2335 lines
128 KiB
Python
2335 lines
128 KiB
Python
# !/usr/bin/env python
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# -*- coding: UTF-8 -*-
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import random
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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,Image
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import torch.nn.functional as F
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import copy
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from pathlib import PureWindowsPath
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from tqdm import tqdm
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from .utils.utils import get_comic
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from .model_loader_utils import (phi2narry,replicate_data_by_indices,get_float,gc_cleanup,tensor_to_image,photomaker_clip,tensortopil_list_upscale,tensortopil_list,extract_content_from_brackets_,
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narry_list_pil,pre_text2infer,cf_clip,get_phrases_idx_cf,get_eot_idx_cf,get_ms_phrase_emb,get_extra_function,photomaker_clip_v2,adjust_indices,load_clip_clipvsion,
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get_scheduler,apply_style_positive,load_lora_for_unet_only,tensortolist,
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nomarl_upscale,SAMPLER_NAMES,SCHEDULER_NAMES,lora_lightning_list)
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from .utils.gradio_utils import cal_attn_indice_xl_effcient_memory,is_torch2_available
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from .ip_adapter.attention_processor import IPAttnProcessor2_0
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if is_torch2_available():
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from .utils.gradio_utils import AttnProcessor2_0 as AttnProcessor
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else:
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from .utils.gradio_utils import AttnProcessor
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import folder_paths
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from comfy.model_management import total_vram
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import comfy
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import latent_preview
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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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weigths_gguf_current_path = os.path.join(folder_paths.models_dir, "gguf")
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if not os.path.exists(weigths_gguf_current_path):
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os.makedirs(weigths_gguf_current_path)
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folder_paths.add_model_folder_path("gguf", weigths_gguf_current_path) # use gguf dir
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global total_count, attn_count_, cur_step, mask1024, mask4096, attn_procs_, unet_,sa32, sa64,write,height_s, width_s
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infer_type_g=torch.float16 if device=="cuda" else torch.float32 #TODO
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class EasyFunction_Lite:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"repo1": ("STRING", { "default": ""}),
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"repo2": ("STRING", { "default": ""}),
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"unet":(["none"] +folder_paths.get_filename_list("diffusion_models"),),
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"gguf": (["none"] + folder_paths.get_filename_list("gguf"),),
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"clip1": (["none"] + folder_paths.get_filename_list("clip"),),
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"clip2": (["none"] + folder_paths.get_filename_list("clip"),),
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"clip_vision1": (["none"] + folder_paths.get_filename_list("clip_vision"),),
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"clip_vision2": (["none"] + folder_paths.get_filename_list("clip_vision"),),
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"lora1": (["none"] +folder_paths.get_filename_list("loras"),),
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"lora2": (["none"] +folder_paths.get_filename_list("loras"),),
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"controlnet":(["none"] +folder_paths.get_filename_list("controlnet"),),
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"special_mode": (["none","tag", "glm"],),
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"tag_temperature": (
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"FLOAT", {"default": 0.7, "min": 0.1, "max": 1.0, "step": 0.1, "round": 0.01}),
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}}
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RETURN_TYPES = ("MODEL","CLIP","STORY_CONDITIONING_1")
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RETURN_NAMES = ("model","clip","info")
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FUNCTION = "easy_function_main"
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CATEGORY = "Storydiffusion"
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def easy_function_main(self, repo1,repo2,unet,gguf,clip1,clip2,clip_vision1,clip_vision2,lora1,lora2,controlnet,special_mode,tag_temperature):
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repo1_path=PureWindowsPath(repo1).as_posix() if repo1 else None
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repo2_path=PureWindowsPath(repo2).as_posix() if repo2 else None
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gguf_path=folder_paths.get_full_path("gguf", gguf) if gguf != "none" else None
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unet_path=folder_paths.get_full_path("unet", unet) if unet != "none" else None
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clip1_path=folder_paths.get_full_path("clip", clip1) if clip1 != "none" else None
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clip2_path=folder_paths.get_full_path("clip", clip2) if clip2 != "none" else None
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lora1_path =folder_paths.get_full_path("loras", lora1) if lora1 != "none" else None
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lora2_path =folder_paths.get_full_path("loras", lora2) if lora2 != "none" else None
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clip_vision1_path=folder_paths.get_full_path("clip_vision", clip_vision1) if clip_vision1 != "none" else None
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clip_vision2_path=folder_paths.get_full_path("clip_vision", clip_vision2) if clip_vision2 != "none" else None
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controlnet_path=folder_paths.get_full_path("controlnet", controlnet) if controlnet != "none" else None
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clip_glm,tag_model,pipe,svdq_repo=None,None,None,None
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repo_list=[i for i in [repo1_path,repo2_path] if i is not None]
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if repo1_path is not None or repo2_path is not None:
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find_svdq=[i for i in repo_list if "svdq" in i ]
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svdq_repo=find_svdq[0] if find_svdq else None
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if special_mode=="tag":
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from .model_loader_utils import StoryLiteTag
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tag_model = StoryLiteTag(device, tag_temperature,repo2_path, repo1_path) #No repo will load default
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elif special_mode=="glm": #kolor only
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from .model_loader_utils import GLM_clip
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if clip1_path is not None:
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clip_glm=GLM_clip(dir_path,clip1_path)
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elif clip2_path is not None and clip1_path is None:
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clip_glm=GLM_clip(dir_path,clip2_path)
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else:
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clip_glm=None
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else:
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pass
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info={"gguf_path":gguf_path,"unet_path":unet_path,"tag_model":tag_model,"clip_vision1_path":clip_vision1_path,"controlnet_path":controlnet_path,
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"clip1_path":clip1_path,"clip2_path":clip2_path,"repo1_path":repo1_path,"repo2_path":repo2_path,"svdq_repo":svdq_repo,
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"lora1_path":lora1_path,"lora2_path":lora2_path,"clip_vision2_path":clip_vision2_path,}
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return (pipe,clip_glm,info)
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class StoryDiffusion_Apply:
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{
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"model": ("MODEL",),
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"vae": ("VAE",),
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"infer_mode": (["story", "classic","flux_pulid","infiniteyou","uno","realcustom","instant_character","dreamo","qwen_image","flux_omi","bagel_edit","story_maker","story_and_maker","consistory","kolor_face","msdiffusion" ],),
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"photomake_ckpt": (["none"] + [i for i in folder_paths.get_filename_list("photomaker") if "v1" in i or "v2" in i],),
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"ipadapter_ckpt": (["none"] + folder_paths.get_filename_list("photomaker"),),
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"quantize_mode": ([ "fp8", "nf4","fp16", ],),
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"lora_scale": ("FLOAT", {"default": 0.8, "min": 0.1, "max": 1.0, "step": 0.1}),
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"extra_function":("STRING", {"default": ""}),
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},
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"optional":{
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"info": ("STORY_CONDITIONING_1",),
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"CLIP_VISION": ("CLIP_VISION",),
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}
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}
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RETURN_TYPES = ("MODEL","DIFFCONDI",)
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RETURN_NAMES = ("model","switch",)
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FUNCTION = "main_apply"
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CATEGORY = "Storydiffusion"
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def main_apply(self,model,vae,infer_mode,photomake_ckpt,ipadapter_ckpt,quantize_mode,lora_scale,extra_function, **kwargs):
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print(f"infer model is {infer_mode}")
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extra_info=kwargs.get("info",{})
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# pre data
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photomake_ckpt_path = None if photomake_ckpt == "none" else folder_paths.get_full_path("photomaker", photomake_ckpt)
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ipadapter_ckpt_path = None if ipadapter_ckpt == "none" else folder_paths.get_full_path("photomaker", ipadapter_ckpt)
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if extra_function:
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extra_function=PureWindowsPath(extra_function).as_posix()
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save_quantezed=True if "save" in extra_function and quantize_mode=="fp8" else False
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clip_vision1_path=extra_info.get("clip_vision1_path") if extra_info else None
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clip_vision2_path=extra_info.get("clip_vision2_path") if extra_info else None
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repo1_path=extra_info.get("repo1_path",None)
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repo2_path=extra_info.get("repo2_path",None)
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lora1_path=extra_info.get("lora1_path") if extra_info else None
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lora2_path=extra_info.get("lora2_path") if extra_info else None
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unet_path=extra_info.get("unet_path") if extra_info else None
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gguf_path=extra_info.get("gguf_path") if extra_info else None
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repo_list=[i for i in [repo1_path,repo2_path] if i is not None]
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lora_list=[i for i in [lora1_path,lora2_path] if i is not None]
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dreamo_version="v1.0" if "v1.0" in extra_function else "v1.1"
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vae_encoder,vae_downsample_factor,vae_config,vision_model_config_ar,image_proj_model,no_dif_quantization,find_Kolors=None,None,None,None,None,False,None
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# per clip vision
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CLIP_VISION=kwargs.get("CLIP_VISION")
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unet_type=torch.float16 #use for sdxl
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if infer_mode=="flux_pulid" or infer_mode=="kolor_face":# 2种加载clip vision的方式
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from comfy.clip_vision import load as clip_load
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if CLIP_VISION is not None:
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clip_vision_path=CLIP_VISION
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elif clip_vision1_path is not None:
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clip_vision_path=clip_load(clip_vision1_path).model
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elif clip_vision2_path is not None:
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clip_vision_path=clip_load(clip_vision2_path).model
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else:
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if infer_mode=="kolor_face":
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pass
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else:
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raise ValueError("Please specify one of CLIP_VISION or clip_vision1_path or clip_vision2_path")
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if infer_mode=="msdiffusion" or infer_mode in ["story_maker" ,"story_and_maker"]:
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if CLIP_VISION is not None:
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pass
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else:
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from comfy.clip_vision import load as clip_load
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if clip_vision1_path is not None:
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CLIP_VISION=clip_load(clip_vision1_path).model
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elif clip_vision2_path is not None:
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CLIP_VISION=clip_load(clip_vision2_path).model
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else:
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raise ValueError("Please provide a CLIP_VISION or CLIP_VISION1 or CLIP_VISION2,Msdiffusion need a clipvison g model,story_maker need a clipvison H model")
