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Allegro

Text-to-Video
Diffusers
Safetensors
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AllegroPipeline
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metadata
license: apache-2.0
language:
  - en
library_name: diffusers

Gallery 路 GitHub 路 Blog 路 Paper 路 Discord

Gallery

For more demos and corresponding prompts, see the Allegro Gallery.

Key Feature

  • 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.
  • 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.

Model info

Model Allegro
Description Text-to-Video Generation Model
Download Hugging Face
Parameter VAE: 175M
DiT: 2.8B
Inference Precision VAE: FP32/TF32/BF16/FP16 (best in FP32/TF32)
DiT/T5: BF16/FP32/TF32
Context Length 79.2K
Resolution 720 x 1280
Frames 88
Video Length 6 seconds @ 15 fps
Single GPU Memory Usage 9.3G BF16 (with cpu_offload)

Quick start

  1. Download the Allegro GitHub code.
  2. Install the necessary requirements. a. Ensure Python >= 3.10, PyTorch >= 2.4, CUDA >= 12.4. For details, see requirements.txt. b. It is recommended to use Anaconda to create a new environment (Python >= 3.10) to run the following example.
  3. Download the Allegro model weights.
  4. Run inference.
python single_inference.py \
--user_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.' \
--vae your/path/to/vae \
--dit your/path/to/transformer \
--text_encoder your/path/to/text_encoder \
--tokenizer your/path/to/tokenizer \
--guidance_scale 7.5 \
--num_sampling_steps 100 \
--seed 42

License

This repo is released under the Apache 2.0 License.