diff --git a/README.md b/README.md
index ca3f0af..882319f 100644
--- a/README.md
+++ b/README.md
@@ -112,8 +112,8 @@ The detailed of Linux:
 - GPU：Nvidia-V100 16G & Nvidia-A10 24G & Nvidia-A100 40G & Nvidia-A100 80G
 
 We need about 60GB available on disk (for saving weights), please check!
-The video size for EasyAnimateV5-12B can be generated by different GPU Memory, including:
 
+The video size for EasyAnimateV5-12B can be generated by different GPU Memory, including:
 | GPU memory | 384x672x72 | 384x672x49 | 576x1008x25 | 576x1008x49 | 768x1344x25 | 768x1344x49 |
 |------------|------------|------------|------------|------------|------------|------------|
 | 16GB       | 🧡         | 🧡         | ❌         | ❌         | ❌         | ❌         |
@@ -121,6 +121,15 @@ The video size for EasyAnimateV5-12B can be generated by different GPU Memory, i
 | 40GB       | ✅         | ✅         | ✅         | ✅         | ❌         | ❌         |
 | 80GB       | ✅         | ✅         | ✅         | ✅         | ✅         | ✅         |
 
+The video size for EasyAnimateV5-7B can be generated by different GPU Memory, including:
+| GPU memory | 384x672x72 | 384x672x49 | 576x1008x25 | 576x1008x49 | 768x1344x25 | 768x1344x49 |
+|------------|------------|------------|------------|------------|------------|------------|
+| 16GB       | 🧡         | 🧡         | ❌         | ❌         | ❌         | ❌         |
+| 24GB       | ✅         | ✅         | 🧡         | 🧡         | ❌         | ❌         |
+| 40GB       | ✅         | ✅         | ✅         | ✅         | ❌         | ❌         |
+| 80GB       | ✅         | ✅         | ✅         | ✅         | ✅         | ✅         |
+
+
 ✅ indicates it can run under "model_cpu_offload", 🧡 represents it can run under "model_cpu_offload_and_qfloat8", ⭕️ indicates it can run under "sequential_cpu_offload", ❌ means it can't run. Please note that running with sequential_cpu_offload will be slower.
 
 Some GPUs that do not support torch.bfloat16, such as 2080ti and V100, require changing the weight_dtype in app.py and predict files to torch.float16 in order to run.
@@ -400,6 +409,13 @@ For details on setting some parameters, please refer to [Readme Train](scripts/R
 
 EasyAnimateV5:
 
+7B:
+| Name | Type | Storage Space | Hugging Face | Model Scope | Description |
+|--|--|--|--|--|--|
+| EasyAnimateV5-7b-zh-InP | EasyAnimateV5 | 22 GB | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV5-7b-zh-InP) | [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV5-7b-zh-InP) | Official 7B image-to-video weights. Supports video prediction at multiple resolutions (512, 768, 1024), trained with 49 frames at 8 frames per second, and supports bilingual prediction in Chinese and English. |
+| EasyAnimateV5-7b-zh | EasyAnimateV5 | 22 GB | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV5-7b-zh) | [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV5-7b-zh) | Official 7B text-to-video weights. Supports video prediction at multiple resolutions (512, 768, 1024), trained with 49 frames at 8 frames per second, and supports bilingual prediction in Chinese and English. |
+
+12B:
 | Name | Type | Storage Space | Hugging Face | Model Scope | Description |
 |--|--|--|--|--|--|
 | EasyAnimateV5-12b-zh-InP | EasyAnimateV5 | 34 GB | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV5-12b-zh-InP) | [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV5-12b-zh-InP) | Official image-to-video weights. Supports video prediction at multiple resolutions (512, 768, 1024), trained with 49 frames at 8 frames per second, and supports bilingual prediction in Chinese and English. |
@@ -409,28 +425,29 @@ EasyAnimateV5:
 <details>
   <summary>(Obsolete) EasyAnimateV4:</summary>
 
-| Name | Type | Storage Space | Url | Hugging Face | Description |
+| Name | Type | Storage Space | Hugging Face | Model Scope | Description |
 |--|--|--|--|--|--|
-| EasyAnimateV4-XL-2-InP.tar.gz | EasyAnimateV4 | Before extraction: 8.9 GB \/ After extraction: 14.0 GB | [Download](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Diffusion_Transformer/EasyAnimateV4-XL-2-InP.tar.gz) | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV4-XL-2-InP)| Our official graph-generated video model is capable of predicting videos at multiple resolutions (512, 768, 1024, 1280) and has been trained on 144 frames at a rate of 24 frames per second. |
+| EasyAnimateV4-XL-2-InP.tar.gz | EasyAnimateV4 | Before extraction: 8.9 GB \/ After extraction: 14.0 GB |[🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV4-XL-2-InP)| [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV4-XL-2-InP)| | Our official graph-generated video model is capable of predicting videos at multiple resolutions (512, 768, 1024, 1280) and has been trained on 144 frames at a rate of 24 frames per second. |
 </details>
 