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# pre dreamo lora
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if infer_mode =="dreamo" and lora1_path is not None and lora2_path is not None:
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if "distill" in lora1_path.lower():
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cfg_distill_path=lora1_path
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dreamo_lora_path=lora2_path
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else:
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cfg_distill_path=lora2_path
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dreamo_lora_path=lora1_path
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else:
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cfg_distill_path=None
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dreamo_lora_path=None
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if infer_mode in ["story_maker" ,"story_and_maker"] and ipadapter_ckpt_path is None:
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raise "story_maker need a mask.bin"
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# check vram only using in flux pulid or UNO
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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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if infer_mode in["story", "story_and_maker","msdiffusion"]:# mix mode,use maker or ms to make 2 roles in on image
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from .model_loader_utils import Loader_storydiffusion
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model = Loader_storydiffusion(model,photomake_ckpt_path,vae)
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elif infer_mode =="story_maker":
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from .model_loader_utils import Loader_story_maker
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model = Loader_story_maker(model,ipadapter_ckpt_path,vae,False,lora_scale)
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elif infer_mode == "flux_pulid":
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from .PuLID.app_flux import get_models
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from .model_loader_utils import Loader_Flux_Pulid
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if unet_path is None:
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raise "PuLID can't link a normal comfyui model,you need load a flux unet model "
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model_=get_models("flux-dev",unet_path,False,aggressive_offload,device=device,offload=offload,quantized_mode=quantize_mode,)
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model = Loader_Flux_Pulid(model_,model,ipadapter_ckpt_path,quantize_mode,aggressive_offload,offload,False,clip_vision_path)
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elif infer_mode == "infiniteyou":
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from .model_loader_utils import Loader_InfiniteYou
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assert extra_info ,"you need to provide extra_info"
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model,image_proj_model = Loader_InfiniteYou(extra_info,vae,quantize_mode)
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elif infer_mode == "consistory":
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from .model_loader_utils import load_pipeline_consistory
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model = load_pipeline_consistory(model,vae)
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elif infer_mode == "instant_character":
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from .model_loader_utils import load_pipeline_instant_character
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assert extra_info ,"you need to provide extra_info"
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model = load_pipeline_instant_character(extra_info,ipadapter_ckpt_path,vae,quantize_mode)
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elif infer_mode == "realcustom":
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from .model_loader_utils import load_pipeline_realcustom,load_realcustom_vae
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if ipadapter_ckpt_path is None:
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raise "realcustom need a realcustom model which in photomaker folder, and chocie it in ipadapter_ckpt_path"
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model,vision_model_config_ar,_ = load_pipeline_realcustom(model,ipadapter_ckpt_path)
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vae_encoder,vae_downsample_factor,vae_config=load_realcustom_vae(vae,device)
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elif infer_mode == "kolor_face":
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from .model_loader_utils import Loader_KOLOR
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find_Kolors =[i for i in repo_list if "kolor" in i.lower()]
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if not find_Kolors:
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raise ValueError("No Kolor model found in the repo")
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model = Loader_KOLOR(find_Kolors[0],clip_vision_path,ipadapter_ckpt_path)
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elif infer_mode == "uno":
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from .model_loader_utils import Loader_UNO
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model = Loader_UNO(extra_info,offload,quantize_mode,save_quantezed,lora_rank=512)
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elif infer_mode == "dreamo":
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from.model_loader_utils import Loader_Dreamo
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if dreamo_lora_path is None or cfg_distill_path is None or ipadapter_ckpt_path is None:
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raise "dreamo need a dreamo lora and cfg distill and turbo lora in ipadapter menu"
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model = Loader_Dreamo(extra_info,vae,quantize_mode,dreamo_lora_path,cfg_distill_path,ipadapter_ckpt_path,device,dreamo_version)
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elif infer_mode == "bagel_edit":
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from .Bagel.app import load_bagel_model
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if not repo_list :
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raise "EasyFunction_Lite node repo1 or repo2 must fill bagel repo"
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max_mem_per_gpu=str(int(total_vram/1000))+"GIB"
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model = load_bagel_model(repo_list[0],quantize_mode,max_mem_per_gpu)
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elif infer_mode == "flux_omi":
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from .model_loader_utils import Loader_Flux_Diffuser
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no_dif_quantization=True if extra_info.get("unet_path") or extra_info.get("svdq_repo") or extra_info.get("gguf_path") else False
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model = Loader_Flux_Diffuser(extra_info,ipadapter_ckpt_path,vae,quantize_mode)
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elif infer_mode == "qwen_image":
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from.qwen_image.inferencer import load_quwen_image
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df_repo=repo_list[0] if repo_list else None
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model,edit_mode = load_quwen_image(cpu_offload=True,cpu_offload_blocks=16,no_pin_memory=True, dir_path =dir_path, repo=df_repo,unet_path=unet_path,gguf_path=gguf_path,lora_path=lora_list[0] if lora_list else None)
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else: # can not choice a mode
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print("infer use comfyui classic mode")
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story_img=True if photomake_ckpt_path and infer_mode in["story","story_maker","story_and_maker","msdiffusion"] else False
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model_=model if infer_mode=="flux_pulid" or story_img else None
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switch={"infer_mode":infer_mode,"ipadapter_ckpt_path":ipadapter_ckpt_path,"photomake_ckpt_path":photomake_ckpt_path,
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"vision_model_config_ar":vision_model_config_ar,"no_dif_quantization":no_dif_quantization,"edit_mode":edit_mode,
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"lora_scale":lora_scale,"image_proj_model":image_proj_model, "vae_encoder":vae_encoder,"vae_downsample_factor":vae_downsample_factor,"vae_config":vae_config,"dreamo_version":dreamo_version,
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"CLIP_VISION":CLIP_VISION,"VAE":vae,"find_Kolors":find_Kolors,"model_":model_,"unet_type":unet_type,"extra_function":extra_function,}
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switch.update(extra_info)
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return (model,switch,)
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class StoryDiffusion_CLIPTextEncode:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"clip": ("CLIP",),
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"switch": ("DIFFCONDI", ),
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"width": ("INT", {"default": 768, "min": 256, "max": 2048, "step": 16, "display": "number"}),
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"height": ("INT", {"default": 768, "min": 256, "max": 2048, "step": 16, "display": "number"}),
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"role_text": ("STRING", {"multiline": True,"default": "[Taylor] a woman img, wearing a white T-shirt, blue loose hair.\n""[Lecun] a man img,wearing a suit,black hair."}),
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"scene_text":("STRING", {"multiline": True,
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"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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"pos_text": ("STRING", {"multiline": True,"default": ",best"}),
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"neg_text": ("STRING", {"multiline": True,
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"default": "bad anatomy, bad hands, missing fingers, extra fingers,three hands, three legs, bad arms, missing legs, missing arms, poorly drawn "
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"face, bad face, fused face, cloned face, three crus, fused feet, fused thigh, extra crus, ugly fingers, horn,amputation, disconnected limbs"}),
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"lora_trigger_words": ("STRING", {"default": "best quality"}),
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"add_style": (["No_style", "Realistic", "Japanese_Anime", "Digital_Oil_Painting", "Pixar_Disney_Character","Photographic", "Comic_book","Line_art", "Black_and_White_Film_Noir", "Isometric_Rooms"],),
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"mask_threshold": (
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"FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.1, "round": 0.01}),
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||
"extra_param":("STRING", {"default": ""}),
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"guidance_list": ("STRING", {"multiline": True, "default": "0., 0.25, 0.4, 0.75;0.6, 0.25, 1., 0.75"}),
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||
},
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||
"optional": {
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||
"image":("IMAGE",),
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||
"control_image":("IMAGE",),
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||
}
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||
}
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||
RETURN_TYPES = ("CONDITIONING","CONDITIONING","DIFFINFO","INT","INT",)
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||
RETURN_NAMES = ("positive", "negative","condition","width","height",)
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||
FUNCTION = "encode"
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||
CATEGORY = "Storydiffusion"
|
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|
||
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def encode(self, clip,switch,width,height, role_text,scene_text,pos_text,neg_text,lora_trigger_words,add_style,mask_threshold,extra_param,guidance_list,**kwargs):
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infer_mode=switch.get("infer_mode")
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||
CLIP_VISION = switch.get("CLIP_VISION")
|
||
extra_function=switch.get("extra_function")
|
||
photomake_ckpt_path=switch.get("photomake_ckpt_path")
|
||
unet_type=switch.get("unet_type")
|
||
model_=switch.get("model_")
|
||
vae=switch.get("VAE")
|
||
|
||
|
||
image=kwargs.get("image",None)
|
||
control_image=kwargs.get("control_image",None)
|
||
|
||
lora_list=[i for i in [switch.get("lora1_path"),switch.get("lora2_path")] if i is not None]
|
||
repo_list=[i for i in [switch.get("repo1_path"),switch.get("repo2_path")] if i is not None]
|
||
|
||
use_lora =True if lora_list else False
|
||
|
||
|
||
if extra_function:
|
||
extra_function=PureWindowsPath(extra_function).as_posix()
|
||
tag_list,text_model,vision_model,siglip_path,dino_path=None,None,None,None,None,
|
||
|
||
# 反推功能
|
||
if switch.get("tag_model") is not None and isinstance(image,torch.Tensor):
|
||
tag_img_list=tensortopil_list(image)
|
||
tag_list=[]
|
||
for i in tag_img_list:
|
||
tag_text=switch.get("tag_model").run_tag(i)
|
||
tag_list.append(tag_text)
|
||
|
||
|
||
|
||
|
||
siglip_path_=switch.get("clip_vision1_path")
|
||
dino_path_=switch.get("clip_vision2_path")
|
||
clip1_path=switch.get("clip1_path")
|
||
clip2_path=switch.get("clip2_path")
|
||
|
||
|
||
siglip_path, dino_path = siglip_path_, dino_path_
|
||
|
||
if (siglip_path_ is not None and dino_path_ is not None and
|
||
"sig" in dino_path_ and "dino" in siglip_path_):
|
||
siglip_path, dino_path = dino_path_, siglip_path_i
|
||
|
||
|
||
if infer_mode == "realcustom" and (siglip_path is None or dino_path is None):
|
||
print ("if use realcustom mode, u must linke add_function to your node,if not ,will auto load clip_vision_path and clip_path")
|
||
siglip_path="" if siglip_path is None else siglip_path
|
||
dino_path="" if dino_path is None else dino_path
|
||
|
||
|
||
auraface,use_photov2,img2img_mode,cached,inject,onnx_provider,dreamo_mode,trigger_words_dual,dual_lora_scale=get_extra_function(extra_function,extra_param,photomake_ckpt_path,image,infer_mode)
|
||
|
||
|
||
|
||
(replace_prompts,role_index_dict,invert_role_index_dict,ref_role_index_dict,ref_role_totals,role_list,role_dict,
|
||
nc_txt_list,nc_indexs,positions_index_char_1,positions_index_char_2,positions_index_dual,prompts_dual,index_char_1_list,index_char_2_list)=pre_text2infer(role_text,scene_text,lora_trigger_words,use_lora,tag_list)
|
||
|
||
|
||
global character_index_dict, invert_character_index_dict, cur_character, ref_indexs_dict, ref_totals, character_dict
|
||
character_index_dict=role_index_dict
|
||
invert_character_index_dict=invert_role_index_dict
|
||
ref_indexs_dict=ref_role_index_dict
|
||
ref_totals=ref_role_totals
|
||
character_dict=role_dict
|
||
|
||
_, style_neg = apply_style_positive(add_style, " ") #get n
|
||
neg_text = neg_text + style_neg
|
||
|
||
replace_prompts=[i+pos_text for i in replace_prompts]
|
||
|
||
only_role_list=[apply_style_positive(add_style,i)[0] for i in replace_prompts]
|
||
|
||
|
||
if len(role_list)==1:
|
||
role_key_list=role_list*len(only_role_list)
|
||
else:
|
||
role_key_list=replicate_data_by_indices(role_list, index_char_1_list, index_char_2_list)
|
||
