Stable Diffusion 只做AI动画是基于把原有视频按照帧进行提取之后对每一帧的图像进行标准化流程操作 
  
  
  
  
  
  
，中间可以掺杂Controlnet对人物进行控制
  
  
  
  
  
  
 ，使用关键词对画面进行控制
 


 


 


 


 


 


 ，但是很多小伙伴不太会掌握一些编辑视频软件或者python的操作导致视频转帧 
 
 
 
 
 
 
，帧转视频会出现一些问题             。

这里分享2套方法 
  
 
  
 
  
 
  
 
  
 
  
 
 。

自制Python脚本

在你的文件目录下和我一致即可













。

视频转帧 fps_jpg.py

from moviepy.editor import * import os import cv2 # 加载视频文件 dir_list = os.listdir("video") video_capture = cv2.VideoCapture("video/" + dir_list[0]) # 初始化帧计数器 frame_count = 0 # 逐帧读取视频并保存图像 while True: # 读取视频帧 ret, frame = video_capture.read() # 检查是否成功读取帧 if not ret: break # 保存图像 cv2.imwrite(f"video_img/frame{frame_count}.jpg", frame) # 帧计数器加1 frame_count += 1 # 释放视频捕捉对象 video_capture.release()

帧转视频 jpg_fps.py

from moviepy.editor import * import os import numpy as np # 按帧合成视频 os.system("ffmpeg -i video_img/frame%d.jpg -acodec libvo_aacenc -vcodec mpeg4 -r 60 video_out/merged.mp4") # os.system("ffmpeg -i video_img/%6d.png -acodec libvo_aacenc -vcodec mpeg4 -r 60 video_out/merged.mp4") # 共读取原视频 dir_list = os.listdir("video") video_source = VideoFileClip("video/" + dir_list[0]) # 提取视频中的音频 video_mp3 = video_source.audio # 提取视频中的时长 video_duration = video_source.duration # 读取merge视频 video_merge = VideoFileClip("video_out/merged.mp4") # 提取视频中的时长 merge_duration = video_merge.duration # 计算加速的倍数 factor = merge_duration / video_duration result = video_merge.speedx(factor) # 设置视频的音频 result = result.set_audio(video_mp3) result.write_videofile("video_merge/diy_result.mp4")

Stable Diffusion 插件

自行下载脚本 sd-webui-video-frames

脚本放到你的 Stable Diffusion 的 Script 下 
  
  
  
  
  
  。

在你的Stable Diffusion中会看到对应的选项卡 
  
  
  
  
  
  
 。

生成的视频转图像帧

 


 


 


 


 


 


。

AI动画脚本

感谢原作者提供脚本 multi-frame-rendering-for-stablediffusion

脚本安装

脚本放到你的 Stable Diffusion 的 Script 下
   
   
   
   
   
   
 ，但是原作者这个脚本似乎有点问题
 

 

 

 

 

 

 ，图片超过2800张之后就无法处理了 

 

 

 

 

 

 
，所以对这个脚本进行了一些修改             ，代码在最后先看流程在操作 
 
 
 
 
 
 。

制作第一帧图像

这样进行基础的文生图的样子 
   
   
   
   
   
   
 。

然后添加Controlnet控制人物

 
 

 
 

 
 

 
 

 
 

 
 

 ，这里建议添加openpose和canny
 

 

 

 

 

 

。

批量生成

点击生成好的图像到图生图界面












，复制图像的种子
 

 

 

 

 

 

 。

勾选和之前文生图中Controlnet相同的配置                    ，但是这里不需要加入图片             。

打开下方的脚本选择输入和输出的文件路径就按照下图配置好久可以点击生成 

 
 

 
 

 
 

 
 

 
 

 
 

。自己制定好图片输入输入的路径就可以了
 
 
 
 
 
 
 
 
 
 
 