 <details>
   <summary>(Obsolete) EasyAnimateV3:</summary>
 
-| Name | Type | Storage Space | Url | Hugging Face | Description |
+| Name | Type | Storage Space | Hugging Face | Model Scope | Description |
 |--|--|--|--|--|--|
-| EasyAnimateV3-XL-2-InP-512x512.tar | EasyAnimateV3 | 18.2GB | [Download](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Diffusion_Transformer/EasyAnimateV3-XL-2-InP-512x512.tar) | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV3-XL-2-InP-512x512) | EasyAnimateV3 official weights for 512x512 text and image to video resolution. Training with 144 frames and fps 24 |
-| EasyAnimateV3-XL-2-InP-768x768.tar | EasyAnimateV3 | 18.2GB | [Download](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Diffusion_Transformer/EasyAnimateV3-XL-2-InP-768x768.tar) | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV3-XL-2-InP-768x768) | EasyAnimateV3 official weights for 768x768 text and image to video resolution. Training with 144 frames and fps 24 |
-| EasyAnimateV3-XL-2-InP-960x960.tar | EasyAnimateV3 | 18.2GB | [Download](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Diffusion_Transformer/EasyAnimateV3-XL-2-InP-960x960.tar) | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV3-XL-2-InP-960x960) | EasyAnimateV3 official weights for 960x960 text and  image to video resolution. Training with 144 frames and fps 24 |
+| EasyAnimateV3-XL-2-InP-512x512.tar | EasyAnimateV3 | 18.2GB | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV3-XL-2-InP-512x512)| [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV3-XL-2-InP-512x512) | EasyAnimateV3 official weights for 512x512 text and image to video resolution. Training with 144 frames and fps 24 |
+| EasyAnimateV3-XL-2-InP-768x768.tar | EasyAnimateV3 | 18.2GB | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV3-XL-2-InP-768x768) | [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV3-XL-2-InP-768x768) | EasyAnimateV3 official weights for 768x768 text and image to video resolution. Training with 144 frames and fps 24 |
+| EasyAnimateV3-XL-2-InP-960x960.tar | EasyAnimateV3 | 18.2GB | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV3-XL-2-InP-960x960) | [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV3-XL-2-InP-960x960) | EasyAnimateV3 official weights for 960x960 text and  image to video resolution. Training with 144 frames and fps 24 |
 </details>
 
 <details>
   <summary>(Obsolete) EasyAnimateV2:</summary>
-| Name | Type | Storage Space | Url | Hugging Face | Description |
-|--|--|--|--|--|--|
-| EasyAnimateV2-XL-2-512x512.tar | EasyAnimateV2 | 16.2GB | [Download](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Diffusion_Transformer/EasyAnimateV2-XL-2-512x512.tar) | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV2-XL-2-512x512) | EasyAnimateV2 official weights for 512x512 resolution. Training with 144 frames and fps 24 |
-| EasyAnimateV2-XL-2-768x768.tar | EasyAnimateV2 | 16.2GB | [Download](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Diffusion_Transformer/EasyAnimateV2-XL-2-768x768.tar) | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV2-XL-2-768x768) | EasyAnimateV2 official weights for 768x768 resolution. Training with 144 frames and fps 24 |
-| easyanimatev2_minimalism_lora.safetensors | Lora of Pixart | 485.1MB | [Download](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Personalized_Model/easyanimatev2_minimalism_lora.safetensors) | - | A lora training with a specifial type images. Images can be downloaded from [Url](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/asset/v2/Minimalism.zip). |
+
+| Name | Type | Storage Space | Url | Hugging Face | Model Scope | Description |
+|--|--|--|--|--|--|--|
+| EasyAnimateV2-XL-2-512x512.tar | EasyAnimateV2 | 16.2GB | - | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV2-XL-2-512x512)| [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV2-XL-2-512x512)| EasyAnimateV2 official weights for 512x512 resolution. Training with 144 frames and fps 24 |
+| EasyAnimateV2-XL-2-768x768.tar | EasyAnimateV2 | 16.2GB | - | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV2-XL-2-768x768) | [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV2-XL-2-768x768)| EasyAnimateV2 official weights for 768x768 resolution. Training with 144 frames and fps 24 |
+| easyanimatev2_minimalism_lora.safetensors | Lora of Pixart | 485.1MB | [Download](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Personalized_Model/easyanimatev2_minimalism_lora.safetensors)| - | - | A lora training with a specifial type images. Images can be downloaded from [Url](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/asset/v2/Minimalism.zip). |
 </details>
 
