739 lines
38 KiB
Python
739 lines
38 KiB
Python
# !/usr/bin/env python
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# -*- coding: UTF-8 -*-
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import gc
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import logging
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import numpy as np
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import torch
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import os
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from PIL import ImageFont
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from huggingface_hub import hf_hub_download
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from diffusers import (StableDiffusionXLPipeline, DiffusionPipeline,EulerDiscreteScheduler, UNet2DConditionModel,UniPCMultistepScheduler, AutoencoderKL,)
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from transformers import CLIPVisionModelWithProjection
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from transformers import CLIPImageProcessor
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import folder_paths
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from comfy.model_management import cleanup_models
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from comfy.clip_vision import load as clip_load
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from comfy.model_management import total_vram
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from .utils.utils import get_comic
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from .utils.gradio_utils import character_to_dict
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from .utils.load_models_utils import load_models, get_instance_path
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from .model_loader_utils import (story_maker_loader,kolor_loader,phi2narry,
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extract_content_from_brackets,narry_list,remove_punctuation_from_strings,phi_list,center_crop_s,center_crop,
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narry_list_pil,setup_seed,find_directories,
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apply_style,get_scheduler,set_attention_processor,load_character_files_on_running,
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save_results,nomarl_upscale,process_generation,SAMPLER_NAMES,SCHEDULER_NAMES,lora_lightning_list,pre_checkpoint,get_easy_function)
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global total_count, attn_count, cur_step, mask1024, mask4096, attn_procs, unet
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global sa32, sa64
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global write
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global height_s, width_s
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photomaker_dir=os.path.join(folder_paths.models_dir, "photomaker")
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device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
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MAX_SEED = np.iinfo(np.int32).max
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dir_path = os.path.dirname(os.path.abspath(__file__))
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fonts_path = os.path.join(dir_path, "fonts")
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fonts_lists = os.listdir(fonts_path)
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base_pt = os.path.join(photomaker_dir,"pt")
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if not os.path.exists(base_pt):
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os.makedirs(base_pt)
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class Storydiffusion_Model_Loader:
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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 {
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"required": {
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"character_prompt": ("STRING", {"multiline": True,
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"default": "[Taylor] a woman img, wearing a white T-shirt, blue loose hair.\n"
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"[Lecun] a man img,wearing a suit,black hair."}),
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"repo_id": ("STRING", {"default": ""}),
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"ckpt_name": (["none"]+folder_paths.get_filename_list("checkpoints"),),
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"vae_id":(["none"]+folder_paths.get_filename_list("vae"),),
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"character_weights": (["none"]+find_directories(base_pt),),
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"lora": (["none"] + folder_paths.get_filename_list("loras"),),
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"lora_scale": ("FLOAT", {"default": 0.8, "min": 0.1, "max": 1.0, "step": 0.1}),
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"controlnet_model": (["none"] + folder_paths.get_filename_list("controlnet"),),
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"clip_vision": (["none"] + folder_paths.get_filename_list("clip_vision"),),
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"trigger_words": ("STRING", {"default": "best quality"}),
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"sampeler_name": (SAMPLER_NAMES,),
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"scheduler": (SCHEDULER_NAMES,),
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"sa32_degree": (
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"FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.1, "round": 0.01}),
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"sa64_degree": (
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"FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.1, "round": 0.01}),
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"width": ("INT", {"default": 768, "min": 256, "max": 2048, "step": 32, "display": "number"}),
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"height": ("INT", {"default": 768, "min": 256, "max": 2048, "step": 32, "display": "number"}),
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"photomake_mode": (["v1", "v2"],),
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"easy_function":("STRING", {"default": ""}),
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},
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"optional": {"image": ("IMAGE",),
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"control_image": ("IMAGE",),
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"model": ("MODEL",),
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"clip":("CLIP",),
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"vae":("VAE",),
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},
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}
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RETURN_TYPES = ("STORY_DICT", )
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RETURN_NAMES = ("model", )
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FUNCTION = "story_model_loader"
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CATEGORY = "Storydiffusion"
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def story_model_loader(self,character_prompt, repo_id, ckpt_name, vae_id, character_weights, lora, lora_scale,controlnet_model, clip_vision,trigger_words,sampeler_name, scheduler,
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sa32_degree, sa64_degree, width, height, photomake_mode, easy_function,**kwargs):
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cf_model=kwargs.get("model")
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clip = kwargs.get("clip")
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front_vae=kwargs.get("vae")
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scheduler_choice = get_scheduler(sampeler_name,scheduler)
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scheduler={"name":sampeler_name,"scheduler":scheduler}
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id_number=len(character_prompt.splitlines())
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if id_number > 2:
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id_number=2
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image = kwargs.get("image")
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if isinstance(image,torch.Tensor):
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print(image.shape)
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batch_num,_,_,_=image.size()
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model_type="img2img"
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if batch_num!=id_number:
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raise "role prompt numbers don't match input image numbers...example:2 roles need 2 input images,"
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else:
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model_type = "txt2img"
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image=None
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logging.info(f"Process using {id_number} roles,mode is {model_type}....")
