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96 lines
3.3 KiB
Markdown
96 lines
3.3 KiB
Markdown
# 当图像模型遇见 AnimateDiff——模型组合技术
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我们已经领略到了 Stable Diffusion 模型及其生态模型的强大图像生成能力，现在我们引入一个新的模块：AnimateDiff，这样一来就可以把图像模型的能力迁移到视频中。在本篇文章中，我们为您展示基于 DiffSynth-Studio 搭建的动漫风格视频渲染方案：Diffutoon。
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## 下载模型
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接下来的例子会用到很多模型，我们先把它们下载好。
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* 一个动漫风格的 Stable Diffusion 架构模型
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* 两个 ControlNet 模型
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* 一个 Textual Inversion 模型
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* 一个 AnimateDiff 模型
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```python
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from diffsynth import download_models
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download_models([
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"AingDiffusion_v12",
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"AnimateDiff_v2",
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"ControlNet_v11p_sd15_lineart",
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"ControlNet_v11f1e_sd15_tile",
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"TextualInversion_VeryBadImageNegative_v1.3"
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])
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```
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## 下载视频
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你可以随意选择任何你喜欢的视频，我们使用[这个视频](https://www.bilibili.com/video/BV1iG411a7sQ)作为演示，你可以通过以下命令下载这个视频文件，但请注意，在没有获得视频原作者的商用版权时，请不要将其用作商业用途。
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```
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modelscope download --dataset Artiprocher/examples_in_diffsynth data/examples/diffutoon/input_video.mp4 --local_dir ./
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```
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## 生成动漫
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```python
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from diffsynth import ModelManager, SDVideoPipeline, ControlNetConfigUnit, VideoData, save_video
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import torch
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# Load models
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model_manager = ModelManager(torch_dtype=torch.float16, device="cuda")
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model_manager.load_models([
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"models/stable_diffusion/aingdiffusion_v12.safetensors",
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"models/AnimateDiff/mm_sd_v15_v2.ckpt",
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"models/ControlNet/control_v11p_sd15_lineart.pth",
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"models/ControlNet/control_v11f1e_sd15_tile.pth",
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])
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# Build pipeline
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pipe = SDVideoPipeline.from_model_manager(
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model_manager,
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[
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ControlNetConfigUnit(
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processor_id="tile",
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model_path="models/ControlNet/control_v11f1e_sd15_tile.pth",
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scale=0.5
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),
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ControlNetConfigUnit(
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processor_id="lineart",
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model_path="models/ControlNet/control_v11p_sd15_lineart.pth",
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scale=0.5
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)
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]
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)
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pipe.prompter.load_textual_inversions(["models/textual_inversion/verybadimagenegative_v1.3.pt"])
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# Load video
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video = VideoData(
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video_file="data/examples/diffutoon/input_video.mp4",
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height=1536, width=1536
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)
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input_video = [video[i] for i in range(30)]
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# Generate
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torch.manual_seed(0)
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output_video = pipe(
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prompt="best quality, perfect anime illustration, light, a girl is dancing, smile, solo",
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negative_prompt="verybadimagenegative_v1.3",
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cfg_scale=7, clip_skip=2,
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input_frames=input_video, denoising_strength=1.0,
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controlnet_frames=input_video, num_frames=len(input_video),
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num_inference_steps=10, height=1536, width=1536,
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animatediff_batch_size=16, animatediff_stride=8,
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)
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# Save video
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save_video(output_video, "output_video.mp4", fps=30)
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```
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## 效果展示
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<video width="512" height="256" controls>
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<source src="https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/b54c05c5-d747-4709-be5e-b39af82404dd" type="video/mp4">
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您的浏览器不支持Video标签。
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</video>
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