUT_NODE = True
|
|
|
|
CATEGORY = "ttN/text"
|
|
|
|
@staticmethod
|
|
def write(print_to_console, text, prompt, extra_pnginfo, my_unique_id):
|
|
if print_to_console == True:
|
|
|
|
input_node = prompt[my_unique_id]["inputs"]["text"]
|
|
|
|
input_from = None
|
|
for node in extra_pnginfo["workflow"]["nodes"]:
|
|
if node['id'] == int(input_node[0]):
|
|
input_from = node['outputs'][input_node[1]]['name']
|
|
|
|
print(f'\033[92m[ttN textDebug_{my_unique_id}] - \033[0;31m\'{input_from}\':\033[0m{text}')
|
|
return {"ui": {"text": text},
|
|
"result": (text,)}
|
|
|
|
class ttN_concat:
|
|
def __init__(self):
|
|
pass
|
|
"""
|
|
Concatenate 2 strings
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"text1": ("STRING", {"multiline": True, "default": ''}),
|
|
"text2": ("STRING", {"multiline": True, "default": ''}),
|
|
"text3": ("STRING", {"multiline": True, "default": ''}),
|
|
"delimiter": ("STRING", {"default":",","multiline": False}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING",)
|
|
RETURN_NAMES = ("concat",)
|
|
FUNCTION = "conmeow"
|
|
|
|
CATEGORY = "ttN/text"
|
|
|
|
def conmeow(self, text1='', text2='', text3='', delimiter=''):
|
|
text1 = '' if text1 == 'undefined' else text1
|
|
text2 = '' if text2 == 'undefined' else text2
|
|
text3 = '' if text3 == 'undefined' else text3
|
|
|
|
concat = delimiter.join([text1, text2, text3])
|
|
|
|
return concat
|
|
|
|
class ttN_text3BOX_3WAYconcat:
|
|
def __init__(self):
|
|
pass
|
|
"""
|
|
Concatenate 3 strings, in various ways.
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"text1": ("STRING", {"multiline": True, "default": ''}),
|
|
"text2": ("STRING", {"multiline": True, "default": ''}),
|
|
"text3": ("STRING", {"multiline": True, "default": ''}),
|
|
"delimiter": ("STRING", {"default":",","multiline": False}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING", "STRING", "STRING", "STRING",)
|
|
RETURN_NAMES = ("text1", "text2", "text3", "1 & 2", "1 & 3", "2 & 3", "concat",)
|
|
FUNCTION = "conmeow"
|
|
|
|
CATEGORY = "ttN/text"
|
|
|
|
def conmeow(self, text1='', text2='', text3='', delimiter=''):
|
|
text1 = '' if text1 == 'undefined' else text1
|
|
text2 = '' if text2 == 'undefined' else text2
|
|
text3 = '' if text3 == 'undefined' else text3
|
|
|
|
t_1n2 = delimiter.join([text1, text2])
|
|
t_1n3 = delimiter.join([text1, text3])
|
|
t_2n3 = delimiter.join([text2, text3])
|
|
concat = delimiter.join([text1, text2, text3])
|
|
|
|
return text1, text2, text3, t_1n2, t_1n3, t_2n3, concat
|
|
|
|
class ttN_text7BOX_concat:
|
|
def __init__(self):
|
|
pass
|
|
"""
|
|
Concatenate many strings
|
|
"""
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"text1": ("STRING", {"multiline": True, "default": ''}),
|
|
"text2": ("STRING", {"multiline": True, "default": ''}),
|
|
"text3": ("STRING", {"multiline": True, "default": ''}),
|
|
"text4": ("STRING", {"multiline": True, "default": ''}),
|
|
"text5": ("STRING", {"multiline": True, "default": ''}),
|
|
"text6": ("STRING", {"multiline": True, "default": ''}),
|
|
"text7": ("STRING", {"multiline": True, "default": ''}),
|
|
"delimiter": ("STRING", {"default":",","multiline": False}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING", "STRING", "STRING", "STRING", "STRING",)
|
|
RETURN_NAMES = ("text1", "text2", "text3", "text4", "text5", "text6", "text7", "concat",)
|
|
FUNCTION = "conmeow"
|
|
|
|
CATEGORY = "ttN/text"
|
|
|
|
def conmeow(self, text1, text2, text3, text4, text5, text6, text7, delimiter):
|
|
text1 = '' if text1 == 'undefined' else text1
|
|
text2 = '' if text2 == 'undefined' else text2
|
|
text3 = '' if text3 == 'undefined' else text3
|
|
text4 = '' if text4 == 'undefined' else text4
|
|
text5 = '' if text5 == 'undefined' else text5
|
|
text6 = '' if text6 == 'undefined' else text6
|
|
text7 = '' if text7 == 'undefined' else text7
|
|
|
|
