| #!/usr/bin/env python3 |
| import torch |
| from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler |
| import numpy as np |
| import gc |
| from diffusers.utils import export_to_video |
| from PIL import Image |
|
|
| 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.enable_xformers_memory_efficient_attention() |
| pipe.enable_vae_slicing() |
|
|
| prompt = "spiderman running in the desert" |
| video_frames = pipe(prompt, num_inference_steps=2, height=320, width=576, num_frames=24, output_type="latent".frames |
| # video_path = export_to_video(video_frames, output_video_path="/home/patrick/videos/video_576_spiderman_24.mp4") |
|
|
| pipe.to("cpu") |
| del pipe |
| gc.collect() |
| torch.cuda.empty_cache() |
|
|
| import ipdb; ipdb.set_trace() |
|
|
| 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_xformers_memory_efficient_attention() |
| pipe.enable_vae_slicing() |
|
|
| video = [Image.fromarray(frame).resize((1024, 576)) for frame in video_frames] |
|
|
| video_frames = pipe(prompt, video=video, num_inference_steps=2, strength=0.6).frames |
| video_path = export_to_video(video_frames, output_video_path="/home/patrick/videos/video_1024_spiderman_24.mp4") |
|
|
