Allegro T2V 40x360P

🚀 Allegro

Allegro is an open - source text - to - video model that can generate high - quality, diverse videos with small and efficient models.

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🚀 Quick Start

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

2. Run inference

import torch
from diffusers import AutoencoderKLAllegro, AllegroPipeline
from diffusers.utils import export_to_video
vae = AutoencoderKLAllegro.from_pretrained("rhymes-ai/Allegro-T2V-40x360P", subfolder="vae", torch_dtype=torch.float32)
vae.tile_overlap_t = 8
vae.tile_overlap_h = 144
vae.tile_overlap_w = 64
vae.stride = (16,112,192)

pipe = AllegroPipeline.from_pretrained(
    "rhymes-ai/Allegro-T2V-40x360P", 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, but the inference time will increase significantly.

3. (Optional) Interpolate the video to 30 FPS

It is recommended to use EMA-VFI to interpolate the video from 15 FPS to 30 FPS. For better visual quality, please use imageio to save the video.

4. For faster inference

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

✨ 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 2 to 6 - second videos at 15 FPS with 368x640 and 720x1280 resolution, which can be interpolated to 30 FPS with 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

Gallery

Model info

📄 License

This repo is released under the Apache 2.0 License.