texts = [text1, text2, text3, text4, text5, text6, text7]
|
|
concat = delimiter.join(text for text in texts if text)
|
|
return text1, text2, text3, text4, text5, text6, text7, concat
|
|
#---------------------------------------------------------------ttN/text END------------------------------------------------------------------------#
|
|
|
|
#---------------------------------------------------------------ttN/util START----------------------------------------------------------------------#
|
|
class ttN_INT:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"int": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
}}
|
|
|
|
RETURN_TYPES = ("INT", "FLOAT", "STRING",)
|
|
RETURN_NAMES = ("int", "float", "text",)
|
|
FUNCTION = "convert"
|
|
|
|
CATEGORY = "ttN/util"
|
|
|
|
@staticmethod
|
|
def convert(int):
|
|
return int, float(int), str(int)
|
|
|
|
class ttN_FLOAT:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"float": ("FLOAT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
}}
|
|
|
|
RETURN_TYPES = ("FLOAT", "INT", "STRING",)
|
|
RETURN_NAMES = ("float", "int", "text",)
|
|
FUNCTION = "convert"
|
|
|
|
CATEGORY = "ttN/util"
|
|
|
|
@staticmethod
|
|
def convert(float):
|
|
return float, int(float), str(float)
|
|
|
|
class ttN_SEED:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
|
}}
|
|
|
|
RETURN_TYPES = ("INT",)
|
|
RETURN_NAMES = ("seed",)
|
|
FUNCTION = "plant"
|
|
OUTPUT_NODE = True
|
|
|
|
CATEGORY = "ttN/util"
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|
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@staticmethod
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def plant(seed):
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return seed,
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#---------------------------------------------------------------ttN/util End------------------------------------------------------------------------#
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#---------------------------------------------------------------ttN/image START---------------------------------------------------------------------#
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# ttN RemBG
|
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try:
|
|
from rembg import remove
|
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class ttN_imageREMBG:
|
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def __init__(self):
|
|
pass
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@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"image": ("IMAGE",),
|
|
"image_output": (["Hide", "Preview", "Save", "Hide/Save"],{"default": "Preview"}),
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|
"save_prefix": ("STRING", {"default": "ComfyUI"}),
|
|
},
|
|
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID",},
|
|
}
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|
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|
|
|
RETURN_TYPES = ("IMAGE", "MASK")
|
|
RETURN_NAMES = ("image", "mask")
|
|
FUNCTION = "remove_background"
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|
CATEGORY = "ttN/image"
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|
OUTPUT_NODE = True
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|
|
|
def remove_background(self, image, image_output, save_prefix, prompt, extra_pnginfo, my_unique_id):
|
|
image = remove(tensor2pil(image))
|
|
tensor = pil2tensor(image)
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|
|
|
#Get alpha mask
|
|
if image.getbands() != ("R", "G", "B", "A"):
|
|
image = image.convert("RGBA")
|
|
mask = None
|
|
if "A" in image.getbands():
|
|
mask = np.array(image.getchannel("A")).astype(np.float32) / 255.0
|
|
mask = torch.from_numpy(mask)
|
|
mask = 1. - mask
|
|
else:
|
|
mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
|
|
|
|
if image_output == "Disabled":
|
|
results = None
|
|
else:
|