role_key_list=[i for i in role_key_list if i is not None]
|
||
|
||
if len(role_list)>1: #重新整理prompt排序，便于后续emb和ID的对应
|
||
nc_dual_list=[]
|
||
for i in nc_indexs:
|
||
nc_dual_list.append(i)
|
||
for i in positions_index_dual:
|
||
nc_dual_list.append(i)
|
||
adjusted_a = adjust_indices(index_char_1_list, nc_dual_list)
|
||
adjusted_b = adjust_indices(index_char_2_list, nc_dual_list)
|
||
a_list = [only_role_list[i] for i in adjusted_a if 0 <= i < len(only_role_list)]
|
||
b_list = [only_role_list[i] for i in adjusted_b if 0 <= i < len(only_role_list)]
|
||
|
||
inf_list_split=[a_list,b_list]
|
||
else:
|
||
inf_list_split=[only_role_list]
|
||
|
||
|
||
|
||
if not img2img_mode and infer_mode=="flux_pulid":
|
||
raise "flux_pulid mode only support image2image"
|
||
|
||
if infer_mode=="msdiffusion" and not prompts_dual:
|
||
raise "use msdiffusion mode need have [role1] and [role2] in sence txt."
|
||
|
||
if img2img_mode and photomake_ckpt_path is None and infer_mode=="story":
|
||
raise "need chocie photomake v1 or v2 when use img2img mode"
|
||
|
||
if infer_mode=="story_maker" and image is None:
|
||
raise "story_maker only support img2img mode,you can use story_and_maker or link a iamge"
|
||
|
||
if infer_mode=="msdiffusion" and photomake_ckpt_path and not img2img_mode:
|
||
raise "if use msdiffusion txt2img mode,can not use photomake_ckpt_path"
|
||
|
||
uno_pe="d" #Literal['d', 'h', 'w', 'o']
|
||
|
||
only_role_emb_ne,x_1_refs_dict,image_emb,x_1_refs_dual=None,None,None,None
|
||
|
||
input_id_images_dict={}
|
||
image_list=[]
|
||
|
||
if img2img_mode and image is not None:
|
||
if len(role_list)==1:
|
||
image_pil=nomarl_upscale(image, width, height)
|
||
input_id_images_dict[role_list[0]]=image_pil
|
||
image_list=[image_pil]
|
||
else:
|
||
f1, _, _, _ = image.size()
|
||
img_list = list(torch.chunk(image, chunks=f1))
|
||
image_list = [nomarl_upscale(img, width, height) for img in img_list]
|
||
for index, key in enumerate(role_list):
|
||
input_id_images_dict[key]=image_list[index]
|
||
|
||
if img2img_mode:
|
||
if infer_mode == "story_and_maker" or infer_mode == "story_maker":
|
||
k1, _, _, _ = image.size()
|
||
if k1 == 1:
|
||
image_emb = [CLIP_VISION.encode_image(image)["penultimate_hidden_states"]]
|
||
else:
|
||
img_list = list(torch.chunk(image, chunks=k1))
|
||
image_emb=[]
|
||
for i in img_list:
|
||
image_emb.append(CLIP_VISION.encode_image(i)["penultimate_hidden_states"].to(device, dtype=unet_type))
|
||
elif infer_mode=="story":
|
||
image_emb = None # story的图片模式的emb要从ip的模型里单独拿，
|
||
elif infer_mode=="msdiffusion":
|
||
image_emb = CLIP_VISION.encode_image(image)["penultimate_hidden_states"] #MS分情况，图生图直接在前面拿，文生图在在sample拿
|
||
if not image_emb.is_cuda:#确保emb在cuda,以及type的正确
|
||
image_emb = image_emb.to(device,dtype=unet_type)
|
||
elif infer_mode == "instant_character":
|
||
from .model_loader_utils import load_dual_clip,instant_character_id_clip
|
||
if extra_function is not None and extra_param:
|
||
if "sig" in extra_function and "dino" in extra_param: #TODO
|
||
siglip_path_i=extra_function
|
||
dino_path_i=extra_param
|
||
else:
|
||
siglip_path_i=extra_param
|
||
dino_path_i=extra_function
|
||
else:
|
||
dino_path_i="facebook/dinov2-giant"
|
||
siglip_path_i="google/siglip-so400m-patch14-384"
|
||
siglip_image_encoder,siglip_image_processor,dino_image_encoder_2,dino_image_processor_2=load_dual_clip(siglip_path_i,dino_path_i,device,torch.bfloat16)
|
||
image_emb=[]
|
||
for img in tensortopil_list(image):
|
||
id_emb=instant_character_id_clip(img,siglip_image_encoder,siglip_image_processor,dino_image_encoder_2,dino_image_processor_2,device,torch.bfloat16)
|
||
image_emb.append(id_emb)
|
||
siglip_image_encoder.to("cpu")
|
||
dino_image_encoder_2.to("cpu")
|
||
gc_cleanup()
|
||
elif infer_mode == "flux_pulid":
|
||
|
||
# get emb use insightface
|
||
pass
|
||
elif infer_mode == "bagel_edit":
|
||
image_emb=input_id_images_dict
|
||
elif infer_mode == "flux_omi":
|
||
image_emb=input_id_images_dict
|
||
elif infer_mode == "dreamo":
|
||
from .model_loader_utils import Dreamo_image_encoder
|
||
from huggingface_hub import hf_hub_download
|
||
|
||
BEN2_path_list=[i for i in repo_list if 'BEN2_Base.pth' in i]
|
||
if not BEN2_path_list:
|
||
BEN2_path= hf_hub_download(repo_id='PramaLLC/BEN2', filename='BEN2_Base.pth', local_dir='ComfyUI/models')
|
||
else:
|
||
BEN2_path=BEN2_path_list[0]
|
||
ref_list=tensortopil_list_upscale(image,width,height)
|
||
if control_image is not None and dreamo_mode=="id":#如果是id加ip模式，角色可以多次换装，但是要考虑衣服的数量
|
||
control_pil_list=tensortopil_list_upscale(control_image,width,height)
|
||
if len(control_pil_list) != len(only_role_list):
|
||
raise "when use dreamo id + ip,control image must same size as prompts,dreamo的id模式,多少句角色prompt,就要有多少件衣服在control节点输入"
|
||
else:
|
||
if len(role_list)>1:
|
||
nc_dual_list=[]
|
||
for i in nc_indexs:
|
||
nc_dual_list.append(i)
|
||
for i in positions_index_dual:
|
||
nc_dual_list.append(i)
|
||
adjusted_a = adjust_indices(index_char_1_list, nc_dual_list)
|
||
adjusted_b = adjust_indices(index_char_2_list, nc_dual_list)
|
||
# print("adjusted_a",adjusted_a)
|
||
# print("adjusted_b",adjusted_b)
|
||
a_list = [control_pil_list[i] for i in adjusted_a if 0 <= i < len(control_pil_list)]
|
||
b_list = [control_pil_list[i] for i in adjusted_b if 0 <= i < len(control_pil_list)]
|
||
|
||
control_pil_list=[a_list,b_list]
|
||
else:
|
||
control_pil_list=[control_pil_list]
|
||
else:
|
||
control_pil_list=None
|
||
|
||
#task_list=["ip", "id", "style"] #TODO,
|
||
image_emb={}
|
||
if control_pil_list is not None and dreamo_mode=="id":
|
||
for key,role_img ,control_list in zip(role_list,ref_list,control_pil_list):
|
||
id_role_emb=[]
|
||
for i in control_list:
|
||
role_emb=Dreamo_image_encoder(BEN2_path,role_img,i,dreamo_mode,"ip",ref_res=512) #TODO
|
||
id_role_emb.append(role_emb)
|
||
image_emb[key]=id_role_emb
|
||
else:
|
||
for key,role_img in zip(role_list,ref_list):
|
||
role_emb=Dreamo_image_encoder(BEN2_path,role_img,None,dreamo_mode,"ip",ref_res=512) #TODO
|
||
image_emb[key]=[role_emb]*len(only_role_list) #改成列表方便协同id模式
|
||
|
||
elif infer_mode == "realcustom":
|
||
if "g" in os.path.basename(clip1_path).lower():
|
||
clip1_path, clip2_path = clip2_path, clip1_path
|
||
|
||
text_model,vision_model=load_clip_clipvsion([clip1_path,clip2_path],
|
||
[os.path.join(dir_path, "config/clip_1"),os.path.join(dir_path, "config/clip_2")],
|
||
dino_path,siglip_path,switch.get("vision_model_config_ar"))
|
||
|
||
elif infer_mode == "uno":
|
||
|
||
from .UNO.uno.flux.pipeline import preprocess_ref
|
||
|
||
ref_pil_list=tensortopil_list(image) #原方法需要图片缩放，先转回pil
|
||
if prompts_dual:
|
||
x_1_refs_dual_=[phi2narry(preprocess_ref(i, 320)) for i in ref_pil_list ]
|
||
x_1_refs_dual=[[vae.encode(i[:,:,:,:3]).to(torch.bfloat16) for i in x_1_refs_dual_]*len(prompts_dual)]#[[1,2],[1,2]] 不考虑cn
|
||
|
||
if control_image is not None:
|
||
control_pil_list=tensortopil_list(control_image)
|
||
else:
|
||
control_pil_list=None
|
||
|
||
if control_pil_list is None:# 无控制图时,默认时单图模式,x_1_refs用key取值
|
||
x_1_refs_dict={}
|
||
for key ,role_pil,prompts in zip(role_list,ref_pil_list,inf_list_split):
|
||
role_tensor=phi2narry(preprocess_ref(role_pil, 512))#单图默认512
|
||
x_1_refs_dict[key]=[[vae.encode(role_tensor[:,:,:,:3]).to(torch.bfloat16)]*len(prompts)] # {key:[[1],[2],key2:[[3],[4]]}
|
||
else:
|
||
if len(control_pil_list) != len(only_role_list):
|
||
raise "control image must same size as prompts,多少句话就多少张图"
|
||
else:
|
||
if len(role_list)>1:
|
||
nc_dual_list=[]
|
||
for i in nc_indexs:
|
||
nc_dual_list.append(i)
|
||
for i in positions_index_dual:
|
||
nc_dual_list.append(i)
|
||
adjusted_a = adjust_indices(index_char_1_list, nc_dual_list)
|
||
adjusted_b = adjust_indices(index_char_2_list, nc_dual_list)
|
||
# print("adjusted_a",adjusted_a)
|
||
# print("adjusted_b",adjusted_b)
|
||
a_list = [control_pil_list[i] for i in adjusted_a if 0 <= i < len(control_pil_list)]
|
||
b_list = [control_pil_list[i] for i in adjusted_b if 0 <= i < len(control_pil_list)]
|
||
|
||
control_pil_list=[a_list,b_list]
|
||
else:
|
||
control_pil_list=[control_pil_list]
|
||
x_1_refs_dict={}
|
||
for key ,role_pil,control_pil in zip(role_list,ref_pil_list,control_pil_list):
|
||
mix_list=[]
|
||
role_tensor=phi2narry(preprocess_ref(role_pil, 320) )#多图默认320
|
||
for c in (control_pil):
|
||
c_tensor=phi2narry(preprocess_ref(c, 320))
|
||
mix_list.append([vae.encode(role_tensor[:,:,:,:3]).to(torch.bfloat16),vae.encode(c_tensor[:,:,:,:3]).to(torch.bfloat16)])
|
||
x_1_refs_dict[key]=mix_list #{key:[[1,2],[3,4]]}
|
||
|
||
|
||
# pre insight face model and emb
|
||
if img2img_mode:
|
||
if infer_mode in ["story_and_maker","story_maker","flux_pulid","kolor_face","infiniteyou"] or (use_photov2 and infer_mode=="story"):
|
||
|
||
from .model_loader_utils import insight_face_loader,get_insight_dict
|
||
|
||
|
||
find_mask=[i for i in repo_list if "rmgb" in i.lower()] if repo_list else None
|
||
if find_mask is not None:
|
||
mask_repo=find_mask[0]
|
||
else:
|
||
mask_repo="briaai/RMBG-1.4"
|
||
app_face,pipeline_mask,app_face_=insight_face_loader(infer_mode,use_photov2, auraface,onnx_provider,mask_repo)
|
||
image_list=tensortopil_list_upscale(image, 640, 640)
|
||
|
||
input_id_emb_s_dict,input_id_img_s_dict,input_id_emb_un_dict,input_id_cloth_dict=get_insight_dict(app_face,app_face_,pipeline_mask,infer_mode,use_photov2,image_list,
|
||
role_list,control_image,width, height,model_,switch.get("image_proj_model")) # CHECK role_list
|
||
|
||
else:
|
||
input_id_emb_s_dict,input_id_img_s_dict,input_id_emb_un_dict,input_id_cloth_dict={}, {}, {}, {}
|
||
|
||
else:
|
||
input_id_emb_s_dict,input_id_img_s_dict,input_id_emb_un_dict,input_id_cloth_dict={}, {}, {}, {}
|
||
|
||
|
||
|
||
# pre clip txt emb
|
||
|
||
noise_x,inp_neg_list,letent_real=[],[],{}
|
||
|
||
if infer_mode=="consistory":
|
||
only_role_emb=None
|
||
elif infer_mode=="instant_character":
|
||
from .model_loader_utils import cf_flux_prompt_clip
|
||
only_role_emb={}
|
||
for key ,prompts in zip(role_list,inf_list_split):
|
||
emb_list_=[]
|
||
for prompt in prompts:
|
||
p_,pool_,ind_=cf_flux_prompt_clip(clip,prompt)
|
||
emb_list_.append([p_,pool_,ind_])
|
||
only_role_emb[key]=emb_list_
|
||
|
||
elif infer_mode=="realcustom":
|
||
from .model_loader_utils import realcustom_clip_emb
|
||
samples_per_prompt=1
|
||
guidance_weight=3.5
|
||
role_text_list = role_text.splitlines()
|
||
roel_text_c=''.join(role_text_list)
|
||
|
||
if '(' in roel_text_c and ')' in roel_text_c:
|
||
object_prompt = extract_content_from_brackets_(roel_text_c) # 提取prompt的object list
|
||
#print(f"object_prompt:{object_prompt}")
|
||
object_prompt=[i.strip() for i in object_prompt]
|
||
for i in object_prompt:
|
||
if " " in i:
|
||
raise "when using [object],object must be a word,any blank in it will cause error."
|
||
|
||
object_prompt=[i for i in object_prompt ]
|
||
target_phrases = sorted(list(set(object_prompt)),key=lambda x: list(object_prompt).index(x)) # 清除同名物体,保持原有顺序
|
||
#print(f"object_prompt:{phrases}",len(phrases))
|
||
assert len(target_phrases)>=2,"when using msdiffusion ,object must be more than 2."
|
||
if len(target_phrases)>2:
|
||
target_phrases=target_phrases[:2] #只取前两个物体
|
||
else:
|
||
raise "when using realcustom ,(objectA) and (objectA) must be in the role prompt."
|
||
|
||
print(f"object_prompt:{target_phrases}")
|
||
image_list=tensortopil_list_upscale(image, width, height)
|
||
|
||
only_role_emb,letent_real={},{}
|
||
|
||
for key ,prompts,role_image,target_phrase in zip(role_list,inf_list_split,image_list,target_phrases):
|
||
emb_dict_real_list,latent_dict_list=[],[]
|
||
for p,n in zip(prompts,[neg_text]*len(prompts)):
|
||
|
||
emb_dict_real,latent_dict=realcustom_clip_emb(text_model,vision_model,switch.get("vae_config"),switch.get("vae_downsample_factor"),p,n,role_image,target_phrase,
|
||
width,height,device,samples_per_prompt,guidance_weight)
|
||
emb_dict_real_list.append(emb_dict_real)
|
||
latent_dict_list.append(latent_dict)
|
||
only_role_emb[key]=emb_dict_real_list
|
||
letent_real[key]=latent_dict_list
|
||
vision_model.to("cpu")
|
||
gc_cleanup()
|
||
elif infer_mode=="flux_pulid":
|
||
from .PuLID.flux.util import load_clip, load_t5
|
||
from .PuLID.app_flux import get_emb_flux_pulid
|
||
|
||
#repo_in="flux-dev" if not repo else repo
|
||
if_repo =False
|
||
t5_ = load_t5("flux-dev",clip,if_repo,device, max_length=128)
|
||
clip_ = load_clip("flux-dev",clip,if_repo,device)
|
||
only_role_emb,noise_x,inp_neg_list={},{},{}
|
||
|
||
for key ,prompts in zip(role_list,inf_list_split):
|
||
ip_emb,inp_n=[],[]
|
||
|
||
for p,n in zip(prompts,[neg_text]*len(prompts)):
|
||
inp,inp_neg=get_emb_flux_pulid(t5_,clip_,if_repo,p,n,width,height,num_steps=20,guidance=3.5,device=device)
|
||
ip_emb.append(inp)
|
||
inp_n.append(inp_neg)
|
||
only_role_emb[key]=ip_emb
|
||
inp_neg_list[key]=inp_n
|
||
elif infer_mode == "uno":
|
||
only_role_emb={}
|
||
from .UNO.uno.flux.sampling import prepare_multi_ip_wrapper
|
||
|
||
for key ,prompts in zip(role_list,inf_list_split):
|
||
ip_emb=[]
|
||
for p,x_1 in zip(prompts,x_1_refs_dict[key]):
|
||
inp = prepare_multi_ip_wrapper(clip,prompt=p, ref_imgs=x_1, pe=uno_pe,device=device,h=height,w=width)
|
||
ip_emb.append(inp)
|
||
only_role_emb[key]=ip_emb
|
||
elif infer_mode=="kolor_face":
|
||
from .model_loader_utils import glm_single_encode
|
||
from .kolors.models.tokenization_chatglm import ChatGLMTokenizer
|
||
|
||
tokenizer = ChatGLMTokenizer.from_pretrained(os.path.join(switch.get("find_Kolors"),'text_encoder'))
|
||
assert clip is not None, "clip is None,check your clip path"
|
||
chatglm3_model = {
|
||
'text_encoder': clip,
|
||
'tokenizer': tokenizer
|
||
}
|
||
only_role_emb,only_role_emb_ne=glm_single_encode(chatglm3_model, inf_list_split,role_list, neg_text, 1)
|
||
elif infer_mode=="flux_omi" and switch.get("no_dif_quantization"): #need comfyclip
|
||
from .model_loader_utils import cf_flux_prompt_clip
|
||
only_role_emb={}
|
||
for key ,prompts in zip(role_list,inf_list_split):
|
||
emb_list_=[]
|
||
for prompt in prompts:
|
||
p_,pool_,ind_=cf_flux_prompt_clip(clip,prompt)
|
||
emb_list_.append([p_,pool_,ind_])
|
||
only_role_emb[key]=emb_list_
|
||
|
||
else:
|
||
if photomake_ckpt_path is not None and img2img_mode and infer_mode in["story","story_and_maker","msdiffusion"]: #img2img模式下SDXL的story的clip要特殊处理，有2个imgencoder进程，所以分离出来 TODO
|
||
if use_photov2:
|
||
if len(role_list)==1:
|
||
emb_dict=photomaker_clip_v2(clip,model_,only_role_list,neg_text,image_list,input_id_emb_s_dict[role_key_list[0]][0])
|
||
only_role_emb=[emb_dict]
|
||
else:
|
||
|
||
only_role_emb=[]
|
||
for role_list_s,role_list_id,key in zip(inf_list_split,image_list,role_key_list):
|
||
#print(input_id_emb_s_dict[key][0].shape) #torch.Size([512])
|
||
emb_dict=photomaker_clip_v2(clip,model_,role_list_s,neg_text,[role_list_id],input_id_emb_s_dict[key][0])
|
||
only_role_emb.append(emb_dict)
|
||
|
||
else:
|
||
if len(role_list)==1:
|
||
emb_dict=photomaker_clip(clip,model_,only_role_list,neg_text,image_list)
|
||
only_role_emb=[emb_dict]
|
||
else:
|
||
|
||
only_role_emb=[]
|
||
for role_list_s,role_list_id in zip(inf_list_split,image_list):
|
||
emb_dict=photomaker_clip(clip,model_,role_list_s,neg_text,[role_list_id])
|
||
only_role_emb.append(emb_dict)
|
||
|
||
else:
|
||
if infer_mode=="classic": #TODO 逆序的角色会出现iD不匹配，受影响的有story文生图
|
||
only_role_emb= cf_clip(only_role_list, clip, infer_mode,role_list) #story模式需要拆分prompt，所以这里需要传入role_list
|
||
elif infer_mode=="dreamo" or infer_mode=="bagel_edit" or (infer_mode=="flux_omi" and not switch.get("no_dif_quantization")):
|
||
pass # TODO 暂时不支持dreamo
|
||
elif infer_mode=="qwen_image":
|
||
from .qwen_image.inferencer import get_emb_data
|
||
image_list_=tensortolist(image,width,height)
|
||
only_role_emb= get_emb_data(clip,vae,inf_list_split,image_list_,role_list)
|
||
else:
|
||
only_role_emb= cf_clip(inf_list_split, clip, infer_mode,role_list) #story,story_maker,story_and_maker,msdiffusion,infinite
|
||
if len (role_list)==1 :
|
||
only_role_emb_dict={}
|
||
only_role_emb_dict[role_list[0]]=only_role_emb # [ [cond_p, output_p],...]