 <details>
diff --git a/README_ja-JP.md b/README_ja-JP.md
index ee7e866..b6bc319 100644
--- a/README_ja-JP.md
+++ b/README_ja-JP.md
@@ -121,6 +121,14 @@ EasyAnimateV5-12Bのビデオサイズは異なるGPUメモリにより生成で
 | 40GB | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 
 | 80GB | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 
 
+EasyAnimateV5-7Bのビデオサイズは異なるGPUメモリにより生成できます。以下の表をご覧ください：
+| GPUメモリ |384x672x72|384x672x49|576x1008x25|576x1008x49|768x1344x25|768x1344x49|
+|----------|----------|----------|----------|----------|----------|----------|
+| 16GB | 🧡 | 🧡 | ❌ | ❌ | ❌ | ❌ | 
+| 24GB | ✅ | ✅ | 🧡 | 🧡 | ❌ | ❌ | 
+| 40GB | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 
+| 80GB | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 
+
 ✅ は"model_cpu_offload"の条件で実行可能であることを示し、🧡は"model_cpu_offload_and_qfloat8"の条件で実行可能を示し、⭕️ は"sequential_cpu_offload"の条件では実行可能であることを示しています。❌は実行できないことを示します。sequential_cpu_offloadにより実行する場合は遅くなります。
 
 一部のGPU（例：2080ti、V100）はtorch.bfloat16をサポートしていないため、app.pyおよびpredictファイル内のweight_dtypeをtorch.float16に変更する必要があります。
@@ -395,6 +403,13 @@ sh scripts/train.sh
 
 EasyAnimateV5:
 
+7B:
+| 名前 | 種類 | ストレージスペース | Hugging Face | Model Scope | 説明 |
+|--|--|--|--|--|--|
+| EasyAnimateV5-7b-zh-InP | EasyAnimateV5 | 22 GB | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV5-7b-zh-InP) | [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV5-7b-zh-InP) | 公式の画像から動画への重み。複数の解像度（512、768、1024）での動画予測をサポートし、49フレーム、毎秒8フレームでトレーニングされ、中国語と英語のバイリンガル予測をサポートします。 |
+| EasyAnimateV5-7b-zh | EasyAnimateV5 | 22 GB | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV5-7b-zh) | [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV5-7b-zh) | 公式のテキストから動画への重み。複数の解像度（512、768、1024）での動画予測をサポートし、49フレーム、毎秒8フレームでトレーニングされ、中国語と英語のバイリンガル予測をサポートします。 |
+
+12B:
 | 名前 | 種類 | ストレージスペース | Hugging Face | Model Scope | 説明 |
 |--|--|--|--|--|--|
 | EasyAnimateV5-12b-zh-InP | EasyAnimateV5 | 34 GB | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV5-12b-zh-InP) | [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV5-12b-zh-InP) | 公式の画像から動画への重み。複数の解像度（512、768、1024）での動画予測をサポートし、49フレーム、毎秒8フレームでトレーニングされ、中国語と英語のバイリンガル予測をサポートします。 |
@@ -404,29 +419,29 @@ EasyAnimateV5:
 <details>
   <summary>(Obsolete) EasyAnimateV4:</summary>
 
-| 名前 | 種類 | ストレージスペース | URL | Hugging Face | 説明 |
+| 名前 | 種類 | ストレージスペース | Hugging Face | Model Scope | 説明 |
 |--|--|--|--|--|--|
-| EasyAnimateV4-XL-2-InP.tar.gz | EasyAnimateV4 | 解凍前: 8.9 GB / 解凍後: 14.0 GB | [ダウンロード](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Diffusion_Transformer/EasyAnimateV4-XL-2-InP.tar.gz) | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV4-XL-2-InP)| 公式のグラフ生成動画モデル。複数の解像度（512、768、1024、1280）での動画予測をサポートし、144フレーム、毎秒24フレームでトレーニングされています。 |
+| EasyAnimateV4-XL-2-InP.tar.gz | EasyAnimateV4 | 解凍前: 8.9 GB / 解凍後: 14.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV4-XL-2-InP)| [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV4-XL-2-InP) | 公式のグラフ生成動画モデル。複数の解像度（512、768、1024、1280）での動画予測をサポートし、144フレーム、毎秒24フレームでトレーニングされています。 |
 </details>
 
