rename the comfyui files and new readme (#53)
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@@ -31,7 +31,7 @@ EasyAnimate is a pipeline based on the transformer architecture that can be used
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We will support quick pull-ups from different platforms, refer to [Quick Start](#quick-start).
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What's New:
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- Support ComfyUI, please refer to [ComfyUI README](easyanimate/comfyui/README.md) for details. [ 2024.07.12 ]
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- Support ComfyUI, please refer to [ComfyUI README](comfyui/README.md) for details. [ 2024.07.12 ]
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- Updated to v3, supports up to 720p 144 frames (960x960, 6s, 24fps) video generation, and supports text and image generated video models. [ 2024.07.01 ]
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- ModelScope-Sora "Data Directors" creative sprint has been annouced using EasyAnimate as the training backbone to investigate the influence of data preprocessing. Please visit the competition's [official website](https://tianchi.aliyun.com/competition/entrance/532219) for more information. [ 2024.06.17 ]
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- Updated to v2, supports a maximum of 144 frames (768x768, 6s, 24fps) for generation. [ 2024.05.26 ]
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@@ -62,7 +62,7 @@ Aliyun provide free GPU time in [Freetier](https://free.aliyun.com/?product=9602
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[](https://gallery.pai-ml.com/#/preview/deepLearning/cv/easyanimate)
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#### b. From ComfyUI
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Our ComfyUI is as follows, please refer to [ComfyUI README](easyanimate/comfyui/README.md) for details.
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Our ComfyUI is as follows, please refer to [ComfyUI README](comfyui/README.md) for details.
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#### c. From docker
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+2
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@@ -31,7 +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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- 支持comfyui，详情查看[ComfyUI README](easyanimate/comfyui/README.md)。[ 2024.07.12 ]
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- 支持comfyui，详情查看[ComfyUI README](comfyui/README.md)。[ 2024.07.12 ]
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- 更新到v3版本，最大支持720p 144帧(960x960, 6s, 24fps)视频生成，支持文与图生视频模型。[ 2024.07.01 ]
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- ModelScope-Sora“数据导演”创意竞速——第三届Data-Juicer大模型数据挑战赛已经正式启动！其使用EasyAnimate作为基础模型，探究数据处理对于模型训练的作用。立即访问[竞赛官网](https://tianchi.aliyun.com/competition/entrance/532219)，了解赛事详情。[ 2024.06.17 ]
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- 更新到v2版本，最大支持144帧(768x768, 6s, 24fps)生成。[ 2024.05.26 ]
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@@ -59,7 +59,7 @@ DSW 有免费 GPU 时间，用户可申请一次，申请后3个月内有效。
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[](https://gallery.pai-ml.com/#/preview/deepLearning/cv/easyanimate)
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#### b. 通过ComfyUI
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我们的ComfyUI界面如下，具体查看[ComfyUI README](easyanimate/comfyui/README.md)。
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我们的ComfyUI界面如下，具体查看[ComfyUI README](comfyui/README.md)。
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#### c. 通过docker
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+1
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@@ -1,3 +1,3 @@
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from .easyanimate.comfyui.comfyui_nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
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from .comfyui.comfyui_nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
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@@ -18,9 +18,17 @@ Easily use EasyAnimate inside ComfyUI!
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TBD
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### Option 2: Install manually
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The EasyAnimate repository needs to be placed at `ComfyUI/custom_nodes/EasyAnimate/`.
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```
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cd ComfyUI/custom_nodes/
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# Git clone the easyanimate itself
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git clone https://github.com/aigc-apps/EasyAnimate.git
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# Git clone the video outout node
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git clone https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite.git
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cd EasyAnimate/
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python install.py
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```
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@@ -45,14 +53,14 @@ EasyAnimateV3:
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## Example workflows
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### Image to video
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Our ui is shown as follow:
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### Image to video generation
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Our ui is shown as follow, this is the [download link](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/asset/v3/easyanimatev3_workflow_i2v.json) of the json:
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You can run the demo using following photo:
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### Image to video generation (high FPS w/ frame interpolation)
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Our ui is shown as follow:
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### Text to video generation
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Our ui is shown as follow, this is the [download link](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/asset/v3/easyanimatev3_workflow_t2v.json) of the json:
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@@ -16,16 +16,16 @@ import comfy.model_management as mm
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import folder_paths
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from comfy.utils import ProgressBar, load_torch_file
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from ..models.autoencoder_magvit import AutoencoderKLMagvit
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from ..models.transformer3d import Transformer3DModel
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from ..pipeline.pipeline_easyanimate_inpaint import EasyAnimateInpaintPipeline
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from ..utils.utils import get_image_to_video_latent
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from ..data.bucket_sampler import ASPECT_RATIO_512, get_closest_ratio
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from ..easyanimate.models.autoencoder_magvit import AutoencoderKLMagvit
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from ..easyanimate.models.transformer3d import Transformer3DModel
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from ..easyanimate.pipeline.pipeline_easyanimate_inpaint import EasyAnimateInpaintPipeline
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from ..easyanimate.utils.utils import get_image_to_video_latent
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from ..easyanimate.data.bucket_sampler import ASPECT_RATIO_512, get_closest_ratio
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# Compatible with Alibaba EAS for quick launch
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eas_cache_dir = '/stable-diffusion-cache/models'
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# The directory of the easyanimate
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script_directory = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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def tensor2pil(image):
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return Image.fromarray(np.clip(255. * image.cpu().numpy(), 0, 255).astype(np.uint8))
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