Question about Table. 1

Thank you for your excellent work.

I have some questions about latency.

I attempted to run StableVideoDiffusionPipeline from diffusers directly to reproduce the latency result in Tab. 1 with an A100 80G GPU. However, my testing results are higher than what you reported. Could you help to check if something wrong about the implementation?

import torch
from PIL import Image
from diffusers import StableVideoDiffusionPipeline
from diffusers.utils import load_image
import packaging

# import torch._dynamo
# torch._dynamo.reset()

if packaging.version.parse(torch.__version__) >= packaging.version.parse('1.12.0'):
    torch.backends.cuda.matmul.allow_tf32 = True

# weight_dtype = torch.bfloat16
weight_dtype = torch.float16

pipeline = StableVideoDiffusionPipeline.from_pretrained(
    "stabilityai/stable-video-diffusion-img2vid", variant="fp16"
).to("cuda", dtype=weight_dtype)

# test 1-step inference speed
img = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png")
generator = torch.manual_seed(42)
for _ in range(10):
    with torch.no_grad(), torch.autocast("cuda", dtype=weight_dtype):
        frames = pipeline(img, decode_chunk_size=7, generator=generator, motion_bucket_id=127, fps=7, max_guidance_scale=1.0, num_inference_steps=1).frames[0] # set max_guidance_scale=1.0 to disable cfg

The inference speed is attached as follow:

snap-research / SF-V

Question about Table. 1 #3