344 lines
12 KiB
Python
344 lines
12 KiB
Python
# !/usr/bin/env python
|
|
# -*- coding: UTF-8 -*-
|
|
import hashlib
|
|
import random
|
|
import re
|
|
import time
|
|
from typing import Literal, Dict, Optional
|
|
import fire
|
|
from torchvision.io import read_video, write_video
|
|
from tqdm import tqdm
|
|
from .utils.wrapper import StreamV2VWrapper
|
|
import torch
|
|
import os
|
|
from PIL import Image
|
|
import numpy as np
|
|
import sys
|
|
import folder_paths
|
|
import cv2
|
|
import logging
|
|
from diffusers import LCMScheduler, StableDiffusionPipeline
|
|
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
|
|
path_dir = os.path.dirname(CURRENT_DIR)
|
|
file_path = os.path.dirname(path_dir)
|
|
BIGMAX = (2 ** 53 - 1)
|
|
input_path = folder_paths.get_input_directory()
|
|
|
|
paths = []
|
|
for search_path in folder_paths.get_folder_paths("diffusers"):
|
|
if os.path.exists(search_path):
|
|
for root, subdir, files in os.walk(search_path, followlinks=True):
|
|
if "model_index.json" in files:
|
|
paths.append(os.path.relpath(root, start=search_path))
|
|
|
|
if paths != []:
|
|
paths = ["none"] + [x for x in paths if x]
|
|
else:
|
|
paths = ["none", ]
|
|
|
|
|
|
def tensor_to_image(tensor):
|
|
# tensor = tensor.cpu()
|
|
image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
|
|
image = Image.fromarray(image_np, mode='RGB')
|
|
return image
|
|
|
|
|
|
def get_video_img(tensor):
|
|
outputs = []
|
|
for x in tensor:
|
|
x = tensor_to_image(x)
|
|
outputs.append(x)
|
|
return outputs
|
|
|
|
|
|
def phi2narry(img):
|
|
img = torch.from_numpy(np.array(img).astype(np.float32) / 255.0).unsqueeze(0)
|
|
return img
|
|
|
|
|
|
def narry_list(list_in):
|
|
for i in range(len(list_in)):
|
|
value = list_in[i]
|
|
modified_value = phi2narry(value)
|
|
list_in[i] = modified_value
|
|
return list_in
|
|
|
|
|
|
def get_local_path(file_path, model_path):
|
|
path = os.path.join(file_path, "models", "diffusers", model_path)
|
|
model_path = os.path.normpath(path)
|
|
if sys.platform == 'win32':
|
|
model_path = model_path.replace('\\', "/")
|
|
return model_path
|
|
|
|
|
|
def get_instance_path(path):
|
|
instance_path = os.path.normpath(path)
|
|
if sys.platform == 'win32':
|
|
instance_path = instance_path.replace('\\', "/")
|
|
return instance_path
|
|
|
|
|
|
def get_lora(lora):
|
|
lora_path = folder_paths.get_full_path("loras", lora)
|
|
lora_path = get_instance_path(lora_path)
|
|
return lora_path
|
|
|
|
|
|
def instance_path(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
|
|
|
|
# class Img_In:
|
|
# def __init__(self):
|
|
# pass
|
|
# def process_img(self,img):
|
|
# img= tensor_to_image
|
|
# return img
|
|
|
|
|
|
class Stream_Model_Loader:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"repo_id": ("STRING", {"default": "runwayml/stable-diffusion-v1-5"}),
|
|
"local_model":(paths,),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("MODEL",)
|
|
RETURN_NAMES = ("pipe",)
|
|
FUNCTION = "main"
|
|
CATEGORY = "StreamV2V_Plus"
|
|
|
|
def main(self,repo_id,local_model,):
|
|
device="cuda"
|
|
#加载pipe
|
|
model_id = instance_path(local_model, repo_id)
|
|
try:
|
|
# Attempt to load the model from a local directory
|
|
pipe = StableDiffusionPipeline.from_pretrained(model_id).to(device=device, dtype=torch.float16)
|
|
except ValueError:
|
|
# If the model is not found locally, load from Hugging Face
|
|
try:
|
|
pipe = StableDiffusionPipeline.from_single_file(model_id).to(device=device, dtype=torch.float16)