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if controlnet_model=="none":
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controlnet_path=None
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control_image=None
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else:
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controlnet_path=folder_paths.get_full_path("controlnet", controlnet_model)
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control_image=kwargs.get("control_image")
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if not isinstance(control_image, torch.Tensor):
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raise "if using controlnet,need input a image in control_image"
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photomaker_path = os.path.join(photomaker_dir, f"photomaker-{photomake_mode}.bin")
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photomake_mode_=photomake_mode
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# load model
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(auraface, NF4, save_model, kolor_face,flux_pulid_name,pulid,quantized_mode,story_maker,make_dual_only,
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clip_vision_path,char_files,ckpt_path,lora,lora_path,use_kolor,photomake_mode,use_flux,onnx_provider)=get_easy_function(
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easy_function,clip_vision,character_weights,ckpt_name,lora,repo_id,photomake_mode)
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photomaker_path,face_ckpt,photomake_mode,pulid_ckpt,face_adapter,kolor_ip_path=pre_checkpoint(
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photomaker_path, photomake_mode, kolor_face, pulid, story_maker, clip_vision_path,use_kolor,model_type)
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if total_vram > 45000.0:
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aggressive_offload = False
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offload = False
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elif 17000.0<total_vram < 45000.0:
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aggressive_offload = False
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offload = True
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else:
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aggressive_offload = True
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offload = True
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logging.info(f"total_vram is {total_vram},aggressive_offload is {aggressive_offload},offload is {offload}")
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# global
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global attn_procs
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global sa32, sa64, write, height_s, width_s
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global attn_count, total_count, id_length, total_length, cur_step
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sa32 = sa32_degree
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sa64 = sa64_degree
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attn_count = 0
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total_count = 0
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cur_step = 0
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id_length = id_number
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total_length = 5
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attn_procs = {}
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write = False
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height_s = height
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width_s = width
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use_cf=False
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use_storydif=False
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if not repo_id and not ckpt_path and not cf_model:
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raise "you need choice a model or repo_id or a comfyUI model..."
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elif not repo_id and not ckpt_path and cf_model:
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from comfy.utils import load_torch_file as load_torch_file_
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from comfy.sd import VAE as cf_vae
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from .utils.comfy_normal import CFGenerator
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if vae_id != "none":
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vae_path = folder_paths.get_full_path("vae", vae_id)
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sd = load_torch_file_(vae_path)
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vae = cf_vae(sd=sd)
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else:
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if not front_vae:
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raise "Now,using comfyUI normal processing must choice 'vae' or 'ae' from 'vae' menu."
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else:
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vae = front_vae
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if not clip:
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raise "Now,using comfyUI normal processing must need comfyUI clip,if using flux need dual clip ."
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use_cf = True
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if cf_model.model.model_type.value==8:
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use_flux=True
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cf_model_type="FLUX"
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elif cf_model.model.model_type.value==4:
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cf_model_type = "CASCADE"
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elif cf_model.model.model_type.value==6:
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cf_model_type = "FLOW"
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elif cf_model.model.model_type.value==5:
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cf_model_type = "EDM"
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elif cf_model.model.model_type.value==1:
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cf_model_type = "EPS"
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elif cf_model.model.model_type.value == 2:
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cf_model_type = "V_PREDICTION"
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elif cf_model.model.model_type.value == 3:
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cf_model_type = "V_PREDICTION_EDM"
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elif cf_model.model.model_type.value == 7:
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cf_model_type = "PREDICTION_CONTINUOUS"
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else:
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try:
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use_flux = True
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cf_model_type = "FLUX"
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except:
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raise "unsupport checkpoints"
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pipe=CFGenerator(cf_model,clip,vae,cf_model_type,device)
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pulid = False
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elif not repo_id and ckpt_path: # load ckpt
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if_repo = False
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if story_maker:
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if not make_dual_only: #default dual
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logging.info("start story-make processing...")
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pipe=story_maker_loader(clip_load,clip_vision_path,dir_path,ckpt_path, face_adapter,UniPCMultistepScheduler)
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else:
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use_storydif=True
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logging.info("start story-diffusion and story-make processing...")