|
||
only_role_emb=only_role_emb_dict
|
||
# pre nc txt emb
|
||
if nc_txt_list and not infer_mode=="consistory":
|
||
nc_txt_list=[i+pos_text for i in nc_txt_list]
|
||
if photomake_ckpt_path is not None and img2img_mode and infer_mode in["story","story_maker","story_and_maker","msdiffusion"]: #img2img模式下SDXL的story的clip要特殊处理，有2个imgencoder进程，所以分离出来 TODO
|
||
nc_emb=[]
|
||
for i in nc_txt_list:
|
||
if use_photov2:
|
||
empty_emb_zero = torch.zeros_like(input_id_emb_s_dict[role_list[0]][0]).to(device,dtype=torch.float16)
|
||
emb_dict_=photomaker_clip_v2(clip,model_,[i],neg_text,image_list,empty_emb_zero,nc_flag=True)
|
||
else:
|
||
emb_dict_=photomaker_clip(clip,model_,[i],neg_text,image_list,nc_flag=True)
|
||
nc_emb.append(emb_dict_)
|
||
else:
|
||
if infer_mode!="kolor_face":
|
||
nc_emb=cf_clip(nc_txt_list, clip, infer_mode,role_list,input_split=False)
|
||
elif infer_mode=="dreamo" or infer_mode=="bagel_edit" or infer_mode=="flux_omi":
|
||
pass # TODO 暂时不支持dreamo
|
||
|
||
else:
|
||
nc_emb,_= glm_single_encode(chatglm3_model, nc_txt_list,role_list, neg_text, 1,nc_mode=True)
|
||
else:
|
||
nc_emb=None
|
||
# pre dual role txt emb
|
||
grounding_kwargs=None
|
||
cross_attention_kwargs=None
|
||
if prompts_dual and infer_mode in["story_maker","story_and_maker","msdiffusion"] : #忽略不支持的模式
|
||
|
||
if infer_mode=="msdiffusion": #[A] a (pig) play whith [B] a (doll) in the garden
|
||
prompts_dual=[i.replace(role_list[0] ,role_dict[role_list[0]]) for i in prompts_dual if role_list[0] in i ]
|
||
prompts_dual = [i.replace(role_list[1], role_dict[role_list[1]]) for i in prompts_dual if role_list[1] in i]
|
||
if '(' in prompts_dual[0] and ')' in prompts_dual[0]:
|
||
object_prompt = extract_content_from_brackets_(prompts_dual[0]) # 提取prompt的object list
|
||
#print(f"object_prompt:{object_prompt}")
|
||
object_prompt=[i.strip() for i in object_prompt]
|
||
for i in object_prompt:
|
||
if " " in i:
|
||
raise "when using [object],object must be a word,any blank in it will cause error."
|
||
|
||
object_prompt=[i for i in object_prompt ]
|
||
phrases = sorted(list(set(object_prompt)),key=lambda x: list(object_prompt).index(x)) # 清除同名物体,保持原有顺序
|
||
#print(f"object_prompt:{phrases}",len(phrases))
|
||
assert len(phrases)>=2,"when using msdiffusion ,object must be more than 2."
|
||
if len(phrases)>2:
|
||
phrases=phrases[:2] #只取前两个物体
|
||
else:
|
||
raise "when using msdiffusion ,(objectA) and (objectA) must be in the prompt."
|
||
if use_lora:
|
||
prompts_dual=[i+lora_trigger_words for i in prompts_dual]
|
||
|
||
prompts_dual=[apply_style_positive(add_style,i+pos_text)[0] for i in prompts_dual] #[' T a (pig) play whith a (doll) in the garden,best 8k,RAW']
|
||
|
||
|
||
prompts_dual=[i.replace("("," ").replace(")"," ") for i in prompts_dual] #clear the bracket
|
||
|
||
box_add = [] # 获取预设box
|
||
guidance_list = guidance_list.strip().split(";")
|
||
for i in range(len(guidance_list)):
|
||
box_add.append(get_float(guidance_list[i]))
|
||
|
||
if mask_threshold == 0:
|
||
mask_threshold = None
|
||
if mask_threshold:
|
||
boxes = [box_add[:2]] # boxes = [[[0., 0.25, 0.4, 0.75], [0.6, 0.25, 1., 0.75]]] # man+women
|
||
else:
|
||
boxes = [[[0, 0, 0, 0], [0, 0, 0, 0]]] # used if you want no layout guidance
|
||
print(f"Roles position on {boxes}")
|
||
from transformers import CLIPTokenizer
|
||
tokenizer_=CLIPTokenizer.from_pretrained(os.path.join(dir_path, "local_repo/tokenizer"))
|
||
for i in prompts_dual:
|
||
phrase_idxes = [get_phrases_idx_cf(tokenizer_, phrases[0], i)]
|
||
eot_idxes = [[get_eot_idx_cf(tokenizer_, i)] * len(phrases[0])]
|
||
cross_attention_kwargs, grounding_kwargs = get_ms_phrase_emb(boxes, device, infer_type_g,
|
||
[0], 1, phrase_idxes,
|
||
1, eot_idxes, phrases, clip,tokenizer_)
|
||
daul_emb = cf_clip(prompts_dual, clip, infer_mode,role_list,input_split=False)
|
||
else:
|
||
prompts_dual=[i.replace(role_list[0] ,role_dict[role_list[0]]) for i in prompts_dual if role_list[0] in i ]
|
||
prompts_dual = [i.replace(role_list[1], role_dict[role_list[1]]) for i in prompts_dual if role_list[1] in i]
|
||
if use_lora:
|
||
prompts_dual=[i+lora_trigger_words for i in prompts_dual]
|
||
prompts_dual=[apply_style_positive(add_style,i+pos_text)[0] for i in prompts_dual] #[' The figurine play whith The pig in the garden,best 8k,RAW']
|
||
daul_emb=cf_clip(prompts_dual, clip, infer_mode,role_list,input_split=False) # maker
|
||
elif prompts_dual and infer_mode == "uno": #UNO双角色图片要单独处理
|
||
prompts_dual=[i.replace(role_list[0] ,role_dict[role_list[0]]) for i in prompts_dual if role_list[0] in i ]
|
||
prompts_dual = [i.replace(role_list[1], role_dict[role_list[1]]) for i in prompts_dual if role_list[1] in i]
|
||
prompts_dual=[apply_style_positive(add_style,i+pos_text)[0] for i in prompts_dual] #[' The figurine play whith The pig in the garden,best 8k,RAW']
|
||
|
||
daul_emb=[]
|
||
for dual_t,x_1 in zip(prompts_dual,x_1_refs_dual): # dual_t:The figurine play whith The pig in the garden best 8k,RAW
|
||
inp = prepare_multi_ip_wrapper(clip,prompt=dual_t, ref_imgs=x_1, pe=uno_pe,device=device,h=height,w=width)
|
||
daul_emb.append(inp)
|
||
elif prompts_dual and infer_mode == "dreamo":
|
||
from .model_loader_utils import Dreamo_image_encoder
|
||
from huggingface_hub import hf_hub_download
|
||
BEN2_path_list=[i for i in repo_list if "BEN2_Base.pth" in i]
|
||
if not BEN2_path_list:
|
||
BEN2_path= hf_hub_download(repo_id='PramaLLC/BEN2', filename='BEN2_Base.pth', local_dir='ComfyUI/models')
|
||
else:
|
||
BEN2_path=BEN2_path_list[0]
|
||
ref_list=tensortopil_list_upscale(image,width,height)
|
||
#task_list=["ip", "id", "style"] #TODO
|
||
images_emb=Dreamo_image_encoder(BEN2_path,ref_list[0],ref_list[1],"ip","ip",ref_res=512) #TODO
|
||
prompts_dual=[i.replace(role_list[0] ,role_dict[role_list[0]]) for i in prompts_dual if role_list[0] in i ]
|
||
prompts_dual = [i.replace(role_list[1], role_dict[role_list[1]]) for i in prompts_dual if role_list[1] in i]
|
||
prompts_dual=[apply_style_positive(add_style,i+pos_text)[0] for i in prompts_dual] #[' The figurine play whith The pig in the garden,best 8k,RAW']
|
||
daul_emb=[images_emb,prompts_dual]
|
||
else:
|
||
daul_emb=None
|
||
# neg
|
||
if infer_mode=="consistory":
|
||
negative = None
|
||
postive_dict={}
|
||
elif infer_mode=="instant_character":
|
||
postive_dict= {"role": only_role_emb, "nc": None, "daul": daul_emb}
|
||
negative = None
|
||
elif infer_mode=="dreamo":
|
||
only_role_emb={}
|
||
for key ,prompts in zip(role_list,inf_list_split):
|
||
only_role_emb[key]=prompts
|
||
postive_dict= {"role": only_role_emb, "nc": None, "daul": daul_emb}
|
||
negative = neg_text # TODO
|
||
elif infer_mode=="bagel_edit":
|
||
only_role_emb={}
|
||
for key ,prompts in zip(role_list,inf_list_split):
|
||
only_role_emb[key]=prompts
|
||
postive_dict= {"role": only_role_emb, "nc": None, "daul": None}
|
||
negative = neg_text # TODO
|
||
elif infer_mode=="flux_omi":
|
||
if switch.get("no_dif_quantization"):
|
||
postive_dict= {"role": only_role_emb, "nc": None, "daul": None} #only_role_emb:p_,pool_,ind_
|
||
negative = neg_text # TODO
|
||
else:
|
||
only_role_emb={}
|
||
for key,prompts in zip(role_list,inf_list_split):
|
||
only_role_emb[key]=prompts
|
||
postive_dict= {"role": only_role_emb, "nc": None, "daul": None}
|
||
negative = neg_text # TODO
|
||
elif infer_mode=="realcustom":
|
||
postive_dict= {"role": only_role_emb, "nc": None, "daul": daul_emb}
|
||
negative=[letent_real]
|
||
elif infer_mode=="flux_pulid":
|
||
postive_dict= {"role": only_role_emb, "nc": None, "daul": daul_emb} #不支持NC
|
||
negative = [inp_neg_list,noise_x]
|
||
elif infer_mode=="uno":
|
||
postive_dict= {"role": only_role_emb, "nc": None, "daul": daul_emb} #TODO
|
||
negative=None
|
||
elif infer_mode=="kolor_face":
|
||
postive_dict = {"role": only_role_emb, "nc": nc_emb, "daul": daul_emb}
|
||
negative = only_role_emb_ne[0]
|
||
elif infer_mode=="qwen_image":
|
||
postive_dict = {"role": only_role_emb, "nc": nc_emb, "daul": daul_emb}
|
||
from .qwen_image.inferencer import get_emb_data
|
||
|
||
image_list_=tensortolist(image, width, height) if not switch.get("edit_mode") else None
|
||
neg_list=[[neg_text]] if len(role_list)==1 else [[neg_text],[neg_text]]
|
||
negative= get_emb_data(clip,vae,neg_list,image_list_,role_list)
|
||
|
||
else:
|
||
tokens_n = clip.tokenize(neg_text)
|
||
output_n = clip.encode_from_tokens(tokens_n, return_pooled=True, return_dict=True) #{"pooled_output":tensor}
|
||
cond_n = output_n.pop("cond")
|
||
if cond_n.shape[1] /77>1 and infer_mode != "classic":
|
||
logging.warning("nagetive prompt'tokens length is abvoe 77,will split it.")