 <details>
   <summary>(Obsolete) EasyAnimateV3:</summary>
 
-| 名前 | 種類 | ストレージスペース | URL | Hugging Face | 説明 |
+| 名前 | 種類 | ストレージスペース | Hugging Face | Model Scope | 説明 |
 |--|--|--|--|--|--|
-| EasyAnimateV3-XL-2-InP-512x512.tar | EasyAnimateV3 | 18.2GB | [ダウンロード](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Diffusion_Transformer/EasyAnimateV3-XL-2-InP-512x512.tar) | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV3-XL-2-InP-512x512) | EasyAnimateV3公式の512x512テキストおよび画像から動画への重み。144フレーム、毎秒24フレームでトレーニングされています。 |
-| EasyAnimateV3-XL-2-InP-768x768.tar | EasyAnimateV3 | 18.2GB | [ダウンロード](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Diffusion_Transformer/EasyAnimateV3-XL-2-InP-768x768.tar) | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV3-XL-2-InP-768x768) | EasyAnimateV3公式の768x768テキストおよび画像から動画への重み。144フレーム、毎秒24フレームでトレーニングされています。 |
-| EasyAnimateV3-XL-2-InP-960x960.tar | EasyAnimateV3 | 18.2GB | [ダウンロード](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Diffusion_Transformer/EasyAnimateV3-XL-2-InP-960x960.tar) | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV3-XL-2-InP-960x960) | EasyAnimateV3公式の960x960テキストおよび画像から動画への重み。144フレーム、毎秒24フレームでトレーニングされています。 |
+| EasyAnimateV3-XL-2-InP-512x512.tar | EasyAnimateV3 | 18.2GB | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV3-XL-2-InP-512x512)| [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV3-XL-2-InP-512x512) | EasyAnimateV3公式の512x512テキストおよび画像から動画への重み。144フレーム、毎秒24フレームでトレーニングされています。 |
+| EasyAnimateV3-XL-2-InP-768x768.tar | EasyAnimateV3 | 18.2GB | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV3-XL-2-InP-768x768) | [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV3-XL-2-InP-768x768) | EasyAnimateV3公式の768x768テキストおよび画像から動画への重み。144フレーム、毎秒24フレームでトレーニングされています。 |
+| EasyAnimateV3-XL-2-InP-960x960.tar | EasyAnimateV3 | 18.2GB | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV3-XL-2-InP-960x960) | [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV3-XL-2-InP-960x960) | EasyAnimateV3公式の960x960テキストおよび画像から動画への重み。144フレーム、毎秒24フレームでトレーニングされています。 |
 </details>
 
 <details>
   <summary>(Obsolete) EasyAnimateV2:</summary>
 
-| 名前 | 種類 | ストレージスペース | URL | Hugging Face | 説明 |
-|--|--|--|--|--|--|
-| EasyAnimateV2-XL-2-512x512.tar | EasyAnimateV2 | 16.2GB | [ダウンロード](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Diffusion_Transformer/EasyAnimateV2-XL-2-512x512.tar) | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV2-XL-2-512x512) | EasyAnimateV2公式の512x512解像度の重み。144フレーム、毎秒24フレームでトレーニングされています。 |
-| EasyAnimateV2-XL-2-768x768.tar | EasyAnimateV2 | 16.2GB | [ダウンロード](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Diffusion_Transformer/EasyAnimateV2-XL-2-768x768.tar) | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV2-XL-2-768x768) | EasyAnimateV2公式の768x768解像度の重み。144フレーム、毎秒24フレームでトレーニングされています。 |
-| easyanimatev2_minimalism_lora.safetensors | Lora of Pixart | 485.1MB | [ダウンロード](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Personalized_Model/easyanimatev2_minimalism_lora.safetensors) | - | 特定のタイプの画像でトレーニングされたLora。画像は[URL](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/asset/v2/Minimalism.zip)からダウンロードできます。 |
+| 名前 | 種類 | ストレージスペース | URL | Hugging Face | Model Scope | 説明 |
+|--|--|--|--|--|--|--|
+| EasyAnimateV2-XL-2-512x512.tar | EasyAnimateV2 | 16.2GB |  - | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV2-XL-2-512x512)| [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV2-XL-2-512x512) | EasyAnimateV2公式の512x512解像度の重み。144フレーム、毎秒24フレームでトレーニングされています。 |
+| EasyAnimateV2-XL-2-768x768.tar | EasyAnimateV2 | 16.2GB | - | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV2-XL-2-768x768) | [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV2-XL-2-768x768) | EasyAnimateV2公式の768x768解像度の重み。144フレーム、毎秒24フレームでトレーニングされています。 |
+| easyanimatev2_minimalism_lora.safetensors | Lora of Pixart | 485.1MB | [ダウンロード](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Personalized_Model/easyanimatev2_minimalism_lora.safetensors) | - | - | 特定のタイプの画像でトレーニングされたLora。画像は[URL](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/asset/v2/Minimalism.zip)からダウンロードできます。 |
 </details>
 
 <details>
diff --git a/README_zh-CN.md b/README_zh-CN.md
index 4dd25e7..2b514f3 100644
--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@@ -119,6 +119,14 @@ EasyAnimateV5-12B的视频大小可以由不同的GPU Memory生成，包括：
 | 40GB | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | 
 | 80GB | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 
 
