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@@ -31,6 +31,7 @@ EasyAnimate is a pipeline based on the transformer architecture, designed for ge
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We will support quick pull-ups from different platforms, refer to [Quick Start](#quick-start).
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**New Features:**
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- EasyAnimate-V5.1 is now supported in diffusers. For more implementation details, please refer to the [PR](https://github.com/huggingface/diffusers/pull/10626). Relevant weights can be downloaded from [EasyAnimate-V5.1-diffusers](https://huggingface.co/collections/alibaba-pai/easyanimate-v51-diffusers-67c81d1d19b236e056675cce). For usage instructions, please refer to [Usage](https://huggingface.co/alibaba-pai/EasyAnimateV5.1-7b-zh-diffusers#a%E3%80%81text-to-video). [ 2025.03.06 ]
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- **Updated to version v5.1**, the Qwen2 VL is used as the text encoder, and Flow is used as the sampling method. It supports bilingual prediction in both Chinese and English. In addition to common controls such as Canny and Pose, it also supports trajectory control, camera control. [2025.01.21]
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- Use reward backpropagation to train Lora and optimize the video, aligning it better with human preferences, detailes in [here](scripts/README_TRAIN_REWARD.md). EasyAnimateV5-7b is released now. [2024.11.27]
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- **Updated to v5**, supporting video generation up to 1024x1024, 49 frames, 6s, 8fps, with expanded model scale to 12B, incorporating the MMDIT structure, and enabling control models with diverse inputs; supports bilingual predictions in Chinese and English. [2024.11.08]
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@@ -31,6 +31,7 @@ EasyAnimateは、トランスフォーマーアーキテクチャに基づいた
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異なるプラットフォームからのクイックプルアップをサポートします。詳細は[クイックスタート](#クイックスタート)を参照してください。
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**新機能:**
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- EasyAnimate-V5.1は現在diffusersでサポートされています。実装の詳細については、[PR](https://github.com/huggingface/diffusers/pull/10626)をご覧ください。関連する重みは[EasyAnimate-V5.1-diffusers](https://huggingface.co/collections/alibaba-pai/easyanimate-v51-diffusers-67c81d1d19b236e056675cce)からダウンロードできます。使用方法については、[Usage](https://huggingface.co/alibaba-pai/EasyAnimateV5.1-7b-zh-diffusers#a%E3%80%81text-to-video)を参照してください。 [ 2025.03.06 ]
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- **バージョンv5.1に更新**、Qwen2 VLがテキストエンコーダーとして使用され、Flowがサンプリング方法として使用されます。中国語と英語の両方でバイリンガル予測をサポートしています。CannyやPoseといった一般的なコントロールに加えて、軌道制御やカメラ制御もサポートしています。[2025.01.21]
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- インセンティブ逆伝播を使用してLoraを訓練し、人間の好みに合うようにビデオを最適化します。詳細は、[ここ]（scripts/README _ train _ REVARD.md）を参照してください。EasyAnimateV 5-7 bがリリースされました。[2024.11.27]
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- **v5に更新**、1024x1024までの動画生成をサポート、49フレーム、6秒、8fps、モデルスケールを12Bに拡張、MMDIT構造を組み込み、さまざまな入力を持つ制御モデルをサポート。中国語と英語のバイリンガル予測をサポート。[2024.11.08]
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@@ -31,6 +31,7 @@ EasyAnimate是一个基于transformer结构的pipeline，可用于生成AI图片
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我们会逐渐支持从不同平台快速启动，请参阅 [快速启动](#快速启动)。
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新特性：
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- EasyAnimate-V5.1现已在diffusers中得到支持，更多实现细节请参考[PR](https://github.com/huggingface/diffusers/pull/10626)，相关权重可以在[EasyAnimate-V5.1-diffusers](https://huggingface.co/collections/alibaba-pai/easyanimate-v51-diffusers-67c81d1d19b236e056675cce)下载，使用方案请参考[Usage](https://huggingface.co/alibaba-pai/EasyAnimateV5.1-7b-zh-diffusers#a%E3%80%81text-to-video)。 [ 2025.03.06 ]
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- 更新到v5.1版本，应用Qwen2 VL作为文本编码器，支持多语言预测，使用Flow作为采样方式，除去常见控制如Canny、Pose外，还支持轨迹控制，相机控制等。[ 2025.01.21 ]
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- 使用奖励反向传播来训练Lora并优化视频，使其更好地符合人类偏好，详细信息请参见[此处](scripts/README_train_REVARD.md)。EasyAnimateV5-7b现已发布。[ 2024.11.27 ]
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- 更新到v5版本，最大支持1024x1024，49帧, 6s, 8fps视频生成，拓展模型规模到12B，应用MMDIT结构，支持不同输入的控制模型，支持中文与英文双语预测。[ 2024.11.08 ]
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0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.397959184 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.428571429 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.459183673 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.489795918 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.520408163 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.551020408 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.581632653 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.612244898 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.642857143 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.673469388 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.704081633 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.734693878 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.765306122 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.795918367 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.826530612 