|
||
cond_n=torch.chunk(cond_n,cond_n.shape[1] //77,dim=1)[0]
|
||
|
||
if infer_mode == "classic":
|
||
if nc_emb is not None:
|
||
for index,i in zip(nc_indexs,nc_emb):
|
||
only_role_emb.insert(index,i)
|
||
if daul_emb is not None:
|
||
for index,i in zip(positions_index_dual,daul_emb):
|
||
only_role_emb.insert(index,i)
|
||
postive_dict = only_role_emb
|
||
negative = [[cond_n, output_n]]
|
||
else:
|
||
postive_dict = {"role": only_role_emb, "nc": nc_emb, "daul": daul_emb}
|
||
negative = [cond_n, output_n]
|
||
|
||
|
||
# Pre emb for maker
|
||
|
||
if img2img_mode and infer_mode in ["story_and_maker","story_maker"]:
|
||
from .StoryMaker.pipeline_sdxl_storymaker_wrapper import encode_prompt_image_emb_
|
||
num_images_per_prompt=1
|
||
make_img,make_mask_img,make_face_info,make_cloth_info=[],[],[],[]
|
||
|
||
for key in role_list:
|
||
img_ = input_id_emb_un_dict[key][0]
|
||
# print(character_key_str,input_id_images_dict)
|
||
mask_image_ = input_id_img_s_dict[key][0] #mask_image
|
||
face_info_ = input_id_emb_s_dict[key][0]
|
||
cloth_info_ = None
|
||
if isinstance(control_image, torch.Tensor):
|
||
cloth_info_ = input_id_cloth_dict[key][0]
|
||
make_img.append(img_)
|
||
make_mask_img.append(mask_image_)
|
||
make_face_info.append(face_info_)
|
||
make_cloth_info.append(cloth_info_)
|
||
|
||
mask_image_2=None
|
||
face_info_2=None
|
||
cloth_2=None
|
||
image_2=None
|
||
prompt_image_emb_dual=None
|
||
if len(role_list)>1:
|
||
|
||
if isinstance(control_image, torch.Tensor):
|
||
cn_image_list=tensortopil_list_upscale(control_image,width,height)
|
||
if len(cn_image_list)<len(only_role_list): #防止溢出
|
||
cn_image_list=cn_image_list + cn_image_list[0]*len(only_role_list)-len(cn_image_list)
|
||
nc_dual_list=[]
|
||
for i in nc_indexs:
|
||
nc_dual_list.append(i)
|
||
for i in positions_index_dual:
|
||
nc_dual_list.append(i)
|
||
|
||
adjusted_a_ = adjust_indices(index_char_1_list, nc_dual_list)
|
||
adjusted_b_ = adjust_indices(index_char_2_list, nc_dual_list)
|
||
a_list = [cn_image_list[i] for i in adjusted_a_ if 0 <= i < len(cn_image_list)]
|
||
b_list = [cn_image_list[i] for i in adjusted_b_ if 0 <= i < len(cn_image_list)]
|
||
|
||
prompt_image_emb,maker_control_image={},{}
|
||
for j,(key,cnlist) in enumerate(zip(role_list,[a_list,b_list])):
|
||
p_list,maker_cn_list=[],[]
|
||
|
||
for cn_img in cnlist:
|
||
prompt_image_emb_,maker_control_image_=encode_prompt_image_emb_([make_img[j],cn_img[i]], a_list, make_mask_img[j], b_list, make_face_info[j], face_info_2, make_cloth_info[j],
|
||
cloth_2,device, num_images_per_prompt, unet_type, CLIP_VISION,vae,do_classifier_free_guidance=True)
|
||
p_list.append(prompt_image_emb_)
|
||
maker_cn_list.append(maker_control_image_)
|
||
prompt_image_emb[key]=p_list#输出改为字典
|
||
maker_control_image[key]=maker_cn_list#输出改为字典
|
||
# 还要将每个元素转为列表，改为[img,cn_img]
|
||
|
||
else:
|
||
prompt_image_emb,maker_control_image={},{}
|
||
|
||
for i,key in enumerate(role_list):
|
||
prompt_image_emb_,maker_control_image_=encode_prompt_image_emb_(make_img[i], image_2, make_mask_img[i], mask_image_2, make_face_info[i], face_info_2, make_cloth_info[i], cloth_2,device, num_images_per_prompt, unet_type, CLIP_VISION,vae,do_classifier_free_guidance=True)
|
||
prompt_image_emb[key]=[prompt_image_emb_]#输出改为字典
|
||
maker_control_image[key]=[maker_control_image_]#输出改为字典
|
||
|
||
else:
|
||
prompt_image_emb_1,maker_control_image_1=encode_prompt_image_emb_(make_img[0], image_2, make_mask_img[0], mask_image_2, make_face_info[0], face_info_2, make_cloth_info[0], cloth_2,device, num_images_per_prompt, unet_type, CLIP_VISION,vae,do_classifier_free_guidance=True)
|
||
|
||
prompt_image_emb={role_list[0]:len(only_role_list)*[prompt_image_emb_1]}#输出改为字典
|
||
maker_control_image={role_list[0]:len(only_role_list)*[maker_control_image_1]}#输出改为字典
|
||
|
||
maker_control_image_dual=None
|
||
if daul_emb is not None:
|
||
prompt_image_emb_dual,maker_control_image_dual=encode_prompt_image_emb_(make_img[0], make_img[1], make_mask_img[0], make_mask_img[1], make_face_info[0], make_face_info[1], make_cloth_info[0], make_cloth_info[1],device, num_images_per_prompt, unet_type, CLIP_VISION,vae,do_classifier_free_guidance=True)
|
||
prompt_image_emb_dual=len(prompts_dual)*[prompt_image_emb_dual]
|
||
maker_control_image_dual=len(prompts_dual)*[maker_control_image_dual]
|
||
else:
|
||
|
||
prompt_image_emb,maker_control_image,prompt_image_emb_dual,maker_control_image_dual=None,None,None,None
|
||
|
||
|
||
|
||
# switch
|
||
new_dict={
|
||
"id_len":len(role_list),"role_list":role_list,"invert_role_index_dict":invert_role_index_dict,"image_list":image_list,"inf_list_split":inf_list_split,
|
||
"nc_index":nc_indexs,"dual_index":positions_index_dual,"grounding_kwargs":grounding_kwargs,"cross_attention_kwargs":cross_attention_kwargs,
|
||
"image_embeds":image_emb,"img2img_mode":img2img_mode,"positions_index_char_1":positions_index_char_1,"dreamo_mode":dreamo_mode,
|
||
"positions_index_char_2":positions_index_char_2,"mask_threshold":mask_threshold,"lora_list":lora_list,
|
||
"input_id_emb_s_dict":input_id_emb_s_dict,"input_id_img_s_dict":input_id_img_s_dict,"input_id_emb_un_dict":input_id_emb_un_dict,"trigger_words_dual":trigger_words_dual,
|
||
"maker_control_image":maker_control_image,"prompt_image_emb":prompt_image_emb,"prompt_image_emb_dual":prompt_image_emb_dual,"dual_lora_scale":dual_lora_scale,
|
||
"maker_control_image_dual":maker_control_image_dual,"prompts_dual":prompts_dual,"control_image":control_image,"trigger_words":lora_trigger_words,
|
||
"only_role_list":only_role_list,"nc_txt_list":nc_txt_list,"neg_text":neg_text,"role_text":role_text,"cf_clip":clip,"cached":cached,
|
||
"inject":inject,"role_key_list":role_key_list,"input_id_images_dict":input_id_images_dict,"id_index":(index_char_1_list,index_char_2_list)
|
||
}
|
||
switch.update(new_dict)
|
||
|
||
|
||
|
||
return (postive_dict,negative,switch,width,height)
|
||
|
||
|
||
class StoryDiffusion_KSampler:
|
||
@classmethod
|
||
def INPUT_TYPES(s):
|
||
return {
|
||
"required": {
|
||
"model": ("MODEL", {"tooltip": "The model used for denoising the input latent."}),
|
||
"seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED,
|
||
"tooltip": "The random seed used for creating the noise."}),
|
||
"steps": ("INT", {"default": 20, "min": 1, "max": 10000,
|
||
"tooltip": "The number of steps used in the denoising process."}),
|
||
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01,
|
||
"tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."}),
|
||
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, {
|
||
"tooltip": "The algorithm used when sampling, this can affect the quality, speed, and style of the generated output."}),
|
||
"scheduler": (comfy.samplers.KSampler.SCHEDULERS,
|
||
{"tooltip": "The scheduler controls how noise is gradually removed to form the image."}),
|
||
"positive": ("CONDITIONING", {
|
||
"tooltip": "The conditioning describing the attributes you want to include in the image."}),
|
||
"negative": ("CONDITIONING", {
|
||
"tooltip": "The conditioning describing the attributes you want to exclude from the image."}),
|
||
"condition":("DIFFINFO",{
|
||
"tooltip": "Switch infer mode witch your chocie."}),
|
||
"latent_image": ("LATENT", {"tooltip": "The latent image to denoise."}),
|
||
"sa32_degree": (
|
||
"FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.1, "round": 0.01}),
|
||
"sa64_degree": (
|
||
"FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.1, "round": 0.01}),
|
||
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01,
|
||
"tooltip": "The amount of denoising applied, lower values will maintain the structure of the initial image allowing for image to image sampling."}),
|
||
},
|
||
|
||
}
|
||
|
||
RETURN_TYPES = ("LATENT",)
|
||
OUTPUT_TOOLTIPS = ("The denoised latent.",)
|
||
FUNCTION = "sample"
|
||
|
||
CATEGORY = "Storydiffusion"
|
||
DESCRIPTION = "Uses the provided model, positive and negative conditioning to denoise the latent image."
|
||
|
||
def common_ksampler(self,model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0,
|
||
disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
|
||
|
||
latent_image = latent["samples"]
|
||
latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
|
||
|
||
if disable_noise:
|
||
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
|
||
else:
|
||
batch_inds = latent["batch_index"] if "batch_index" in latent else None
|
||
noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
|
||
|
||
noise_mask = None
|
||
if "noise_mask" in latent:
|
||
noise_mask = latent["noise_mask"]
|
||
|
||
callback = latent_preview.prepare_callback(model, steps)
|
||
disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
|
||
samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative,
|
||
latent_image,
|
||
denoise=denoise, disable_noise=disable_noise, start_step=start_step,
|
||
last_step=last_step,
|
||
force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback,
|
||
disable_pbar=disable_pbar, seed=seed)
|
||
# out = latent.copy()
|
||
# out["samples"] = samples
|
||
return samples
|
||
def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative,condition, latent_image,sa32_degree,sa64_degree, denoise=1.0, **kwargs):
|
||
|
||
infer_mode=condition.get("infer_mode")
|
||
id_len=condition.get("id_len")
|
||
nc_index=condition.get("nc_index")
|
||
dual_index=condition.get("dual_index")
|
||
role_list=condition.get("role_list")
|
||
mask_threshold=condition.get("mask_threshold")
|
||
grounding_kwargs=condition.get("grounding_kwargs")
|
||
cross_attention_kwargs=condition.get("cross_attention_kwargs")
|
||
image_embeds=condition.get("image_embeds")
|
||
img2img_mode=condition.get("img2img_mode")
|
||
num_images_per_prompt=1
|
||
photomake_ckpt_path = condition.get("photomake_ckpt_path")
|
||
ipadapter_ckpt_path = condition.get("ipadapter_ckpt_path")
|
||
prompt_image_emb=condition.get("prompt_image_emb")
|
||
maker_control_image=condition.get("maker_control_image")
|
||
prompt_image_emb_dual=condition.get("prompt_image_emb_dual")
|
||
maker_control_image_dual=condition.get("maker_control_image_dual")
|
||
controlnet=condition.get("controlnet")
|
||
prompts_dual=condition.get("prompts_dual")
|
||
input_id_emb_un_dict=condition.get("input_id_emb_un_dict")
|
||
input_id_img_s_dict=condition.get("input_id_img_s_dict")
|
||
input_id_emb_s_dict=condition.get("input_id_emb_s_dict")
|
||
input_id_cloth_dict=condition.get("input_id_cloth_dict")
|
||
only_role_list=condition.get("only_role_list")
|
||
nc_txt_list=condition.get("nc_txt_list")
|
||
neg_text=condition.get("neg_text")
|
||
cached=condition.get("cached")
|
||
inject=condition.get("inject")
|
||
inf_list_split=condition.get("inf_list_split")
|
||
input_id_images_dict=condition.get("input_id_images_dict")
|
||
dreamo_mode=condition.get("dreamo_mode")
|
||
invert_role_index_dict=condition.get("invert_role_index_dict")
|
||
latent_init=latent_image["samples"]
|
||
|
||
|
||
scheduler_choice = get_scheduler(sampler_name, scheduler)
|
||
#from latent get h & w
|
||
batch_size,_,height, width = latent_init.size()
|
||
height=height*8
|
||
width=width*8
|
||
empty_img_init = Image.new('RGB', (height, width), (255, 255, 255))
|
||
zero_tensor = torch.zeros((height, width), dtype=torch.float32, device="cpu")
|
||
|
||
control_image_zero = Image.fromarray(np.zeros([height, width, 3]).astype(np.uint8))
|
||
start_merge_step = int(float(20) / 100 * steps) # TODO: 20% of steps
|
||
if start_merge_step > 30:
|
||
start_merge_step = 30
|
||
|
||
if infer_mode =="classic":
|
||
samples_list=[]
|
||
for i in positive:
|
||
seed_random=random.randint(0, seed)
|
||
samples=self.common_ksampler(model, seed_random, steps, cfg, sampler_name, scheduler, i, negative, latent_image,
|
||
denoise=denoise) #torch.Size([1, 4, 64, 64])
|
||
|
||
samples_list.append(samples)
|
||
out = latent_image.copy()
|
||
out["samples"] = torch.cat(samples_list,dim=0)
|
||
return (out,)
|
||
else:
|
||
|
||
lora_list=condition.get("lora_list",[])
|
||
trigger_words=condition.get("trigger_words","best")
|
||
trigger_words_dual=condition.get("trigger_words_dual","best")
|
||
lora_scale=condition.get("lora_scale",1.0)
|
||
dual_lora_scale=condition.get("dual_lora_scale",1.0)
|
||
if infer_mode in ["story_maker" ,"msdiffusion" ,"story_and_maker" ,"story",]:
|
||
|
||
if lora_list:
|
||
if len(lora_list)==2:
|
||
lora_list_l = [ i for i in lora_list if os.path.basename(i) in lora_lightning_list]
|
||
|
||
if lora_list_l:
|
||
if 1==len(lora_list_l):
|
||
model=load_lora_for_unet_only(model,lora_list_l[0],trigger_words)
|
||
|
||
else:
|
||
for i,j,k in zip(lora_list_l,[trigger_words,trigger_words_dual],[lora_scale,dual_lora_scale]):
|
||
model=load_lora_for_unet_only(model,i,j,k)
|
||
else:
|
||
for i,j ,k in zip(lora_list,[trigger_words,trigger_words_dual],[lora_scale,dual_lora_scale]):
|
||
model=load_lora_for_unet_only(model,i,j,k)
|
||
else:
|
||
model=load_lora_for_unet_only(model,lora_list[0],trigger_words,lora_scale)
|
||
|
||
if condition.get("controlnet_path") is not None and infer_mode!="story":
|
||
controlnet_path =condition.get("controlnet")
|
||
from diffusers import ControlNetModel
|
||
from safetensors.torch import load_file
|
||
controlnet = ControlNetModel.from_unet(model.unet)
|
||
cn_state_dict = load_file(controlnet_path, device="cpu")
|
||
controlnet.load_state_dict(cn_state_dict, strict=False)
|
||
del cn_state_dict
|
||
gc_cleanup()
|
||
controlnet.to(torch.float16)
|
||
if infer_mode=="story_maker" :
|
||
model.controlnet = controlnet
|
||
|
||
|
||
|
||
#get emb
|
||
only_role_emb=positive.get("role")
|
||
nc_emb=positive.get("nc")
|
||
daul_emb=positive.get("daul")
|
||
daul_emb_ms=daul_emb
|
||
|
||
if infer_mode in["story" ,"msdiffusion","story_and_maker"]: #三者都调用story的unet方法，只是双角色引入ms或者maker
|
||
if ipadapter_ckpt_path is None and infer_mode=="msdiffusion":
|
||
raise "msdiffusion need a ms_adapter.bin file at ipadapter_ckpt menu."