+EasyAnimateV5-7B的视频大小可以由不同的GPU Memory生成，包括：
+| GPU memory |384x672x72|384x672x49|576x1008x25|576x1008x49|768x1344x25|768x1344x49|
+|----------|----------|----------|----------|----------|----------|----------|
+| 16GB | 🧡 | 🧡 | ❌ | ❌ | ❌ | ❌ | 
+| 24GB | ✅ | ✅ | 🧡 | 🧡 | ❌ | ❌ | 
+| 40GB | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | 
+| 80GB | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 
+
 ✅ 表示它可以在"model_cpu_offload"的情况下运行，🧡代表它可以在"model_cpu_offload_and_qfloat8"的情况下运行，⭕️ 表示它可以在"sequential_cpu_offload"的情况下运行，❌ 表示它无法运行。请注意，使用sequential_cpu_offload运行会更慢。
 
 有一些不支持torch.bfloat16的卡型，如2080ti、V100，需要将app.py、predict文件中的weight_dtype修改为torch.float16才可以运行。
@@ -395,6 +403,13 @@ sh scripts/train.sh
 # 模型地址
 EasyAnimateV5:
 
+7B:
+| 名称 | 种类 | 存储空间 | Hugging Face | Model Scope | 描述 |
+|--|--|--|--|--|--|
+| EasyAnimateV5-7b-zh-InP | EasyAnimateV5 | 22 GB | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV5-7b-zh-InP) | [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV5-7b-zh-InP)| 官方的7B图生视频权重。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以49帧、每秒8帧进行训练，支持中文与英文双语预测 |
+| EasyAnimateV5-7b-zh | EasyAnimateV5 | 22 GB | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV5-7b-zh) | [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV5-12b-zh)| 官方的7B文生视频权重。可用于进行下游任务的fientune。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以49帧、每秒8帧进行训练，支持中文与英文双语预测 |
+
+12B:
 | 名称 | 种类 | 存储空间 | Hugging Face | Model Scope | 描述 |
 |--|--|--|--|--|--|
 | EasyAnimateV5-12b-zh-InP | EasyAnimateV5 | 34 GB | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV5-12b-zh-InP) | [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV5-12b-zh-InP)| 官方的图生视频权重。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以49帧、每秒8帧进行训练，支持中文与英文双语预测 |
@@ -404,29 +419,29 @@ EasyAnimateV5:
 <details>
   <summary>(Obsolete) EasyAnimateV4:</summary>
 
-| 名称 | 种类 | 存储空间 | 下载地址 | Hugging Face | 描述 |
+| 名称 | 种类 | 存储空间 | Hugging Face | Model Scope | 描述 |
 |--|--|--|--|--|--|
-| EasyAnimateV4-XL-2-InP.tar.gz | EasyAnimateV4 | 解压前 8.9 GB / 解压后 14.0 GB | [Download](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Diffusion_Transformer/EasyAnimateV4-XL-2-InP.tar.gz) | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV4-XL-2-InP)| 官方的图生视频权重。支持多分辨率（512，768，1024，1280）的视频预测，以144帧、每秒24帧进行训练 |
+| EasyAnimateV4-XL-2-InP.tar.gz | EasyAnimateV4 | 解压前 8.9 GB / 解压后 14.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV4-XL-2-InP)| [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV4-XL-2-InP)| 官方的图生视频权重。支持多分辨率（512，768，1024，1280）的视频预测，以144帧、每秒24帧进行训练 |
 </details>
 
 <details>
   <summary>(Obsolete) EasyAnimateV3:</summary>
 
-| 名称 | 种类 | 存储空间 | 下载地址 | Hugging Face | 描述 |
+| 名称 | 种类 | 存储空间 | Hugging Face | Model Scope | 描述 |
 |--|--|--|--|--|--|
-| EasyAnimateV3-XL-2-InP-512x512.tar | EasyAnimateV3 | 18.2GB | [Download](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Diffusion_Transformer/EasyAnimateV3-XL-2-InP-512x512.tar) | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV3-XL-2-InP-512x512)| 官方的512x512分辨率的图生视频权重。以144帧、每秒24帧进行训练 |
-| EasyAnimateV3-XL-2-InP-768x768.tar | EasyAnimateV3 | 18.2GB | [Download](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Diffusion_Transformer/EasyAnimateV3-XL-2-InP-768x768.tar) | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV3-XL-2-InP-768x768) | 官方的768x768分辨率的图生视频权重。以144帧、每秒24帧进行训练 |
-| EasyAnimateV3-XL-2-InP-960x960.tar | EasyAnimateV3 | 18.2GB | [Download](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Diffusion_Transformer/EasyAnimateV3-XL-2-InP-960x960.tar) | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV3-XL-2-InP-960x960) | 官方的960x960（720P）分辨率的图生视频权重。以144帧、每秒24帧进行训练 |
+| EasyAnimateV3-XL-2-InP-512x512.tar | EasyAnimateV3 | 18.2GB| [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV3-XL-2-InP-512x512)| [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV3-XL-2-InP-512x512)| 官方的512x512分辨率的图生视频权重。以144帧、每秒24帧进行训练 |
+| EasyAnimateV3-XL-2-InP-768x768.tar | EasyAnimateV3 | 18.2GB | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV3-XL-2-InP-768x768) | [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV3-XL-2-InP-768x768)| 官方的768x768分辨率的图生视频权重。以144帧、每秒24帧进行训练 |
+| EasyAnimateV3-XL-2-InP-960x960.tar | EasyAnimateV3 | 18.2GB | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV3-XL-2-InP-960x960) | [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV3-XL-2-InP-960x960)| 官方的960x960（720P）分辨率的图生视频权重。以144帧、每秒24帧进行训练 |
 </details>
 
