JLNTV_IPD/CogVideo
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update diffusers code

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zR 2024-11-09 22:07:32 +08:00
parent a8205b575d
commit c8c7b62aa1
3 changed files with 49 additions and 62 deletions
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48
inference/cli_demo.py
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@ -3,15 +3,15 @@ This script demonstrates how to generate a video using the CogVideoX model with
The script supports different types of video generation, including text-to-video (t2v), image-to-video (i2v),
and video-to-video (v2v), depending on the input data and different weight.
- text-to-video: THUDM/CogVideoX-5b or THUDM/CogVideoX-2b
- video-to-video: THUDM/CogVideoX-5b or THUDM/CogVideoX-2b
- image-to-video: THUDM/CogVideoX-5b-I2V
- text-to-video: THUDM/CogVideoX-5b, THUDM/CogVideoX-2b or THUDM/CogVideoX1.5-5b
- video-to-video: THUDM/CogVideoX-5b, THUDM/CogVideoX-2b or THUDM/CogVideoX1.5-5b
- image-to-video: THUDM/CogVideoX-5b-I2V or THUDM/CogVideoX1.5-5b-I2V
Running the Script:
To run the script, use the following command with appropriate arguments:
```bash
$ python cli_demo.py --prompt "A girl riding a bike." --model_path THUDM/CogVideoX-5b --generate_type "t2v"
$ python cli_demo.py --prompt "A girl riding a bike." --model_path THUDM/CogVideoX1.5-5b --generate_type "t2v"
```
Additional options are available to specify the model path, guidance scale, number of inference steps, video generation type, and output paths.
@ -23,7 +23,6 @@ from typing import Literal
import torch
from diffusers import (
CogVideoXPipeline,
CogVideoXDDIMScheduler,
CogVideoXDPMScheduler,
CogVideoXImageToVideoPipeline,
CogVideoXVideoToVideoPipeline,
@ -37,6 +36,7 @@ def generate_video(
model_path: str,
lora_path: str = None,
lora_rank: int = 128,
num_frames=81,
output_path: str = "./output.mp4",
image_or_video_path: str = "",
num_inference_steps: int = 50,
@ -45,6 +45,7 @@ def generate_video(
dtype: torch.dtype = torch.bfloat16,
generate_type: str = Literal["t2v", "i2v", "v2v"], # i2v: image to video, v2v: video to video
seed: int = 42,
fps: int = 8,
):
"""
Generates a video based on the given prompt and saves it to the specified path.
@ -56,11 +57,13 @@ def generate_video(
- lora_rank (int): The rank of the LoRA weights.
- output_path (str): The path where the generated video will be saved.
- num_inference_steps (int): Number of steps for the inference process. More steps can result in better quality.
- num_frames (int): Number of frames to generate.
- 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.bfloat16).
- generate_type (str): The type of video generation (e.g., 't2v', 'i2v', 'v2v').·
- seed (int): The seed for reproducibility.
- fps (int): The frames per second for the generated video.
"""
# 1. Load the pre-trained CogVideoX pipeline with the specified precision (bfloat16).
@ -109,11 +112,11 @@ def generate_video(
if generate_type == "i2v":
video_generate = pipe(
prompt=prompt,
image=image, # The path of the image to be used as the background of the video
image=image, # The path of the image, the resolution of video will be the same as the image for CogVideoX1.5-5B-I2V, otherwise it will be 720 * 480
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=49, # Number of frames to generate，changed to 49 for diffusers version `0.30.3` and after.
use_dynamic_cfg=True, # This id used for DPM Sechduler, for DDIM scheduler, it should be False
num_frames=num_frames, # Number of frames to generate
