1980 lines
90 KiB
Python
1980 lines
90 KiB
Python
# !/usr/bin/env python
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# -*- coding: UTF-8 -*-
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import datetime
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import gc
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import json
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import cv2
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import numpy as np
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import safetensors
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import torch
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import copy
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import os
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import random
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from PIL import ImageFont
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from safetensors.torch import load_file
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from .ip_adapter.attention_processor import IPAttnProcessor2_0
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import sys
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import re
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from .utils.gradio_utils import (
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character_to_dict,
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process_original_prompt,
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get_ref_character,
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cal_attn_mask_xl,
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cal_attn_indice_xl_effcient_memory,
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is_torch2_available,
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)
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from PIL import Image
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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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from huggingface_hub import hf_hub_download
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from diffusers import (StableDiffusionXLPipeline, DiffusionPipeline, DDIMScheduler, ControlNetModel,
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KDPM2AncestralDiscreteScheduler, LMSDiscreteScheduler,
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DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler,
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EulerDiscreteScheduler, HeunDiscreteScheduler, UNet2DConditionModel,
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KDPM2DiscreteScheduler,
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EulerAncestralDiscreteScheduler, UniPCMultistepScheduler, AutoencoderKL,
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StableDiffusionXLControlNetPipeline, DDPMScheduler, LCMScheduler)
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from omegaconf import OmegaConf
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from transformers import CLIPVisionModelWithProjection
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from transformers import CLIPImageProcessor
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from .msdiffusion.models.projection import Resampler
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from .msdiffusion.models.model import MSAdapter
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from .msdiffusion.utils import get_phrase_idx, get_eot_idx
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from diffusers.schedulers.scheduling_ddim import DDIMScheduler
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import torch.nn.functional as F
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from .utils.utils import get_comic
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from .utils.style_template import styles
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from .utils.load_models_utils import load_models, get_instance_path, get_lora_dict
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import folder_paths
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from comfy.utils import common_upscale
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global total_count, attn_count, cur_step, mask1024, mask4096, attn_procs, unet
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global sa32, sa64
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global write
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global height, width
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STYLE_NAMES = list(styles.keys())
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photomaker_dir=os.path.join(folder_paths.models_dir, "photomaker")
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device = (
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"cuda"
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if torch.cuda.is_available()
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else "mps" if torch.backends.mps.is_available() else "cpu"
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)
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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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path_dir = os.path.dirname(dir_path)
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file_path = os.path.dirname(path_dir)
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fonts_path = os.path.join(dir_path, "fonts")
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fonts_lists = os.listdir(fonts_path)
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lora_get = get_lora_dict()
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lora_lightning_list = lora_get["lightning_xl_lora"]
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diff_paths = []
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for search_path in folder_paths.get_folder_paths("diffusers"):
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if os.path.exists(search_path):
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for root, subdir, files in os.walk(search_path, followlinks=True):
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if "model_index.json" in files:
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diff_paths.append(os.path.relpath(root, start=search_path))
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if diff_paths:
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diff_paths = ["none"] + [x for x in diff_paths if x]
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else:
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diff_paths = ["none", ]
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clip_lists = folder_paths.get_filename_list("clip_vision")
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if clip_lists:
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clip_paths = ["none"] + [x for x in clip_lists if x]
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else:
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clip_paths = ["none",]
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control_paths = []
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paths_a = []
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for search_path in folder_paths.get_folder_paths("diffusers"):
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if os.path.exists(search_path):
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for root, subdir, files in os.walk(search_path, followlinks=True):
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if "model_index.json" in files:
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control_paths.append(os.path.relpath(root, start=search_path))
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if "config.json" in files:
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paths_a.append(os.path.relpath(root, start=search_path))
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paths_a = ([z for z in paths_a if "controlnet-canny-sdxl-1.0" in z]
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+ [p for p in paths_a if "MistoLine" in p]
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+ [o for o in paths_a if "lcm-sdxl" in o]
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+ [Q for Q in paths_a if "controlnet-openpose-sdxl-1.0" in Q]
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+ [Z for Z in paths_a if "controlnet-scribble-sdxl-1.0" in Z]
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+ [a for a in paths_a if "controlnet-depth-sdxl-1.0" in a]
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+[b for b in paths_a if "controlnet-tile-sdxl-1.0" in b]
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+[c for c in paths_a if "controlnet-zoe-depth-sdxl-1.0" in c]
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+[d for d in paths_a if "sdxl-controlnet-seg " in d])
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if control_paths != [] or paths_a != []:
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control_paths = ["none"] + [x for x in control_paths if x] + [y for y in paths_a if y]
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else:
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control_paths = ["none", ]
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scheduler_list = [
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"Euler", "Euler a", "DDIM", "DDPM", "DPM++ 2M", "DPM++ 2M Karras", "DPM++ 2M SDE", "DPM++ 2M SDE Karras",
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"DPM++ SDE", "DPM++ SDE Karras", "DPM2",
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"DPM2 Karras", "DPM2 a", "DPM2 a Karras", "Heun", "LCM", "LMS", "LMS Karras", "UniPC"
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]
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def phi2narry(img):
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img = torch.from_numpy(np.array(img).astype(np.float32) / 255.0).unsqueeze(0)
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return img
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def get_instance_path(path):
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instance_path = os.path.normpath(path)
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if sys.platform == 'win32':
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instance_path = instance_path.replace('\\', "/")
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return instance_path
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def add_pil(list, list_add, num):
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new_list = list[:num] + list_add + list[num:]
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return new_list
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def tensor_to_image(tensor):
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image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
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image = Image.fromarray(image_np, mode='RGB')
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return image
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# get fonts list
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def has_parentheses(s):
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return bool(re.search(r'\(.*?\)', s))
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def contains_brackets(s):
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return '[' in s or ']' in s
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def extract_content_from_brackets(text):
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# 正则表达式匹配多对方括号内的内容
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return re.findall(r'\[(.*?)\]', text)
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def narry_list(list_in):
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for i in range(len(list_in)):
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value = list_in[i]
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modified_value = phi2narry(value)
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list_in[i] = modified_value
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return list_in
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def remove_punctuation_from_strings(lst):
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pattern = r"[\W]+$" # 匹配字符串末尾的所有非单词字符
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return [re.sub(pattern, '', s) for s in lst]
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def format_punctuation_from_strings(lst):
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pattern = r"[\W]+$" # 匹配字符串末尾的所有非单词字符
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return [re.sub(pattern, ';', s) for s in lst]
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def phi_list(list_in):
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for i in range(len(list_in)):
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value = list_in[i]
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list_in[i] = value
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return list_in
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def narry_list_pil(list_in):
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for i in range(len(list_in)):
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value = list_in[i]
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modified_value = tensor_to_image(value)
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list_in[i] = modified_value
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return list_in
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def get_local_path(file_path, model_path):
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path = os.path.join(file_path, "models", "diffusers", model_path)
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model_path = os.path.normpath(path)
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if sys.platform.startswith('win32'):
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model_path = model_path.replace('\\', "/")
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return model_path
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def setup_seed(seed):
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torch.manual_seed(seed)
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if device == "cuda":
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torch.cuda.manual_seed_all(seed)
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np.random.seed(seed)
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random.seed(seed)
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torch.backends.cudnn.deterministic = True
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def get_image_path_list(folder_name):
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image_basename_list = os.listdir(folder_name)
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image_path_list = sorted(
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[os.path.join(folder_name, basename) for basename in image_basename_list]
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)
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return image_path_list
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def apply_style_positive(style_name: str, positive: str):
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p, n = styles.get(style_name, styles[style_name])
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#print(p, "test0", n)
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return p.replace("{prompt}", positive),n
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def apply_style(style_name: str, positives: list, negative: str = ""):
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p, n = styles.get(style_name, styles[style_name])
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#print(p,"test1",n)
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return [
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p.replace("{prompt}", positive) for positive in positives
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], n + " " + negative
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def load_character_files(character_files: str):
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if character_files == "":
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raise "Please set a character file!"
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character_files_arr = character_files.splitlines()
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primarytext = []
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for character_file_name in character_files_arr:
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character_file = torch.load(
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character_file_name, map_location=torch.device("cpu")
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)
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character_file.eval()
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primarytext.append(character_file["character"] + character_file["description"])
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return array2string(primarytext)
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def array2string(arr):
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stringtmp = ""
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for i, part in enumerate(arr):
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if i != len(arr) - 1:
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stringtmp += part + "\n"
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else:
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stringtmp += part
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return stringtmp
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def find_directories(base_path):
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directories = []
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for root, dirs, files in os.walk(base_path):
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for name in dirs:
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directories.append(name)
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return directories
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base_pt = os.path.join(photomaker_dir,"pt")
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if not os.path.exists(base_pt):
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os.makedirs(base_pt)
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pt_path_list = find_directories(base_pt)
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if pt_path_list:
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character_weights=["none"]+pt_path_list
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else:
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character_weights=["none",]
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def get_scheduler(name):
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scheduler = False
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if name == "Euler":
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scheduler = EulerDiscreteScheduler()
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elif name == "Euler a":
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scheduler = EulerAncestralDiscreteScheduler()
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elif name == "DDIM":
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scheduler = DDIMScheduler()
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elif name == "DDPM":
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scheduler = DDPMScheduler()
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elif name == "DPM++ 2M":
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scheduler = DPMSolverMultistepScheduler()
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elif name == "DPM++ 2M Karras":
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scheduler = DPMSolverMultistepScheduler(use_karras_sigmas=True)
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elif name == "DPM++ 2M SDE":
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scheduler = DPMSolverMultistepScheduler(algorithm_type="sde-dpmsolver++")
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elif name == "DPM++ 2M SDE Karras":
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scheduler = DPMSolverMultistepScheduler(use_karras_sigmas=True, algorithm_type="sde-dpmsolver++")
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elif name == "DPM++ SDE":
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scheduler = DPMSolverSinglestepScheduler()
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elif name == "DPM++ SDE Karras":
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scheduler = DPMSolverSinglestepScheduler(use_karras_sigmas=True)
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elif name == "DPM2":
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scheduler = KDPM2DiscreteScheduler()
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elif name == "DPM2 Karras":
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scheduler = KDPM2DiscreteScheduler(use_karras_sigmas=True)
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elif name == "DPM2 a":
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scheduler = KDPM2AncestralDiscreteScheduler()
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elif name == "DPM2 a Karras":
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scheduler = KDPM2AncestralDiscreteScheduler(use_karras_sigmas=True)
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elif name == "Heun":
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scheduler = HeunDiscreteScheduler()
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elif name == "LCM":
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scheduler = LCMScheduler()
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elif name == "LMS":
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scheduler = LMSDiscreteScheduler()
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elif name == "LMS Karras":
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scheduler = LMSDiscreteScheduler(use_karras_sigmas=True)
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elif name == "UniPC":
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scheduler = UniPCMultistepScheduler()
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return scheduler
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def set_attention_processor(unet, id_length, is_ipadapter=False):
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global attn_procs
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attn_procs = {}
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for name in unet.attn_processors.keys():
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cross_attention_dim = (
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None
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if name.endswith("attn1.processor")
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else unet.config.cross_attention_dim
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)
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if name.startswith("mid_block"):
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hidden_size = unet.config.block_out_channels[-1]
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elif name.startswith("up_blocks"):
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block_id = int(name[len("up_blocks.")])
