| import numpy as np |
| import torch |
| from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler |
| from diffusers.utils import export_to_video |
| from datetime import datetime |
|
|
| pipe = DiffusionPipeline.from_pretrained(r"J:\Projects\Video-Projects\text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16") |
| pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) |
| pipe.enable_model_cpu_offload() |
| timestamp_str = datetime.now().strftime("%Y-%m-%d-%H%M%S") |
|
|
| output_video_path=f"J:/Projects/Video-Projects/text-to-video-ms-1.7b/output_videos/{timestamp_str}.mp4" |
| prompt = "Spiderman is surfing" |
|
|
| video_frames = pipe(prompt, num_inference_steps=25).frames |
| video_frames_np = [np.array(frame) for frame in video_frames] |
| video_frames_np = np.concatenate(video_frames_np, axis=0) |
|
|
| video_path = export_to_video(video_frames_np,output_video_path) |
|
|