use_dynamic_cfg=True, # This id used for DPM scheduler, for DDIM scheduler, it should be False
guidance_scale=guidance_scale,
generator=torch.Generator().manual_seed(seed), # Set the seed for reproducibility
).frames[0]
@ -122,7 +125,7 @@ def generate_video(
prompt=prompt,
num_videos_per_prompt=num_videos_per_prompt,
num_inference_steps=num_inference_steps,
num_frames=49,
num_frames=num_frames,
use_dynamic_cfg=True,
guidance_scale=guidance_scale,
generator=torch.Generator().manual_seed(seed),
@ -133,13 +136,12 @@ def generate_video(
video=video, # The path of the video to be used as the background of the video
num_videos_per_prompt=num_videos_per_prompt,
num_inference_steps=num_inference_steps,
# num_frames=49,
num_frames=num_frames,
use_dynamic_cfg=True,
guidance_scale=guidance_scale,
generator=torch.Generator().manual_seed(seed), # Set the seed for reproducibility
).frames[0]
# 5. Export the generated frames to a video file. fps must be 8 for original video.
export_to_video(video_generate, output_path, fps=8)
export_to_video(video_generate, output_path, fps=fps)
if __name__ == "__main__":
@ -152,24 +154,18 @@ if __name__ == "__main__":
help="The path of the image to be used as the background of the video",
)
parser.add_argument(
"--model_path", type=str, default="THUDM/CogVideoX-5b", help="The path of the pre-trained model to be used"
"--model_path", type=str, default="THUDM/CogVideoX-5b", help="Path of the pre-trained model use"
)
parser.add_argument("--lora_path", type=str, default=None, help="The path of the LoRA weights to be used")
parser.add_argument("--lora_rank", type=int, default=128, help="The rank of the LoRA weights")
parser.add_argument(
"--output_path", type=str, default="./output.mp4", help="The path where the generated video will be saved"
)
parser.add_argument("--output_path", type=str, default="./output.mp4", help="The path save generated video")
parser.add_argument("--guidance_scale", type=float, default=6.0, help="The scale for classifier-free guidance")
parser.add_argument(
"--num_inference_steps", type=int, default=50, help="Number of steps for the inference process"
)
parser.add_argument("--num_inference_steps", type=int, default=50, help="Inference steps")
parser.add_argument("--num_frames", type=int, default=81, help="Number of steps for the inference process")
parser.add_argument("--fps", type=int, default=16, help="Number of steps for the inference process")
parser.add_argument("--num_videos_per_prompt", type=int, default=1, help="Number of videos to generate per prompt")
parser.add_argument(
"--generate_type", type=str, default="t2v", help="The type of video generation (e.g., 't2v', 'i2v', 'v2v')"
)
parser.add_argument(
"--dtype", type=str, default="bfloat16", help="The data type for computation (e.g., 'float16' or 'bfloat16')"
)
parser.add_argument("--generate_type", type=str, default="t2v", help="The type of video generation")
parser.add_argument("--dtype", type=str, default="bfloat16", help="The data type for computation")
parser.add_argument("--seed", type=int, default=42, help="The seed for reproducibility")
args = parser.parse_args()
@ -180,6 +176,7 @@ if __name__ == "__main__":
lora_path=args.lora_path,
lora_rank=args.lora_rank,
output_path=args.output_path,
num_frames=args.num_frames,
image_or_video_path=args.image_or_video_path,
num_inference_steps=args.num_inference_steps,
guidance_scale=args.guidance_scale,
@ -187,4 +184,5 @@ if __name__ == "__main__":
dtype=dtype,
generate_type=args.generate_type,
seed=args.seed,
fps=args.fps,
)