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hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
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elif name.startswith("down_blocks"):
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block_id = int(name[len("down_blocks.")])
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hidden_size = unet.config.block_out_channels[block_id]
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if cross_attention_dim is None:
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if name.startswith("up_blocks"):
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attn_procs[name] = SpatialAttnProcessor2_0(id_length=id_length)
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else:
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attn_procs[name] = AttnProcessor()
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else:
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if is_ipadapter:
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attn_procs[name] = IPAttnProcessor2_0(
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hidden_size=hidden_size,
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cross_attention_dim=cross_attention_dim,
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scale=1,
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num_tokens=4,
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).to(unet.device, dtype=torch.float16)
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else:
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attn_procs[name] = AttnProcessor()
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unet.set_attn_processor(copy.deepcopy(attn_procs))
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def load_single_character_weights(unet, filepath):
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"""
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从指定文件中加载权重到 attention_processor 类的 id_bank 中。
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参数:
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- model: 包含 attention_processor 类实例的模型。
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- filepath: 权重文件的路径。
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"""
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# 使用torch.load来读取权重
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weights_to_load = torch.load(filepath, map_location=torch.device("cpu"))
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weights_to_load.eval()
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character = weights_to_load["character"]
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description = weights_to_load["description"]
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#print(character)
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for attn_name, attn_processor in unet.attn_processors.items():
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if isinstance(attn_processor, SpatialAttnProcessor2_0):
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# 转移权重到GPU（如果GPU可用的话）并赋值给id_bank
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attn_processor.id_bank[character] = {}
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for step_key in weights_to_load[attn_name].keys():
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attn_processor.id_bank[character][step_key] = [
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tensor.to(unet.device)
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for tensor in weights_to_load[attn_name][step_key]
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]
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print("successsfully,load_single_character_weights")
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def load_character_files_on_running(unet, character_files: str):
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if character_files == "":
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return False
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weights_list = os.listdir(character_files)#获取路径下的权重列表
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#character_files_arr = character_files.splitlines()
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for character_file in weights_list:
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path_cur=os.path.join(character_files,character_file)
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load_single_character_weights(unet, path_cur)
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return True
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def save_single_character_weights(unet, character, description, filepath):
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"""
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保存 attention_processor 类中的 id_bank GPU Tensor 列表到指定文件中。
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参数:
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- model: 包含 attention_processor 类实例的模型。
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- filepath: 权重要保存到的文件路径。
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"""
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weights_to_save = {}
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weights_to_save["description"] = description
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weights_to_save["character"] = character
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for attn_name, attn_processor in unet.attn_processors.items():
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if isinstance(attn_processor, SpatialAttnProcessor2_0):
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# 将每个 Tensor 转到 CPU 并转为列表，以确保它可以被序列化
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#print(attn_name, attn_processor)
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weights_to_save[attn_name] = {}
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for step_key in attn_processor.id_bank[character].keys():
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weights_to_save[attn_name][step_key] = [
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tensor.cpu()
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for tensor in attn_processor.id_bank[character][step_key]
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]
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# 使用torch.save保存权重
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torch.save(weights_to_save, filepath)
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def save_results(unet):
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timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
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weight_folder_name =os.path.join(base_pt,f"{timestamp}")
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#创建文件夹
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if not os.path.exists(weight_folder_name):
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os.makedirs(weight_folder_name)
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global character_dict
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for char in character_dict:
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description = character_dict[char]
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save_single_character_weights(unet,char,description,os.path.join(weight_folder_name, f'{char}.pt'))
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class SpatialAttnProcessor2_0(torch.nn.Module):
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r"""
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Attention processor for IP-Adapater for PyTorch 2.0.
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Args:
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hidden_size (`int`):
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The hidden size of the attention layer.
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cross_attention_dim (`int`):
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The number of channels in the `encoder_hidden_states`.
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text_context_len (`int`, defaults to 77):
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The context length of the text features.
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scale (`float`, defaults to 1.0):
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the weight scale of image prompt.
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"""
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def __init__(
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self,
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hidden_size=None,
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cross_attention_dim=None,
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id_length=4,
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device=device,
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dtype=torch.float16,
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):
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super().__init__()
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if not hasattr(F, "scaled_dot_product_attention"):
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raise ImportError(
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"AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
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)
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self.device = device
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self.dtype = dtype
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self.hidden_size = hidden_size
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self.cross_attention_dim = cross_attention_dim
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||
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, width
|
||
global character_dict
|
||
global character_index_dict, invert_character_index_dict, cur_character, ref_indexs_dict, ref_totals, cur_character
|
||
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,
|
||
width,
|
||
device=self.device,
|
||
dtype=self.dtype,
|
||
)
|
||
if write:
|
||
assert len(cur_character) == 1
|
||
if hidden_states.shape[1] == (height // 32) * (width // 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 // 32) * (width // 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,
|
||
width,
|
||
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, width = hidden_states.shape
|
||
hidden_states = hidden_states.view(
|
||
batch_size, channel, height * width
|
||
).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, width
|
||
)
|
||
|
||
if attn.residual_connection:
|
||
hidden_states = hidden_states + residual
|
||
|
||
hidden_states = hidden_states / attn.rescale_output_factor
|
||
|
||
return hidden_states
|
||
|
||
|
||
def process_generation(
|
||
pipe,
|
||
upload_images,
|
||
model_type,
|
||
_num_steps,
|
||
style_name,
|
||
_Ip_Adapter_Strength,
|
||
_style_strength_ratio,
|
||
guidance_scale,
|
||
seed_,
|
||
id_length,
|
||
general_prompt,
|
||
negative_prompt,
|
||
prompt_array,
|
||
width,
|
||
height,
|
||
load_chars,
|
||
lora,
|
||
trigger_words,photomake_mode,use_kolor,use_flux,auraface
|
||
): # Corrected font_choice usage
|
||
|
||
if len(general_prompt.splitlines()) >= 3:
|
||
raise "Support for more than three characters is temporarily unavailable due to VRAM limitations, but this issue will be resolved soon."
|
||
# _model_type = "Photomaker" if _model_type == "Using Ref Images" else "original"
|
||
|
||
if not use_kolor and not use_flux:
|
||
if model_type == "img2img" and "img" not in general_prompt:
|
||
raise 'Please choice img2img typle,and add the triger word " img " behind the class word you want to customize, such as: man img or woman img'
|
||
|
||
global total_length, attn_procs, cur_model_type
|
||
global write
|
||
global cur_step, attn_count
|
||
|
||
#load_chars = load_character_files_on_running(unet, character_files=char_files)
|
||
|
||
prompts_origin = prompt_array.splitlines()
|
||
prompts = [prompt for prompt in prompts_origin if not len(extract_content_from_brackets(prompt))>=2] # 剔除双角色
|
||
if use_kolor:
|
||
add_trigger_words = "," + trigger_words + " " + "风格" + ";"
|
||
else:
|
||
add_trigger_words = "," + trigger_words + " " + "style" + ";"
|
||
if lora :
|
||
if lora in lora_lightning_list:
|
||
prompts = remove_punctuation_from_strings(prompts)
|
||
else:
|
||
prompts = remove_punctuation_from_strings(prompts)
|
||
prompts = [item + add_trigger_words for item in prompts]
|
||
|
||
global character_index_dict, invert_character_index_dict, ref_indexs_dict, ref_totals
|
||
global character_dict
|
||
|
||
character_dict, character_list = character_to_dict(general_prompt, lora, add_trigger_words)
|
||
#print(character_dict)
|
||
start_merge_step = int(float(_style_strength_ratio) / 100 * _num_steps)
|
||
if start_merge_step > 30:
|
||
start_merge_step = 30
|
||
print(f"start_merge_step:{start_merge_step}")
|
||
generator = torch.Generator(device=device).manual_seed(seed_)
|
||
clipped_prompts = prompts[:]
|
||
nc_indexs = []
|
||
for ind, prompt in enumerate(clipped_prompts):
|
||
if "[NC]" in prompt:
|
||
nc_indexs.append(ind)
|
||
if ind < id_length:
|
||
raise f"The first {id_length} row is id prompts, cannot use [NC]!"