 <details>
   <summary>(Obsolete) EasyAnimateV2:</summary>
 
-| 名称 | 种类 | 存储空间 | 下载地址 | Hugging Face | 描述 |
-|--|--|--|--|--|--|
-| EasyAnimateV2-XL-2-512x512.tar | EasyAnimateV2 | 16.2GB | [Download](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Diffusion_Transformer/EasyAnimateV2-XL-2-512x512.tar) | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV2-XL-2-512x512)| 官方的512x512分辨率的重量。以144帧、每秒24帧进行训练 |
-| EasyAnimateV2-XL-2-768x768.tar | EasyAnimateV2 | 16.2GB | [Download](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Diffusion_Transformer/EasyAnimateV2-XL-2-768x768.tar) | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV2-XL-2-768x768) | 官方的768x768分辨率的重量。以144帧、每秒24帧进行训练 |
-| easyanimatev2_minimalism_lora.safetensors | Lora of Pixart | 485.1MB | [Download](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Personalized_Model/easyanimatev2_minimalism_lora.safetensors)| - | 使用特定类型的图像进行lora训练的结果。图片可从这里[下载](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/webui/Minimalism.zip). |
+| 名称 | 种类 | 存储空间 | 下载地址 | Hugging Face | Model Scope | 描述 |
+|--|--|--|--|--|--|--|
+| EasyAnimateV2-XL-2-512x512.tar | EasyAnimateV2 | 16.2GB | - | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV2-XL-2-512x512)| [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV2-XL-2-512x512)| 官方的512x512分辨率的重量。以144帧、每秒24帧进行训练 |
+| EasyAnimateV2-XL-2-768x768.tar | EasyAnimateV2 | 16.2GB | - | [🤗Link](https://huggingface.co/alibaba-pai/EasyAnimateV2-XL-2-768x768) | [😄Link](https://modelscope.cn/models/PAI/EasyAnimateV2-XL-2-768x768)| 官方的768x768分辨率的重量。以144帧、每秒24帧进行训练 |
+| easyanimatev2_minimalism_lora.safetensors | Lora of Pixart | 485.1MB | [Download](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/Personalized_Model/easyanimatev2_minimalism_lora.safetensors)| - | - | 使用特定类型的图像进行lora训练的结果。图片可从这里[下载](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/webui/Minimalism.zip). |
 </details>
 
 <details>
diff --git a/comfyui/comfyui_nodes.py b/comfyui/comfyui_nodes.py
index e37f216..3b20566 100644
--- a/comfyui/comfyui_nodes.py
+++ b/comfyui/comfyui_nodes.py
@@ -69,12 +69,14 @@ class LoadEasyAnimateModel:
                         'EasyAnimateV3-XL-2-InP-768x768',
                         'EasyAnimateV3-XL-2-InP-960x960',
                         'EasyAnimateV4-XL-2-InP',
+                        'EasyAnimateV5-7b-zh-InP',
+                        'EasyAnimateV5-7b-zh',
                         'EasyAnimateV5-12b-zh-InP',
                         'EasyAnimateV5-12b-zh-Control',
                         'EasyAnimateV5-12b-zh',
                     ],
                     {
-                        "default": 'EasyAnimateV5-12b-zh-InP',
+                        "default": 'EasyAnimateV5-7b-zh-InP',
                     }
                 ),
                 "GPU_memory_mode":(
diff --git a/easyanimate/models/autoencoder_magvit.py b/easyanimate/models/autoencoder_magvit.py
index 62ee173..a10f6f0 100644
--- a/easyanimate/models/autoencoder_magvit.py
+++ b/easyanimate/models/autoencoder_magvit.py
@@ -44,6 +44,7 @@ from ..vae.ldm.models.cogvideox_enc_dec import (CogVideoXCausalConv3d,
                                                 CogVideoXDecoder3D,
                                                 CogVideoXEncoder3D,
                                                 CogVideoXSafeConv3d)
+from ..vae.ldm.models.omnigen_enc_dec import CausalConv3d
 from ..vae.ldm.models.omnigen_enc_dec import Decoder as omnigen_Mag_Decoder
 from ..vae.ldm.models.omnigen_enc_dec import Encoder as omnigen_Mag_Encoder
 