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pipe = load_models(ckpt_path, model_type=model_type, single_files=True, use_safetensors=True,
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photomake_mode=photomake_mode, photomaker_path=photomaker_path, lora=lora,
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lora_path=lora_path,
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trigger_words=trigger_words, lora_scale=lora_scale)
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set_attention_processor(pipe.unet, id_length, is_ipadapter=False)
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elif "flux" in ckpt_path.lower():
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use_flux=True
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if pulid:
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logging.info("start flux-pulid processing...")
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from .PuLID.app_flux import FluxGenerator
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if not clip_vision_path:
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raise "need 'EVA02_CLIP_L_336_psz14_s6B.pt' in comfyUI/models/clip_vision"
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if NF4:
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quantized_mode = "nf4"
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if vae_id == "none" :
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if not front_vae:
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raise "Now,using pulid must choice 'ae' from 'vae' menu."
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else:
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raise "Now,using pulid must choice 'ae' from 'vae' menu(come soon)"
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else:
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vae_path = folder_paths.get_full_path("vae", vae_id)
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pipe = FluxGenerator(flux_pulid_name, ckpt_path, "cuda", offload=offload,
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aggressive_offload=aggressive_offload, pretrained_model=pulid_ckpt,
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quantized_mode=quantized_mode, clip_vision_path=clip_vision_path, clip_cf=clip,
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vae_cf=vae_path,if_repo=if_repo,onnx_provider=onnx_provider)
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else:
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raise "need pulid in easy function"
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else:
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logging.info("start story-diffusion mode processing...")
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use_storydif=True
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pipe = load_models(ckpt_path, model_type=model_type, single_files=True, use_safetensors=True,
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photomake_mode=photomake_mode, photomaker_path=photomaker_path, lora=lora,
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lora_path=lora_path,
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trigger_words=trigger_words, lora_scale=lora_scale)
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set_attention_processor(pipe.unet, id_length, is_ipadapter=False)
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else: #if repo or no ckpt,choice repo
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if_repo=True
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if repo_id.rsplit("/")[-1].lower()=="playground-v2.5-1024px-aesthetic":
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logging.info("start playground story-diffusion processing...")
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use_storydif = True
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pipe = DiffusionPipeline.from_pretrained(
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repo_id,
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torch_dtype=torch.float16,
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)
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set_attention_processor(pipe.unet, id_length, is_ipadapter=False)
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elif repo_id.rsplit("/")[-1].lower()=="sdxl-unstable-diffusers-y":
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logging.info("start sdxl-unstable story-diffusion processing...")
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use_storydif = True
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pipe = StableDiffusionXLPipeline.from_pretrained(
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repo_id, torch_dtype=torch.float16,use_safetensors=False
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)
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set_attention_processor(pipe.unet, id_length, is_ipadapter=False)
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elif use_kolor:
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logging.info("start kolor processing...")
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pipe=kolor_loader(repo_id, model_type, set_attention_processor, id_length, kolor_face, clip_vision_path,
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clip_load, CLIPVisionModelWithProjection, CLIPImageProcessor,
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photomaker_dir, face_ckpt, AutoencoderKL, EulerDiscreteScheduler, UNet2DConditionModel)
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pipe.enable_model_cpu_offload()
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elif use_flux:
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from .model_loader_utils import flux_loader
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pipe=flux_loader(folder_paths,ckpt_path,repo_id,AutoencoderKL,save_model,model_type,pulid,clip_vision_path,NF4,vae_id,offload,aggressive_offload,pulid_ckpt,quantized_mode,
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if_repo,dir_path,clip,onnx_provider)
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if lora:
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if not "Hyper" in lora_path : #can't support Hyper now
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if not NF4:
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logging.info("try using lora in flux quantize processing...")
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pipe.load_lora_weights(lora_path)
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pipe.fuse_lora(lora_scale=0.125) # lora_scale=0.125
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else: # SD dif_repo
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if story_maker:
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if not make_dual_only:
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logging.info("start story_maker processing...")
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from .StoryMaker.pipeline_sdxl_storymaker import StableDiffusionXLStoryMakerPipeline
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pipe = StableDiffusionXLStoryMakerPipeline.from_pretrained(
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repo_id, torch_dtype=torch.float16)
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if device != "mps":
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pipe.cuda()
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image_encoder=clip_load(clip_vision_path)
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pipe.load_storymaker_adapter(image_encoder, face_adapter, scale=0.8, lora_scale=0.8)
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pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
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else:
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logging.info("start story_diffusion processing...")