 
 ，其他的地方按照我这里设置即可












。

multi_frame_render.py

复制修改的代码新建一个脚本即可                    。

import numpy as np from tqdm import trange from PIL import Image, ImageSequence, ImageDraw, ImageFilter, PngImagePlugin import modules.scripts as scripts import gradio as gr from modules import processing, shared, sd_samplers, images from modules.processing import Processed from modules.sd_samplers import samplers from modules.shared import opts, cmd_opts, state from modules import deepbooru from modules.script_callbacks import ImageSaveParams, before_image_saved_callback from modules.shared import opts, cmd_opts, state from modules.sd_hijack import model_hijack import pandas as pd import piexif import piexif.helper import os, re def gr_show(visible=True): return {"visible": visible, "__type__": "update"} def gr_show_value_none(visible=True): return {"value": None, "visible": visible, "__type__": "update"} def gr_show_and_load(value=None, visible=True): if value: if value.orig_name.endswith(.csv): value = pd.read_csv(value.name) else: value = pd.read_excel(value.name) else: visible = False return {"value": value, "visible": visible, "__type__": "update"} class Script(scripts.Script): def title(self): return "(Beta) Multi-frame Video rendering" def show(self, is_img2img): return is_img2img def ui(self, is_img2img): with gr.Row(): input_dir = gr.Textbox(label=Input directory, lines=1) output_dir = gr.Textbox(label=Output directory, lines=1) # reference_imgs = gr.UploadButton(label="Upload Guide Frames", file_types = [.png,.jpg,.jpeg], live=True, file_count = "multiple") first_denoise = gr.Slider(minimum=0, maximum=1, step=0.05, label=Initial Denoise Strength, value=1, elem_id=self.elem_id("first_denoise")) append_interrogation = gr.Dropdown(label="Append interrogated prompt at each iteration", choices=["None", "CLIP", "DeepBooru"], value="None") third_frame_image = gr.Dropdown(label="Third Frame Image", choices=["None", "FirstGen", "OriginalImg", "Historical"], value="FirstGen") color_correction_enabled = gr.Checkbox(label="Enable Color Correction", value=False, elem_id=self.elem_id("color_correction_enabled")) unfreeze_seed = gr.Checkbox(label="Unfreeze Seed", value=False, elem_id=self.elem_id("unfreeze_seed")) loopback_source = gr.Dropdown(label="Loopback Source", choices=["PreviousFrame", "InputFrame","FirstGen"], value="InputFrame") with gr.Row(): use_txt = gr.Checkbox(label=Read tags from text files) with gr.Row(): txt_path = gr.Textbox(label=Text files directory (Optional, will load from input dir if not specified), lines=1) with gr.Row(): use_csv = gr.Checkbox(label=Read tabular commands) csv_path = gr.File(label=.csv or .xlsx, file_types=[file], visible=False) with gr.Row(): with gr.Column(): table_content = gr.Dataframe(visible=False, wrap=True) use_csv.change( fn=lambda x: [gr_show_value_none(x), gr_show_value_none(False)], inputs=[use_csv], outputs=[csv_path, table_content], ) csv_path.change( fn=lambda x: gr_show_and_load(x), inputs=[csv_path], outputs=[table_content], ) return [append_interrogation, input_dir, output_dir, first_denoise, third_frame_image, color_correction_enabled, unfreeze_seed, loopback_source, use_csv, table_content, use_txt, txt_path] def run(self, p, append_interrogation, input_dir, output_dir, first_denoise, third_frame_image, color_correction_enabled, unfreeze_seed, loopback_source, use_csv, table_content, use_txt, txt_path): freeze_seed = not unfreeze_seed if use_csv: prompt_list = [i[0] for i in table_content.values.tolist()] prompt_list.insert(0, prompt_list.pop()) reference_imgs = [os.path.join(input_dir, f) for f in os.listdir(input_dir) if re.match(r.+\.(jpg|png)$, f)] print(fWill process following files: {", ".join(reference_imgs)}) if use_txt: if txt_path == "": files = [re.sub(r\.(jpg|png)$, .txt, path) for path in reference_imgs] else: files = [os.path.join(txt_path, os.path.basename(re.sub(r\.(jpg|png)$, .txt, path))) for path in reference_imgs] prompt_list = [open(file, r).read().rstrip(\n) for file in files] loops = len(reference_imgs) processing.fix_seed(p) batch_count = p.n_iter p.batch_size = 1 p.n_iter = 1 output_images, info = None, None initial_seed = None initial_info = None initial_width = p.width initial_img = reference_imgs[0] # p.init_images[0] grids = [] all_images = [] original_init_image = p.init_images original_prompt = p.prompt if original_prompt != "": original_prompt = original_prompt.rstrip(, ) + , if not original_prompt.rstrip().endswith(,) else original_prompt.rstrip() + original_denoise = p.denoising_strength state.job_count = loops * batch_count initial_color_corrections = [processing.setup_color_correction(p.init_images[0])] # for n in range(batch_count): history = None # frames = [] third_image = None third_image_index = 0 frame_color_correction = None # Reset to original init image at the start of each batch p.init_images = original_init_image p.width = initial_width for i in range(loops): if state.interrupted: break filename = os.path.basename(reference_imgs[i]) p.n_iter = 1 p.batch_size = 1 p.do_not_save_grid = True p.control_net_input_image = Image.open(reference_imgs[i]).convert("RGB").resize((initial_width, p.height), Image.ANTIALIAS) if(i > 0): loopback_image = p.init_images[0] if loopback_source == "InputFrame": loopback_image = p.control_net_input_image elif loopback_source == "FirstGen": loopback_image = history if third_frame_image != "None": p.width = initial_width * 3 img = Image.new("RGB", (initial_width*3, p.height)) img.paste(p.init_images[0], (0, 0)) # img.paste(p.init_images[0], (initial_width, 0)) img.paste(loopback_image, (initial_width, 0)) if i == 1: third_image = p.init_images[0] img.paste(third_image, (initial_width*2, 0)) p.init_images = [img] if color_correction_enabled: p.color_corrections = [processing.setup_color_correction(img)] msk = Image.new("RGB", (initial_width*3, p.height)) msk.paste(Image.open(reference_imgs[i-1]).convert("RGB").resize((initial_width, p.height), Image.ANTIALIAS), (0, 0)) msk.paste(p.control_net_input_image, (initial_width, 0)) msk.paste(Image.open(reference_imgs[third_image_index]).convert("RGB").resize((initial_width, p.height), Image.ANTIALIAS), (initial_width*2, 0)) p.control_net_input_image = msk latent_mask = Image.new("RGB", (initial_width*3, p.height), "black") latent_draw = ImageDraw.Draw(latent_mask) latent_draw.rectangle((initial_width,0,initial_width*2,p.height), fill="white") p.image_mask = latent_mask p.denoising_strength = original_denoise else: p.width = initial_width * 2 img = Image.new("RGB", (initial_width*2, p.height)) img.paste(p.init_images[0], (0, 0)) # img.paste(p.init_images[0], (initial_width, 0)) img.paste(loopback_image, (initial_width, 0)) p.init_images = [img] if color_correction_enabled: p.color_corrections = [processing.setup_color_correction(img)] msk = Image.new("RGB", (initial_width*2, p.height)) msk.paste(Image.open(reference_imgs[i-1]).convert("RGB").resize((initial_width, p.height), Image.ANTIALIAS), (0, 0)) msk.paste(p.control_net_input_image, (initial_width, 0)) p.control_net_input_image = msk # frames.append(msk) # latent_mask = Image.new("RGB", (initial_width*2, p.height), "white") # latent_draw = ImageDraw.Draw(latent_mask) # latent_draw.rectangle((0,0,initial_width,p.height), fill="black") latent_mask = Image.new("RGB", (initial_width*2, p.height), "black") latent_draw = ImageDraw.Draw(latent_mask) latent_draw.rectangle((initial_width,0,initial_width*2,p.height), fill="white") # p.latent_mask = latent_mask p.image_mask = latent_mask p.denoising_strength = original_denoise else: latent_mask = Image.new("RGB", (initial_width, p.height), "white") # p.latent_mask = latent_mask p.image_mask = latent_mask p.denoising_strength = first_denoise p.control_net_input_image = p.control_net_input_image.resize((initial_width, p.height)) # frames.append(p.control_net_input_image) # if opts.img2img_color_correction: # p.color_corrections = initial_color_corrections if append_interrogation != "None": p.prompt = original_prompt if append_interrogation == "CLIP": p.prompt += shared.interrogator.interrogate(p.init_images[0]) elif append_interrogation == "DeepBooru": p.prompt += deepbooru.model.tag(p.init_images[0]) if use_csv or use_txt: p.prompt = original_prompt + prompt_list[i] # state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}" processed = processing.process_images(p) if initial_seed is None: initial_seed = processed.seed initial_info = processed.info init_img = processed.images[0] if(i > 0): init_img = init_img.crop((initial_width, 0, initial_width*2, p.height)) comments = {} if len(model_hijack.comments) > 0: for comment in model_hijack.comments: comments[comment] = 1 info = processing.create_infotext( p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, 0, 0) pnginfo = {} if info is not None: pnginfo[parameters] = info params = ImageSaveParams(init_img, p, filename, pnginfo) before_image_saved_callback(params) fullfn_without_extension, extension = os.path.splitext( filename) info = params.pnginfo.get(parameters, None) def exif_bytes(): return piexif.dump({ Exif: { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(info or , encoding=unicode) }, }) if extension.lower() == .png: pnginfo_data = PngImagePlugin.PngInfo() for k, v in params.pnginfo.items(): pnginfo_data.add_text(k, str(v)) init_img.save( os.path.join( output_dir, filename), pnginfo=pnginfo_data) elif extension.lower() in (.jpg, .jpeg, .webp): init_img.save(os.path.join(output_dir, filename)) if opts.enable_pnginfo and info is not None: piexif.insert( exif_bytes(), os.path.join( output_dir, filename)) else: init_img.save(os.path.join(output_dir, filename)) if third_frame_image != "None": if third_frame_image == "FirstGen" and i == 0: third_image = init_img third_image_index = 0 elif third_frame_image == "OriginalImg" and i == 0: third_image = original_init_image[0] third_image_index = 0 elif third_frame_image == "Historical": third_image = processed.images[0].crop((0, 0, initial_width, p.height)) third_image_index = (i-1) p.init_images = [init_img] if(freeze_seed): p.seed = processed.seed else: p.seed = processed.seed + 1 # p.seed = processed.seed if i == 0: history = init_img # history.append(processed.images[0]) # frames.append(processed.images[0]) # grid = images.image_grid(history, rows=1) # if opts.grid_save: # images.save_image(grid, p.outpath_grids, "grid", initial_seed, p.prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename, grid=True, p=p) # grids.append(grid) # # all_images += history + frames # all_images += history # p.seed = p.seed+1 # if opts.return_grid: # all_images = grids + all_images processed = Processed(p, [], initial_seed, initial_info) return processed