Instructions to use RaphaelLiu/Pusa-V0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use RaphaelLiu/Pusa-V0.5 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("RaphaelLiu/Pusa-V0.5", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| import os | |
| import subprocess | |
| import tempfile | |
| import time | |
| import numpy as np | |
| from moviepy.editor import ImageSequenceClip | |
| from PIL import Image | |
| from genmo.lib.progress import get_new_progress_bar | |
| class Timer: | |
| def __init__(self): | |
| self.times = {} # Dictionary to store times per stage | |
| def __call__(self, name): | |
| print(f"Timing {name}") | |
| return self.TimerContextManager(self, name) | |
| def print_stats(self): | |
| total_time = sum(self.times.values()) | |
| # Print table header | |
| print("{:<20} {:>10} {:>10}".format("Stage", "Time(s)", "Percent")) | |
| for name, t in self.times.items(): | |
| percent = (t / total_time) * 100 if total_time > 0 else 0 | |
| print("{:<20} {:>10.2f} {:>9.2f}%".format(name, t, percent)) | |
| class TimerContextManager: | |
| def __init__(self, outer, name): | |
| self.outer = outer # Reference to the Timer instance | |
| self.name = name | |
| self.start_time = None | |
| def __enter__(self): | |
| self.start_time = time.perf_counter() | |
| return self | |
| def __exit__(self, exc_type, exc_value, traceback): | |
| end_time = time.perf_counter() | |
| elapsed = end_time - self.start_time | |
| self.outer.times[self.name] = self.outer.times.get(self.name, 0) + elapsed | |
| def save_video(final_frames, output_path, fps=30): | |
| assert final_frames.ndim == 4 and final_frames.shape[3] == 3, f"invalid shape: {final_frames} (need t h w c)" | |
| if final_frames.dtype != np.uint8: | |
| final_frames = (final_frames * 255).astype(np.uint8) | |
| ImageSequenceClip(list(final_frames), fps=fps).write_videofile(output_path) | |
| def create_memory_tracker(): | |
| import torch | |
| previous = [None] # Use list for mutable closure state | |
| def track(label="all2all"): | |
| current = torch.cuda.memory_allocated() / 1e9 | |
| if previous[0] is not None: | |
| diff = current - previous[0] | |
| sign = "+" if diff >= 0 else "" | |
| print(f"GPU memory ({label}): {current:.2f} GB ({sign}{diff:.2f} GB)") | |
| else: | |
| print(f"GPU memory ({label}): {current:.2f} GB") | |
| previous[0] = current # type: ignore | |
| return track | |