|
|
except Exception as e:
|
|
raise "Failed to load model from Hugging Face"
|
|
|
|
return (pipe,)
|
|
|
|
|
|
class Stream_Sampler:
|
|
def __init__(self):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
video_files = [f for f in os.listdir(input_path) if
|
|
os.path.isfile(os.path.join(input_path, f)) and f.split('.')[-1] in ["mp4", "webm", "mkv",
|
|
"avi"]]
|
|
return {
|
|
"required": {
|
|
"pipe":("MODEL",),
|
|
"prompt": ("STRING", {"multiline": True, "default": "Claymation, a man is giving a talk"}),
|
|
"video": (["none"]+video_files,),
|
|
"sampler_type": (["txt2img", "vdieo2vdieo","WebCam2Video"],),
|
|
"scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1, "round": 0.01}),
|
|
"guidance_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 30.0, "step": 0.1, "round": 0.01}),
|
|
"diffusion_steps": ("INT", {"default": 4, "min": 1, "max": 1000}),
|
|
"num_inference_steps": ("INT", {"default": 50, "min": 1, "max": 1000}),
|
|
"noise_strength": ("FLOAT", {"default": 0.4, "min": 0.0, "max": 1.0, "step": 0.1, "round": 0.01}),
|
|
"seed": ("INT", {"default": 2, "min": 0, "max": 0xffffffffffffffff}),
|
|
"vae_id": ("STRING", {"default": "madebyollin/taesd"}),
|
|
"index_list":(["txt2img","video2video","cam2video"],),
|
|
"acceleration":(["xformers","tensorrt","sfast"],),
|
|
"lora": (["none"] + folder_paths.get_filename_list("loras"),),
|
|
"lora_scale": ("FLOAT", {"default": 0.8, "min": 0.1, "max": 1.0, "step": 0.1}),
|
|
"trigger_word": ("STRING", {"default": "best quality"}),
|
|
"width": ("INT", {"default": 512, "min": 256, "max": 4096, "step": 64, }),
|
|
"height": ("INT", {"default": 512, "min": 256, "max": 4096, "step": 64}),
|
|
"cfg_type":(["none", "full", "self", "initialize"],),},
|
|
"optional":{"image": ("IMAGE",),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE","FLOAT",)
|
|
RETURN_NAMES = ("image","fps",)
|
|
FUNCTION = "main_stream"
|
|
CATEGORY = "StreamV2V_Plus"
|
|
|
|
def main_stream(self,pipe,prompt,video,sampler_type,scale,guidance_scale,diffusion_steps,num_inference_steps,
|
|
noise_strength,seed,vae_id,index_list,acceleration,lora,lora_scale,trigger_word,width,height,cfg_type,**kwargs):
|
|
|
|
|
|
output_dir = os.path.join(file_path, "output")
|
|
fps=25
|
|
height_v=512
|
|
width_v=512
|
|
|
|
image=kwargs.get("image")
|
|
|
|
if sampler_type=="vdieo2vdieo":
|
|
|
|
if video == "none":
|
|
raise "need video input"
|
|
input_video = get_instance_path(os.path.join(input_path, video))
|
|
# if not os.path.exists(output_dir):
|
|
# os.mkdir(output_dir)
|
|
video_info = read_video(input_video) # input path to the file name
|
|
video = video_info[0] / 255
|
|
fps = video_info[2]["video_fps"]
|
|
height_v = int(video.shape[1] * scale)
|
|
width_v = int(video.shape[2] * scale)
|
|
|
|
init_step = int(50 * (1 - noise_strength))
|
|
interval = int(50 * noise_strength) // diffusion_steps
|
|
t_index_list = [init_step + i * interval for i in range(diffusion_steps)]
|
|
|
|
stream = StreamV2VWrapper(
|
|
pipe,
|
|
mode="img2img",
|
|
t_index_list=t_index_list,
|
|
frame_buffer_size=1,
|
|
width=width_v,
|
|
height=height_v,
|
|
warmup=10,
|
|
acceleration="xformers",
|
|
do_add_noise=True,
|
|
output_type="pt",
|
|
enable_similar_image_filter=False,
|
|
similar_image_filter_threshold=0.98,
|
|
use_denoising_batch=True,
|
|
use_cached_attn=True,
|
|
use_feature_injection=True,
|
|
feature_injection_strength=0.8,
|
|
feature_similarity_threshold=0.98,
|
|