|
||
|
||
prompts = [
|
||
prompt if "[NC]" not in prompt else prompt.replace("[NC]", "")
|
||
for prompt in clipped_prompts
|
||
]
|
||
|
||
if lora:
|
||
if lora in lora_lightning_list:
|
||
prompts = [
|
||
prompt.rpartition("#")[0] if "#" in prompt else prompt for prompt in prompts
|
||
]
|
||
else:
|
||
prompts = [
|
||
prompt.rpartition("#")[0] + add_trigger_words if "#" in prompt else prompt for prompt in prompts
|
||
]
|
||
else:
|
||
prompts = [
|
||
prompt.rpartition("#")[0] if "#" in prompt else prompt for prompt in prompts
|
||
]
|
||
|
||
# id_prompts = prompts[:id_length]
|
||
(
|
||
character_index_dict,
|
||
invert_character_index_dict,
|
||
replace_prompts,
|
||
ref_indexs_dict,
|
||
ref_totals,
|
||
) = process_original_prompt(character_dict, prompts, id_length)
|
||
|
||
if model_type == "img2img":
|
||
# _upload_images = [_upload_images]
|
||
input_id_images_dict = {}
|
||
if len(upload_images) != len(character_dict.keys()):
|
||
raise f"You upload images({len(upload_images)}) is not equal to the number of characters({len(character_dict.keys())})!"
|
||
for ind, img in enumerate(upload_images):
|
||
input_id_images_dict[character_list[ind]] = [img] # 已经pil转化了 不用load
|
||
# input_id_images_dict[character_list[ind]] = [load_image(img)]
|
||
# real_prompts = prompts[id_length:]
|
||
# if device == "cuda":
|
||
# torch.cuda.empty_cache()
|
||
write = True
|
||
cur_step = 0
|
||
attn_count = 0
|
||
#id_prompts_no_using, negative_prompt = apply_style(style_name, ["id_prompts"], negative_prompt)
|
||
#setup_seed(seed_)
|
||
total_results = []
|
||
id_images = []
|
||
results_dict = {}
|
||
p_num=0
|
||
if photomake_mode == "v2":
|
||
from .utils.insightface_package import FaceAnalysis2, analyze_faces
|
||
if auraface:
|
||
from huggingface_hub import snapshot_download
|
||
snapshot_download(
|
||
"fal/AuraFace-v1",
|
||
local_dir="models/auraface",
|
||
)
|
||
face_detector = FaceAnalysis2(name="auraface",providers=["CUDAExecutionProvider", "CPUExecutionProvider"],root=".",
|
||
allowed_modules=['detection', 'recognition'])
|
||
else:
|
||
face_detector = FaceAnalysis2(providers=['CUDAExecutionProvider'],
|
||
allowed_modules=['detection', 'recognition'])
|
||
face_detector.prepare(ctx_id=0, det_size=(640, 640))
|
||
|
||
global cur_character
|
||
if not load_chars:
|
||
for character_key in character_dict.keys():# 先生成角色对应第一句场景提示词的图片
|
||
cur_character = [character_key]
|
||
ref_indexs = ref_indexs_dict[character_key]
|
||
current_prompts = [replace_prompts[ref_ind] for ref_ind in ref_indexs]
|
||
setup_seed(seed_)
|
||
generator = torch.Generator(device=device).manual_seed(seed_)
|
||
cur_step = 0
|
||
cur_positive_prompts, cur_negative_prompt = apply_style(
|
||
style_name, current_prompts, negative_prompt
|
||
)
|
||
if use_kolor:
|
||
cur_negative_prompt=[cur_negative_prompt]
|
||
if model_type == "txt2img":
|
||
if use_flux:
|
||
id_images = pipe(
|
||
prompt=cur_positive_prompts,
|
||
num_inference_steps=_num_steps,
|
||
guidance_scale=guidance_scale,
|
||
output_type="pil",
|
||
max_sequence_length=256,
|
||
height=height,
|
||
width=width,
|
||
generator=torch.Generator("cpu").manual_seed(0)
|
||
).images
|
||
else:
|
||
if use_kolor:
|
||
cur_negative_prompt = cur_negative_prompt * len(cur_positive_prompts) if len( cur_negative_prompt) != len(
|
||
cur_positive_prompts) else cur_negative_prompt
|
||
id_images = pipe(
|
||
cur_positive_prompts,
|
||
num_inference_steps=_num_steps,
|
||
guidance_scale=guidance_scale,
|
||
height=height,
|
||
width=width,
|
||
negative_prompt= cur_negative_prompt,
|
||
generator=generator
|
||
).images
|
||
|
||
elif model_type == "img2img":
|
||
if use_kolor:
|
||
pipe.set_ip_adapter_scale([_Ip_Adapter_Strength])
|
||
cur_negative_prompt= cur_negative_prompt*len(cur_positive_prompts) if len( cur_negative_prompt)!=len(cur_positive_prompts) else cur_negative_prompt
|
||
id_images = pipe(
|
||
prompt=cur_positive_prompts,
|
||
ip_adapter_image=input_id_images_dict[character_key],
|
||
negative_prompt= cur_negative_prompt,
|
||
num_inference_steps=_num_steps,
|
||
height=height,
|
||
width=width,
|
||
guidance_scale=guidance_scale,
|
||
num_images_per_prompt=1,
|
||
generator=generator,
|
||
).images
|
||
elif use_flux:
|
||
id_images = pipe(
|
||
prompt=cur_positive_prompts,
|
||
latents=None,
|
||
num_inference_steps=_num_steps,
|
||
height=height,
|
||
width=width,
|
||
output_type="pil",
|
||
max_sequence_length=256,
|
||
guidance_scale=guidance_scale,
|
||
generator=torch.Generator("cpu").manual_seed(0),
|
||
).images
|
||
else:
|
||
if photomake_mode == "v2":
|
||
# 提取id
|
||
# print("v2 mode load_chars", cur_positive_prompts, negative_prompt, character_key)
|
||
img = input_id_images_dict[character_key][
|
||
0] # input_id_images_dict {'[Taylor]': [pil], '[Lecun]': [pil]}
|
||
img = np.array(img)
|
||
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
|
||
|
||
faces = analyze_faces(face_detector, img, )
|
||
id_embed_list = [torch.from_numpy((faces[0]['embedding']))]
|
||
id_embeds = torch.stack(id_embed_list)
|
||
|
||
id_images = pipe(
|
||
cur_positive_prompts,
|
||
input_id_images=input_id_images_dict[character_key],
|
||
num_inference_steps=_num_steps,
|
||
guidance_scale=guidance_scale,
|
||
start_merge_step=start_merge_step,
|
||
height=height,
|
||
width=width,
|
||
negative_prompt= cur_negative_prompt,
|
||
id_embeds=id_embeds,
|
||
generator=generator
|
||
).images
|
||
else:
|
||
# print("v1 mode,load_chars", cur_positive_prompts, negative_prompt,character_key )
|
||
id_images = pipe(
|
||
cur_positive_prompts,
|
||
input_id_images=input_id_images_dict[character_key],
|
||
num_inference_steps=_num_steps,
|
||
guidance_scale=guidance_scale,
|
||
start_merge_step=start_merge_step,
|
||
height=height,
|
||
width=width,
|
||
negative_prompt= cur_negative_prompt,
|
||
generator=generator
|
||
).images
|
||
else:
|
||
raise NotImplementedError(
|
||
"You should choice between original and Photomaker!",
|
||
f"But you choice {model_type}",
|
||
)
|
||
p_num+=1
|
||
# total_results = id_images + total_results
|
||
# yield total_results
|
||
for ind, img in enumerate(id_images):
|
||
results_dict[ref_indexs[ind]] = img
|
||
# real_images = []
|
||
yield [results_dict[ind] for ind in results_dict.keys()]
|
||
write = False
|
||
if not load_chars:
|
||
real_prompts_inds = [
|
||
ind for ind in range(len(prompts)) if ind not in ref_totals
|
||
]
|
||
else:
|
||
real_prompts_inds = [ind for ind in range(len(prompts))]
|
||
|
||
#print(real_prompts_inds)
|
||
real_prompt_no, negative_prompt_style = apply_style_positive(style_name, "real_prompt")
|
||
negative_prompt=str(negative_prompt)+str(negative_prompt_style)
|
||
|
||
for real_prompts_ind in real_prompts_inds: # 非角色流程
|
||
real_prompt = replace_prompts[real_prompts_ind]
|
||
cur_character = get_ref_character(prompts[real_prompts_ind], character_dict)
|
||
#print(cur_character)
|
||
setup_seed(seed_)
|
||
if len(cur_character) > 1 and model_type == "img2img":
|
||
raise "Temporarily Not Support Multiple character in Ref Image Mode!"