|
||
global attn_procs_,sa32, sa64, write, height_s, width_s,attn_count_, total_count, id_length, total_length, cur_step,cur_character
|
||
|
||
sa32 = sa32_degree
|
||
sa64 = sa64_degree
|
||
attn_count_ = 0
|
||
total_count = 0
|
||
cur_step = 0
|
||
id_length = id_len
|
||
total_length = 5
|
||
attn_procs_ = {}
|
||
write = False
|
||
height_s = height
|
||
width_s = width
|
||
|
||
set_attention_processor(model.unet, id_len, is_ipadapter=False)
|
||
|
||
model.scheduler = scheduler_choice.from_config(model.scheduler.config)
|
||
model.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
|
||
|
||
samples_list = []
|
||
if img2img_mode and photomake_ckpt_path:
|
||
generator=torch.Generator(device=device).manual_seed(seed)
|
||
#print(invert_role_index_dict)
|
||
for emb_dict,r_key,key in zip(only_role_emb,role_list,invert_role_index_dict.keys()):
|
||
write = True
|
||
cur_character = invert_role_index_dict[key] #全局角色名称，需要
|
||
#print(input_id_images_dict) #{'[Taylor]': <PIL.Image>, '[Lecun]': <PIL.Image>}
|
||
#print(r_key)# '[Taylor]'
|
||
# print(emb_dict)
|
||
samples = model(
|
||
input_id_images=input_id_images_dict[r_key],
|
||
num_inference_steps=steps,
|
||
guidance_scale=cfg,
|
||
start_merge_step=start_merge_step,
|
||
height=height,
|
||
width=width,
|
||
negative_prompt=neg_text,
|
||
generator=generator,
|
||
emb_dict=emb_dict,
|
||
)[0] # torch.Size([1, 4, 64, 64])
|
||
#print(samples.shape)
|
||
samples_list.append(samples)
|
||
if nc_emb: #no role 无角色的emb
|
||
nc_list=[]
|
||
for i in (nc_emb):
|
||
write = False
|
||
samples = model(input_id_images=[input_id_images_dict[role_list[0]]],
|
||
num_inference_steps=steps,
|
||
guidance_scale=cfg,
|
||
start_merge_step=start_merge_step,
|
||
height=height,
|
||
width=width,
|
||
negative_prompt=neg_text,
|
||
generator=generator,
|
||
emb_dict=i,
|
||
nc_flag=True)[0] # torch.Size([1, 4, 64, 64])
|
||
nc_list.append(samples)
|
||
for index_,sample_nc in zip(nc_index,nc_list):
|
||
samples_list.insert(int(index_), sample_nc)
|
||
else:
|
||
for key in role_list: # i:{a:[[tensor,tensor],[tensor,tensor]],b:[[tensor,tensor],[tensor,tensor]]} 只跑第一张图生成ID
|
||
seed_random = random.randint(0, seed)
|
||
write = True
|
||
cur_character = [key]
|
||
samples = model(height=height, width=width, num_inference_steps=steps, guidance_scale=cfg,
|
||
generator=torch.Generator(device=device).manual_seed(seed_random),
|
||
prompt_embeds=only_role_emb[key][0][0],
|
||
negative_prompt_embeds=negative[0],
|
||
pooled_prompt_embeds=only_role_emb[key][0][1].get("pooled_output"),
|
||
negative_pooled_prompt_embeds=negative[1].get("pooled_output"))[
|
||
0] # torch.Size([1, 4, 64, 64])
|
||
samples_list.append(samples)
|
||
|
||
write = False #关闭角色生成
|
||
|
||
for index,emb in enumerate(only_role_emb[key]):
|
||
if index==0:
|
||
continue
|
||
samples_ = model(height=height, width=width, num_inference_steps=steps, guidance_scale=cfg,
|
||
generator=torch.Generator(device=device).manual_seed(seed_random),
|
||
prompt_embeds=emb[0],
|
||
negative_prompt_embeds=negative[0],
|
||
pooled_prompt_embeds=emb[1].get("pooled_output"),
|
||
negative_pooled_prompt_embeds=negative[1].get("pooled_output"))[
|
||
0] # torch.Size([1, 4, 64, 64])
|
||
samples_list.append(samples_)
|
||
|
||
if nc_emb: #no role 无角色的emb
|
||
for index, i in zip(nc_index, nc_emb):
|
||
seed_random = random.randint(0, seed)
|
||
write = False
|
||
samples = model(height=height, width=width, num_inference_steps=steps, guidance_scale=cfg,
|
||
generator=torch.Generator(device=device).manual_seed(seed_random),
|
||
prompt_embeds=i[0],
|
||
negative_prompt_embeds=negative[0],
|
||
pooled_prompt_embeds=i[1].get("pooled_output"),
|
||
negative_pooled_prompt_embeds=negative[1].get("pooled_output"))[
|
||
0] # torch.Size([1, 4, 64, 64])
|
||
samples_list.insert(index, samples)
|
||
if infer_mode=="msdiffusion" or infer_mode =="story_and_maker":
|
||
daul_emb = None #确保pass后段dual的执行
|
||
if daul_emb_ms:
|
||
# del model
|
||
# gc.collect()
|
||
# torch.cuda.empty_cache()
|
||
|
||
VAE=condition.get("VAE")
|
||
for j ,(index, i) in enumerate(zip(dual_index, daul_emb_ms)):
|
||
seed_random = random.randint(0, seed)
|
||
write = False
|
||
if not img2img_mode: #文生图模式，以文生图第一张为ID参考拿emb
|
||
CLIP_VISION = condition.get("CLIP_VISION")
|
||
|
||
out_1, out_2 = {}, {}
|
||
out_1["samples"]=samples_list[int(condition.get("positions_index_char_1"))]
|
||
out_2["samples"] =samples_list[int(condition.get("positions_index_char_2"))]
|
||
role_1=VAE.decode(out_1["samples"])
|
||
role_2 = VAE.decode(out_2["samples"])
|
||
|
||
if infer_mode=="msdiffusion":
|
||
from .model_loader_utils import Infer_MSdiffusion
|
||
role_tensor=torch.cat((role_1,role_2),dim=0)
|
||
image_embeds=CLIP_VISION.encode_image(role_tensor)["penultimate_hidden_states"]
|
||
image_embeds = image_embeds.to(device, dtype=model.unet.dtype)
|
||
if controlnet:
|
||
model=model #TO DO
|
||
#write = False
|
||
samples = Infer_MSdiffusion(model,ipadapter_ckpt_path,image_embeds, i[0],negative[0],grounding_kwargs,cross_attention_kwargs,
|
||
1,mask_threshold,height,width,steps,seed_random,cfg,i[1].get("pooled_output"),negative[1].get("pooled_output") )
|
||
|
||
else: #maker的emb要单独处理，用列表形式传入 make
|
||
from .StoryMaker.pipeline_sdxl_storymaker_wrapper import encode_prompt_image_emb_
|
||
# image_embeds=[]
|
||
# for X in [role_1,role_2]:
|
||
# image_embeds.append(clip_vision.encode_image(X)["penultimate_hidden_states"].to(device, dtype=model.unet.dtype))
|
||
make_img,make_mask_img,make_face_info,make_cloth_info=[],[],[],[]
|
||
from .model_loader_utils import insight_face_loader,get_insight_dict
|
||
app_face,pipeline_mask,app_face_=insight_face_loader(infer_mode,False, False)
|
||
|
||
image_list = [tensor_to_image(role_1),tensor_to_image(role_2)]
|
||
input_id_emb_s_dict,input_id_img_s_dict,input_id_emb_un_dict,input_id_cloth_dict=get_insight_dict(app_face,app_face_,pipeline_mask,infer_mode,False,image_list,role_list,condition.get("control_image"),width, height)
|
||
for key in role_list:
|
||
img_ = input_id_emb_un_dict[key][0]
|
||
# print(character_key_str,input_id_images_dict)
|
||
mask_image_ = input_id_img_s_dict[key][0] #mask_image
|
||
face_info_ = input_id_emb_s_dict[key][0]
|
||
cloth_info_ = None
|
||
if isinstance(condition.get("control_image"), torch.Tensor):
|
||
cloth_info_ = input_id_cloth_dict[key][0]
|
||
make_img.append(img_)
|
||
make_mask_img.append(mask_image_)
|
||
make_face_info.append(face_info_)
|
||
make_cloth_info.append(cloth_info_)
|
||
|
||
prompt_image_emb_dual,maker_control_image_dual=encode_prompt_image_emb_(make_img[0], make_img[1], make_mask_img[0], make_mask_img[1], make_face_info[0],
|
||
make_face_info[1], make_cloth_info[0], make_cloth_info[1],device, num_images_per_prompt, condition.get("unet_type"), CLIP_VISION,VAE,do_classifier_free_guidance=True)
|
||
prompt_image_emb_dual=len(prompts_dual)*[prompt_image_emb_dual]
|
||
maker_control_image_dual=len(prompts_dual)*[maker_control_image_dual]
|
||
samples = model(height=height, width=width, num_inference_steps=steps, guidance_scale=cfg,
|
||
generator=torch.Generator(device=device).manual_seed(seed_random),
|
||
prompt_embeds=i[0],
|
||
negative_prompt_embeds=negative[0],
|
||
pooled_prompt_embeds=i[1].get("pooled_output"),
|
||
negative_pooled_prompt_embeds=negative[1].get("pooled_output"),
|
||
prompt_image_emb=prompt_image_emb_dual[j],
|
||
control_image=maker_control_image_dual[j],
|
||
)[0] # torch.Size([1, 4, 64, 64])
|
||
else:
|
||
if infer_mode=="msdiffusion":
|
||
#del model
|
||
from .model_loader_utils import Loader_storydiffusion,Infer_MSdiffusion
|
||
model=Loader_storydiffusion(None,None,None,model)
|
||
gc_cleanup()
|
||
samples = Infer_MSdiffusion(model,ipadapter_ckpt_path,image_embeds, i[0],negative[0],grounding_kwargs,cross_attention_kwargs,
|
||
1,mask_threshold,height,width,steps,seed_random,cfg,i[1].get("pooled_output"),negative[1].get("pooled_output") )
|
||
#del model
|
||
else:
|
||
from .model_loader_utils import Loader_story_maker
|
||
print("reload maker")
|
||
model=Loader_story_maker(None,ipadapter_ckpt_path,VAE,False,condition.get("lora_scale"),UNET=model)
|
||
gc_cleanup()
|
||
if controlnet:
|
||
model.controlnet = controlnet
|
||
|
||
seed_random = random.randint(0, seed)
|
||
|
||
#print(i)
|
||
samples = model(height=height, width=width, num_inference_steps=steps, guidance_scale=cfg,
|
||
generator=torch.Generator(device=device).manual_seed(seed_random),
|
||
prompt_embeds=i[0],
|
||
negative_prompt_embeds=negative[0],
|
||
pooled_prompt_embeds=i[1].get("pooled_output"),
|
||
negative_pooled_prompt_embeds=negative[1].get("pooled_output"),
|
||
prompt_image_emb=prompt_image_emb_dual[j],
|
||
control_image=maker_control_image_dual[j],
|
||
)[0] # torch.Size([1, 4, 64, 64])
|
||
samples_list.insert(index,samples)
|
||
|
||
if daul_emb: #双角色的emb，即便不是msdiffusion，其他方法也能用，只是ID不一致而已
|
||
for index, i in zip(dual_index, daul_emb):
|
||
seed_random = random.randint(0, seed)
|
||
write = False
|
||
samples = model(height=height, width=width, num_inference_steps=steps, guidance_scale=cfg,
|
||
generator=torch.Generator(device=device).manual_seed(seed_random),
|
||
prompt_embeds=i[0],
|
||
negative_prompt_embeds=negative[0],
|
||
pooled_prompt_embeds=i[1].get("pooled_output"),
|
||
negative_pooled_prompt_embeds=negative[1].get("pooled_output"),
|
||
)[0] # torch.Size([1, 4, 64, 64])
|
||
samples_list.insert(index, samples)
|
||
out = {}
|
||
out["samples"] = torch.cat(samples_list, dim=0)
|
||
return (out,)
|
||
elif infer_mode == "story_maker" : #单纯使用maker，兼容单体双人及双人同框，目前需要修改源码，将cn的图片与衣服的emb拿出来提前处理，再传入pipe
|
||
model.scheduler = scheduler_choice.from_config(model.scheduler.config)
|
||
seed_random = random.randint(0, seed)
|
||
samples_list = []
|
||
for key in role_list :
|
||
for i,emb in enumerate(only_role_emb[key]):
|
||
samples = model(height=height, width=width, num_inference_steps=steps, guidance_scale=cfg,
|
||
generator=torch.Generator(device=device).manual_seed(seed_random),
|
||
prompt_embeds=emb[0],
|
||
negative_prompt_embeds=negative[0],
|
||
pooled_prompt_embeds=emb[1].get("pooled_output"),
|
||
negative_pooled_prompt_embeds=negative[1].get("pooled_output"),
|
||
prompt_image_emb=prompt_image_emb[key][i],
|
||
control_image=maker_control_image[key][i],
|
||
)[0] # torch.Size([1, 4, 64, 64])
|
||
samples_list.append(samples)
|
||
if nc_emb: #no role 无角色的emb
|
||
id_embeds_z, clip_image_embeds_z, clip_face_embeds_z = torch.zeros_like(prompt_image_emb[0]), torch.zeros_like(prompt_image_emb[1]), torch.zeros_like(prompt_image_emb[2])
|
||
for index, i in zip(nc_index, nc_emb):
|
||
seed_random = random.randint(0, seed)
|
||
write = False
|
||
samples = model(height=height, width=width, num_inference_steps=steps, guidance_scale=cfg,
|
||
generator=torch.Generator(device=device).manual_seed(seed_random),
|
||
prompt_embeds=i[0],
|
||
negative_prompt_embeds=negative[0],
|
||
pooled_prompt_embeds=i[1].get("pooled_output"),
|
||
negative_pooled_prompt_embeds=negative[1].get("pooled_output"),
|
||
prompt_image_emb=(id_embeds_z, clip_image_embeds_z, clip_face_embeds_z),
|
||
control_image=empty_img_init,
|
||
)[0] # torch.Size([1, 4, 64, 64])
|
||
samples_list.insert(index, samples)
|
||
if daul_emb:
|
||
for j ,(index, i) in enumerate(zip(dual_index, daul_emb)):
|
||
seed_random = random.randint(0, seed)
|
||
write = False
|
||
samples = model(height=height, width=width, num_inference_steps=steps, guidance_scale=cfg,
|
||
generator=torch.Generator(device=device).manual_seed(seed_random),
|
||
prompt_embeds=i[0],
|
||
negative_prompt_embeds=negative[0],
|
||
pooled_prompt_embeds=i[1].get("pooled_output"),
|
||
negative_pooled_prompt_embeds=negative[1].get("pooled_output"),
|
||
prompt_image_emb=prompt_image_emb_dual[j],
|
||
control_image=maker_control_image_dual[j],
|
||
)[0] # torch.Size([1, 4, 64, 64])
|
||
samples_list.insert(index, samples)
|
||
out = {}
|
||
out["samples"] = torch.cat(samples_list, dim=0)
|
||
return (out,)
|
||
|
||
elif infer_mode == "consistory":
|
||
from .consistory.consistory_run import run_batch_generation,run_anchor_generation, run_extra_generation
|
||
mask_dropout = 0.5
|
||
same_latent = False
|
||
n_achors = 2
|
||
role_input=condition.get("role_text").splitlines()[0]
|
||
main_role = role_input.replace("]", "").replace("[", "")
|
||
concept_token = [main_role]
|
||
if ")" in role_input:
|
||
object_role = role_input.split(")")[0].split("(")[-1]
|
||
concept_token=[main_role,object_role]
|
||
style = "A photo of "
|
||
subject=f"a {main_role} "
|
||
replace_prompts= [f'{style}{subject} {i}' for i in only_role_list]
|
||
gpu = 0
|
||
torch.cuda.reset_max_memory_allocated(gpu)
|
||
|
||
model.scheduler = scheduler_choice.from_config(model.scheduler.config)
|
||
seed_random = random.randint(0, seed)
|
||
if not cached:
|
||
if not inject:
|
||
model.enable_vae_slicing()
|
||
model.enable_model_cpu_offload()
|
||
else:
|
||
model.to(torch.float16)
|
||
samples_list = run_batch_generation(model, replace_prompts, concept_token,neg_text, seed,n_steps=steps,mask_dropout=mask_dropout,
|
||
same_latent=same_latent, perform_injection=inject,n_achors=n_achors,cf_clip=condition.get("cf_clip"))
|
||
out = {}
|
||
out["samples"] = samples_list
|
||
return (out,zero_tensor)
|
||
else:
|
||