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.857142857 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.887755102 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.918367347 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.948979592 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.979591837 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.010204082 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.040816327 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.071428571 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.102040816 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.132653061 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.163265306 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.193877551 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.224489796 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.255102041 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.285714286 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.316326531 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.346938776 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.377551020 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.408163265 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.438775510 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.469387755 0.000000000 0.000000000 1.000000000 0.000000000
|
||||
Regular → Executable
+20
@@ -185,6 +185,8 @@ class AutoencoderKLMagvit(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
|
||||
self.slice_compression_vae = slice_compression_vae
|
||||
self.cache_compression_vae = cache_compression_vae
|
||||
self.cache_mag_vae = cache_mag_vae
|
||||
self.cache_compression_vae_copy = cache_compression_vae
|
||||
self.cache_mag_vae_copy = cache_mag_vae
|
||||
self.mini_batch_encoder = mini_batch_encoder
|
||||
self.mini_batch_decoder = mini_batch_decoder
|
||||
self.use_slicing = False
|
||||
@@ -197,6 +199,24 @@ class AutoencoderKLMagvit(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
|
||||
self.tile_latent_min_size = int(self.tile_sample_min_size / (2 ** (len(ch_mult) - 1)))
|
||||
self.scaling_factor = scaling_factor
|
||||
|
||||
def disable_cache_in_vae(self):
|
||||
self.cache_compression_vae = False
|
||||
self.encoder.cache_compression_vae = False
|
||||
self.decoder.cache_compression_vae = False
|
||||
|
||||
self.cache_mag_vae = False
|
||||
self.encoder.cache_mag_vae = False
|
||||
self.decoder.cache_mag_vae = False
|
||||
|
||||
def enable_cache_in_vae(self):
|
||||
self.cache_compression_vae = self.cache_compression_vae_copy
|
||||
self.encoder.cache_compression_vae = self.cache_compression_vae_copy
|
||||
self.decoder.cache_compression_vae = self.cache_compression_vae_copy
|
||||
|
||||
self.cache_mag_vae = self.cache_mag_vae_copy
|
||||
self.encoder.cache_mag_vae = self.cache_mag_vae_copy
|
||||
self.decoder.cache_mag_vae = self.cache_mag_vae_copy
|
||||
|
||||
def _set_gradient_checkpointing(self, module, value=False):
|
||||
if isinstance(module, (omnigen_Mag_Encoder, omnigen_Mag_Decoder)):
|
||||
module.gradient_checkpointing = value
|
||||
|
||||
Regular → Executable
+10
-2
@@ -1148,8 +1148,10 @@ def main():
|
||||
if args.vae_gradient_checkpointing:
|
||||
# Since 3D casual VAE need a cache to decode all latents autoregressively, .Thus, gradient checkpointing can only be
|
||||
# enabled when decoding the first batch (i.e. the first three) of latents, in which case the cache is not being used.
|
||||
if args.num_decoded_latents > 3:
|
||||
raise ValueError("The vae_gradient_checkpointing is not supported for num_decoded_latents > 3.")
|
||||
|
||||
# num_decoded_latents > 3 is support in EasyAnimate now.
|
||||
# if args.num_decoded_latents > 3:
|
||||
# raise ValueError("The vae_gradient_checkpointing is not supported for num_decoded_latents > 3.")
|
||||
vae.enable_gradient_checkpointing()
|
||||
|
||||
# Enable TF32 for faster training on Ampere GPUs,
|
||||
@@ -1418,6 +1420,8 @@ def main():
|
||||
|
||||
if hasattr(noise_scheduler, "init_noise_sigma"):
|
||||
latents = latents * noise_scheduler.init_noise_sigma
|
||||
if hasattr(vae, "enable_cache_in_vae"):
|
||||
vae.enable_cache_in_vae()
|
||||
|
||||
# Prepare inpaint latents if it needs.
|
||||
# Use zero latents if we want to t2v.
|
||||
@@ -1553,6 +1557,8 @@ def main():
|
||||
# compute the previous noisy sample x_t -> x_t-1
|
||||
latents = noise_scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
||||
|
||||
if hasattr(vae, "disable_cache_in_vae"):
|
||||
vae.disable_cache_in_vae()
|
||||
# decode latents (tensor)
|
||||
# latents = latents.permute(0, 2, 1, 3, 4) # [B, C, T, H, W]
|
||||
# Since the casual VAE decoding consumes a large amount of VRAM, and we need to keep the decoding
|
||||
@@ -1652,6 +1658,8 @@ def main():
|
||||
args.validation_prompts = validation_prompts[:args.validation_batch_size]
|
||||
validation_prompts_idx = [(i, p) for i, p in enumerate(args.validation_prompts)]
|
||||
|
||||
if hasattr(vae, "enable_cache_in_vae"):
|
||||
vae.enable_cache_in_vae()
|
||||
accelerator.wait_for_everyone()
|
||||
with accelerator.split_between_processes(validation_prompts_idx) as splitted_prompts_idx:
|
||||
validation_loss, validation_reward = log_validation(
|
||||
|
||||
Reference in New Issue
Block a user