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use_storydif = True
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pipe = load_models(repo_id, model_type=model_type, single_files=False, use_safetensors=True,
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photomake_mode=photomake_mode,
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photomaker_path=photomaker_path, lora=lora,
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lora_path=lora_path,
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trigger_words=trigger_words, lora_scale=lora_scale)
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set_attention_processor(pipe.unet, id_length, is_ipadapter=False)
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if vae_id != "none":
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if use_storydif:
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vae_id = folder_paths.get_full_path("vae", vae_id)
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vae_config=os.path.join(dir_path, "local_repo","vae")
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pipe.vae=AutoencoderKL.from_single_file(vae_id, config=vae_config,torch_dtype=torch.float16)
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load_chars = False
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if use_storydif:
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pipe.scheduler = scheduler_choice.from_config(pipe.scheduler.config)
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load_chars = load_character_files_on_running(pipe.unet, character_files=char_files)
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pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
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pipe.enable_vae_slicing()
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pipe.to(device)
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# if device != "mps":
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# pipe.enable_model_cpu_offload()
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torch.cuda.empty_cache()
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# need get emb
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character_name_dict_, character_list_ = character_to_dict(character_prompt, lora, trigger_words)
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#print(character_list_)
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if model_type=="img2img":
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d1, _, _, _ = image.size()
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if d1 == 1:
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image_load = [nomarl_upscale(image, width, height)]
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else:
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img_list = list(torch.chunk(image, chunks=d1))
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image_load = [nomarl_upscale(img, width, height) for img in img_list]
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from .model_loader_utils import insight_face_loader,get_insight_dict
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app_face,pipeline_mask,app_face_=insight_face_loader(photomake_mode, auraface, kolor_face, story_maker, make_dual_only,use_storydif)
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input_id_emb_s_dict, input_id_img_s_dict, input_id_emb_un_dict, input_id_cloth_dict=get_insight_dict(app_face,pipeline_mask,app_face_,image_load,photomake_mode,
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kolor_face,story_maker,make_dual_only,
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pulid,pipe,character_list_,control_image,width, height,use_storydif)
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else:
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input_id_emb_s_dict = {}
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input_id_img_s_dict = {}
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input_id_emb_un_dict = {}
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input_id_cloth_dict = {}
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#print(input_id_img_s_dict)
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role_name_list = [i for i in character_name_dict_.keys()]
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#print( role_name_list)
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model={"pipe":pipe,"use_flux":use_flux,"use_kolor":use_kolor,"photomake_mode":photomake_mode,"trigger_words":trigger_words,"lora_scale":lora_scale,