|
||
generator = torch.Generator(device=device).manual_seed(seed_)
|
||
cur_step = 0
|
||
real_prompt ,negative_prompt_style_no= apply_style_positive(style_name, real_prompt)
|
||
#print(real_prompt)
|
||
if model_type == "txt2img":
|
||
# print(results_dict,real_prompts_ind)
|
||
if use_flux:
|
||
results_dict[real_prompts_ind] = pipe(
|
||
prompt= real_prompt,
|
||
num_inference_steps=_num_steps,
|
||
guidance_scale=guidance_scale,
|
||
output_type="pil",
|
||
max_sequence_length=256,
|
||
height=height,
|
||
width=width,
|
||
generator=torch.Generator("cpu").manual_seed(seed_)
|
||
).images[0]
|
||
else:
|
||
results_dict[real_prompts_ind] = pipe(
|
||
real_prompt,
|
||
num_inference_steps=_num_steps,
|
||
guidance_scale=guidance_scale,
|
||
height=height,
|
||
width=width,
|
||
negative_prompt=negative_prompt,
|
||
generator=generator,
|
||
).images[0]
|
||
|
||
elif model_type == "img2img":
|
||
if use_kolor:
|
||
empty_img=Image.new('RGB', (height, width), (255, 255, 255))
|
||
results_dict[real_prompts_ind] = pipe(
|
||
prompt = real_prompt,
|
||
ip_adapter_image = [(
|
||
input_id_images_dict[cur_character[0]]
|
||
if real_prompts_ind not in nc_indexs
|
||
else empty_img
|
||
),],
|
||
negative_prompt = negative_prompt,
|
||
height = height,
|
||
width = width,
|
||
num_inference_steps = _num_steps,
|
||
guidance_scale = guidance_scale,
|
||
num_images_per_prompt = 1,
|
||
generator = generator,
|
||
nc_flag=True if real_prompts_ind in nc_indexs else False, # nc_flag，用索引标记，主要控制非角色人物的生成，默认false
|
||
).images[0]
|
||
elif use_flux:
|
||
results_dict[real_prompts_ind]=pipe(
|
||
prompt=real_prompt,
|
||
latents=None,
|
||
num_inference_steps=_num_steps,
|
||
height=height,
|
||
width=width,
|
||
output_type="pil",
|
||
max_sequence_length=256,
|
||
guidance_scale=guidance_scale,
|
||
generator=torch.Generator("cpu").manual_seed(seed_),
|
||
).images[0]
|
||
else:
|
||
if photomake_mode == "v2":
|
||
# V2版本必须要有id_embeds，只能用input_id_images作为风格参考
|
||
img = (
|
||
input_id_images_dict[cur_character[0]]
|
||
if real_prompts_ind not in nc_indexs
|
||
else input_id_images_dict[character_list[0]]
|
||
),
|
||
# print(img,type(img[0][0]))
|
||
|
||
img = np.array(img[0][0])
|
||
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
|
||
faces = analyze_faces(face_detector, img, )
|
||
nc_flag = True if real_prompts_ind in nc_indexs else False # 有nc为ture，无则false
|
||
if not nc_flag:
|
||
id_embed_list = [torch.from_numpy((faces[0]['embedding']))]
|
||
else:
|
||
id_embed_list = [torch.zeros_like(torch.from_numpy((faces[0]['embedding'])))]
|
||
id_embeds = torch.stack(id_embed_list)
|
||
|
||
# print(real_prompts_ind, real_prompt,"v2 mode")
|
||
|
||
results_dict[real_prompts_ind] = pipe(
|
||
real_prompt,
|
||
input_id_images=(
|
||
input_id_images_dict[cur_character[0]]
|
||
if real_prompts_ind not in nc_indexs
|
||
else input_id_images_dict[character_list[0]]
|
||
),
|
||
num_inference_steps=_num_steps,
|
||
guidance_scale=guidance_scale,
|
||
start_merge_step=start_merge_step,
|
||
height=height,
|
||
width=width,
|
||
negative_prompt=negative_prompt,
|
||
generator=generator,
|
||
id_embeds=id_embeds,
|
||
nc_flag=True if real_prompts_ind in nc_indexs else False,
|
||
).images[0]
|
||
else:
|
||
#print(real_prompts_ind, real_prompt, "v1 mode", )
|
||
results_dict[real_prompts_ind] = pipe(
|
||
real_prompt,
|
||
input_id_images=(
|
||
input_id_images_dict[cur_character[0]]
|
||
if real_prompts_ind not in nc_indexs
|
||
else input_id_images_dict[character_list[0]]
|
||
),
|
||
num_inference_steps=_num_steps,
|
||
guidance_scale=guidance_scale,
|
||
start_merge_step=start_merge_step,
|
||
height=height,
|
||
width=width,
|
||
negative_prompt=negative_prompt,
|
||
generator=generator,
|
||
nc_flag=True if real_prompts_ind in nc_indexs else False, # nc_flag，用索引标记，主要控制非角色人物的生成，默认false
|
||
).images[0]
|
||
else:
|
||
raise NotImplementedError(
|
||
"You should choice between original and Photomaker!",
|
||
f"But you choice {model_type}",
|
||
)
|
||
yield [results_dict[ind] for ind in results_dict.keys()]
|
||
total_results = [results_dict[ind] for ind in range(len(prompts))]
|
||
torch.cuda.empty_cache()
|
||
yield total_results
|
||
|
||
def nomarl_tensor_upscale(tensor, width, height):
|
||
samples = tensor.movedim(-1, 1)
|
||
samples = common_upscale(samples, width, height, "nearest-exact", "center")
|
||
samples = samples.movedim(1, -1)
|
||
return samples
|
||
|
||
def nomarl_upscale(img, width, height):
|
||
samples = img.movedim(-1, 1)
|
||
img = common_upscale(samples, width, height, "nearest-exact", "center")
|
||
samples = img.movedim(1, -1)
|
||
img = tensor_to_image(samples)
|
||
return img
|
||
def nomarl_upscale_tensor(img, width, height):
|
||
samples = img.movedim(-1, 1)
|
||
img = common_upscale(samples, width, height, "nearest-exact", "center")
|
||
samples = img.movedim(1, -1)
|
||
return samples
|
||
|
||
def get_phrases_idx(tokenizer, phrases, prompt):
|
||
res = []
|
||
phrase_cnt = {}
|
||
for phrase in phrases:
|
||
if phrase in phrase_cnt:
|
||
cur_cnt = phrase_cnt[phrase]
|
||
phrase_cnt[phrase] += 1
|
||
else:
|
||
cur_cnt = 0
|
||
phrase_cnt[phrase] = 1
|
||
res.append(get_phrase_idx(tokenizer, phrase, prompt, num=cur_cnt)[0])
|
||
return res
|
||
|
||
|
||
def main_normal(prompt,pipe,phrases,ms_model,input_images,num_samples,steps,seed,negative_prompt,scale,image_encoder,cfg,image_processor,
|
||
boxes,mask_threshold,start_step,image_proj_type,image_encoder_type,drop_grounding_tokens,height,width,phrase_idxes, eot_idxes,in_img,use_repo):
|
||
if use_repo:
|
||
in_img = None
|
||
images = ms_model.generate(pipe=pipe, pil_images=[input_images],processed_images=in_img, num_samples=num_samples,
|
||
num_inference_steps=steps,
|
||
seed=seed,
|
||
prompt=[prompt], negative_prompt=negative_prompt, scale=scale,
|
||
image_encoder=image_encoder, guidance_scale=cfg,
|
||
image_processor=image_processor, boxes=boxes,
|
||
mask_threshold=mask_threshold,
|
||
start_step=start_step,
|
||
image_proj_type=image_proj_type,
|
||
image_encoder_type=image_encoder_type,
|
||
phrases=phrases,
|
||
drop_grounding_tokens=drop_grounding_tokens,
|
||
phrase_idxes=phrase_idxes, eot_idxes=eot_idxes, height=height,
|
||
width=width)
|
||
return images
|
||
def main_control(prompt,width,height,pipe,phrases,ms_model,input_images,num_samples,steps,seed,negative_prompt,scale,image_encoder,cfg,
|
||
image_processor,boxes,mask_threshold,start_step,image_proj_type,image_encoder_type,drop_grounding_tokens,controlnet_scale,control_image,phrase_idxes, eot_idxes,in_img,use_repo):
|
||
if use_repo:
|
||
in_img=None
|
||
images = ms_model.generate(pipe=pipe, pil_images=[input_images],processed_images=in_img, num_samples=num_samples,
|
||
num_inference_steps=steps,
|
||
seed=seed,
|
||
prompt=[prompt], negative_prompt=negative_prompt, scale=scale,
|
||
image_encoder=image_encoder, guidance_scale=cfg,
|
||
image_processor=image_processor, boxes=boxes,
|
||
mask_threshold=mask_threshold,
|
||
start_step=start_step,
|
||
image_proj_type=image_proj_type,
|
||
image_encoder_type=image_encoder_type,
|
||
phrases=phrases,
|
||
drop_grounding_tokens=drop_grounding_tokens,
|
||
phrase_idxes=phrase_idxes, eot_idxes=eot_idxes, height=height,
|
||
width=width,
|
||
image=control_image, controlnet_conditioning_scale=controlnet_scale)
|
||
|
||
return images
|
||
|
||
def get_float(str_in):
|
||
list_str=str_in.split(",")
|
||
float_box=[float(x) for x in list_str]
|
||
return float_box
|
||
|
||
def msdiffusion_main(pipe, image_1, image_2, prompts_dual, width, height, steps, seed, style_name,char_describe,char_origin,negative_prompt,
|
||
clip_vision, _model_type, lora, lora_path, lora_scale, trigger_words, ckpt_path,dif_repo,
|
||
role_scale, mask_threshold, start_step,controlnet_model_path,control_image,controlnet_scale,cfg,guidance_list,scheduler_choice):
|
||
from comfy.model_management import cleanup_models
|
||
from comfy.clip_vision import load
|
||
tensor_a = phi2narry(image_1.copy())
|
||
tensor_b = phi2narry(image_2.copy())
|
||
in_img = torch.cat((tensor_a, tensor_b), dim=0)
|
||
|
||
original_config_file = os.path.join(dir_path, 'config', 'sd_xl_base.yaml')
|
||
if dif_repo:
|
||
single_files = False