|
# Define preview_prefix
|
|
preview_prefix = "ttNrembg_{:02d}".format(int(my_unique_id))
|
|
results = save_images(self, tensor, preview_prefix, save_prefix, image_output, prompt, extra_pnginfo)
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|
|
|
if image_output in ("Hide", "Hide/Save"):
|
|
return (tensor, mask)
|
|
|
|
# Output image results to ui and node outputs
|
|
return {"ui": {"images": results},
|
|
"result": (tensor, mask)}
|
|
except:
|
|
class ttN_imageREMBG:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"error": ("STRING",{"default": "RemBG is not installed", "multiline": False, 'readonly': True}),
|
|
"link": ("STRING",{"default": "https://github.com/danielgatis/rembg", "multiline": False}),
|
|
},
|
|
}
|
|
|
|
|
|
RETURN_TYPES = ("")
|
|
FUNCTION = "remove_background"
|
|
CATEGORY = "ttN/image"
|
|
|
|
def remove_background(error):
|
|
return None
|
|
|
|
class ttN_imageOUPUT:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {
|
|
"image": ("IMAGE",),
|
|
"image_output": (["Hide", "Preview", "Save", "Hide/Save"],{"default": "Preview"}),
|
|
"save_prefix": ("STRING", {"default": "ComfyUI"}),
|
|
},
|
|
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID",},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
RETURN_NAMES = ("image",)
|
|
FUNCTION = "output"
|
|
CATEGORY = "ttN/image"
|
|
OUTPUT_NODE = True
|
|
|
|
def output(self, image, image_output, save_prefix, prompt, extra_pnginfo, my_unique_id):
|
|
|
|
# Define preview_prefix
|
|
preview_prefix = "ttNimgOUT_{:02d}".format(int(my_unique_id))
|
|
results = save_images(self, image, preview_prefix, save_prefix, image_output, prompt, extra_pnginfo)
|
|
|
|
if image_output in ("Hide", "Hide/Save"):
|
|
return (image,)
|
|
|
|
# Output image results to ui and node outputs
|
|
return {"ui": {"images": results},
|
|
"result": (image,)}
|
|
|
|
class ttN_modelScale:
|
|
upscale_methods = ["nearest-exact", "bilinear", "area"]
|
|
crop_methods = ["disabled", "center"]
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": { "model_name": (folder_paths.get_filename_list("upscale_models"),),
|
|
"image": ("IMAGE",),
|
|
"info": ("INFO", {"default": "Rescale based on model upscale image size ⬇", "multiline": True}),
|
|
"rescale_after_model": ([False, True],{"default": True}),
|
|
"rescale_method": (s.upscale_methods,),
|
|
"rescale": (["by percentage", "to Width/Height"],),
|
|
"percent": ("INT", {"default": 50, "min": 0, "max": 1000, "step": 25}),
|
|
"width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
|
|
"height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
|
|
"crop": (s.crop_methods,),
|
|
"image_output": (["Hide", "Preview", "Save", "Hide/Save"],),
|
|
"save_prefix": ("STRING", {"default": "ComfyUI"}),
|
|
"output_latent": ([False, True],{"default": True}),
|
|
"vae": ("VAE",),},
|
|
"hidden": { "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID",},
|
|
}
|
|
|
|
RETURN_TYPES = ("LATENT", "IMAGE",)
|
|
RETURN_NAMES = ("latent", 'image',)
|
|
|
|
FUNCTION = "upscale"
|
|
CATEGORY = "ttN/image"
|
|
OUTPUT_NODE = True
|
|
|
|
def vae_encode_crop_pixels(self, pixels):
|
|
x = (pixels.shape[1] // 8) * 8
|
|
y = (pixels.shape[2] // 8) * 8
|
|
if pixels.shape[1] != x or pixels.shape[2] != y:
|
|
x_offset = (pixels.shape[1] % 8) // 2
|
|
y_offset = (pixels.shape[2] % 8) // 2
|
|
pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :]
|
|
return pixels
|
|
|
|
def upscale(self, model_name, image, info, rescale_after_model, rescale_method, rescale, percent, width, height, crop, image_output, save_prefix, output_latent, vae, prompt=None, extra_pnginfo=None, my_unique_id=None):
|
|
# Load Model
|
|
model_path = folder_paths.get_full_path("upscale_models", model_name)
|
|
sd = comfy.utils.load_torch_file(model_path, safe_load=True)
|
|
upscale_model = model_loading.load_state_dict(sd).eval()
|
|
|
|
# Model upscale
|
|