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"load_chars":load_chars,"repo_id":repo_id,"lora_path":lora_path,"ckpt_path":ckpt_path,"model_type":model_type, "lora": lora,
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"scheduler":scheduler,"width":width,"height":height,"kolor_face":kolor_face,"pulid":pulid,"story_maker":story_maker,
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"make_dual_only":make_dual_only,"face_adapter":face_adapter,"clip_vision_path":clip_vision_path,
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"controlnet_path":controlnet_path,"character_prompt":character_prompt,"image":image,"control_image":control_image,
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"input_id_emb_s_dict":input_id_emb_s_dict,"input_id_img_s_dict":input_id_img_s_dict,"use_cf":use_cf,
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"input_id_emb_un_dict":input_id_emb_un_dict,"input_id_cloth_dict":input_id_cloth_dict,"role_name_list":role_name_list,"use_storydif":use_storydif}
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return (model,)
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||
class Storydiffusion_Sampler:
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||
def __init__(self):
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||
pass
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
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||
return {
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||
"required": {
|
||
"model": ("STORY_DICT",),
|
||
"scene_prompts": ("STRING", {"multiline": True,
|
||
"default": "[Taylor] wake up in the bed ;\n[Taylor] have breakfast by the window;\n[Lecun] driving a car;\n[Lecun] is working."}),
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||
"negative_prompt": ("STRING", {"multiline": True,
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"default": "bad anatomy, bad hands, missing fingers, extra fingers, "
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"three hands, three legs, bad arms, missing legs, "
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"missing arms, poorly drawn face, bad face, fused face, "
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"cloned face, three crus, fused feet, fused thigh, "
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"extra crus, ugly fingers, horn,"
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"amputation, disconnected limbs"}),
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"img_style": (
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["No_style", "Realistic", "Japanese_Anime", "Digital_Oil_Painting", "Pixar_Disney_Character",
|
||
"Photographic", "Comic_book",
|
||
"Line_art", "Black_and_White_Film_Noir", "Isometric_Rooms"],),
|
||
"seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED}),
|
||
"steps": ("INT", {"default": 20, "min": 1, "max": 100}),
|
||
"cfg": ("FLOAT", {"default": 7, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
|
||
"denoise_or_ip_sacle": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 1.0, "step": 0.1, "round": 0.01}),
|
||
"style_strength_ratio": ("INT", {"default": 20, "min": 1, "max": 100, "step": 1, "display": "number"}),
|
||
"guidance": (
|
||
"FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
|
||
"mask_threshold": (
|
||
"FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.1, "round": 0.01}),
|
||
"start_step": ("INT", {"default": 5, "min": 1, "max": 1024}),
|
||
"save_character": ("BOOLEAN", {"default": False},),
|
||
"controlnet_scale": (
|
||
"FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.1, "round": 0.01}),
|
||
"guidance_list": ("STRING", {"multiline": True, "default": "0., 0.25, 0.4, 0.75;0.6, 0.25, 1., 0.75"}),
|
||
},
|
||
}
|
||
|
||
|
||
RETURN_TYPES = ("IMAGE", "STRING",)
|
||
RETURN_NAMES = ("image", "prompt_array",)
|
||
FUNCTION = "story_sampler"
|
||
CATEGORY = "Storydiffusion"
|
||
|
||
def story_sampler(self, model,scene_prompts, negative_prompt, img_style, seed, steps,
|
||
cfg, denoise_or_ip_sacle, style_strength_ratio,
|
||
guidance, mask_threshold, start_step,save_character,controlnet_scale,guidance_list,):
|
||
# get value from dict
|
||
pipe=model.get("pipe")
|
||
use_flux=model.get("use_flux")
|
||
photomake_mode=model.get("photomake_mode")
|
||
use_kolor=model.get("use_kolor")
|
||
load_chars=model.get("load_chars")
|
||
model_type=model.get("model_type")
|
||
trigger_words=model.get("trigger_words")
|
||
repo_id=model.get("repo_id")
|
||