|
||
elif not dif_repo and ckpt_path:
|
||
single_files = True
|
||
elif dif_repo and ckpt_path:
|
||
single_files = False
|
||
else:
|
||
raise "no model"
|
||
del pipe
|
||
cleanup_models(keep_clone_weights_loaded=False)
|
||
gc.collect()
|
||
torch.cuda.empty_cache()
|
||
if single_files:
|
||
try:
|
||
pipe = StableDiffusionXLPipeline.from_single_file(
|
||
ckpt_path, original_config=original_config_file,
|
||
torch_dtype=torch.float16)
|
||
except:
|
||
try:
|
||
pipe = StableDiffusionXLPipeline.from_single_file(
|
||
ckpt_path, original_config_file=original_config_file,
|
||
torch_dtype=torch.float16)
|
||
except:
|
||
raise "load pipe error!,check you diffusers"
|
||
else:
|
||
pipe = StableDiffusionXLPipeline.from_pretrained(dif_repo, torch_dtype=torch.float16)
|
||
|
||
if controlnet_model_path != "none":
|
||
controlnet_model_path = folder_paths.get_full_path("controlnet", controlnet_model_path)
|
||
controlnet = ControlNetModel.from_unet(pipe.unet)
|
||
cn_state_dict = load_file(controlnet_model_path, device="cpu")
|
||
controlnet.load_state_dict(cn_state_dict, strict=False)
|
||
controlnet.to(torch.float16)
|
||
try:
|
||
pipe = StableDiffusionXLControlNetPipeline.from_single_file(ckpt_path,unet=pipe.unet,vae=pipe.vae, text_encoder=pipe.text_encoder,
|
||
controlnet=controlnet,
|
||
original_config=original_config_file,
|
||
torch_dtype=torch.float16)
|
||
except:
|
||
try:
|
||
pipe = StableDiffusionXLControlNetPipeline.from_single_file(ckpt_path,unet=pipe.unet,vae=pipe.vae, text_encoder=pipe.text_encoder,
|
||
controlnet=controlnet,
|
||
original_config_file=original_config_file,
|
||
torch_dtype=torch.float16)
|
||
except:
|
||
raise "load pipe error!,check you diffusers"
|
||
if lora:
|
||
if lora in lora_lightning_list:
|
||
pipe.load_lora_weights(lora_path)
|
||
pipe.fuse_lora()
|
||
else:
|
||
pipe.load_lora_weights(lora_path, adapter_name=trigger_words)
|
||
pipe.fuse_lora(adapter_names=[trigger_words, ], lora_scale=lora_scale)
|
||
pipe.scheduler = scheduler_choice.from_config(pipe.scheduler.config)
|
||
pipe.enable_xformers_memory_efficient_attention()
|
||
pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
|
||
pipe.enable_vae_slicing()
|
||
|
||
if device != "mps":
|
||
pipe.enable_model_cpu_offload()
|
||
torch.cuda.empty_cache()
|
||
# 预加载 ms
|
||
photomaker_local_path = os.path.join(photomaker_dir, "ms_adapter.bin")
|
||
if not os.path.exists(photomaker_local_path):
|
||
ms_path = hf_hub_download(
|
||
repo_id="doge1516/MS-Diffusion",
|
||
filename="ms_adapter.bin",
|
||
repo_type="model",
|
||
local_dir=photomaker_dir,
|
||
)
|
||
else:
|
||
ms_path = photomaker_local_path
|
||
ms_ckpt = get_instance_path(ms_path)
|
||
image_processor = CLIPImageProcessor()
|
||
image_encoder_type = "clip"
|
||
cleanup_models(keep_clone_weights_loaded=False)
|
||
image_encoder=load(clip_vision)
|
||
use_repo = False
|
||
config_path = os.path.join(dir_path, "config", "config.json")
|
||
image_encoder_config = OmegaConf.load(config_path)
|
||
image_encoder_projection_dim = image_encoder_config["vision_config"]["projection_dim"]
|
||
num_tokens = 16
|
||
image_proj_type = "resampler"
|
||
latent_init_mode = "grounding"
|
||
# latent_init_mode = "random"
|
||
image_proj_model = Resampler(
|
||
dim=1280,
|
||
depth=4,
|
||
dim_head=64,
|
||
heads=20,
|
||
num_queries=num_tokens,
|
||
embedding_dim=image_encoder_config["vision_config"]["hidden_size"],
|
||
output_dim=pipe.unet.config.cross_attention_dim,
|
||
ff_mult=4,
|
||
latent_init_mode=latent_init_mode,
|
||
phrase_embeddings_dim=pipe.text_encoder.config.projection_dim,
|
||
).to(device,dtype=torch.float16)
|
||
ms_model = MSAdapter(pipe.unet, image_proj_model, ckpt_path=ms_ckpt, device=device, num_tokens=num_tokens)
|
||
ms_model.to(device,dtype=torch.float16)
|
||
torch.cuda.empty_cache()
|
||
input_images = [image_1, image_2]
|
||
batch_size = 1
|
||
guidance_list = guidance_list.strip().split(";")
|
||
box_add = [] # 获取预设box
|
||
for i in range(len(guidance_list)):
|
||
box_add.append(get_float(guidance_list[i]))
|
||
|
||
if mask_threshold == 0.:
|
||
mask_threshold = None
|
||
|
||
image_ouput = []
|
||
|
||
# get role name
|
||
role_a = char_origin[0].replace("]", "").replace("[", "")
|
||
role_b = char_origin[1].replace("]", "").replace("[", "")
|
||
# get n p prompt
|
||
prompts_dual, negative_prompt = apply_style(
|
||
style_name, prompts_dual, negative_prompt
|
||
)
|
||
|
||
# 添加Lora trigger
|
||
add_trigger_words = "," + trigger_words + " " + "style" + ";"
|
||
if lora:
|
||
prompts_dual = remove_punctuation_from_strings(prompts_dual)
|
||
if lora not in lora_lightning_list: # 加速lora不需要trigger
|
||
prompts_dual = [item + add_trigger_words for item in prompts_dual]
|
||
|
||
|
||
prompts_dual = [item.replace(char_origin[0],char_describe[0]) for item in prompts_dual if char_origin[0] in item ]
|
||
prompts_dual = [item.replace(char_origin[1], char_describe[1]) for item in prompts_dual if char_origin[1] in item]
|
||
|
||
prompts_dual = [item.replace("[", " ", ).replace("]", " ", ) for item in prompts_dual]
|
||
#print(prompts_dual)
|
||
torch.cuda.empty_cache()
|
||
|
||
phrases = [[role_a, role_b]]
|
||
drop_grounding_tokens = [0] # set to 1 if you want to drop the grounding tokens
|
||
|
||
if mask_threshold:
|
||
boxes = [box_add[:2]]
|
||
print(f"Roles position on {boxes}")
|
||
# boxes = [[[0., 0.25, 0.4, 0.75], [0.6, 0.25, 1., 0.75]]] # man+women
|
||
else:
|
||
zero_list = [0 for _ in range(4)]
|
||
boxes = [zero_list for _ in range(2)]
|
||
boxes = [boxes] # used if you want no layout guidance
|
||
# print(boxes)
|
||
|
||
if controlnet_model_path != "none":
|
||
d1, _, _, _ = control_image.size()
|
||
if d1 == 1:
|
||
control_img_list = [control_image]
|
||
else:
|
||
control_img_list = torch.chunk(control_image, chunks=d1)
|
||
j = 0
|
||
for i, prompt in enumerate(prompts_dual):
|
||
control_image = control_img_list[j]
|
||
control_image = nomarl_upscale(control_image, width, height)
|
||
j += 1
|
||
# used to get the attention map, return zero if the phrase is not in the prompt
|
||
phrase_idxes = [get_phrases_idx(pipe.tokenizer, phrases[0], prompt)]
|
||
eot_idxes = [[get_eot_idx(pipe.tokenizer, prompt)] * len(phrases[0])]
|
||
# print(phrase_idxes, eot_idxes)
|
||
image_main = main_control(prompt, width, height, pipe, phrases, ms_model, input_images, batch_size,
|
||
steps,
|
||
seed, negative_prompt, role_scale, image_encoder, cfg,
|
||
image_processor, boxes, mask_threshold, start_step, image_proj_type,
|
||
image_encoder_type, drop_grounding_tokens, controlnet_scale, control_image,
|
||
phrase_idxes, eot_idxes,in_img,use_repo)
|
||
|
||
image_ouput += image_main
|
||
torch.cuda.empty_cache()
|
||
else:
|
||
for i, prompt in enumerate(prompts_dual):
|
||
# used to get the attention map, return zero if the phrase is not in the prompt
|
||
phrase_idxes = [get_phrases_idx(pipe.tokenizer, phrases[0], prompt)]
|
||
eot_idxes = [[get_eot_idx(pipe.tokenizer, prompt)] * len(phrases[0])]
|
||
# print(phrase_idxes, eot_idxes)
|
||
image_main = main_normal(prompt, pipe, phrases, ms_model, input_images, batch_size, steps, seed,
|
||
negative_prompt, role_scale, image_encoder, cfg, image_processor,
|
||
boxes, mask_threshold, start_step, image_proj_type, image_encoder_type,
|
||
drop_grounding_tokens, height, width, phrase_idxes, eot_idxes,in_img,use_repo)
|
||
image_ouput += image_main
|
||
torch.cuda.empty_cache()
|
||
torch.cuda.empty_cache()
|
||
return image_ouput
|
||
|
||
class Storydiffusion_Model_Loader:
|
||
def __init__(self):
|
||
pass
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"repo_id": ("STRING", {"default": ""}),
|
||
"ckpt_name": (["none"]+folder_paths.get_filename_list("checkpoints"),),
|
||
"vae_id":(["none"]+folder_paths.get_filename_list("vae"),),
|
||
"character_weights": (character_weights,),
|
||
"lora": (["none"] + folder_paths.get_filename_list("loras"),),
|
||
"lora_scale": ("FLOAT", {"default": 0.8, "min": 0.1, "max": 1.0, "step": 0.1}),
|
||
"trigger_words": ("STRING", {"default": "best quality"}),
|
||
"scheduler": (scheduler_list,),
|
||
"model_type": (["img2img", "txt2img"],),
|
||
"id_number": ("INT", {"default": 2, "min": 1, "max": 2, "step": 1, "display": "number"}),
