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(f'Will 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