lora_path=model.get("lora_path")
|
||
ckpt_path=model.get("ckpt_path")
|
||
lora=model.get("lora")
|
||
lora_scale =model.get("lora_scale")
|
||
scheduler=model.get("scheduler")
|
||
height=model.get("height")
|
||
width = model.get("width")
|
||
kolor_face= model.get("kolor_face")
|
||
story_maker=model.get("story_maker")
|
||
face_adapter=model.get("face_adapter")
|
||
pulid= model.get("pulid")
|
||
controlnet_path=model.get("controlnet_path")
|
||
clip_vision_path=model.get("clip_vision_path")
|
||
make_dual_only= model.get("make_dual_only")
|
||
scheduler_choice = get_scheduler(scheduler["name"],scheduler["scheduler"])
|
||
character_prompt=model.get("character_prompt")
|
||
control_image=model.get("control_image")
|
||
image=model.get("image")
|
||
use_cf=model.get("use_cf")
|
||
input_id_emb_s_dict = model.get("input_id_emb_s_dict")
|
||
input_id_img_s_dict = model.get("input_id_img_s_dict")
|
||
input_id_emb_un_dict = model.get("input_id_emb_un_dict")
|
||
input_id_cloth_dict = model.get("input_id_cloth_dict")
|
||
role_name_list=model.get("role_name_list")
|
||
use_storydif=model.get("use_storydif")
|
||
cf_scheduler=scheduler
|
||
#print(input_id_emb_s_dict,input_id_img_s_dict,input_id_emb_un_dict,role_name_list) #'[Taylor]',['[Taylor]']
|
||
|
||
empty_emb_zero = None
|
||
if model_type=="img2img":
|
||
if pulid or kolor_face or (photomake_mode=="v2" and use_storydif):
|
||
empty_emb_zero = torch.zeros_like(input_id_emb_s_dict[role_name_list[0]][0]).to(device)
|
||
|
||
# 格式化文字内容
|
||
scene_prompts.strip()
|
||
character_prompt.strip()
|
||
# 从角色列表获取角色方括号信息
|
||
char_origin = character_prompt.splitlines()
|
||
char_origin=[i for i in char_origin if "[" in i]
|
||
#print(char_origin)
|
||
char_describe = char_origin # [A a men...,B a girl ]
|
||
char_origin = [char.split("]")[0] + "]" for char in char_origin]
|
||
#print(char_origin)
|
||
# 判断是否有双角色prompt，如果有，获取双角色列表及对应的位置列表，
|
||
prompts_origin = scene_prompts.splitlines()
|
||
prompts_origin=[i.strip() for i in prompts_origin]
|
||
prompts_origin = [i for i in prompts_origin if "[" in i]
|
||
#print(prompts_origin)
|
||
positions_dual = [index for index, prompt in enumerate(prompts_origin) if len(extract_content_from_brackets(prompt))>=2] #改成单句中双方括号方法，利于MS组句，[A]... [B]...[C]
|
||
prompts_dual = [prompt for prompt in prompts_origin if len(extract_content_from_brackets(prompt))>=2]
|
||
|
||
if len(char_origin) == 2:
|
||
positions_char_1 = [index for index, prompt in enumerate(prompts_origin) if char_origin[0] in prompt][
|
||
0] # 获取角色出现的索引列表，并获取首次出现的位置
|
||
positions_char_2 = [index for index, prompt in enumerate(prompts_origin) if char_origin[1] in prompt][
|
||
0] # 获取角色出现的索引列表，并获取首次出现的位置
|
||
|
||
if model_type=="img2img":
|
||
d1, _, _, _ = image.size()
|
||
if d1 == 1:
|
||
image_load = [nomarl_upscale(image, width, height)]
|
||
else:
|
||
img_list = list(torch.chunk(image, chunks=d1))
|
||
image_load = [nomarl_upscale(img, width, height) for img in img_list]
|
||
|
||
gen = process_generation(pipe, image_load, model_type, steps, img_style, denoise_or_ip_sacle,
|
||
style_strength_ratio, cfg,
|
||
seed, id_length,
|
||
character_prompt,
|
||
negative_prompt,
|
||
scene_prompts,
|
||
width,
|
||
height,
|
||
load_chars,
|
||
lora,
|
||
trigger_words,photomake_mode,use_kolor,use_flux,make_dual_only,
|
||
kolor_face,pulid,story_maker,input_id_emb_s_dict, input_id_img_s_dict,input_id_emb_un_dict, input_id_cloth_dict,guidance,control_image,empty_emb_zero,use_cf,cf_scheduler)
|
||
|
||
else:
|
||
if story_maker:
|
||
raise "story maker only suppport img2img now"
|
||
upload_images = None
|
||
gen = process_generation(pipe, upload_images, model_type, steps, img_style, denoise_or_ip_sacle,
|
||
style_strength_ratio, cfg,
|
||
seed, id_length,
|
||
character_prompt,
|
||
negative_prompt,
|
||
scene_prompts,
|
||
width,
|
||
height,
|
||
load_chars,
|
||
lora,
|
||
trigger_words,photomake_mode,use_kolor,use_flux,make_dual_only,kolor_face,
|
||
pulid,story_maker,input_id_emb_s_dict, input_id_img_s_dict,input_id_emb_un_dict, input_id_cloth_dict,guidance,control_image,empty_emb_zero,use_cf,cf_scheduler)
|
||
|
||
for value in gen:
|
||
print(type(value))
|
||
image_pil_list = phi_list(value)
|
||
|
||
image_pil_list_ms = image_pil_list.copy()
|
||
if save_character:
|
||
print("saving character...")
|
||
save_results(pipe.unet)
|
||
if prompts_dual:
|
||
if not clip_vision_path:
|
||
raise "need a clip_vison weight."
|
||
if use_flux or use_kolor:
|
||
raise "flux or kolor don't support MS diffsion."
|
||
if model_type == "img2img":
|
||
image_a = image_load[0]
|
||
image_b = image_load[1]
|
||
else:
|
||
image_a = image_pil_list_ms[positions_char_1]
|
||
image_b = image_pil_list_ms[positions_char_2]
|
||
if story_maker:
|
||
print("start sampler dual prompt using story maker")
|
||
if make_dual_only:
|
||
del pipe
|
||
cleanup_models(keep_clone_weights_loaded=False)
|
||
gc.collect()
|
||
torch.cuda.empty_cache()
|
||
pipe=story_maker_loader(clip_load,clip_vision_path,dir_path,ckpt_path,face_adapter,UniPCMultistepScheduler)
|
||
mask_image_1=input_id_img_s_dict[role_name_list[0]][0]
|
||
mask_image_2=input_id_img_s_dict[role_name_list[1]][0]
|
||
|
||
if photomake_mode=="v2":
|
||
face_info_1 = input_id_emb_un_dict[role_name_list[0]][0]
|
||
face_info_2 = input_id_emb_un_dict[role_name_list[1]][0]
|
||
else:
|
||
face_info_1 = input_id_emb_s_dict[role_name_list[0]][0]
|
||
face_info_2 = input_id_emb_s_dict[role_name_list[1]][0]
|
||
|
||
cloth_info_1=None
|
||
cloth_info_2=None
|
||
if isinstance(control_image,torch.Tensor):
|
||
cloth_info_1 = input_id_cloth_dict[role_name_list[0]][0]
|
||
cloth_info_2 = input_id_cloth_dict[role_name_list[1]][0]
|
||
|
||
prompts_dual, negative_prompt = apply_style(
|
||
img_style, prompts_dual, negative_prompt
|
||
)
|
||
# 添加Lora trigger
|
||
add_trigger_words = " " + trigger_words+ " style "
|
||
if lora:
|
||
prompts_dual = remove_punctuation_from_strings(prompts_dual)
|
||
if lora not in lora_lightning_list: # 加速lora不需要trigger
|
||
prompts_dual = [item + add_trigger_words for item in prompts_dual]
|
||
|
||
prompts_dual = [item.replace(char_origin[0], char_describe[0]) for item in prompts_dual if
|
||
char_origin[0] in item]
|
||
prompts_dual = [item.replace(char_origin[1], char_describe[1]) for item in prompts_dual if
|
||
char_origin[1] in item]
|
||
|
||
prompts_dual = [item.replace("[", " ", ).replace("]", " ", ) for item in prompts_dual]
|
||
image_dual = []
|
||
if model_type=="txt2img":
|
||
setup_seed(seed)
|
||
generator = torch.Generator(device=device).manual_seed(seed)
|
||
for i,prompt in enumerate(prompts_dual):
|
||
output = pipe(
|
||
image=image_a, mask_image=mask_image_1, face_info=face_info_1, # first person
|
||
image_2=image_b, mask_image_2=mask_image_2, face_info_2=face_info_2, # second person
|
||
prompt=prompt,
|
||
negative_prompt=negative_prompt,
|
||
ip_adapter_scale=denoise_or_ip_sacle, lora_scale=0.8,
|
||
num_inference_steps=steps,
|
||
guidance_scale=cfg,
|
||
height=height, width=width,
|
||
generator=generator,
|
||
cloth=cloth_info_1,
|
||
cloth_2=cloth_info_2
|
||
).images[0]
|
||
image_dual.append(output)
|
||
else: #using ms diffusion
|
||
print("start sampler dual prompt using ms-diffusion")
|
||
if controlnet_path:
|
||
if not isinstance(control_image,torch.Tensor):
|
||
raise "using controlnet need controlnet image input"
|
||
if width != height:
|
||
square = max(height, width)
|
||
new_height, new_width = square, square
|
||
image_a = center_crop(image_a)
|
||
image_b = center_crop(image_b)
|
||
else:
|
||
new_width = width
|
||
new_height = height
|
||
del pipe
|
||
cleanup_models(keep_clone_weights_loaded=False)
|
||
gc.collect()
|
||
torch.cuda.empty_cache()
|
||
from .model_loader_utils import msdiffusion_main
|
||
image_dual = msdiffusion_main(image_a, image_b, prompts_dual, new_width, new_height, steps, seed,
|
||
img_style, char_describe, char_origin, negative_prompt, clip_vision_path,
|
||
model_type, lora, lora_path, lora_scale,
|
||
trigger_words, ckpt_path, repo_id, guidance,
|
||
mask_threshold, start_step, controlnet_path, control_image,
|
||
controlnet_scale, cfg, guidance_list, scheduler_choice)
|
||
j = 0
|
||
for i in positions_dual: # 重新将双人场景插入原序列
|
||
if width != height:
|
||
img = center_crop_s(image_dual[j], width, height)
|
||
else:
|
||
img = image_dual[j]
|
||
image_pil_list.insert(int(i), img)
|
||
j += 1
|
||
image_list = narry_list(image_pil_list)
|
||
torch.cuda.empty_cache()
|
||
else:
|
||
image_list = narry_list(image_pil_list)
|
||
image = torch.from_numpy(np.fromiter(image_list, np.dtype((np.float32, (height, width, 3)))))
|
||
torch.cuda.empty_cache()
|
||
return (image, scene_prompts,)
|
||
|
||
class Comic_Type:
|
||
@classmethod
|
||
def INPUT_TYPES(s):
|
||
return {"required": {"image": ("IMAGE",),
|
||
"scene_prompts": ("STRING", {"multiline": True, "forceInput": True, "default": ""}),
|
||
"fonts_list": (fonts_lists,),
|
||
"text_size": ("INT", {"default": 40, "min": 1, "max": 100}),
|
||
"comic_type": (["Four_Pannel", "Classic_Comic_Style"],),
|
||
"split_lines": ("STRING", {"default": "；"}),
|
||
}}
|
||
|
||
RETURN_TYPES = ("IMAGE",)
|
||
ETURN_NAMES = ("image",)
|
||
FUNCTION = "comic_gen"
|
||
CATEGORY = "Storydiffusion"
|
||
|
||
def comic_gen(self, image, scene_prompts, fonts_list, text_size, comic_type, split_lines):
|
||
result = [item for index, item in enumerate(image)]
|
||
total_results = narry_list_pil(result)
|
||
font_choice = os.path.join(dir_path, "fonts", fonts_list)
|
||
captions = scene_prompts.splitlines()
|
||
if len(captions) > 1:
|
||
captions = [caption.replace("(", "").replace(")", "") if "(" or ")" in caption else caption
|
||
for caption in captions] # del ()
|
||
captions = [caption.replace("[NC]", "") for caption in captions]
|
||
captions = [caption.replace("]", "").replace("[", "") for caption in captions]
|
||
captions = [
|
||
caption.split("#")[-1] if "#" in caption else caption
|
||
for caption in captions
|
||
]
|
||
else:
|
||
prompt_array = scene_prompts.replace(split_lines, "\n")
|
||
captions = prompt_array.splitlines()
|
||
font = ImageFont.truetype(font_choice, text_size)
|
||
images = (
|
||
get_comic(total_results, comic_type, captions=captions, font=font)
|
||
+ total_results
|
||
)
|
||
images = phi2narry(images[0])
|
||
return (images,)
|
||
|
||
|
||
class Pre_Translate_prompt:
|
||
@classmethod
|
||
def INPUT_TYPES(s):
|
||
return {"required": {"scene_prompts": ("STRING", {"forceInput": True, "default": ""}),
|
||
"keep_character_name": ("BOOLEAN", {"default": False},)
|
||
}}
|
||
|
||
RETURN_TYPES = ("STRING",)
|
||
ETURN_NAMES = ("prompt_array",)
|
||
FUNCTION = "translate_prompt"
|
||
CATEGORY = "Storydiffusion"
|
||
|
||
def translate_prompt(self, scene_prompts, keep_character_name):
|
||
captions = scene_prompts.splitlines()
|
||
if not keep_character_name:
|
||
captions = [caption.split(")", 1)[-1] if ")" in caption else caption
|
||
for caption in captions] # del character
|
||
else:
|
||
captions = [caption.replace("(", "").replace(")", "") if "(" or ")" in caption else caption
|
||
for caption in captions] # del ()
|
||
captions = [caption.replace("[NC]", "") for caption in captions]
|
||
if not keep_character_name:
|
||
captions = [caption.split("]", 1)[-1] for caption in captions]
|
||
else:
|
||
captions = [caption.replace("]", "").replace("[", "") for caption in captions]
|
||
captions = [
|
||
caption.split("#")[-1] if "#" in caption else caption
|
||
for caption in captions
|
||
]
|
||
scene_prompts = ''.join(captions)
|
||
return (scene_prompts,)
|
||
|
||
class Story_Easy_Function:
|
||
@classmethod
|
||
def INPUT_TYPES(s):
|
||
return {"required": {"function_repo": ("STRING", {"default": ""}),
|
||
"function_ckpt": (["none"] + folder_paths.get_filename_list("checkpoints"),),
|
||
"function_cli": ("STRING", {"default": ""}),
|
||
}}
|
||
|
||
RETURN_TYPES = ("CLIP",)
|
||
ETURN_NAMES = ("clip",)
|
||
FUNCTION = "funcion_main"
|
||
CATEGORY = "Storydiffusion"
|
||
|
||
def funcion_main(self, function_repo, function_ckpt,function_cli):
|
||
if "glm" in function_cli:
|
||
pass
|
||
#print("test")
|
||
return (function_repo,)
|
||
|
||
|
||
NODE_CLASS_MAPPINGS = {
|
||
"Storydiffusion_Model_Loader": Storydiffusion_Model_Loader,
|
||
"Storydiffusion_Sampler": Storydiffusion_Sampler,
|
||
"Pre_Translate_prompt": Pre_Translate_prompt,
|
||
"Comic_Type": Comic_Type,
|
||
"Story_Easy_Function":Story_Easy_Function
|
||
}
|
||
|
||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||
"Storydiffusion_Model_Loader": "Storydiffusion_Model_Loader",
|
||
"Storydiffusion_Sampler": "Storydiffusion_Sampler",
|
||
"Pre_Translate_prompt": "Pre_Translate_prompt",
|
||
"Comic_Type": "Comic_Type",
|
||
"Story_Easy_Function":"Story_Easy_Function"
|
||
}
|