|
||
"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}),
|
||
"img_width": ("INT", {"default": 768, "min": 256, "max": 2048, "step": 32, "display": "number"}),
|
||
"img_height": ("INT", {"default": 768, "min": 256, "max": 2048, "step": 32, "display": "number"}),
|
||
"photomake_mode": (["v1", "v2"],),
|
||
"easy_function":("STRING", {"default": ""}),
|
||
}
|
||
}
|
||
|
||
|
||
RETURN_TYPES = ("STORY_DICT", )
|
||
RETURN_NAMES = ("model", )
|
||
FUNCTION = "story_model_loader"
|
||
CATEGORY = "Storydiffusion"
|
||
|
||
def instance_path(self,path, repo):
|
||
if repo == "":
|
||
if path == "none":
|
||
repo = "none"
|
||
else:
|
||
model_path = get_local_path(file_path, path)
|
||
repo = get_instance_path(model_path)
|
||
return repo
|
||
def story_model_loader(self,repo_id, ckpt_name,vae_id, character_weights, lora, lora_scale, trigger_words, scheduler,
|
||
model_type, id_number, sa32_degree, sa64_degree, img_width, img_height,photomake_mode,easy_function):
|
||
|
||
scheduler_choice = get_scheduler(scheduler)
|
||
if ckpt_name=="none":
|
||
ckpt_path = None
|
||
else:
|
||
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
|
||
|
||
if character_weights!="none":
|
||
character_weights_path = get_instance_path(os.path.join(base_pt, character_weights))
|
||
weights_list = os.listdir(character_weights_path)
|
||
if weights_list:
|
||
char_files=character_weights_path
|
||
else:
|
||
char_files=""
|
||
else:
|
||
char_files = ""
|
||
|
||
easy_function=easy_function.strip().lower()
|
||
|
||
if easy_function=="auraface":
|
||
auraface=True
|
||
else:
|
||
auraface = False
|
||
|
||
if easy_function=="nf4":
|
||
NF4=True
|
||
else:
|
||
NF4 = False
|
||
|
||
|
||
|
||
|
||
photomaker_path = os.path.join(photomaker_dir, f"photomaker-{photomake_mode}.bin")
|
||
if photomake_mode=="v1":
|
||
if not os.path.exists(photomaker_path):
|
||
photomaker_path = hf_hub_download(
|
||
repo_id="TencentARC/PhotoMaker",
|
||
filename="photomaker-v1.bin",
|
||
local_dir=photomaker_dir,
|
||
)
|
||
else:
|
||
if not os.path.exists(photomaker_path):
|
||
photomaker_path = hf_hub_download(
|
||
repo_id="TencentARC/PhotoMaker-V2",
|
||
filename="photomaker-v2.bin",
|
||
local_dir=photomaker_dir,
|
||
)
|
||
|
||
if lora != "none":
|
||
lora_path = folder_paths.get_full_path("loras", lora)
|
||
lora_path = get_instance_path(lora_path)
|
||
if "/" in lora:
|
||
lora = lora.split("/")[-1]
|
||
if "\\" in lora:
|
||
lora = lora.split("\\")[-1]
|
||
else:
|
||
lora_path = None
|
||
lora=None
|
||
|
||
# global
|
||
global attn_procs
|
||
global sa32, sa64, write, height, width
|
||
global attn_count, total_count, id_length, total_length, cur_step
|
||
|
||
sa32 = sa32_degree
|
||
sa64 = sa64_degree
|
||
attn_count = 0
|
||
total_count = 0
|
||
cur_step = 0
|
||
id_length = id_number
|
||
total_length = 5
|
||
attn_procs = {}
|
||
write = False
|
||
height = img_height
|
||
width = img_width
|
||
|
||
# load model
|
||
use_kolor = False
|
||
use_flux = False
|
||
if repo_id:
|
||
if repo_id.rsplit("/")[-1] in "Kwai-Kolors/Kolors":
|
||
use_kolor=True
|
||
if repo_id.rsplit("/")[-1] in "black-forest-labs/FLUX.1-dev,black-forest-labs/FLUX.1-schnell":
|
||
use_flux = True
|
||
|
||
if not repo_id and not ckpt_path:
|
||
raise "you need choice a model or repo_id"
|
||
elif not repo_id and ckpt_path: # load ckpt
|
||
pipe = load_models(ckpt_path, model_type=model_type,single_files=True,use_safetensors=True, photomake_mode=photomake_mode,photomaker_path=photomaker_path, lora=lora,
|
||
lora_path=lora_path,
|
||
trigger_words=trigger_words, lora_scale=lora_scale)
|
||
set_attention_processor(pipe.unet, id_length, is_ipadapter=False)
|
||
else: #if repo or no ckpt,choice repo
|
||
if repo_id.rsplit("/")[-1]=="playground-v2.5-1024px-aesthetic":
|
||
pipe = DiffusionPipeline.from_pretrained(
|
||
repo_id,
|
||
torch_dtype=torch.float16,
|
||
)
|
||
set_attention_processor(pipe.unet, id_length, is_ipadapter=False)
|
||
elif repo_id.rsplit("/")[-1]=="sdxl-unstable-diffusers-y":
|
||
pipe = StableDiffusionXLPipeline.from_pretrained(
|
||
repo_id, torch_dtype=torch.float16,use_safetensors=False
|
||
)
|
||
set_attention_processor(pipe.unet, id_length, is_ipadapter=False)
|
||
elif use_kolor:
|
||
from .kolors.pipelines.pipeline_stable_diffusion_xl_chatglm_256 import \
|
||
StableDiffusionXLPipeline as StableDiffusionXLPipelineKolors
|
||
from .kolors.models.modeling_chatglm import ChatGLMModel
|
||
from .kolors.models.tokenization_chatglm import ChatGLMTokenizer
|
||
from .kolors.models.unet_2d_condition import UNet2DConditionModel as UNet2DConditionModelkolor
|
||
from .kolors.pipelines.pipeline_stable_diffusion_xl_chatglm_256_ipadapter import \
|
||
StableDiffusionXLPipeline as StableDiffusionXLPipelinekoloripadapter
|
||
text_encoder = ChatGLMModel.from_pretrained(
|
||
f'{repo_id}/text_encoder',torch_dtype=torch.float16).half()
|
||
vae = AutoencoderKL.from_pretrained(f"{repo_id}/vae", revision=None).half()
|
||
tokenizer = ChatGLMTokenizer.from_pretrained(f'{repo_id}/text_encoder')
|
||
scheduler = EulerDiscreteScheduler.from_pretrained(f"{repo_id}/scheduler")
|
||
if model_type=="txt2img":
|
||
unet = UNet2DConditionModel.from_pretrained(f"{repo_id}/unet", revision=None,
|
||
use_safetensors=True).half()
|
||
pipe = StableDiffusionXLPipelineKolors(
|
||
vae=vae,
|
||
text_encoder=text_encoder,
|
||
tokenizer=tokenizer,
|
||
unet=unet,
|
||
scheduler=scheduler,
|
||
force_zeros_for_empty_prompt=False, )
|
||
set_attention_processor(pipe.unet, id_length, is_ipadapter=False)
|
||
else:
|
||
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
|
||
f'{repo_id}/Kolors-IP-Adapter-Plus/image_encoder', ignore_mismatched_sizes=True).to(dtype=torch.float16)
|
||
ip_img_size = 336
|
||
clip_image_processor = CLIPImageProcessor(size=ip_img_size, crop_size=ip_img_size)
|
||
unet = UNet2DConditionModelkolor.from_pretrained(f"{repo_id}/unet", revision=None,).half()
|
||
pipe=StableDiffusionXLPipelinekoloripadapter(
|
||
vae=vae,
|
||
text_encoder=text_encoder,
|
||
tokenizer=tokenizer,
|
||
unet=unet,
|
||
scheduler=scheduler,
|
||
image_encoder=image_encoder,
|
||
feature_extractor=clip_image_processor,
|
||
force_zeros_for_empty_prompt=False
|
||
)
|
||
if hasattr(pipe.unet, 'encoder_hid_proj'):
|
||
pipe.unet.text_encoder_hid_proj = pipe.unet.encoder_hid_proj
|
||
|
||
pipe.load_ip_adapter(f'{repo_id}/Kolors-IP-Adapter-Plus', subfolder="",
|
||
weight_name=["ip_adapter_plus_general.bin"])
|
||
pipe.enable_model_cpu_offload()
|
||
elif use_flux:
|
||
# pip install optimum-quanto
|
||
# https://gist.github.com/AmericanPresidentJimmyCarter/873985638e1f3541ba8b00137e7dacd9
|
||
timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
|
||
weight_transformer = os.path.join(folder_paths.models_dir,"checkpoints",f"transformer_{timestamp}.pt")
|
||
dtype = torch.bfloat16
|
||
if not ckpt_path:
|
||
from optimum.quanto import freeze, qfloat8, quantize
|
||
from diffusers.pipelines.flux.pipeline_flux import FluxPipeline
|
||
from diffusers import FlowMatchEulerDiscreteScheduler
|
||
from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel
|
||
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
|
||
revision = "refs/pr/1"
|
||
scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler",
|
||
revision=revision)
|
||
text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype)
|
||
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype)
|
||
text_encoder_2 = T5EncoderModel.from_pretrained(repo_id, subfolder="text_encoder_2",
|
||
torch_dtype=dtype,
|
||
revision=revision)
|
||
tokenizer_2 = T5TokenizerFast.from_pretrained(repo_id, subfolder="tokenizer_2",
|
||
torch_dtype=dtype,
|
||
revision=revision)
|
||
vae = AutoencoderKL.from_pretrained(repo_id, subfolder="vae", torch_dtype=dtype,
|
||
revision=revision)
|
||
transformer = FluxTransformer2DModel.from_pretrained(repo_id, subfolder="transformer",
|
||
torch_dtype=dtype, revision=revision)
|
||
quantize(transformer, weights=qfloat8)
|
||
freeze(transformer)
|
||
print(f"saving fp8 pt on '{weight_transformer}'")
|
||
torch.save(transformer,
|
||
weight_transformer) # https://pytorch.org/tutorials/beginner/saving_loading_models.html.
