この記事は以下の記事と比較して読んでいただくとより分かりやすいかと思います。
![](https://sf-fantasy.space/wp-content/uploads/2023/10/animediff少女-300x300.gif)
拡張機能comfyui-animatediffによるノード図
まず512×512の出来上がり図はこちらになります。
![](https://sf-fantasy.space/wp-content/uploads/2023/10/AnimateDiff_00003_.gif)
インストール
Managerからインストールできるcomfyui-animatediffをGUIからインストールします。
モデルmm_sd_v15_v2.ckptはStable Diffusion WebUIのものを転用しました。
ダウンロードした後ComfyUI>custom_nodes>comfyui-animatediff>modelsに入れます。
それ以外のmm_sd_v14.ckpt、mm_sd_v15.ckptやmm-Stablized_mid.pthは私の環境では上手く動きませんでした。
基本最小構成のノード図解説
![](https://sf-fantasy.space/wp-content/uploads/2023/10/Screenshot-2023-10-29-at-19.25.34-1024x547.png)
以下デフォの構成から作ることを仮定します。
1,まずKSamplerをAnimate Diff Samplerと交換して接続します。
2,次にSave ImageをAnimate Diff Combineと交換して同様に接続します。
3, Animate Diff Module Loaderの右端motion_moduleとAnimate Diff Samplerの左端MOTION_MODULEの緑点を結びます。
その時のログを参考のために載せておきます。
Starting server
To see the GUI go to: http://127.0.0.1:8188
FETCH DATA from: /Users/***/ComfyUI/custom_nodes/ComfyUI-Manager/extension-node-map.json
got prompt
[AnimateDiff] - INFO - Loading motion module mm_sd_v15_v2.ckpt
[AnimateDiff] - INFO - Converting motion module to fp16.
model_type EPS
adm 0
Using split attention in VAE
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
Using split attention in VAE
missing {'cond_stage_model.text_projection', 'cond_stage_model.logit_scale'}
left over keys: dict_keys(['cond_stage_model.transformer.text_model.embeddings.position_ids', 'model_ema.decay', 'model_ema.num_updates'])
Requested to load SD1ClipModel
Loading 1 new model
[AnimateDiff] - INFO - Injecting motion module with method default.
Requested to load BaseModel
Loading 1 new model
DDIM Sampler: 100%|█████████████████████████████| 25/25 [03:33<00:00, 8.54s/it]
/Users/***/ComfyUI/comfy/model_base.py:49: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32))
/Users/***/ComfyUI/comfy/model_base.py:50: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32))
[AnimateDiff] - INFO - Ejecting motion module with method default.
Prompt executed in 222.11 seconds
注意点
モデルはmm_sd_v15.ckptはruntimeエラーで動かず、mm_sd_v15_v2.ckptを使ったら動きました。
個人の感想として、Stable Diffusion WebUIの時は動作の仕組みが分かり辛かったのがこの図を見るとブラックボックスを開けたみたいにプログラミング動作の仕組みが少し謎が解けてきたようにも感じました。
LoRA
生成AI出力結果
![](https://sf-fantasy.space/wp-content/uploads/2023/10/AnimateDiff_00026_.gif)
使用モデルは
Checkpoint:beautifulRealistic_brav5.safetensors
LoRA:JapaneseDollLikeness_v15.safetensors
になります。
Stable Diffusion WebUIの使い回しモデルで大丈夫です。
ノード図
![](https://sf-fantasy.space/wp-content/uploads/2023/10/Screenshot-2023-10-30-at-10.44.38-1024x484.png)
拡張機能のLoRAノードは動作しなかったので、デフォでついているLoad LoRAを使いました。
この結果がベストとは思いませんが可能性という具体例として共有させていただきます。
ログ
got prompt
model_type EPS
adm 0
Using split attention in VAE
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
Using split attention in VAE
missing {'cond_stage_model.text_projection', 'cond_stage_model.logit_scale'}