@@ -96,6 +97,7 @@ class AutoencoderKLMagvit(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
         out_channels: int = 3,
         ch =  128,
         ch_mult = [ 1,2,4,4 ],
+        block_out_channels = [128, 256, 512, 512],
         use_gc_blocks = None,
         down_block_types: tuple = None,
         up_block_types: tuple = None,
@@ -109,6 +111,7 @@ class AutoencoderKLMagvit(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
         latent_channels: int = 4,
         norm_num_groups: int = 32,
         scaling_factor: float = 0.1825,
+        force_upcast: float = True,
         slice_mag_vae=True,
         slice_compression_vae=False,
         cache_compression_vae=False,
@@ -130,8 +133,9 @@ class AutoencoderKLMagvit(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
             in_channels=in_channels,
             out_channels=latent_channels,
             down_block_types=down_block_types,
-            ch = ch,
-            ch_mult = ch_mult,
+            ch=ch,
+            ch_mult=ch_mult,
+            block_out_channels=block_out_channels,
             use_gc_blocks=use_gc_blocks,
             mid_block_type=mid_block_type,
             mid_block_use_attention=mid_block_use_attention,
@@ -154,8 +158,9 @@ class AutoencoderKLMagvit(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
             in_channels=latent_channels,
             out_channels=out_channels,
             up_block_types=up_block_types,
-            ch = ch,
-            ch_mult = ch_mult,
+            ch=ch,
+            ch_mult=ch_mult,
+            block_out_channels=block_out_channels,
             use_gc_blocks=use_gc_blocks,
             mid_block_type=mid_block_type,
             mid_block_use_attention=mid_block_use_attention,
@@ -272,6 +277,11 @@ class AutoencoderKLMagvit(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
 
         self.set_attn_processor(processor)
 
+    def _clear_conv_cache(self):
+        for name, module in self.named_modules():
+            if isinstance(module, CausalConv3d):
+                module._clear_conv_cache()
+
     @apply_forward_hook
     def encode(
         self, x: torch.FloatTensor, return_dict: bool = True
@@ -308,6 +318,7 @@ class AutoencoderKLMagvit(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
         moments = self.quant_conv(h)
         posterior = DiagonalGaussianDistribution(moments)
 
+        self._clear_conv_cache()
         if not return_dict:
             return (posterior,)
 
@@ -355,6 +366,7 @@ class AutoencoderKLMagvit(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
         else:
             decoded = self._decode(z).sample
 
+        self._clear_conv_cache()
         if not return_dict:
             return (decoded,)
 