|
||
quantize(text_encoder_2, weights=qfloat8)
|
||
freeze(text_encoder_2)
|
||
pipe = FluxPipeline(
|
||
scheduler=scheduler,
|
||
text_encoder=text_encoder,
|
||
tokenizer=tokenizer,
|
||
text_encoder_2=None,
|
||
tokenizer_2=tokenizer_2,
|
||
vae=vae,
|
||
transformer=None,
|
||
)
|
||
pipe.text_encoder_2 = text_encoder_2
|
||
pipe.transformer = transformer
|
||
|
||
else: # flux diff unet ,diff 0.30
|
||
from diffusers import FluxTransformer2DModel, FluxPipeline
|
||
from transformers import T5EncoderModel, CLIPTextModel
|
||
from optimum.quanto import freeze, qfloat8, quantize
|
||
if NF4:
|
||
#https://github.com/huggingface/diffusers/issues/9165
|
||
from accelerate.utils import set_module_tensor_to_device, compute_module_sizes
|
||
from accelerate import init_empty_weights
|
||
from .utils.convert_nf4_flux import _replace_with_bnb_linear, create_quantized_param, \
|
||
check_quantized_param
|
||
import safetensors.torch
|
||
import gc
|
||
dtype = torch.bfloat16
|
||
is_torch_e4m3fn_available = hasattr(torch, "float8_e4m3fn")
|
||
original_state_dict = safetensors.torch.load_file(ckpt_path)
|
||
config_file = os.path.join(dir_path, "config.json")
|
||
with init_empty_weights():
|
||
config = FluxTransformer2DModel.load_config(config_file)
|
||
model = FluxTransformer2DModel.from_config(config).to(dtype)
|
||
expected_state_dict_keys = list(model.state_dict().keys())
|
||
|
||
_replace_with_bnb_linear(model, "nf4")
|
||
|
||
for param_name, param in original_state_dict.items():
|
||
if param_name not in expected_state_dict_keys:
|
||
continue
|
||
|
||
is_param_float8_e4m3fn = is_torch_e4m3fn_available and param.dtype == torch.float8_e4m3fn
|
||
if torch.is_floating_point(param) and not is_param_float8_e4m3fn:
|
||
param = param.to(dtype)
|
||
|
||
if not check_quantized_param(model, param_name):
|
||
set_module_tensor_to_device(model, param_name, device=0, value=param)
|
||
else:
|
||
create_quantized_param(
|
||
model, param, param_name, target_device=0, state_dict=original_state_dict,
|
||
pre_quantized=True
|
||
)
|
||
|
||
del original_state_dict
|
||
gc.collect()
|
||
pipe = FluxPipeline.from_pretrained(repo_id, transformer=model,torch_dtype=dtype)
|
||
|
||
else:
|
||
if os.path.splitext(ckpt_path)[-1] == ".pt":
|
||
transformer = torch.load(ckpt_path)
|
||
transformer.eval()
|
||
else:
|
||
#config_file = f"{repo_id}/transformer/config.json"
|
||
config_file = os.path.join(dir_path,"utils", "config.json")
|
||
transformer = FluxTransformer2DModel.from_single_file(ckpt_path, config=config_file,
|
||
torch_dtype=dtype)
|
||
quantize(transformer, weights=qfloat8)
|
||
freeze(transformer)
|
||
text_encoder_2 = T5EncoderModel.from_pretrained(repo_id, subfolder="text_encoder_2",
|
||
torch_dtype=dtype)
|
||
quantize(text_encoder_2, weights=qfloat8)
|
||
freeze(text_encoder_2)
|
||
|
||
pipe = FluxPipeline.from_pretrained(repo_id, transformer=None, text_encoder_2=None,
|
||
torch_dtype=dtype)
|
||
pipe.transformer = transformer
|
||
pipe.text_encoder_2 = text_encoder_2
|
||
|
||
pipe.enable_model_cpu_offload()
|
||
if lora:
|
||
if not "Hyper" in lora_path : #can't support Hyper now
|
||
if not NF4:
|
||
pipe.load_lora_weights(lora_path)
|
||
pipe.fuse_lora(lora_scale=0.125) # lora_scale=0.125
|
||
|
||
else: # SD dif_repo
|
||
pipe = load_models(repo_id, model_type=model_type, single_files=False, use_safetensors=True,photomake_mode=photomake_mode,
|
||
photomaker_path=photomaker_path, lora=lora,
|
||
lora_path=lora_path,
|
||
trigger_words=trigger_words, lora_scale=lora_scale)
|
||
set_attention_processor(pipe.unet, id_length, is_ipadapter=False)
|
||
if vae_id != "none":
|
||
if not use_flux:
|
||
vae_id = folder_paths.get_full_path("vae", vae_id)
|
||
pipe.vae=AutoencoderKL.from_single_file(vae_id, torch_dtype=torch.float16)
|
||
load_chars = False
|
||
if not use_kolor and not use_flux:
|
||
pipe.scheduler = scheduler_choice.from_config(pipe.scheduler.config)
|
||
pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
|
||
pipe.enable_vae_slicing()
|
||
unet = pipe.unet
|
||
load_chars = load_character_files_on_running(unet, character_files=char_files)
|
||
if device != "mps":
|
||
pipe.to("cuda")
|
||
# if device != "mps":
|
||
# pipe.enable_model_cpu_offload()
|
||
torch.cuda.empty_cache()
|
||
model={"pipe":pipe,"use_flux":use_flux,"use_kolor":use_kolor,"photomake_mode":photomake_mode,"trigger_words":trigger_words,"lora_scale":lora_scale,
|
||
"load_chars":load_chars,"repo_id":repo_id,"lora_path":lora_path,"ckpt_path":ckpt_path,"model_type":model_type, "lora": lora,"scheduler":scheduler,"auraface":auraface}
|
||
return (model,)
|
||
|
||
|
||
class Storydiffusion_Sampler:
|
||
def __init__(self):
|
||
pass
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"model": ("STORY_DICT",),
|
||
"character_prompt": ("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."}),
|
||
"scene_prompts": ("STRING", {"multiline": True,
|
||
"default": "[Taylor]wake up in the bed,medium shot;\n[Taylor]have breakfast by the window;\n[Lecun] drving on the road,medium shot;\n[Lecun]work in the company."}),
|
||
"split_prompt": ("STRING", {"default": ""}),
|
||
"negative_prompt": ("STRING", {"multiline": True,
|
||
"default": "bad anatomy, bad hands, missing fingers, extra fingers, "
|
||
"three hands, three legs, bad arms, missing legs, "
|
||
"missing arms, poorly drawn face, bad face, fused face, "
|
||
"cloned face, three crus, fused feet, fused thigh, "
|
||
"extra crus, ugly fingers, horn,"
|
||
"amputation, disconnected limbs"}),
|
||
"img_style": (
|
||
["No_style", "Realistic", "Japanese_Anime", "Digital_Oil_Painting", "Pixar_Disney_Character",
|
||
"Photographic", "Comic_book",
|
||
"Line_art", "Black_and_White_Film_Noir", "Isometric_Rooms"],),
|
||
"seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED}),
|
||
"steps": ("INT", {"default": 20, "min": 1, "max": 100}),
|
||
"cfg": ("FLOAT", {"default": 7, "min": 0.1, "max": 10.0, "step": 0.1, "round": 0.01}),
|
||
"ip_adapter_strength": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 1.0, "step": 0.1, "round": 0.01}),
|
||
"style_strength_ratio": ("INT", {"default": 20, "min": 10, "max": 50, "step": 1, "display": "number"}),
|
||
"clip_vision": (clip_paths,),
|
||
"role_scale": (
|
||
"FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.1, "round": 0.01}),
|
||
"mask_threshold": (
|
||
"FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.1, "round": 0.01}),
|
||
"start_step": ("INT", {"default": 5, "min": 1, "max": 1024}),
|
||
"save_character": ("BOOLEAN", {"default": False},),
|
||
"controlnet_model_path": (["none"] + folder_paths.get_filename_list("controlnet"),),
|
||
"controlnet_scale": (
|
||
"FLOAT", {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.1, "round": 0.01}),
|
||
"guidance_list": ("STRING", {"multiline": True, "default": "0., 0.25, 0.4, 0.75;0.6, 0.25, 1., 0.75"}),
|
||
},
|
||
"optional": {"image": ("IMAGE",),
|
||
"control_image": ("IMAGE",),},
|
||
}
|
||
|
||
|
||
RETURN_TYPES = ("IMAGE", "STRING",)
|
||
RETURN_NAMES = ("image", "prompt_array",)
|
||
FUNCTION = "story_sampler"
|
||
CATEGORY = "Storydiffusion"
|
||
|
||
def story_sampler(self, model, character_prompt,scene_prompts,split_prompt, negative_prompt, img_style, seed, steps,
|
||
cfg, ip_adapter_strength, style_strength_ratio,clip_vision,
|
||
role_scale, mask_threshold, start_step,save_character,controlnet_model_path,controlnet_scale,guidance_list,**kwargs):
|
||
# get value from dict
|
||
pipe=model.get("pipe")
|
||
use_flux=model.get("use_flux")
|
||
photomake_mode=model.get("photomake_mode")
|
||
use_kolor=model.get("use_kolor")
|
||
load_chars=model.get("load_chars")
|
||
model_type=model.get("model_type")
|
||
trigger_words=model.get("trigger_words")
|
||
repo_id=model.get("repo_id")
|
||
lora_path=model.get("lora_path")
|
||
ckpt_path=model.get("ckpt_path")
|
||
lora=model.get("lora")
|
||
lora_scale =model.get("lora_scale")
|
