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THUDM
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CogVideoX-2b

Text-to-Video
Diffusers
Safetensors
English
CogVideoXPipeline
cogvideox
video-generation
thudm
Model card Files Community
13
zR commited on
Commit
4bbfb1d
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1 Parent(s): 0d6a353

update GPU memory to 24GB

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Files changed (2) hide show
  1. README.md +14 -13
  2. README_zh.md +14 -13
README.md CHANGED
@@ -88,18 +88,17 @@ inference: false
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  CogVideoX is an open-source video generation model that shares the same origins as [清影](https://chatglm.cn/video).
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  The table below provides a list of the video generation models we currently offer, along with their basic information.
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- | Model Name | CogVideoX-2B (Current Repos) |
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- |--------------------------------------------|------------------------------|
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- | Supported Prompt Language | English |
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- | GPU Memory Required for Inference | 36GB |
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- | GPU Memory Required for Fine-tuning (bs=1) | 42GB |
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- | Prompt Length | 226 Tokens |
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- | Video Length | 6 seconds |
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- | Frames Per Second | 8 frames |
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- | Resolution | 720 * 480 |
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- | Positional Embeddings | Sinusoidal |
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- | Quantized Inference | Not Supported |
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- | Multi-card Inference | Not Supported |
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  **Note** Using [SAT](https://github.com/THUDM/SwissArmyTransformer) model cost 18GB for inference. Check our github.
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@@ -128,7 +127,9 @@ prompt = "A panda, dressed in a small, red jacket and a tiny hat, sits on a wood
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  pipe = CogVideoXPipeline.from_pretrained(
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  "THUDM/CogVideoX-2b",
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  torch_dtype=torch.float16
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- ).to("cuda")
 
 
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  prompt_embeds, _ = pipe.encode_prompt(
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  prompt=prompt,
 
88
  CogVideoX is an open-source video generation model that shares the same origins as [清影](https://chatglm.cn/video).
89
  The table below provides a list of the video generation models we currently offer, along with their basic information.
90
 
91
+ | Model Name | CogVideoX-2B |
92
+ |-------------------------------------------|--------------------------------------|
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+ | Prompt Language | English |
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+ | Single GPU Inference (FP16) | 23.9GB |
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+ | Multi GPUs Inference (FP16) | 20GB minimum per GPU using diffusers |
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+ | GPU Memory Required for Fine-tuning(bs=1) | 40GB |
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+ | Prompt Max Length | 226 Tokens |
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+ | Video Length | 6 seconds |
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+ | Frames Per Second | 8 frames |
100
+ | Resolution | 720 * 480 |
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+ | Quantized Inference | Not Supported |
 
102
 
103
  **Note** Using [SAT](https://github.com/THUDM/SwissArmyTransformer) model cost 18GB for inference. Check our github.
104
 
 
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  pipe = CogVideoXPipeline.from_pretrained(
128
  "THUDM/CogVideoX-2b",
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  torch_dtype=torch.float16
130
+ )
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+
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+ pipe.enable_model_cpu_offload()
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  prompt_embeds, _ = pipe.encode_prompt(
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  prompt=prompt,
README_zh.md CHANGED
@@ -73,18 +73,17 @@
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74
  CogVideoX是 [清影](https://chatglm.cn/video) 同源的开源版本视频生成模型。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。
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- | Model Name | CogVideoX-2B (当前仓库) |
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- |---------------|---------------------|
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- | 提示词语言 | English |
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- | 推理显存消耗 | 36GB |
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- | 微调显存消耗 (bs=1) | 42GB |
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- | 提示词长度上限 | 226 Tokens |
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- | 视频生成长度 | 6 seconds |
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- | 视频生成帧率 (每秒) | 8 frames |
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- | 视频生成分辨率 | 720 * 480 |
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- | 位置编码 | Sinusoidal |
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- | 量化 | 不支持 |
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- | 多卡推理 | 不支持 |
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  **Note** 使用 [SAT](https://github.com/THUDM/SwissArmyTransformer) 推理SAT版本模型仅需18G显存。欢迎前往我们的github查看。
90
 
@@ -112,7 +111,9 @@ prompt = "A panda, dressed in a small, red jacket and a tiny hat, sits on a wood
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  pipe = CogVideoXPipeline.from_pretrained(
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  "THUDM/CogVideoX-2b",
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  torch_dtype=torch.float16
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- ).to("cuda")
 
 
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  prompt_embeds, _ = pipe.encode_prompt(
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  prompt=prompt,
 
73
 
74
  CogVideoX是 [清影](https://chatglm.cn/video) 同源的开源版本视频生成模型。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。
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+ | 模型名 | CogVideoX-2B |
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+ |---------------------|--------------------------------------|
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+ | 提示词语言 | English |
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+ | 单GPU推理 (FP-16) 显存消耗 | 23.9GB |
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+ | 多GPU推理 (FP-16) 显存消耗 | 20GB minimum per GPU using diffusers |
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+ | 微调显存消耗 (bs=1) | 42GB |
82
+ | 提示词长度上限 | 226 Tokens |
83
+ | 视频长度 | 6 seconds |
84
+ | 帧率（每秒） | 8 frames |
85
+ | 视频分辨率 | 720 * 480 |
86
+ | 量化推理 | 不支持 |
 
87
 
88
  **Note** 使用 [SAT](https://github.com/THUDM/SwissArmyTransformer) 推理SAT版本模型仅需18G显存。欢迎前往我们的github查看。
89
 
 
111
  pipe = CogVideoXPipeline.from_pretrained(
112
  "THUDM/CogVideoX-2b",
113
  torch_dtype=torch.float16
114
+ )
115
+
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+ pipe.enable_model_cpu_offload()
117
 
118
  prompt_embeds, _ = pipe.encode_prompt(
119
  prompt=prompt,