device = comfy.model_management.get_torch_device()
|
|
upscale_model.to(device)
|
|
in_img = image.movedim(-1,-3).to(device)
|
|
|
|
tile = 128 + 64
|
|
overlap = 8
|
|
steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap)
|
|
pbar = comfy.utils.ProgressBar(steps)
|
|
s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar)
|
|
upscale_model.cpu()
|
|
s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0)
|
|
|
|
# Post Model Rescale
|
|
if rescale_after_model == True:
|
|
samples = s.movedim(-1, 1)
|
|
if rescale == "by percentage" and percent != 0:
|
|
height = percent / 100 * samples.shape[2]
|
|
width = percent / 100 * samples.shape[3]
|
|
if (width > MAX_RESOLUTION):
|
|
width = MAX_RESOLUTION
|
|
if (height > MAX_RESOLUTION):
|
|
height = MAX_RESOLUTION
|
|
|
|
width = int(enforce_mul_of_64(width))
|
|
height = int(enforce_mul_of_64(height))
|
|
|
|
s = comfy.utils.common_upscale(samples, width, height, rescale_method, crop)
|
|
s = s.movedim(1,-1)
|
|
|
|
# vae encode
|
|
if output_latent == True:
|
|
pixels = self.vae_encode_crop_pixels(s)
|
|
t = vae.encode(pixels[:,:,:,:3])
|
|
else:
|
|
t = None
|
|
|
|
preview_prefix = "ttNhiresfix_{:02d}".format(int(my_unique_id))
|
|
results = save_images(self, s, preview_prefix, save_prefix, image_output, prompt, extra_pnginfo)
|
|
|
|
if image_output in ("Hide", "Hide/Save"):
|
|
return ({"samples":t}, s,)
|
|
|
|
return {"ui": {"images": results},
|
|
"result": ({"samples":t}, s,)}
|
|
|
|
#---------------------------------------------------------------ttN/image END-----------------------------------------------------------------------#
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
#ttN/pipe
|
|
"ttN pipeLoader": ttN_TSC_pipeLoader,
|
|
"ttN pipeKSampler": ttN_TSC_pipeKSampler,
|
|
"ttN pipeIN": ttN_pipe_IN,
|
|
"ttN pipeOUT": ttN_pipe_OUT,
|
|
"ttN pipeEDIT": ttN_pipe_EDIT,
|
|
"ttN pipe2BASIC": ttN_pipe_2BASIC,
|
|
"ttN pipe2DETAILER": ttN_pipe_2DETAILER,
|
|
|
|
#ttN/text
|
|
"ttN text": ttN_text,
|
|
"ttN textDebug": ttN_textDebug,
|
|
"ttN concat": ttN_concat,
|
|
"ttN text3BOX_3WAYconcat": ttN_text3BOX_3WAYconcat,
|
|
"ttN text7BOX_concat": ttN_text7BOX_concat,
|
|
|
|
#ttN/image
|
|
"ttN imageOutput": ttN_imageOUPUT,
|
|
"ttN imageREMBG": ttN_imageREMBG,
|
|
"ttN hiresfixScale": ttN_modelScale,
|
|
|
|
#ttN/util
|
|
"ttN int": ttN_INT,
|
|
"ttN float": ttN_FLOAT,
|
|
"ttN seed": ttN_SEED
|
|
}
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
#ttN/pipe
|
|
"ttN pipeLoader": "pipeLoader",
|
|
"ttN pipeKSampler": "pipeKSampler",
|
|
"ttN pipeIN": "pipeIN",
|
|
"ttN pipeOUT": "pipeOUT",
|
|
"ttN pipeEDIT": "pipeEDIT",
|
|
"ttN pipe2BASIC": "pipe > basic_pipe",
|
|
"ttN pipe2DETAILER": "pipe > detailer_pipe",
|
|
|
|
#ttN/text
|
|
"ttN text": "text",
|
|
"ttN textDebug": "textDebug",
|
|
"ttN concat": "textConcat",
|
|
"ttN text7BOX_concat": "7x TXT Loader Concat",
|
|
"ttN text3BOX_3WAYconcat": "3x TXT Loader MultiConcat",
|
|
|
|
#ttN/image
|
|
"ttN imageREMBG": "imageRemBG",
|
|
"ttN imageOutput": "imageOutput",
|
|
"ttN hiresfixScale": "hiresfixScale",
|
|
|
|
#ttN/util
|
|
"ttN int": "int",
|
|
"ttN float": "float",
|
|
"ttN seed": "seed"
|
|
}
|
|
|
|
print("\033[92m[t ttNodes Loaded t]\033[0m")
|
|
|
|
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|
|
# (KSampler Modified from TSC Efficiency Nodes) - https://github.com/LucianoCirino/efficiency-nodes-comfyui #
|
|
# (upscale from QualityOfLifeSuite_Omar92) - https://github.com/omar92/ComfyUI-QualityOfLifeSuit_Omar92 #
|
|
# (Node weights from BlenderNeko/ComfyUI_ADV_CLIP_emb) - https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb #
|
|
# (misc. from WAS node Suite) - https://github.com/WASasquatch/was-node-suite-comfyui #
|
|
#---------------------------------------------------------------------------------------------------------------------------------------------------#
|