File size: 1,338 Bytes
2acb9f2 941d256 2acb9f2 7f2323b 2acb9f2 941d256 e990e13 941d256 2acb9f2 e990e13 7f2323b 2acb9f2 e990e13 941d256 7f2323b 941d256 7f2323b 941d256 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 |
#!/usr/bin/env python3
import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from diffusers.utils import export_to_video
from PIL import Image
# Make sure CUDA has < 13GB VRAM
# torch.cuda.set_per_process_memory_fraction(0.5)
pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
# pipe.enable_model_cpu_offload()
pipe.to("cuda")
pipe.enable_vae_slicing()
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
prompt = "Darth Vader is surfing on waves"
video_frames = pipe(prompt, num_inference_steps=40, height=320, width=576, num_frames=36).frames
video_path = export_to_video(video_frames, output_video_path="/home/patrick/videos/video_576_darth_vader_36.mp4")
pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_XL", torch_dtype=torch.float16)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.enable_vae_slicing()
video = [Image.fromarray(frame).resize((1024, 576)) for frame in video_frames]
video_frames = pipe(prompt, video=video, strength=0.6).frames
video_path = export_to_video(video_frames, output_video_path="/home/patrick/videos/video_1024_darth_vader_36.mp4")
|