diff --git a/easyanimate/vae/ldm/models/omnigen_casual3dcnn.py b/easyanimate/vae/ldm/models/omnigen_casual3dcnn.py
index 12692da..cba33e1 100644
--- a/easyanimate/vae/ldm/models/omnigen_casual3dcnn.py
+++ b/easyanimate/vae/ldm/models/omnigen_casual3dcnn.py
@@ -95,6 +95,7 @@ class AutoencoderKLMagvit_fromOmnigen(pl.LightningModule):
         out_channels: int = 3,
         ch =  128,
         ch_mult = [ 1,2,4,4 ],
+        block_out_channels = [128, 256, 512, 512],
         use_gc_blocks = None,
         down_block_types: tuple = None,
         up_block_types: tuple = None,
@@ -129,8 +130,9 @@ class AutoencoderKLMagvit_fromOmnigen(pl.LightningModule):
             in_channels=in_channels,
             out_channels=latent_channels,
             down_block_types=down_block_types,
-            ch = ch,
-            ch_mult = ch_mult,
+            ch=ch,
+            ch_mult=ch_mult,
+            block_out_channels=block_out_channels,
             use_gc_blocks=use_gc_blocks,
             mid_block_type=mid_block_type,
             mid_block_use_attention=mid_block_use_attention,
@@ -144,6 +146,7 @@ class AutoencoderKLMagvit_fromOmnigen(pl.LightningModule):
             slice_mag_vae=slice_mag_vae,
             slice_compression_vae=slice_compression_vae,
             cache_compression_vae=cache_compression_vae,
+            cache_mag_vae=cache_mag_vae,
             spatial_group_norm=spatial_group_norm,
             mini_batch_encoder=mini_batch_encoder,
         )
@@ -152,8 +155,9 @@ class AutoencoderKLMagvit_fromOmnigen(pl.LightningModule):
             in_channels=latent_channels,
             out_channels=out_channels,
             up_block_types=up_block_types,
-            ch = ch,
-            ch_mult = ch_mult,
+            ch=ch,
+            ch_mult=ch_mult,
+            block_out_channels=block_out_channels,
             use_gc_blocks=use_gc_blocks,
             mid_block_type=mid_block_type,
             mid_block_use_attention=mid_block_use_attention,
diff --git a/easyanimate/vae/ldm/models/omnigen_enc_dec.py b/easyanimate/vae/ldm/models/omnigen_enc_dec.py
index ff501f1..3c1f359 100644
--- a/easyanimate/vae/ldm/models/omnigen_enc_dec.py
+++ b/easyanimate/vae/ldm/models/omnigen_enc_dec.py
@@ -58,6 +58,7 @@ class Encoder(nn.Module):
         down_block_types = ("SpatialDownBlock3D",),
         ch = 128,
         ch_mult = [1,2,4,4,],
+        block_out_channels = [128, 256, 512, 512],
         use_gc_blocks = None,
         mid_block_type: str = "MidBlock3D",
         mid_block_use_attention: bool = True,
@@ -77,7 +78,8 @@ class Encoder(nn.Module):
         verbose = False,
     ):
         super().__init__()
-        block_out_channels = [ch * i for i in ch_mult]
+        if block_out_channels is None:
+            block_out_channels = [ch * i for i in ch_mult]
         assert len(down_block_types) == len(block_out_channels), (
             "Number of down block types must match number of block output channels."
         )
@@ -364,6 +366,7 @@ class Decoder(nn.Module):
         up_block_types  = ("SpatialUpBlock3D",),
         ch = 128,
         ch_mult = [1,2,4,4,],
+        block_out_channels = [128, 256, 512, 512],
         use_gc_blocks = None,
         mid_block_type: str = "MidBlock3D",
         mid_block_use_attention: bool = True,
@@ -382,7 +385,8 @@ class Decoder(nn.Module):
         verbose = False,
     ):
         super().__init__()
-        block_out_channels = [ch * i for i in ch_mult]
+        if block_out_channels is None:
+            block_out_channels = [ch * i for i in ch_mult]
         assert len(up_block_types) == len(block_out_channels), (
             "Number of up block types must match number of block output channels."
         )
diff --git a/easyanimate/vae/ldm/modules/vaemodules/common.py b/easyanimate/vae/ldm/modules/vaemodules/common.py
index f13f355..f865527 100755
--- a/easyanimate/vae/ldm/modules/vaemodules/common.py
+++ b/easyanimate/vae/ldm/modules/vaemodules/common.py
@@ -77,6 +77,10 @@ class CausalConv3d(nn.Conv3d):
             **kwargs,
         )
 
+    def _clear_conv_cache(self):
+        del self.prev_features
+        self.prev_features = None
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         # x: (B, C, T, H, W)
         dtype = x.dtype
@@ -97,7 +101,11 @@ class CausalConv3d(nn.Conv3d):
                 mode="replicate",     # TODO: check if this is necessary
             )
             x = x.to(dtype=dtype)
-            self.prev_features = x[:, :, -self.temporal_padding:]
+
+            # Clear cache before
+            self._clear_conv_cache()
+            # We could move these to the cpu for a lower VRAM
+            self.prev_features = x[:, :, -self.temporal_padding:].clone()
 
             b, c, f, h, w = x.size()
             outputs = []
@@ -117,7 +125,11 @@ class CausalConv3d(nn.Conv3d):
                     [self.prev_features, x], dim = 2
                 )
             x = x.to(dtype=dtype)
-            self.prev_features = x[:, :, -self.temporal_padding:]
+
+            # Clear cache before
+            self._clear_conv_cache()
+            # We could move these to the cpu for a lower VRAM
+            self.prev_features = x[:, :, -self.temporal_padding:].clone()
 
             b, c, f, h, w = x.size()
             outputs = []
@@ -134,7 +146,12 @@ class CausalConv3d(nn.Conv3d):
                 mode="replicate",     # TODO: check if this is necessary
             )
             x = x.to(dtype=dtype)
-            self.prev_features = x[:, :, -self.temporal_padding:]
+
+            # Clear cache before
+            self._clear_conv_cache()
+            # We could move these to the cpu for a lower VRAM
+            self.prev_features = x[:, :, -self.temporal_padding:].clone()
+            
             return super().forward(x)
         elif self.padding_flag == 6:
             if self.t_stride == 2:
@@ -145,7 +162,12 @@ class CausalConv3d(nn.Conv3d):
                 x = torch.concat(
                     [self.prev_features, x], dim = 2
                 )
-            self.prev_features = x[:, :, -self.temporal_padding:]
+
+            # Clear cache before
+            self._clear_conv_cache()
+            # We could move these to the cpu for a lower VRAM
+            self.prev_features = x[:, :, -self.temporal_padding:].clone()
+            
             x = x.to(dtype=dtype)
             return super().forward(x)
         else: