Allegro/README.md

---
license: apache-2.0
language:
- en
library_name: diffusers
---
<p align="center">
<img src="https://huggingface.co/rhymes-ai/Allegro/resolve/main/banner.gif" width="500" height="400"/>
</p>
<p align="center">
 <a href="https://rhymes.ai/" target="_blank"> Gallery</a> · <a href="https://github.com/rhymes-ai/Aria" target="_blank">GitHub</a> · <a href="https://www.rhymes.ai/blog-details/" target="_blank">Blog</a> · <a href="https://arxiv.org/pdf/2410.05993" target="_blank">Paper</a> · <a href="https://discord" target="_blank">Discord</a> 
 
</p> 

# Gallery
<img src="https://huggingface.co/rhymes-ai/Allegro/resolve/main/gallery.gif" width="1000" height="800"/>For more demos and corresponding prompts, see the [Allegro Gallery](TBD).


# Key Feature 
- **High-Quality Output**: Generate detailed 6-second videos at 15 FPS with 720x1280 resolution, which can be interpolated to 30 FPS with EMA-VFI.
- **Small and Efficient**: Features a 175M parameter VAE and a 2.8B parameter DiT model. Supports multiple precisions (FP32, BF16, FP16) and uses 9.3 GB of GPU memory in BF16 mode with CPU offloading.
- **Extensive Context Length**: Handles up to 79.2k tokens, providing rich and comprehensive text-to-video generation capabilities.
- **Versatile Content Creation**: Capable of generating a wide range of content, from close-ups of humans and animals to diverse dynamic scenes.


# Model info 

<table>
  <tr>
    <th>Model</th>
    <td>Allegro</td>
  </tr>
  <tr>
    <th>Description</th>
    <td>Text-to-Video Generation Model</td>
  </tr>
  <tr>
    <th>Download</th>
    <td>&lt;HF link - TBD&gt;</td>
  </tr>
  <tr>
    <th rowspan="2">Parameter</th>
    <td>VAE: 175M</td>
  </tr>
  <tr>
    <td>DiT: 2.8B</td>
  </tr>
  <tr>
    <th rowspan="2">Inference Precision</th>
    <td>VAE: FP32/TF32/BF16/FP16 (best in FP32/TF32)</td>
  </tr>
  <tr>
    <td>DiT/T5: BF16/FP32/TF32</td>
  </tr>
  <tr>
    <th>Context Length</th>
    <td>79.2k</td>
  </tr>
  <tr>
    <th>Resolution</th>
    <td>720 x 1280</td>
  </tr>
  <tr>
    <th>Frames</th>
    <td>88</td>
  </tr>
  <tr>
    <th>Video Length</th>
    <td>6 seconds @ 15 fps</td>
  </tr>
  <tr>
    <th>Single GPU Memory Usage</th>
    <td>9.3G BF16 (with cpu_offload)</td>
  </tr>
</table>


# Quick start
You can quickly get started with Allegro using the Hugging Face Diffusers library.
For more tutorials, see Allegro GitHub (link-tbd).

1. Install necessary requirements. Please refer to [requirements.txt](https://github.com/rhymes-ai) on Allegro GitHub.
2. Perform inference on a single GPU.
```python
from diffusers import DiffusionPipeline
import torch

allegro_pipeline = DiffusionPipeline.from_pretrained(
"rhymes-ai/Allegro", trust_remote_code=True, torch_dtype=torch.bfloat16
).to("cuda")

allegro_pipeline.vae = allegro_pipeline.vae.to(torch.float32)

prompt = "a video of an astronaut riding a horse on mars"

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.
"""

num_sampling_steps, guidance_scale, seed = 100, 7.5, 42

user_prompt = positive_prompt.format(args.user_prompt.lower().strip())
out_video = allegro_pipeline(
    user_prompt, 
    negative_prompt=negative_prompt, 
    num_frames=88,
    height=720,
    width=1280,
    num_inference_steps=num_sampling_steps,
    guidance_scale=guidance_scale,
    max_sequence_length=512,
    generator = torch.Generator(device="cuda:0").manual_seed(seed)
).video[0]

imageio.mimwrite("test_video.mp4", out_video, fps=15, quality=8)
```
Tip: 
- It is highly recommended to use a video frame interpolation model (such as EMA-VFI) to enhance the result to 30 FPS.
- For more tutorials, see [Allegro GitHub](https://github.com/rhymes-ai).

# License
This repo is released under the Apache 2.0 License.