||
auraface=model.get("auraface")
|
||
scheduler=model.get("scheduler")
|
||
scheduler_choice = get_scheduler(scheduler)
|
||
image = kwargs.get("image")
|
||
# 格式化文字内容
|
||
if split_prompt:
|
||
scene_prompts.replace("\n", "").replace(split_prompt, ";\n").strip()
|
||
character_prompt.replace("\n", "").replace(split_prompt, ";\n").strip()
|
||
else:
|
||
scene_prompts.strip()
|
||
character_prompt.strip()
|
||
if "\n" not in scene_prompts:
|
||
scene_prompts.replace(";", ";\n").strip()
|
||
if "\n" in character_prompt:
|
||
if character_prompt.count("\n") > 1:
|
||
character_prompt.replace("\n", "").replace("[", "\n[").strip()
|
||
if character_prompt.count("\n") > 1:
|
||
character_prompt.replace("\n", "").replace("[", "\n[", 2).strip() # 多行角色在这里强行转为双角色
|
||
|
||
# 从角色列表获取角色方括号信息
|
||
char_origin = character_prompt.splitlines()
|
||
char_describe = char_origin # [A a men...,B a girl ]
|
||
char_origin = [char.split("]")[0] + "]" for char in char_origin]
|
||
|
||
# 判断是否有双角色prompt，如果有，获取双角色列表及对应的位置列表，
|
||
prompts_origin = scene_prompts.splitlines()
|
||
positions_dual = [index for index, prompt in enumerate(prompts_origin) if len(extract_content_from_brackets(prompt))>=2] #改成单句中双方括号方法，利于MS组句，[A]... [B]...[C]
|
||
prompts_dual = [prompt for prompt in prompts_origin if len(extract_content_from_brackets(prompt))>=2]
|
||
|
||
# positions_dual = [index for index, prompt in enumerate(prompts_origin) if has_parentheses(prompt)]
|
||
# prompts_dual = [prompt for prompt in prompts_origin if has_parentheses(prompt)]
|
||
|
||
if len(char_origin) == 2:
|
||
positions_char_1 = [index for index, prompt in enumerate(prompts_origin) if char_origin[0] in prompt][
|
||
0] # 获取角色出现的索引列表，并获取首次出现的位置
|
||
positions_char_2 = [index for index, prompt in enumerate(prompts_origin) if char_origin[1] in prompt][
|
||
0] # 获取角色出现的索引列表，并获取首次出现的位置
|
||
|
||
if model_type=="img2img":
|
||
d1, _, _, _ = image.size()
|
||
if d1 == 1:
|
||
image_load = [nomarl_upscale(image, width, height)]
|
||
else:
|
||
img_list = list(torch.chunk(image, chunks=d1))
|
||
image_load = [nomarl_upscale(img, width, height) for img in img_list]
|
||
|
||
gen = process_generation(pipe, image_load, model_type, steps, img_style, ip_adapter_strength,
|
||
style_strength_ratio, cfg,
|
||
seed, id_length,
|
||
character_prompt,
|
||
negative_prompt,
|
||
scene_prompts,
|
||
width,
|
||
height,
|
||
load_chars,
|
||
lora,
|
||
trigger_words,photomake_mode,use_kolor,use_flux,auraface)
|
||
|
||
else:
|
||
upload_images = None
|
||
_Ip_Adapter_Strength = 0.5
|
||
_style_strength_ratio = 20
|
||
gen = process_generation(pipe, upload_images, model_type, steps, img_style, _Ip_Adapter_Strength,
|
||
_style_strength_ratio, cfg,
|
||
seed, id_length,
|
||
character_prompt,
|
||
negative_prompt,
|
||
scene_prompts,
|
||
width,
|
||
height,
|
||
load_chars,
|
||
lora,
|
||
trigger_words,photomake_mode,use_kolor,use_flux,auraface)
|
||
|
||
for value in gen:
|
||
print(type(value))
|
||
image_pil_list = phi_list(value)
|
||
|
||
image_pil_list_ms = image_pil_list.copy()
|
||
if save_character:
|
||
print("saving character...")
|
||
save_results(pipe.unet)
|
||
if clip_vision=="none":
|
||
clip_vision =None
|
||
else:
|
||
clip_vision = folder_paths.get_full_path("clip_vision", clip_vision)
|
||
if prompts_dual:
|
||
if not clip_vision:
|
||
raise "need a clip_vison weight."
|
||
if use_flux or use_kolor:
|
||
raise "flux or kolor don't support MS diffsion."
|
||
print("start sampler dual prompt")
|
||
control_image = None
|
||
if controlnet_model_path!="none":
|
||
control_image = kwargs["control_image"]
|
||
image_a = image_pil_list_ms[positions_char_1]
|
||
image_b = image_pil_list_ms[positions_char_2]
|
||
image_dual = msdiffusion_main(pipe, image_a, image_b, prompts_dual, width, height, steps, seed,
|
||
img_style, char_describe,char_origin,negative_prompt, clip_vision, model_type, lora, lora_path, lora_scale,
|
||
trigger_words, ckpt_path,repo_id, role_scale,
|
||
mask_threshold, start_step,controlnet_model_path,control_image,controlnet_scale,cfg,guidance_list,scheduler_choice)
|
||
j = 0
|
||
for i in positions_dual: #重新将双人场景插入原序列
|
||
img = image_dual[j]
|
||
image_pil_list.insert(int(i), img)
|
||
j += 1
|
||
image_list = narry_list(image_pil_list)
|
||
torch.cuda.empty_cache()
|
||
else:
|
||
image_list = narry_list(image_pil_list)
|
||
image = torch.from_numpy(np.fromiter(image_list, np.dtype((np.float32, (height, width, 3)))))
|
||
torch.cuda.empty_cache()
|
||
return (image, scene_prompts,)
|
||
|
||
|
||
class Comic_Type:
|
||
@classmethod
|
||
def INPUT_TYPES(s):
|
||
return {"required": {"image": ("IMAGE",),
|
||
"scene_prompts": ("STRING", {"multiline": True, "forceInput": True, "default": ""}),
|
||
"fonts_list": (fonts_lists,),
|
||
"text_size": ("INT", {"default": 40, "min": 1, "max": 100}),
|
||
"comic_type": (["Four_Pannel", "Classic_Comic_Style"],),
|
||
"split_lines": ("STRING", {"default": "；"}),
|
||
}}
|
||
|
||
RETURN_TYPES = ("IMAGE",)
|
||
ETURN_NAMES = ("image",)
|
||
FUNCTION = "comic_gen"
|
||
CATEGORY = "Storydiffusion"
|
||
|
||
def comic_gen(self, image, scene_prompts, fonts_list, text_size, comic_type, split_lines):
|
||
result = [item for index, item in enumerate(image)]
|
||
total_results = narry_list_pil(result)
|
||
font_choice = os.path.join(dir_path, "fonts", fonts_list)
|
||
captions = scene_prompts.splitlines()
|
||
if len(captions) > 1:
|
||
captions = [caption.replace("(", "").replace(")", "") if "(" or ")" in caption else caption
|
||
for caption in captions] # del ()
|
||
captions = [caption.replace("[NC]", "") for caption in captions]
|
||
captions = [caption.replace("]", "").replace("[", "") for caption in captions]
|
||
captions = [
|
||
caption.split("#")[-1] if "#" in caption else caption
|
||
for caption in captions
|
||
]
|
||
else:
|
||
prompt_array = scene_prompts.replace(split_lines, "\n")
|
||
captions = prompt_array.splitlines()
|
||
font = ImageFont.truetype(font_choice, text_size)
|
||
images = (
|
||
get_comic(total_results, comic_type, captions=captions, font=font)
|
||
+ total_results
|
||
)
|
||
images = phi2narry(images[0])
|
||
return (images,)
|
||
|
||
|
||
class Pre_Translate_prompt:
|
||
@classmethod
|
||
def INPUT_TYPES(s):
|
||
return {"required": {"scene_prompts": ("STRING", {"forceInput": True, "default": ""}),
|
||
"keep_character_name": ("BOOLEAN", {"default": False},)
|
||
}}
|
||
|
||
RETURN_TYPES = ("STRING",)
|
||
ETURN_NAMES = ("prompt_array",)
|
||
FUNCTION = "translate_prompt"
|
||
CATEGORY = "Storydiffusion"
|
||
|
||
def translate_prompt(self, scene_prompts, keep_character_name):
|
||
captions = scene_prompts.splitlines()
|
||
if not keep_character_name:
|
||
captions = [caption.split(")", 1)[-1] if ")" in caption else caption
|
||
for caption in captions] # del character
|
||
else:
|
||
captions = [caption.replace("(", "").replace(")", "") if "(" or ")" in caption else caption
|
||
for caption in captions] # del ()
|
||
captions = [caption.replace("[NC]", "") for caption in captions]
|
||
if not keep_character_name:
|
||
captions = [caption.split("]", 1)[-1] for caption in captions]
|
||
else:
|
||
captions = [caption.replace("]", "").replace("[", "") for caption in captions]
|
||
captions = [
|
||
caption.split("#")[-1] if "#" in caption else caption
|
||
for caption in captions
|
||
]
|
||
scene_prompts = ''.join(captions)
|
||
return (scene_prompts,)
|
||
|
||
|
||
NODE_CLASS_MAPPINGS = {
|
||
"Storydiffusion_Model_Loader": Storydiffusion_Model_Loader,
|
||
"Storydiffusion_Sampler": Storydiffusion_Sampler,
|
||
"Pre_Translate_prompt": Pre_Translate_prompt,
|
||
"Comic_Type": Comic_Type,
|
||
}
|
||
|
||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||
"Storydiffusion_Model_Loader": "Storydiffusion_Model_Loader",
|
||
"Storydiffusion_Sampler": "Storydiffusion_Sampler",
|
||
"Pre_Translate_prompt": "Pre_Translate_prompt",
|
||
"Comic_Type": "Comic_Type",
|
||
}
|