change qingying page

README.md

@@ -109,7 +109,7 @@ inference: false
 
 ## Model Introduction
 
-CogVideoX is an open-source version of the video generation model originating from [QingYing](https://chatglm.cn/video?fr=osm_cogvideo). The table below displays the list of video generation models we currently offer, along with their foundational information.
+CogVideoX is an open-source version of the video generation model originating from [QingYing](https://chatglm.cn/video?lang=en?fr=osm_cogvideo). The table below displays the list of video generation models we currently offer, along with their foundational information.
 
 <table style="border-collapse: collapse; width: 100%;">
   <tr>
@@ -194,8 +194,6 @@ CogVideoX is an open-source version of the video generation model originating fr
 + Using [SAT](https://github.com/THUDM/SwissArmyTransformer)  for inference and fine-tuning of SAT version
   models. Feel free to visit our GitHub for more information.
 
-
-
 ## Quick Start 🤗
 
 This model supports deployment using the huggingface diffusers library. You can deploy it by following these steps.

README_zh.md

@@ -116,8 +116,8 @@ CogVideoX是 [清影](https://chatglm.cn/video?fr=osm_cogvideo) 同源的开源
   </tr>
   <tr>
     <td style="text-align: center;">单GPU显存消耗<br></td>
-    <td style="text-align: center;">FP16: 18GB using <a href="https://github.com/THUDM/SwissArmyTransformer">SAT</a> / <b>12.5GB* using diffusers</b><br><b>INT8: 7.8GB* using diffusers</b></td>
-    <td style="text-align: center;">BF16: 26GB using <a href="https://github.com/THUDM/SwissArmyTransformer">SAT</a> / <b>20.7GB* using diffusers</b><br><b>INT8: 11.4GB* using diffusers</b></td>
+    <td style="text-align: center;">FP16: 18GB using <a href="https://github.com/THUDM/SwissArmyTransformer">SAT</a> / <b>12.5GB* using diffusers</b><br><b>INT8: 7.8GB* using diffusers  with torchao</b></td>
+    <td style="text-align: center;">BF16: 26GB using <a href="https://github.com/THUDM/SwissArmyTransformer">SAT</a> / <b>20.7GB* using diffusers</b><br><b>INT8: 11.4GB* using diffusers  with torchao</b></td>
   </tr>
   <tr>
     <td style="text-align: center;">多GPU推理显存消耗</td>
@@ -226,6 +226,62 @@ video = pipe(
 
 export_to_video(video, "output.mp4", fps=8)
 ```
+## Quantized Inference
+
+[PytorchAO](https://github.com/pytorch/ao) 和 [Optimum-quanto](https://github.com/huggingface/optimum-quanto/)
+可以用于对文本编码器、Transformer 和 VAE 模块进行量化，从而降低 CogVideoX 的内存需求。这使得在免费的 T4 Colab 或较小 VRAM 的
+GPU 上运行该模型成为可能！值得注意的是，TorchAO 量化与 `torch.compile` 完全兼容，这可以显著加快推理速度。
+
+```diff
+# To get started, PytorchAO needs to be installed from the GitHub source and PyTorch Nightly.
+# Source and nightly installation is only required until next release.
+
+import torch
+from diffusers import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel, CogVideoXPipeline
+from diffusers.utils import export_to_video
++ from transformers import T5EncoderModel
++ from torchao.quantization import quantize_, int8_weight_only, int8_dynamic_activation_int8_weight
+
++ quantization = int8_weight_only
+
++ text_encoder = T5EncoderModel.from_pretrained("THUDM/CogVideoX-5b", subfolder="text_encoder", torch_dtype=torch.bfloat16)
++ quantize_(text_encoder, quantization())
+
++ transformer = CogVideoXTransformer3DModel.from_pretrained("THUDM/CogVideoX-5b", subfolder="transformer", torch_dtype=torch.bfloat16)
++ quantize_(transformer, quantization())
+
++ vae = AutoencoderKLCogVideoX.from_pretrained("THUDM/CogVideoX-5b", subfolder="vae", torch_dtype=torch.bfloat16)
++ quantize_(vae, quantization())
+
+# Create pipeline and run inference
+pipe = CogVideoXPipeline.from_pretrained(
+    "THUDM/CogVideoX-5b",
++    text_encoder=text_encoder,
++    transformer=transformer,
++    vae=vae,
+    torch_dtype=torch.bfloat16,
+)
+pipe.enable_model_cpu_offload()
+pipe.vae.enable_tiling()
+
+prompt = "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance."
+
+video = pipe(
+    prompt=prompt,
+    num_videos_per_prompt=1,
+    num_inference_steps=50,
+    num_frames=49,
+    guidance_scale=6,
+    generator=torch.Generator(device="cuda").manual_seed(42),
+).frames[0]
+
+export_to_video(video, "output.mp4", fps=8)
+```
+
+此外，这些模型可以通过使用PytorchAO以量化数据类型序列化并存储，从而节省磁盘空间。你可以在以下链接中找到示例和基准测试。
+
+- [torchao](https://gist.github.com/a-r-r-o-w/4d9732d17412888c885480c6521a9897)
+- [quanto](https://gist.github.com/a-r-r-o-w/31be62828b00a9292821b85c1017effa)
 
 ## 深入研究