53
inference/cli_demo_quantization.py
View File

@ -3,7 +3,7 @@ This script demonstrates how to generate a video from a text prompt using CogVid
Note:
Must install the `torchao`，`torch`,`diffusers`,`accelerate` library FROM SOURCE to use the quantization feature.
Must install the `torchao`，`torch` library FROM SOURCE to use the quantization feature.
Only NVIDIA GPUs like H100 or higher are supported om FP-8 quantization.
ALL quantization schemes must use with NVIDIA GPUs.
@ -51,6 +51,9 @@ def generate_video(
num_videos_per_prompt: int = 1,
quantization_scheme: str = "fp8",
dtype: torch.dtype = torch.bfloat16,
num_frames: int = 81,
fps: int = 8,
seed: int = 42,
):
"""
Generates a video based on the given prompt and saves it to the specified path.
@ -65,7 +68,6 @@ def generate_video(
- quantization_scheme (str): The quantization scheme to use ('int8', 'fp8').
- dtype (torch.dtype): The data type for computation (default is torch.bfloat16).
"""
text_encoder = T5EncoderModel.from_pretrained(model_path, subfolder="text_encoder", torch_dtype=dtype)
text_encoder = quantize_model(part=text_encoder, quantization_scheme=quantization_scheme)
transformer = CogVideoXTransformer3DModel.from_pretrained(model_path, subfolder="transformer", torch_dtype=dtype)
@ -80,54 +82,38 @@ def generate_video(
torch_dtype=dtype,
)
pipe.scheduler = CogVideoXDPMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
# Using with compile will run faster. First time infer will cost ~30min to compile.
# pipe.transformer.to(memory_format=torch.channels_last)
# for FP8 should remove pipe.enable_model_cpu_offload()
pipe.enable_model_cpu_offload()
# This is not for FP8 and INT8 and should remove this line
# pipe.enable_sequential_cpu_offload()
pipe.vae.enable_slicing()
pipe.vae.enable_tiling()
video = pipe(
prompt=prompt,
num_videos_per_prompt=num_videos_per_prompt,
num_inference_steps=num_inference_steps,
num_frames=49,
num_frames=num_frames,
use_dynamic_cfg=True,
guidance_scale=guidance_scale,
generator=torch.Generator(device="cuda").manual_seed(42),
generator=torch.Generator(device="cuda").manual_seed(seed),
).frames[0]
export_to_video(video, output_path, fps=8)
export_to_video(video, output_path, fps=fps)
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-5b", help="Path of the pre-trained model")
parser.add_argument("--output_path", type=str, default="./output.mp4", help="Path to save generated video")
parser.add_argument("--num_inference_steps", type=int, default=50, help="Inference steps")
parser.add_argument("--guidance_scale", type=float, default=6.0, help="Classifier-free guidance scale")
parser.add_argument("--num_videos_per_prompt", type=int, default=1, help="Videos to generate per prompt")
parser.add_argument("--dtype", type=str, default="bfloat16", help="Data type (e.g., 'float16', 'bfloat16')")
parser.add_argument(
"--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"
)
parser.add_argument(
"--num_inference_steps", type=int, default=50, help="Number of steps for the inference process"
)
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="bfloat16", help="The data type for computation (e.g., 'float16', 'bfloat16')"
)
parser.add_argument(
"--quantization_scheme",
type=str,
default="bf16",
choices=["int8", "fp8"],
help="The quantization scheme to use (int8, fp8)",
"--quantization_scheme", type=str, default="fp8", choices=["int8", "fp8"], help="Quantization scheme"
)
parser.add_argument("--num_frames", type=int, default=81, help="Number of frames in the video")
parser.add_argument("--fps", type=int, default=16, help="Frames per second for output video")
parser.add_argument("--seed", type=int, default=42, help="Random seed for reproducibility")
args = parser.parse_args()
dtype = torch.float16 if args.dtype == "float16" else torch.bfloat16
@ -140,4 +126,7 @@ if __name__ == "__main__":
num_videos_per_prompt=args.num_videos_per_prompt,
quantization_scheme=args.quantization_scheme,
dtype=dtype,
num_frames=args.num_frames,
fps=args.fps,
seed=args.seed,
)

10
requirements.txt
View File

@ -1,14 +1,14 @@
diffusers>=0.31.0
accelerate>=1.0.1
transformers>=4.46.1
accelerate>=1.1.1
transformers>=4.46.2
numpy==1.26.0
torch>=2.5.0
torchvision>=0.20.0
sentencepiece>=0.2.0
SwissArmyTransformer>=0.4.12
gradio>=5.4.0
gradio>=5.5.0
imageio>=2.35.1
imageio-ffmpeg>=0.5.1
openai>=1.53.0
openai>=1.54.0
moviepy>=1.0.3
scikit-video>=1.1.11
scikit-video>=1.1.11