cache_interval=4,
|
|
cache_maxframes=1,
|
|
use_tome_cache=True,
|
|
seed=seed,
|
|
)
|
|
|
|
if lora == "none":
|
|
raise "need style lora"
|
|
lora_path = get_lora(lora)
|
|
stream.stream.load_lora(lora_path, adapter_name=trigger_word)
|
|
stream.stream.pipe.set_adapters(adapter_names=["lcm", trigger_word], adapter_weights=[1.0, lora_scale])
|
|
print(f"Use LORA: {trigger_word} in {lora}")
|
|
prompt = prompt + " " + trigger_word + "style"
|
|
stream.prepare(
|
|
prompt=prompt,
|
|
num_inference_steps=num_inference_steps,
|
|
guidance_scale=guidance_scale,
|
|
)
|
|
|
|
video_result = torch.zeros(video.shape[0], height, width, 3)
|
|
|
|
for _ in range(stream.batch_size):
|
|
stream(image=video[0].permute(2, 0, 1))
|
|
|
|
inference_time = []
|
|
for i in tqdm(range(video.shape[0])):
|
|
iteration_start_time = time.time()
|
|
output_image = stream(video[i].permute(2, 0, 1))
|
|
video_result[i] = output_image.permute(1, 2, 0)
|
|
iteration_end_time = time.time()
|
|
inference_time.append(iteration_end_time - iteration_start_time)
|
|
print(f'Avg time: {sum(inference_time[20:]) / len(inference_time[20:])}')
|
|
|
|
gen = get_video_img(video_result)
|
|
gen = narry_list(gen) # 列表排序
|
|
images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (height, width, 3)))))
|
|
return (images, fps)
|
|
|
|
elif sampler_type=="txt2img":
|
|
stream = StreamV2VWrapper(
|
|
pipe,
|
|
t_index_list=[0, 16, 32, 45],
|
|
lora_dict=None,
|
|
output_type="pil",
|
|
mode="txt2img",
|
|
lcm_lora_id=None,
|
|
vae_id=vae_id,
|
|
device="cuda",
|
|
dtype=torch.float16,
|
|
frame_buffer_size=1,
|
|
warmup=10,
|
|
width=width,
|
|
height=height,
|
|
acceleration="xformers",
|
|
do_add_noise=True,
|
|
use_denoising_batch=False,
|
|
use_cached_attn=True,
|
|
use_feature_injection=True,
|
|
feature_injection_strength=0.8,
|
|
feature_similarity_threshold=0.98,
|
|
cache_interval=1,
|
|
cache_maxframes=4,
|
|
use_tome_cache=True,
|
|
cfg_type="none",
|
|
seed=seed,
|
|
use_safety_checker=False
|
|
)
|
|
prompt = prompt + " " + trigger_word + "style"
|
|
images = phi2narry(stream.txt2img(prompt))
|
|
return (images,)
|
|
|
|
else:
|
|
stream = StreamV2VWrapper(
|
|
pipe,
|
|
mode="img2img",
|
|
t_index_list=[30, 35, 40, 45],
|
|
frame_buffer_size=1,
|
|
width=width,
|
|
height=height,
|
|
warmup=10,
|
|
dtype=torch.float16,
|
|
device="cuda",
|
|
acceleration="xformers",
|
|
do_add_noise=True,
|
|
output_type="pil",
|
|
use_denoising_batch=True,
|
|
use_cached_attn=True,
|
|
use_feature_injection=True,
|
|
feature_injection_strength=0.8,
|
|
feature_similarity_threshold=0.98,
|
|
cache_interval=4,
|
|
cache_maxframes=1,
|
|
use_tome_cache=True,
|
|
seed=seed,
|
|
)
|
|
if lora == "none":
|
|
raise "need style lora"
|
|
lora_path = get_lora(lora)
|
|
stream.stream.load_lora(lora_path, adapter_name=trigger_word)
|
|
stream.stream.pipe.set_adapters(adapter_names=["lcm", trigger_word], adapter_weights=[1.0, lora_scale])
|
|
print(f"Use LORA: {trigger_word} in {lora}")
|
|
prompt = prompt + " " + trigger_word + "style"
|
|
image = tensor_to_image(image)
|
|
output_image = stream(image=image, prompt=prompt)
|
|
print(type(output_image))
|
|
images = phi2narry(output_image)
|
|
return (images,)
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"Stream_Model_Loader":Stream_Model_Loader,
|
|
"Stream_Sampler": Stream_Sampler,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"Load_Stream":"Load_Stream",
|
|
"Stream_Sampler": "Stream_Sampler",
|
|
}
|