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24
README.md
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@ -57,18 +57,18 @@ to [清影](https://chatglm.cn/video).
The table below shows the list of video generation models we currently provide,
along with related basic information:
| Model Name | CogVideoX-2B |
|-------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------|
| Prompt Language | English |
| GPU Memory Required for Inference (FP16) | 36GB using diffusers (will be optimized before the PR is merged) and 25G using [SAT](https://github.com/THUDM/SwissArmyTransformer) |
| GPU Memory Required for Fine-tuning(bs=1) | 42GB |
| Prompt Max Length | 226 Tokens |
| Video Length | 6 seconds |
| Frames Per Second | 8 frames |
| Resolution | 720 * 480 |
| Quantized Inference | Not Supported |
| Multi-card Inference | Not Supported |
| Download Link | 🤗 [CogVideoX-2B](https://huggingface.co/THUDM/CogVideoX-2B) |
| Model Name | CogVideoX-2B |
|-------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------|
| Prompt Language | English |
| GPU Memory Required for Inference (FP16) | 36GB using diffusers (will be optimized before the PR is merged) and 18GB using [SAT](https://github.com/THUDM/SwissArmyTransformer) |
| GPU Memory Required for Fine-tuning(bs=1) | 42GB |
| Prompt Max Length | 226 Tokens |
| Video Length | 6 seconds |
| Frames Per Second | 8 frames |
| Resolution | 720 * 480 |
| Quantized Inference | Not Supported |
| Multi-card Inference | Not Supported |
| Download Link | 🤗 [CogVideoX-2B](https://huggingface.co/THUDM/CogVideoX-2B) |
## Project Structure

24
README_zh.md
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@ -54,18 +54,18 @@ CogVideoX是 [清影](https://chatglm.cn/video) 同源的开源版本视频生
下表战展示目前我们提供的视频生成模型列表，以及相关基础信息:
| 模型名字 | CogVideoX-2B |
|----------------|-------------------------------------------------------------------------------------------------------------------------------------|
| 提示词语言 | English |
| 推理显存消耗 (FP-16) | 36GB using diffusers (will be optimized before the PR is merged) and 25G using [SAT](https://github.com/THUDM/SwissArmyTransformer) |
| 微调显存消耗 (bs=1) | 42GB |
| 提示词长度上限 | 226 Tokens |
| 视频长度 | 6 seconds |
| 帧率（每秒） | 8 frames |
| 视频分辨率 | 720 * 480 |
| 量化推理 | 不支持 |
| 多卡推理 | 不支持 |
| 权重地址 | 🤗 [CogVideoX-2B](https://huggingface.co/THUDM/CogVideoX-2B) |
| 模型名字 | CogVideoX-2B |
|----------------|--------------------------------------------------------------------------------------------------------------------------------------|
| 提示词语言 | English |
| 推理显存消耗 (FP-16) | 36GB using diffusers (will be optimized before the PR is merged) and 18GB using [SAT](https://github.com/THUDM/SwissArmyTransformer) |
| 微调显存消耗 (bs=1) | 42GB |
| 提示词长度上限 | 226 Tokens |
| 视频长度 | 6 seconds |
| 帧率（每秒） | 8 frames |
| 视频分辨率 | 720 * 480 |
| 量化推理 | 不支持 |
| 多卡推理 | 不支持 |
| 权重地址 | 🤗 [CogVideoX-2B](https://huggingface.co/THUDM/CogVideoX-2B) |
## 项目结构

2
inference/cli_demo.py
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@ -43,7 +43,7 @@ def generate_video(
# Load the pre-trained CogVideoX pipeline with the specified precision (float16) and move it to the specified device
pipe = CogVideoXPipeline.from_pretrained(model_path, torch_dtype=dtype).to(device)
pipe.enable_sequential_cpu_offload() # Enable sequential CPU offload for faster inference
# Encode the prompt to get the prompt embeddings
prompt_embeds, _ = pipe.encode_prompt(
prompt=prompt, # The textual description for video generation

2
inference/cli_vae_demo.py
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@ -4,7 +4,7 @@ This script demonstrates how to encode video frames using a pre-trained CogVideo
Note:
This script requires the `diffusers>=0.30.0` library to be installed.
If the video appears “completely green” and cannot be viewed, please switch to a different player to watch it. This is a normal phenomenon.
Cost 71GB of GPU memory for encoding a 1-minute video at 720p resolution.
Cost 71GB of GPU memory for encoding a 6s video at 720p resolution.
Run the script:
$ python cli_demo.py --model_path THUDM/CogVideoX-2b --video_path path/to/video.mp4 --output_path path/to/output