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cli_demo_quantization.py
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cli_demo_quantization.py
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"""
This script demonstrates how to generate a video from a text prompt using CogVideoX with quantization.
Note:
Must install the `torchao`，`torch`,`diffusers`,`accelerate` 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 using with NVIDIA GPUs.
# Run the script:
python cli_demo_quantization.py --prompt "A girl riding a bike." --model_path THUDM/CogVideoX-2b --quantization_scheme fp8 --dtype float16
python cli_demo_quantization.py --prompt "A girl riding a bike." --model_path THUDM/CogVideoX-5b --quantization_scheme fp8 --dtype bfloat16
"""
import argparse
import os
import torch
import torch._dynamo
from diffusers import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel, CogVideoXPipeline, CogVideoXDPMScheduler
from diffusers.utils import export_to_video
from transformers import T5EncoderModel
from torchao.quantization import quantize_, int8_weight_only
from torchao.float8.inference import ActivationCasting, QuantConfig, quantize_to_float8
os.environ["TORCH_LOGS"] = "+dynamo,output_code,graph_breaks,recompiles"
torch._dynamo.config.suppress_errors = True
torch.set_float32_matmul_precision("high")
torch._inductor.config.conv_1x1_as_mm = True
torch._inductor.config.coordinate_descent_tuning = True
torch._inductor.config.epilogue_fusion = False
torch._inductor.config.coordinate_descent_check_all_directions = True
def quantize_model(part, quantization_scheme):
if quantization_scheme == "int8":
quantize_(part, int8_weight_only())
elif quantization_scheme == "fp8":
quantize_to_float8(part, QuantConfig(ActivationCasting.DYNAMIC))
return part
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,
quantization_scheme: str = "fp8",
dtype: torch.dtype = torch.bfloat16,
):
"""
Generates a video based on the given prompt and saves it to the specified path.
Parameters:
- prompt (str): The description of the video to be generated.
- model_path (str): The path of the pre-trained model to be used.
- 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.
- 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.
- 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)
transformer = quantize_model(part=transformer, quantization_scheme=quantization_scheme)
vae = AutoencoderKLCogVideoX.from_pretrained(model_path, subfolder="vae", torch_dtype=dtype)
vae = quantize_model(part=vae, quantization_scheme=quantization_scheme)
pipe = CogVideoXPipeline.from_pretrained(
model_path,
text_encoder=text_encoder,
transformer=transformer,
vae=vae,
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,
use_dynamic_cfg=True, ## This id used for DPM Sechduler, for DDIM scheduler, it should be False
guidance_scale=guidance_scale,
generator=torch.Generator(device="cuda").manual_seed(42),
).frames[0]
export_to_video(video, output_path, fps=8)
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="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)",
)
args = parser.parse_args()
dtype = torch.float16 if args.dtype == "float16" else torch.bfloat16
generate_video(
prompt=args.prompt,
model_path=args.model_path,
output_path=args.output_path,
num_inference_steps=args.num_inference_steps,
guidance_scale=args.guidance_scale,
num_videos_per_prompt=args.num_videos_per_prompt,
quantization_scheme=args.quantization_scheme,
dtype=dtype,
)