Text-to-Video
Safetensors
MLX
Wan2.2
mlx-gen
mflux
apple-silicon
bf16
wan
video-generation
image-to-video
Instructions to use AbstractFramework/wan2.2-ti2v-5b-diffusers-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AbstractFramework/wan2.2-ti2v-5b-diffusers-bf16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir wan2.2-ti2v-5b-diffusers-bf16 AbstractFramework/wan2.2-ti2v-5b-diffusers-bf16
- Wan2.2
How to use AbstractFramework/wan2.2-ti2v-5b-diffusers-bf16 with Wan2.2:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Update TI2V-5B memory and validation card
Browse files
README.md
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- mlx-gen
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- mflux
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- apple-silicon
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- wan
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- wan2.2
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- video-generation
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# wan2.2-ti2v-5b-diffusers-bf16
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This repository contains MLX-Gen saved weights for
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It uses the mflux/MLX saved-weight layout. It is not a Diffusers or Transformers
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## Source Model
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Original model: [`Wan-AI/Wan2.2-TI2V-5B-Diffusers`](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers).
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## License and Access
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This prepared derivative follows the Apache 2.0 license of the source model.
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##
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## Usage
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mlxgen generate \
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--model AbstractFramework/wan2.2-ti2v-5b-diffusers-bf16 \
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--prompt "
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--width 1280 \
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--height 704 \
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--frames
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--steps
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--guidance 5 \
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--fps 24 \
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--seed
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--output video.mp4
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```
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## Attribution
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MLX-Gen is based on [mflux](https://github.com/filipstrand/mflux) by Filip Strand and the original
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Prepared and contributed by [@lpalbou](https://huggingface.co/lpalbou).
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- mlx-gen
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- mflux
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- apple-silicon
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- bf16
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- wan
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- wan2.2
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- video-generation
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---
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# wan2.2-ti2v-5b-diffusers-bf16
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This repository contains BF16 MLX-Gen saved weights for
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[`Wan-AI/Wan2.2-TI2V-5B-Diffusers`](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers).
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It is designed for local Apple Silicon inference with
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[`mlx-gen`](https://github.com/lpalbou/mlx-gen).
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It uses the mflux/MLX saved-weight layout. It is not a Diffusers or Transformers
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`from_pretrained()` checkpoint.
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## Source Model
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Original model: [`Wan-AI/Wan2.2-TI2V-5B-Diffusers`](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers).
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This prepared derivative follows the Apache 2.0 license of the source model.
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## Precision
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The upstream TI2V-5B source snapshot is not uniformly 16-bit on disk: the transformer and VAE
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safetensors are FP32, while the UMT5 text encoder is BF16. MLX-Gen loads Wan transformer/VAE
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weights at BF16 runtime precision, so this prepared BF16 package reduces storage and download size
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but is not a runtime-memory optimization versus source generation.
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Use this package when you want a smaller reusable MLX-Gen folder that preserves source behavior.
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Use the mixed q8/BF16 package when you want a smaller model footprint.
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## Measurements
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Measured on 2026-06-04 with `mlx-gen 0.18.10` on an Apple M5 Max with 128 GiB unified memory.
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Validation profile: `1280x704`, 17 frames, 20 denoising steps, guidance `5`, 24 fps, seed `321`,
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explicit empty negative prompt.
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| Layout | Storage | Logical Model | Full-Process Physical Peak | Max RSS | MLX Peak | Total Time | Output |
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| Upstream source snapshot | 31.9 GiB | 10.6 GiB | 102.7 GiB | 13.7 GiB | 58.5 GiB | 216.2 s | [base-source.mp4](validation/ti2v5b-clean/base-source.mp4) |
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| This BF16 package | 21.2 GiB | 10.6 GiB | 102.6 GiB | 14.5 GiB | 58.5 GiB | 261.6 s | [prepared-bf16.mp4](validation/ti2v5b-clean/prepared-bf16.mp4) |
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| Mixed q8/BF16 package | 16.9 GiB | 6.3 GiB | 103.7 GiB | 13.8 GiB | 54.2 GiB | 243.4 s | [mixed-q8-bf16.mp4](validation/ti2v5b-clean/mixed-q8-bf16.mp4) |
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The source and this BF16 package produced byte-identical decoded MP4 frames. The mixed q8/BF16
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package stayed visually in the same family with mean frame MAE `1.66` versus source/BF16.
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`Storage` is the Hugging Face repository total. `Logical Model` is the loaded Wan transformer plus
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VAE tensor footprint measured from MLX arrays. `Full-Process Physical Peak` is Darwin
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`phys_footprint` sampled from model initialization through MP4 save and health validation.
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Validation assets:
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- [contact-sheet.png](validation/ti2v5b-clean/contact-sheet.png)
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- [metrics.json](validation/ti2v5b-clean/metrics.json)
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## Usage
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mlxgen generate \
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--model AbstractFramework/wan2.2-ti2v-5b-diffusers-bf16 \
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--prompt "A short cinematic video of a glowing orange glass sphere floating above calm teal water, soft reflections, gentle camera movement" \
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--negative-prompt "" \
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--width 1280 \
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--height 704 \
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--frames 17 \
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--steps 20 \
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--guidance 5 \
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--fps 24 \
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--seed 321 \
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--output video.mp4
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```
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TI2V-5B also supports first-frame image-to-video in MLX-Gen when one input image is supplied.
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## Attribution
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MLX-Gen is based on [mflux](https://github.com/filipstrand/mflux) by Filip Strand and the original
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mflux contributors.
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Prepared and contributed by [@lpalbou](https://huggingface.co/lpalbou).
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