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README.md

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-# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
-# Doc / guide: https://huggingface.co/docs/hub/model-cards
+#型号卡片元数据参考见规范：https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+#文件/指南： https://huggingface.co/docs/hub/model-cards
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-# Stable Video Diffusion Image-to-Video Model Card
+#稳定视频扩散图像到视频模型卡
 
-<!-- Provide a quick summary of what the model is/does. -->
+<！ --提供模型功能的快速摘要。 -->
 ![row01](output_tile.gif)
-Stable Video Diffusion (SVD) Image-to-Video is a diffusion model that takes in a still image as a conditioning frame, and generates a video from it. 
+稳定视频扩散(SVD)图像到视频(Image-to-Video)是一种扩散模型，它将静止图像作为调节帧，并从中生成视频。
 
-## Model Details
+##模型详细信息
 
-### Model Description
+###型号说明
 
-(SVD) Image-to-Video is a latent diffusion model trained to generate short video clips from an image conditioning. 
-This model was trained to generate 25 frames at resolution 576x1024 given a context frame of the same size, finetuned from [SVD Image-to-Video [14 frames]](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid).
-We also finetune the widely used [f8-decoder](https://huggingface.co/docs/diffusers/api/models/autoencoderkl#loading-from-the-original-format) for temporal consistency. 
-For convenience, we additionally provide the model with the 
-standard frame-wise decoder [here](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/blob/main/svd_xt_image_decoder.safetensors).
+(SVD)图像到视频(Image-to-Video)是一种潜在扩散模型，其被训练为从图像调节生成短视频剪辑。
+该模型被训练为生成分辨率为576x1024的25帧，给定相同大小的上下文帧，从[SVD图像到视频微调[14帧]](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid).
+我们还微调了广泛使用的[F8-译码器](https://huggingface.co/docs/diffusers/api/models/autoencoderkl#从原始格式加载)为了时间的一致性。
+为方便起见，我们还为模型提供了
+标准逐帧译码器[在这里](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/blob/main/svd_xt_image_decoder.safetensors).
 
 
-- **Developed by:** Stability AI
-- **Funded by:** Stability AI
-- **Model type:** Generative image-to-video model
-- **Finetuned from model:** SVD Image-to-Video [14 frames]
+-**编制单位：**稳定性AI
+-**资金来源：**稳定性AI
+-**型号类型：**生成图像到视频模型
+-**根据模型进行微调：**SVD图像到视频[14帧]
 
-### Model Sources
+###模型源
 
-For research purposes, we recommend our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), 
-which implements the most popular diffusion frameworks (both training and inference).
+出于研究目的，我们建议`生成模型`GitHub存储库(https://github.com/Stability-AI/generative-models),
+它实现了最流行的传播框架(训练和推理)。
 
-- **Repository:** https://github.com/Stability-AI/generative-models
-- **Paper:** https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets
+-**存储库：** https://github.com/Stability-AI/generative-models
+-**纸：** https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets
 
 
-## Evaluation
-![comparison](comparison.png)
-The chart above evaluates user preference for SVD-Image-to-Video over [GEN-2](https://research.runwayml.com/gen2) and [PikaLabs](https://www.pika.art/).
-SVD-Image-to-Video is preferred by human voters in terms of video quality. For details on the user study, we refer to the [research paper](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets)
+##评价
+![比较](comparison.png)
+上图评估了用户对SVD-图像到视频的偏好，而不是[GEN-2](https://research.runwayml.com/gen2)和[PikaLabs](https://www.pika.art/).[GEN-2](https://research.runwayml.com/gen2)和[PikaLabs](https://www.pika.art/).
+就视频质量而言，SVD-图像到视频是人类选民的首选。 有关用户研究的详情，请参阅[研究文件](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets)[研究论文](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets)
 
-## Uses
+##uses
 
-### Direct Use
+###直接使用
 
-The model is intended for research purposes only. Possible research areas and tasks include
+该模型仅用于研究目的。 可能的研究领域和任务包括
 
-- Research on generative models.
-- Safe deployment of models which have the potential to generate harmful content.
-- Probing and understanding the limitations and biases of generative models.
-- Generation of artworks and use in design and other artistic processes.
-- Applications in educational or creative tools.
+--研究生成模型。
+--安全部署可能产生有害内容的模型。
+--探索和理解生成模型的局限性和偏差。
+--艺术品的产生和在设计和其他艺术过程中的使用。
+--在教育或创意工具中的应用。
 
-Excluded uses are described below.
+排除的用途描述如下。
 
-### Out-of-Scope Use
+###超出范围使用
 
-The model was not trained to be factual or true representations of people or events, 
-and therefore using the model to generate such content is out-of-scope for the abilities of this model.
-The model should not be used in any way that violates Stability AI's [Acceptable Use Policy](https://stability.ai/use-policy).
+模型并没有被训练成真实或真实的人或事件的表现，
+因此，使用该模型来生成这样的内容超出了该模型的能力的范围。
+该模型不得以任何违反稳定性AI[可接受使用政策]的方式使用(https://stability.ai/use-policy).[可接受的使用政策](https://stability.ai/use-policy).
 
-## Limitations and Bias
+##限制和偏差
 
-### Limitations
-- The generated videos are rather short (<= 4sec), and the model does not achieve perfect photorealism.
-- The model may generate videos without motion, or very slow camera pans.
-- The model cannot be controlled through text.
-- The model cannot render legible text.
-- Faces and people in general may not be generated properly.
-- The autoencoding part of the model is lossy.
+###限制
+--生成的视频相当短(<=4sec)，模型没有达到完美的照片真实感。
+--模型可能生成没有运动的视频，或者生成速度非常慢的摄像盘。
+--无法通过文本控制模型。
+--模型无法呈现清晰可见的文本。
+--面部和一般人物可能无法正确生成。
+--模型的自动编码部分有损耗。
 
 
-### Recommendations
+###推荐
 
-The model is intended for research purposes only.
+该模型仅用于研究目的。
 
-## How to Get Started with the Model
+##如何开始使用模型
 
-Check out https://github.com/Stability-AI/generative-models
+结帐https://github.com/Stability-AI/generative-models