samples_list = []
|
||
if len(replace_prompts)>2:
|
||
spilit_prompt=replace_prompts[:2]
|
||
else:
|
||
spilit_prompt=replace_prompts
|
||
|
||
anchor_out_images, anchor_cache_first_stage, anchor_cache_second_stage = run_anchor_generation(
|
||
model, spilit_prompt, concept_token,neg_text,
|
||
seed=seed, n_steps=steps, mask_dropout=mask_dropout, same_latent=same_latent,perform_injection=inject,
|
||
cache_cpu_offloading=True,cf_clip=condition.get("cf_clip"))
|
||
samples_list.append(anchor_out_images)
|
||
if len(replace_prompts) > 2:
|
||
left_prompt=replace_prompts[2:]
|
||
else:
|
||
left_prompt=replace_prompts[:1] # use default
|
||
|
||
for extra_prompt in left_prompt:
|
||
extra_image = run_extra_generation(model, [extra_prompt], concept_token,neg_text,
|
||
anchor_cache_first_stage,
|
||
anchor_cache_second_stage,
|
||
seed=seed, n_steps=steps,
|
||
mask_dropout=mask_dropout,
|
||
same_latent=same_latent,
|
||
perform_injection=inject,
|
||
cache_cpu_offloading=True,cf_clip=condition.get("cf_clip"))
|
||
samples_list.append(extra_image)
|
||
out = {}
|
||
out["samples"] = torch.cat(samples_list, dim=0)
|
||
return (out,)
|
||
|
||
elif infer_mode == "kolor_face":
|
||
samples_list = []
|
||
for key in role_list:
|
||
for emb in only_role_emb[key]:
|
||
samples = model(
|
||
prompt=None,
|
||
prompt_embeds=emb[0],
|
||
negative_prompt_embeds=negative[0],
|
||
pooled_prompt_embeds=emb[1],
|
||
negative_pooled_prompt_embeds=negative[1],
|
||
height=height,
|
||
width=width,
|
||
num_inference_steps=steps,
|
||
guidance_scale=cfg,
|
||
num_images_per_prompt=1,
|
||
generator=torch.Generator(device=device).manual_seed(seed),
|
||
face_crop_image=input_id_img_s_dict[key][0],
|
||
face_insightface_embeds=input_id_emb_s_dict[key][0].to(device, dtype=torch.float16),
|
||
).images
|
||
#print(samples.shape)
|
||
samples_list.append(samples)
|
||
if nc_emb:
|
||
for emb in nc_emb:
|
||
samples = model(
|
||
prompt=None,
|
||
prompt_embeds=emb[0],
|
||
negative_prompt_embeds=negative[0],
|
||
pooled_prompt_embeds=emb[1],
|
||
negative_pooled_prompt_embeds=negative[1],
|
||
height=height,
|
||
width=width,
|
||
num_inference_steps=steps,
|
||
guidance_scale=cfg,
|
||
num_images_per_prompt=1,
|
||
generator=torch.Generator(device=device).manual_seed(seed),
|
||
face_crop_image=Image.new('RGB', (336, 336), (255, 255, 255)),
|
||
face_insightface_embeds=torch.zeros_like(input_id_emb_s_dict[role_list[0]][0]).to(device, dtype=torch.float16),
|
||
).images
|
||
#print(samples.shape)
|
||
samples_list.append(samples)
|
||
out = {}
|
||
out["samples"] = torch.cat(samples_list, dim=0)
|
||
return (out,)
|
||
|
||
elif infer_mode == "infiniteyou":
|
||
samples_list = []
|
||
|
||
for key in role_list:
|
||
if isinstance(input_id_img_s_dict[key][0], list) and (len(input_id_img_s_dict[key][0]) < len(only_role_emb[key])):#输入的图片数量小于emb数量
|
||
cn_img=input_id_img_s_dict[key][0]+(len(only_role_emb[key])-len(input_id_img_s_dict[key][0]))*[control_image_zero]
|
||
else:
|
||
cn_img=input_id_img_s_dict[key][0]
|
||
|
||
for index,emb in enumerate(only_role_emb[key]):
|
||
|
||
samples = model (id_embed=input_id_emb_s_dict[key][0],
|
||
prompt_embeds=emb[0],
|
||
pooled_prompt_embeds=emb[1].get("pooled_output"),
|
||
control_image=cn_img[index] if isinstance(cn_img , list) else input_id_img_s_dict[key][0],
|
||
guidance_scale=cfg,
|
||
num_steps=steps,
|
||
seed=random.randint(0, seed),
|
||
infusenet_conditioning_scale=1.0,
|
||
infusenet_guidance_start=0,
|
||
infusenet_guidance_end=1.0,
|
||
height=height,
|
||
width=width,
|
||
)
|
||
samples_list.append(samples)
|
||
out = {}
|
||
out["samples"] = torch.cat(samples_list, dim=0)
|
||
return (out,)
|
||
elif infer_mode == "flux_pulid":
|
||
samples_list = []
|
||
for key in role_list:
|
||
for index,emb in enumerate(only_role_emb[key]):
|
||
samples = model.generate_image(
|
||
width=width,
|
||
height=height,
|
||
num_steps=steps,
|
||
start_step=2,
|
||
guidance=cfg,
|
||
seed=random.randint(0, seed),
|
||
inp=emb,
|
||
inp_neg=negative[0][key][index],
|
||
id_embeddings=input_id_emb_s_dict[key][0],
|
||
uncond_id_embeddings=input_id_emb_un_dict[key][0],
|
||
) # torch.Size([1, 4, 64, 64])
|
||
|
||
samples_list.append(samples)
|
||
out = {}
|
||
out["samples"] = torch.cat(samples_list, dim=0)
|
||
return (out,)
|
||
elif infer_mode=="uno":
|
||
samples_list = []
|
||
for key in role_list:
|
||
for index,emb in enumerate(only_role_emb[key]):
|
||
#print(emb)
|
||
samples = model(
|
||
width=width,
|
||
height=height,
|
||
guidance=cfg,
|
||
num_steps=steps,
|
||
inp_cond=emb,
|
||
seed=random.randint(0, seed),
|
||
|
||
) # torch.Size([1, 4, 64, 64])
|
||
|
||
samples_list.append(samples)
|
||
|
||
if daul_emb:
|
||
for index, emb in zip(dual_index, daul_emb):
|
||
samples = model(
|
||
width=width,
|
||
height=height,
|
||
guidance=cfg,
|
||
num_steps=steps,
|
||
inp_cond=emb,
|
||
seed=random.randint(0, seed)
|
||
) # torch.Size([1, 4, 64, 64])
|
||
samples_list.insert(index, samples)
|
||
|
||
out = {}
|
||
out["samples"] = torch.cat(samples_list, dim=0)
|
||
return (out,)
|
||
elif infer_mode=="realcustom":
|
||
from .model_loader_utils import realcustom_infer
|
||
write = False
|
||
samples_list = []
|
||
for key in role_list:
|
||
for emb,latent_ in zip(only_role_emb[key],negative[0][key]):
|
||
samples = realcustom_infer(
|
||
model,
|
||
sample_steps=steps,
|
||
mask_reused_step=12,
|
||
emb_dict=emb,
|
||
latent_dict=latent_,
|
||
mask_scope=0.20,
|
||
mask_strategy="max_norm", #"min_max_per_channel", "max_norm"
|
||
guidance_weight=cfg,
|
||
height=height,
|
||
width=width,
|
||
seed=random.randint(0, seed),
|
||
device=device,
|
||
)
|
||
samples_list.append(samples)
|
||
out = {}
|
||
out["samples"] = torch.cat(samples_list, dim=0)
|
||
return (out,)
|
||
elif infer_mode=="instant_character":
|
||
samples_list = []
|
||
#model.to(device)
|
||
for key,id_emb in zip(role_list,image_embeds):
|
||
for emb_list in only_role_emb[key]:
|
||
samples = model(
|
||
prompt_embeds=emb_list[0],
|
||
pooled_prompt_embeds=emb_list[1],
|
||
num_inference_steps=steps,
|
||
guidance_scale=cfg,
|
||
subject_image=True,
|
||
subject_scale=0.9,
|
||
generator=torch.manual_seed(random.randint(0, seed)),
|
||
text_ids=emb_list[2],
|
||
subject_image_embeds_dict=id_emb,
|
||
)[0] # torch.Size([1, 4, 64, 64])
|
||
#print(samples.shape)
|
||
samples_list.append(samples)
|
||
|
||
out = {}
|
||
out["samples"] = torch.cat(samples_list, dim=0)
|
||
return (out,)
|
||
elif infer_mode=="dreamo":
|
||
samples_list = []
|
||
cfg=4.5 if condition.get("dreamo_version")=="v1.1" else 3.5
|
||
|
||
first_step_guidance=0
|
||
for key in role_list:
|
||
for index,prompt in tqdm(enumerate(only_role_emb[key]),desc=f"Processing {key}"):
|
||
samples = model(prompt=prompt,
|
||
width=width,
|
||
height=height,
|
||
num_inference_steps=steps,
|
||
guidance_scale=cfg,
|
||
ref_conds=image_embeds[key][index],
|
||
generator=torch.Generator(device="cpu").manual_seed(seed),
|
||
true_cfg_scale=1,
|
||
true_cfg_start_step=0,
|
||
true_cfg_end_step=0,
|
||
negative_prompt=neg_text,
|
||
neg_guidance_scale=cfg,
|
||
first_step_guidance_scale=first_step_guidance if first_step_guidance > 0 else cfg,
|
||
).images
|
||
#print(samples.shape)
|
||
samples_list.append(samples)
|
||
if daul_emb:
|
||
for index, prompt in tqdm(zip(dual_index, daul_emb[1]),desc="Processing dual prompts"): #daul_emb [emb,prompts]
|
||
samples = model(prompt=prompt,
|
||
width=width,
|
||
height=height,
|
||
num_inference_steps=steps,
|
||
guidance_scale=cfg,
|
||
ref_conds=daul_emb[0],
|
||
generator=torch.Generator(device="cpu").manual_seed(seed),
|
||
true_cfg_scale=1,
|
||
true_cfg_start_step=0,
|
||
true_cfg_end_step=0,
|
||
negative_prompt=neg_text,
|
||
neg_guidance_scale=cfg,
|
||
first_step_guidance_scale=first_step_guidance if first_step_guidance > 0 else cfg,
|
||
).images
|
||
samples_list.insert(index, samples)
|
||
out = {}
|
||
out["samples"] = torch.cat(samples_list, dim=0)
|
||
return (out,)
|
||
elif infer_mode =="bagel_edit":
|
||
|
||
VAE=condition.get("VAE")
|
||
samples_list = []
|
||
if img2img_mode:
|
||
from .Bagel.app import edit_image
|
||
for key in role_list:
|
||
for prompt in only_role_emb[key]:
|
||
samples,text_=edit_image(
|
||
model,
|
||
image=image_embeds[key],
|
||
prompt=prompt,
|
||
show_thinking=False,
|
||
cfg_text_scale=cfg,
|
||
cfg_img_scale=2.0,
|
||
cfg_interval=0.0,
|
||
timestep_shift=3.0,
|
||
num_timesteps=steps,
|
||
cfg_renorm_min=0.0,
|
||
cfg_renorm_type="text_channel",
|
||
max_think_token_n=1024,
|
||
do_sample=False,
|
||
text_temperature=0.3,
|
||
seed=seed) # tuple (image,str)
|
||
print(f'thinking text is {text_}')
|
||
samples_list.append(samples)
|
||
else:
|
||
from .Bagel.app import text_to_image
|
||
if width==height: #todo
|
||
image_ratio="1:1"
|
||
elif width/height==0.75:
|
||
image_ratio="4:3"
|
||
elif width/height==3/4:
|
||
image_ratio="3:4"
|
||
elif width/height==9/16:
|
||
image_ratio="9:16"
|
||
elif width/height==16/9:
|
||
image_ratio="16:9"
|
||
else:
|
||
image_ratio="1:1"
|
||
|
||
for key in role_list:
|
||
for prompt in only_role_emb[key]:
|
||
samples,text_=text_to_image(
|
||
model,
|
||
prompt=prompt,
|
||
show_thinking=False,
|
||
cfg_text_scale=cfg,
|
||
cfg_interval=0.4,
|
||
timestep_shift=3.0,
|
||
num_timesteps=steps,
|
||
cfg_renorm_min=0.0,
|
||
cfg_renorm_type="global",
|
||
max_think_token_n=1024,
|
||
do_sample=False,
|
||
text_temperature=0.3,
|
||
seed=seed,
|
||
image_ratio=image_ratio) # tuple (image,str)
|
||
print(f'thinking text is {text_}')
|
||
samples_list.append(samples)
|
||
|
||
out = {}
|
||
samples_list=[VAE.encode(phi2narry(pixels)[:,:,:,:3]) for pixels in samples_list] # TODO pil to tensor
|
||
out["samples"] = torch.cat(samples_list, dim=0)
|
||
return (out,)
|
||
elif infer_mode =="flux_omi":
|
||
samples_list = []
|
||
if condition.get("no_dif_quantization"):
|
||
for key in role_list:
|
||
for ebm_list in only_role_emb[key]:
|
||
samples=model(
|
||
prompt=None,
|
||
height=height,
|
||
width=width,
|
||
guidance_scale=cfg if cfg==3.5 else 3.5,
|
||
num_inference_steps=steps,
|
||
max_sequence_length=512,
|
||
generator=torch.Generator("cpu").manual_seed(seed),
|
||
prompt_embeds=ebm_list[0],
|
||
pooled_prompt_embeds=ebm_list[1],
|
||
spatial_images=[image_embeds[key]],
|
||
subject_images=[], #TODO TRY ON
|
||
cond_size=512,
|
||
).images
|
||
#print(samples.shape)
|
||
samples_list.append(samples)
|
||
else:
|
||
for key in role_list:
|
||
for prompt in only_role_emb[key]:
|
||
samples=model(
|
||
prompt=prompt,
|
||
height=height,
|
||
width=width,
|
||
guidance_scale=cfg if cfg==3.5 else 3.5,
|
||
num_inference_steps=steps,
|
||
max_sequence_length=512,
|
||
generator=torch.Generator("cpu").manual_seed(seed),
|
||
spatial_images=[image_embeds[key]],
|
||
subject_images=[], #TODO TRY ON
|
||
cond_size=512,
|
||
).images
|
||
#print(samples.shape)
|
||
samples_list.append(samples)
|
||
out = {}
|
||
out["samples"] = torch.cat(samples_list, dim=0)
|
||
# Clear cache after generation
|
||
from .OmniConsistency.infer import clear_cache
|
||
clear_cache(model.transformer)
|
||
return (out,)
|
||
elif infer_mode =="qwen_image":
|
||
from .qwen_image.inferencer import infer_qwen_image_edit
|
||
samples_list = []
|
||
# import comfy.model_management as model_management
|
||
# model_management.unload_all_models()
|
||
|
||
image_list=condition.get("image_list") if condition.get("edit_mode") else [None,None]
|
||
for key,image in zip(role_list,image_list):
|
||
negative_prompt_embeds,_=negative[key][0]
|
||
|
||
for prompt in only_role_emb[key]:
|
||
samples=infer_qwen_image_edit(
|
||
model,
|
||
image=image,
|
||
prompt_embeds=prompt[0],
|
||
prompt_embeds_mask=torch.zeros_like(prompt[0]),
|
||
negative_prompt_embeds=negative_prompt_embeds,
|
||
negative_prompt_embeds_mask=torch.zeros_like(negative_prompt_embeds),
|
||
seed=seed,
|
||
num_inference_steps=steps,
|
||
true_cfg_scale=cfg,
|
||
image_latents=prompt[1],
|
||
edit_mode=condition.get("edit_mode"),
|
||
latents=latent_init,
|
||
)
|
||
#print(samples.shape)
|
||
samples_list.append(samples)
|
||
out = {}
|
||
out["samples"] = torch.cat(samples_list, dim=0)
|
||
return (out,)
|
||
else: #none:
|
||
return
|
||
|
||
|
||
|
||
class Comic_Type:
|
||
@classmethod
|
||
def INPUT_TYPES(s):
|
||
return {"required": {"image": ("IMAGE",),
|
||
"scene_prompts": ("STRING", {"multiline": True, "forceInput": True, "default": ""}),
|
||
"fonts_list": (os.listdir(os.path.join(dir_path, "fonts")),),
|
||
"text_size": ("INT", {"default": 40, "min": 1, "max": 100}),
|
||
"comic_type": (["Four_Pannel", "Classic_Comic_Style"],),
|
||
"split_lines": ("STRING", {"default": "；"}),
|
||
}}
|
||
|
||
RETURN_TYPES = ("IMAGE",)
|
||
RETURN_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,)
|
||
|
||
|
||
def set_attention_processor(unet_, id_length, is_ipadapter=False):
|
||
global attn_procs_
|
||
attn_procs_ = {}
|
||
for name in unet_.attn_processors.keys():
|
||
cross_attention_dim = (
|
||
None
|
||
if name.endswith("attn1.processor")
|
||
else unet_.config.cross_attention_dim
|
||
)
|
||
if name.startswith("mid_block"):
|
||
hidden_size = unet_.config.block_out_channels[-1]
|
||
elif name.startswith("up_blocks"):
|
||
block_id = int(name[len("up_blocks.")])
|
||
hidden_size = list(reversed(unet_.config.block_out_channels))[block_id]
|
||
elif name.startswith("down_blocks"):
|
||
block_id = int(name[len("down_blocks.")])
|
||
hidden_size = unet_.config.block_out_channels[block_id]
|
||
if cross_attention_dim is None:
|
||
if name.startswith("up_blocks"):
|
||
attn_procs_[name] = SpatialAttnProcessor2_0(id_length=id_length)
|
||
else:
|
||
attn_procs_[name] = AttnProcessor()
|
||
else:
|
||
if is_ipadapter:
|
||
attn_procs_[name] = IPAttnProcessor2_0(
|
||
hidden_size=hidden_size,
|
||
cross_attention_dim=cross_attention_dim,
|
||
scale=1,
|
||
num_tokens=4,
|
||
).to(unet_.device, dtype=torch.float16)
|
||
else:
|
||
attn_procs_[name] = AttnProcessor()
|
||
|
||
unet_.set_attn_processor(copy.deepcopy(attn_procs_))
|
||
|
||
|
||
class SpatialAttnProcessor2_0(torch.nn.Module):
|
||
r"""
|
||
Attention processor for IP-Adapater for PyTorch 2.0.
|
||
Args:
|
||
hidden_size (`int`):
|
||
The hidden size of the attention layer.
|
||
cross_attention_dim (`int`):
|
||
The number of channels in the `encoder_hidden_states`.
|
||
text_context_len (`int`, defaults to 77):
|
||
The context length of the text features.
|
||
scale (`float`, defaults to 1.0):
|
||
the weight scale of image prompt.
|
||
"""
|
||
|
||
def __init__(
|
||
self,
|
||
hidden_size=None,
|
||
cross_attention_dim=None,
|
||
id_length=4,
|
||
device=device,
|
||
dtype=torch.float16,
|
||
):
|
||
super().__init__()
|
||
if not hasattr(F, "scaled_dot_product_attention"):
|
||
raise ImportError(
|
||
"AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
|
||
)
|
||
self.device = device
|
||
self.dtype = dtype
|
||
self.hidden_size = hidden_size
|
||
self.cross_attention_dim = cross_attention_dim
|
||
self.total_length = id_length + 1
|
||
self.id_length = id_length
|
||
self.id_bank = {}
|
||
|
||
def __call__(
|
||
self,
|
||
attn,
|
||
hidden_states,
|
||
encoder_hidden_states=None,
|
||
attention_mask=None,
|
||
temb=None,
|
||
):
|
||
# un_cond_hidden_states, cond_hidden_states = hidden_states.chunk(2)
|
||
# un_cond_hidden_states = self.__call2__(attn, un_cond_hidden_states,encoder_hidden_states,attention_mask,temb)
|
||
# 生成一个0到1之间的随机数
|
||
global total_count, attn_count_, cur_step, indices1024, indices4096
|
||
global sa32, sa64
|
||
global write
|
||
global height_s, width_s
|
||
global character_dict
|
||
global character_index_dict, invert_character_index_dict, cur_character, ref_indexs_dict, ref_totals
|
||
if attn_count_ == 0 and cur_step == 0:
|
||
indices1024, indices4096 = cal_attn_indice_xl_effcient_memory(
|
||
self.total_length,
|
||
self.id_length,
|
||
sa32,
|
||
sa64,
|
||
height_s,
|
||
width_s,
|
||
device=self.device,
|
||
dtype=self.dtype,
|
||
)
|
||
if write:
|
||
assert len(cur_character) == 1
|
||
if hidden_states.shape[1] == (height_s // 32) * (width_s // 32):
|
||
indices = indices1024
|
||
else:
|
||
indices = indices4096
|
||
# print(f"white:{cur_step}")
|
||
total_batch_size, nums_token, channel = hidden_states.shape
|
||
img_nums = total_batch_size // 2
|
||
hidden_states = hidden_states.reshape(-1, img_nums, nums_token, channel)
|
||
# print(img_nums,len(indices),hidden_states.shape,self.total_length)
|
||
if cur_character[0] not in self.id_bank:
|
||
self.id_bank[cur_character[0]] = {}
|
||
self.id_bank[cur_character[0]][cur_step] = [
|
||
hidden_states[:, img_ind, indices[img_ind], :]
|
||
.reshape(2, -1, channel)
|
||
.clone()
|
||
for img_ind in range(img_nums)
|
||
]
|
||
hidden_states = hidden_states.reshape(-1, nums_token, channel)
|
||
# self.id_bank[cur_step] = [hidden_states[:self.id_length].clone(), hidden_states[self.id_length:].clone()]
|
||
else:
|
||
# encoder_hidden_states = torch.cat((self.id_bank[cur_step][0].to(self.device),self.id_bank[cur_step][1].to(self.device)))
|
||
# TODO: ADD Multipersion Control
|
||
encoder_arr = []
|
||
for character in cur_character:
|
||
encoder_arr = encoder_arr + [
|
||
tensor.to(self.device)
|
||
for tensor in self.id_bank[character][cur_step]
|
||
]
|
||
# 判断随机数是否大于0.5
|
||
if cur_step < 1:
|
||
hidden_states = self.__call2__(
|
||
attn, hidden_states, None, attention_mask, temb
|
||
)
|
||
else: # 256 1024 4096
|
||
random_number = random.random()
|
||
if cur_step < 20:
|
||
rand_num = 0.3
|
||
else:
|
||
rand_num = 0.1
|
||
# print(f"hidden state shape {hidden_states.shape[1]}")
|
||
if random_number > rand_num:
|
||
if hidden_states.shape[1] == (height_s // 32) * (width_s // 32):
|
||
indices = indices1024
|
||
else:
|
||
indices = indices4096
|
||
# print("before attention",hidden_states.shape,attention_mask.shape,encoder_hidden_states.shape if encoder_hidden_states is not None else "None")
|
||
if write:
|
||
total_batch_size, nums_token, channel = hidden_states.shape
|
||
img_nums = total_batch_size // 2
|
||
hidden_states = hidden_states.reshape(
|
||
-1, img_nums, nums_token, channel
|
||
)
|
||
encoder_arr = [
|
||
hidden_states[:, img_ind, indices[img_ind], :].reshape(
|
||
2, -1, channel
|
||
)
|
||
for img_ind in range(img_nums)
|
||
]
|
||
for img_ind in range(img_nums):
|
||
# print(img_nums)
|
||
# assert img_nums != 1
|
||
img_ind_list = [i for i in range(img_nums)]
|
||
# print(img_ind_list,img_ind)
|
||
img_ind_list.remove(img_ind)
|
||
# print(img_ind,invert_character_index_dict[img_ind])
|
||
# print(character_index_dict[invert_character_index_dict[img_ind]])
|
||
# print(img_ind_list)
|
||
# print(img_ind,img_ind_list)
|
||
encoder_hidden_states_tmp = torch.cat(
|
||
[encoder_arr[img_ind] for img_ind in img_ind_list]
|
||
+ [hidden_states[:, img_ind, :, :]],
|
||
dim=1,
|
||
)
|
||
|
||
hidden_states[:, img_ind, :, :] = self.__call2__(
|
||
attn,
|
||
hidden_states[:, img_ind, :, :],
|
||
encoder_hidden_states_tmp,
|
||
None,
|
||
temb,
|
||
)
|
||
else:
|
||
_, nums_token, channel = hidden_states.shape
|
||
# img_nums = total_batch_size // 2
|
||
# encoder_hidden_states = encoder_hidden_states.reshape(-1,img_nums,nums_token,channel)
|
||
hidden_states = hidden_states.reshape(2, -1, nums_token, channel)
|
||
|
||
# encoder_arr = [encoder_hidden_states[:,img_ind,indices[img_ind],:].reshape(2,-1,channel) for img_ind in range(img_nums)]
|
||
encoder_hidden_states_tmp = torch.cat(
|
||
encoder_arr + [hidden_states[:, 0, :, :]], dim=1
|
||
)
|
||
|
||
hidden_states[:, 0, :, :] = self.__call2__(
|
||
attn,
|
||
hidden_states[:, 0, :, :],
|
||
encoder_hidden_states_tmp,
|
||
None,
|
||
temb,
|
||
)
|
||
hidden_states = hidden_states.reshape(-1, nums_token, channel)
|
||
else:
|
||
hidden_states = self.__call2__(
|
||
attn, hidden_states, None, attention_mask, temb
|
||
)
|
||
attn_count_ += 1
|
||
if attn_count_ == total_count:
|
||
attn_count_ = 0
|
||
cur_step += 1
|
||
indices1024, indices4096 = cal_attn_indice_xl_effcient_memory(
|
||
self.total_length,
|
||
self.id_length,
|
||
sa32,
|
||
sa64,
|
||
height_s,
|
||
width_s,
|
||
device=self.device,
|
||
dtype=self.dtype,
|
||
)
|
||
|
||
return hidden_states
|
||
|
||
def __call2__(
|
||
self,
|
||
attn,
|
||
hidden_states,
|
||
encoder_hidden_states=None,
|
||
attention_mask=None,
|
||
temb=None,
|
||
):
|
||
residual = hidden_states
|
||
|
||
if attn.spatial_norm is not None:
|
||
hidden_states = attn.spatial_norm(hidden_states, temb)
|
||
|
||
input_ndim = hidden_states.ndim
|
||
|
||
if input_ndim == 4:
|
||
batch_size, channel, height_s, width_s = hidden_states.shape
|
||
hidden_states = hidden_states.view(
|
||
batch_size, channel, height_s * width_s
|
||
).transpose(1, 2)
|
||
|
||
batch_size, sequence_length, channel = hidden_states.shape
|
||
|
||
if attention_mask is not None:
|
||
attention_mask = attn.prepare_attention_mask(
|
||
attention_mask, sequence_length, batch_size
|
||
)
|
||
# scaled_dot_product_attention expects attention_mask shape to be
|
||
# (batch, heads, source_length, target_length)
|
||
attention_mask = attention_mask.view(
|
||
batch_size, attn.heads, -1, attention_mask.shape[-1]
|
||
)
|
||
|
||
if attn.group_norm is not None:
|
||
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
|
||
1, 2
|
||
)
|
||
|
||
query = attn.to_q(hidden_states)
|
||
|
||
if encoder_hidden_states is None:
|
||
encoder_hidden_states = hidden_states # B, N, C
|
||
# else:
|
||
# encoder_hidden_states = encoder_hidden_states.view(-1,self.id_length+1,sequence_length,channel).reshape(-1,(self.id_length+1) * sequence_length,channel)
|
||
|
||
key = attn.to_k(encoder_hidden_states)
|
||
value = attn.to_v(encoder_hidden_states)
|
||
|
||
inner_dim = key.shape[-1]
|
||
head_dim = inner_dim // attn.heads
|
||
|
||
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
||
|
||
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
||
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
||
|
||
# the output of sdp = (batch, num_heads, seq_len, head_dim)
|
||
# TODO: add support for attn.scale when we move to Torch 2.1
|
||
hidden_states = F.scaled_dot_product_attention(
|
||
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
|
||
)
|
||
|
||
hidden_states = hidden_states.transpose(1, 2).reshape(
|
||
batch_size, -1, attn.heads * head_dim
|
||
)
|
||
hidden_states = hidden_states.to(query.dtype)
|
||
|
||
# linear proj
|
||
hidden_states = attn.to_out[0](hidden_states)
|
||
# dropout
|
||
hidden_states = attn.to_out[1](hidden_states)
|
||
|
||
if input_ndim == 4:
|
||
hidden_states = hidden_states.transpose(-1, -2).reshape(
|
||
batch_size, channel, height_s, width_s
|
||
)
|
||
|
||
if attn.residual_connection:
|
||
hidden_states = hidden_states + residual
|
||
|
||
hidden_states = hidden_states / attn.rescale_output_factor
|
||
|
||
return hidden_states
|
||
|
||
|
||
NODE_CLASS_MAPPINGS = {
|
||
"Pre_Translate_prompt": Pre_Translate_prompt,
|
||
"Comic_Type": Comic_Type,
|
||
"EasyFunction_Lite":EasyFunction_Lite,
|
||
"StoryDiffusion_Apply":StoryDiffusion_Apply,
|
||
"StoryDiffusion_CLIPTextEncode":StoryDiffusion_CLIPTextEncode,
|
||
"StoryDiffusion_KSampler":StoryDiffusion_KSampler,
|
||
|
||
}
|
||
|
||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||
"Pre_Translate_prompt": "Pre_Translate_prompt",
|
||
"Comic_Type": "Comic_Type",
|
||
"EasyFunction_Lite":"EasyFunction_Lite",
|
||
"StoryDiffusion_Apply":"StoryDiffusion_Apply",
|
||
"StoryDiffusion_CLIPTextEncode":"StoryDiffusion_CLIPTextEncode",
|
||
"StoryDiffusion_KSampler":"StoryDiffusion_KSampler",
|
||
}
|