Allegro

🚀 Allegro

Allegro is an open - source text - to - video generation model. It can create diverse and high - quality video content, with small model parameters and efficient performance. It offers users a powerful tool for video content creation.

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📷 Gallery

✨ Features

• Open Source: Full model weights and code available to the community, Apache 2.0!
• Versatile Content Creation: Capable of generating a wide range of content, from close - ups of humans and animals to diverse dynamic scenes.
• High - Quality Output: Generate detailed 6 - second videos at 15 FPS with 720x1280 resolution, which can be interpolated to 30 FPS with [EMA - VFI](https://github.com/MCG - NJU/EMA - VFI).
• Small and Efficient: Features a 175M parameter VideoVAE and a 2.8B parameter VideoDiT model. Supports multiple precisions (FP32, BF16, FP16) and uses 9.3 GB of GPU memory in BF16 mode with CPU offloading. Context length is 79.2K, equivalent to 88 frames.

📚 Documentation

🚀 Quick Start

Step 1: Install the necessary requirements

• Ensure Python >= 3.10, PyTorch >= 2.4, CUDA >= 12.4.
• It is recommended to use Anaconda to create a new environment (Python >= 3.10) conda create -n rllegro python=3.10 -y to run the following example.
• Run pip install git+https://github.com/huggingface/diffusers.git torch==2.4.1 transformers==4.40.1 accelerate sentencepiece imageio imageio - ffmpeg beautifulsoup4

Step 2: Run inference

import torch
from diffusers import AutoencoderKLAllegro, AllegroPipeline
from diffusers.utils import export_to_video
vae = AutoencoderKLAllegro.from_pretrained("rhymes-ai/Allegro", subfolder="vae", torch_dtype=torch.float32)
pipe = AllegroPipeline.from_pretrained(
    "rhymes-ai/Allegro", vae=vae, torch_dtype=torch.bfloat16
)
pipe.to("cuda")
pipe.vae.enable_tiling()
prompt = "A seaside harbor with bright sunlight and sparkling seawater, with many boats in the water. From an aerial view, the boats vary in size and color, some moving and some stationary. Fishing boats in the water suggest that this location might be a popular spot for docking fishing boats."

positive_prompt = """
(masterpiece), (best quality), (ultra-detailed), (unwatermarked), 
{} 
emotional, harmonious, vignette, 4k epic detailed, shot on kodak, 35mm photo, 
sharp focus, high budget, cinemascope, moody, epic, gorgeous
"""

negative_prompt = """
nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, 
low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry.
"""

prompt = prompt.format(prompt.lower().strip())

video = pipe(prompt, negative_prompt=negative_prompt, guidance_scale=7.5, max_sequence_length=512, num_inference_steps=100, generator = torch.Generator(device="cuda:0").manual_seed(42)).frames[0]
export_to_video(video, "output.mp4", fps=15)

Use pipe.enable_sequential_cpu_offload() to offload the model into CPU for less GPU memory cost (about 9.3G, compared to 27.5G if CPU offload is not enabled), but the inference time will increase significantly.

Step 3: (Optional) Interpolate the video to 30 FPS

It is recommended to use [EMA - VFI](https://github.com/MCG - NJU/EMA - VFI) to interpolate the video from 15 FPS to 30 FPS. For better visual quality, please use imageio to save the video.

Step 4: Faster inference

For faster inference such as Context Parallel, PAB, please refer to our github repo.

📄 License

This repo is released under the Apache 2.0 License.