update cli_demo

inference/cli_demo.py

@@ -21,8 +21,9 @@ import numpy as np
 import torch
 from diffusers import CogVideoXPipeline, CogVideoXDDIMScheduler
 
+
 def export_to_video_imageio(
-        video_frames: Union[List[np.ndarray], List[PIL.Image.Image]], output_video_path: str = None, fps: int = 8
+    video_frames: Union[List[np.ndarray], List[PIL.Image.Image]], output_video_path: str = None, fps: int = 8
 ) -> str:
     """
     Export the video frames to a video file using imageio lib to Avoid "green screen" issue (for example CogVideoX)
@@ -38,13 +39,13 @@ def export_to_video_imageio(
 
 
 def generate_video(
-        prompt: str,
-        model_path: str,
-        output_path: str = "./output.mp4",
-        num_inference_steps: int = 50,
-        guidance_scale: float = 6.0,
-        num_videos_per_prompt: int = 1,
-        dtype: torch.dtype = torch.float16,
+    prompt: str,
+    model_path: str,
+    output_path: str = "./output.mp4",
+    num_inference_steps: int = 50,
+    guidance_scale: float = 6.0,
+    num_videos_per_prompt: int = 1,
+    dtype: torch.dtype = torch.bloat16,
 ):
     """
     Generates a video based on the given prompt and saves it to the specified path.
@@ -56,11 +57,11 @@ def generate_video(
     - num_inference_steps (int): Number of steps for the inference process. More steps can result in better quality.
     - guidance_scale (float): The scale for classifier-free guidance. Higher values can lead to better alignment with the prompt.
     - num_videos_per_prompt (int): Number of videos to generate per prompt.
-    - dtype (torch.dtype): The data type for computation (default is torch.float16).
+    - dtype (torch.dtype): The data type for computation (default is torch.bfloat16).
 
     """
 
-    # 1.  Load the pre-trained CogVideoX pipeline with the specified precision (float16).
+    # 1.  Load the pre-trained CogVideoX pipeline with the specified precision (bfloat16).
     # add device_map="balanced" in the from_pretrained function and remove the enable_model_cpu_offload()
     # function to use Multi GPUs.
 
@@ -79,8 +80,7 @@ def generate_video(
     torch.cuda.reset_accumulated_memory_stats()
     torch.cuda.reset_peak_memory_stats()
 
-    # Using with diffusers branch `main` to enable tiling. This will cost ONLY 12GB GPU memory.
-    # pipe.vae.enable_tiling()
+    pipe.vae.enable_tiling()
 
     # 4. Generate the video frames based on the prompt.
     # `num_frames` is the Number of frames to generate.
@@ -90,7 +90,7 @@ def generate_video(
         prompt=prompt,
         num_videos_per_prompt=num_videos_per_prompt,  # Number of videos to generate per prompt
         num_inference_steps=num_inference_steps,  # Number of inference steps
-        num_frames=48, # Number of frames to generate，changed to 49 for diffusers version `0.31.0` and after.
+        num_frames=49,  # Number of frames to generate，changed to 49 for diffusers version `0.31.0` and after.
         guidance_scale=guidance_scale,  # Guidance scale for classifier-free guidance
         generator=torch.Generator().manual_seed(42),  # Set the seed for reproducibility
     ).frames[0]
@@ -103,7 +103,7 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Generate a video from a text prompt using CogVideoX")
     parser.add_argument("--prompt", type=str, required=True, help="The description of the video to be generated")
     parser.add_argument(
-        "--model_path", type=str, default="THUDM/CogVideoX-2b", help="The path of the pre-trained model to be used"
+        "--model_path", type=str, default="THUDM/CogVideoX-5b", help="The path of the pre-trained model to be used"
     )
     parser.add_argument(
         "--output_path", type=str, default="./output.mp4", help="The path where the generated video will be saved"
@@ -114,13 +114,13 @@ if __name__ == "__main__":
     parser.add_argument("--guidance_scale", type=float, default=6.0, help="The scale for classifier-free guidance")
     parser.add_argument("--num_videos_per_prompt", type=int, default=1, help="Number of videos to generate per prompt")
     parser.add_argument(
-        "--dtype", type=str, default="float16", help="The data type for computation (e.g., 'float16' or 'float32')"
+        "--dtype", type=str, default="bfloat16", help="The data type for computation (e.g., 'float16' or 'bfloat16')"
     )
 
     args = parser.parse_args()
 
     # Convert dtype argument to torch.dtype, NOT suggest BF16.
-    dtype = torch.float16 if args.dtype == "float16" else torch.float32
+    dtype = torch.float16 if args.dtype == "float16" else torch.bfloat16
 
     # main function to generate video.
     generate_video(

sat/configs/inference.yaml

@@ -1,7 +1,7 @@
 args:
   latent_channels: 16
   mode: inference
-  # load: "{your_CogVideoX-2b-sat_path}/transformer" # This is for Full model without lora adapter
+  load: "{your CogVideoX SAT folder}/transformer" # This is for Full model without lora adapter
   # load: "{your lora folder} such as zRzRzRzRzRzRzR/lora-disney-08-20-13-28" # This is for Full model without lora adapter
 
   batch_size: 1