left over keys: dict_keys(['cond_stage_model.transformer.text_model.embeddings.position_ids', 'model_ema.input_blocks00bias', 'model_ema.input_blocks00weight', 'model_ema.input_blocks100emb_layers1bias', 'model_ema.input_blocks100emb_layers1weight', 'model_ema.input_blocks100in_layers0bias', 'model_ema.input_blocks100in_layers0weight', 'model_ema.input_blocks100in_layers2bias', 'model_ema.input_blocks100in_layers2weight', 'model_ema.input_blocks100out_layers0bias', 'model_ema.input_blocks100out_layers0weight', 'model_ema.input_blocks100out_layers3bias', 'model_ema.input_blocks100out_layers3weight', 'model_ema.input_blocks10emb_layers1bias', 'model_ema.input_blocks10emb_layers1weight', 'model_ema.input_blocks10in_layers0bias', 'model_ema.input_blocks10in_layers0weight', 'model_ema.input_blocks10in_layers2bias', 'model_ema.input_blocks10in_layers2weight', 'model_ema.input_blocks10out_layers0bias', 'model_ema.input_blocks10out_layers0weight', 'model_ema.input_blocks10out_layers3bias', 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'model_ema.time_embed0bias', 'model_ema.time_embed0weight', 'model_ema.time_embed2bias', 'model_ema.time_embed2weight'])
Requested to load SD1ClipModel
Loading 1 new model
[AnimateDiff] - INFO - Injecting motion module with method default.
Requested to load BaseModel
Loading 1 new model
DDIM Sampler: 100%|█████████████████████████████| 25/25 [03:33<00:00, 8.54s/it]
[AnimateDiff] - INFO - Ejecting motion module with method default.
Using split attention in VAE
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
Using split attention in VAE
Prompt executed in 222.30 seconds
LoRA+アップスケール(モデル)
生成AI出力結果
2048×2048の画像になりました。
![](https://sf-fantasy.space/wp-content/uploads/2023/10/AnimateDiff_00027_-1024x1024.gif)
ノード図
![](https://sf-fantasy.space/wp-content/uploads/2023/10/Screenshot-2023-10-30-at-13.34.59-1024x540.png)
アップスケールモデルではRealESRGAN_x4plus.pthを使いました。
ComfyUIの場合512×512でアニメgifを一度作るとそれがストックされており読み出すようになっているようです。
LoRA+アップスケール(モデル+hires fix)
生成AI出力結果
![](https://sf-fantasy.space/wp-content/uploads/2023/10/AnimateDiff_00041_-1024x1024.gif)
使用モデルは
Checkpoint:beautifulRealistic_brav5.safetensors
LoRA:JapaneseDollLikeness_v15.safetensors
ポジ:c(best quality, masterpiece), fine lady looking back at me.
, full body
ネガ:(easynegative:1.0),(worst quality,low quality:1.2),(bad anatomy:1.4),(realistic:1.1),nose,lips,adult,fat,sad, (inaccurate limb:1.2),extra digit,fewer digits,six fingers,(monochrome:0.95)
になります。学習されたモデルが学習してきた写真によって得意なポーズがある程度あるような気がします。
モデル図
![](https://sf-fantasy.space/wp-content/uploads/2023/10/Screenshot-2023-10-30-at-18.26.26-1024x604.png)
hires fixの場合はKSamplerを使う代わりにAnimate Diff Samplerを使います。それ以外は画像生成の場合と同じように追加すれば大丈夫です。
Controlnet
拡張機能ComfyUI-AnimateDiff-Evolvedによるノード図
現在のところMac apple siliconではWarningになって真っ黒なgif画像が生成されてしまいます。主要なコンテンツが非推奨(deprecated)と表示されるので今現在は使えない可能性が高いかもしれません。またgithubの解説記事を再現することそのものができないようです。(2023年10月現在)