Text-to-Video
Safetensors
MLX
Wan2.2
mlx-gen
mflux
apple-silicon
8-bit precision
mixed-q8-bf16
wan
video-generation
wan-a14b
Instructions to use AbstractFramework/wan2.2-t2v-a14b-diffusers-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AbstractFramework/wan2.2-t2v-a14b-diffusers-8bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir wan2.2-t2v-a14b-diffusers-8bit AbstractFramework/wan2.2-t2v-a14b-diffusers-8bit
- Wan2.2
How to use AbstractFramework/wan2.2-t2v-a14b-diffusers-8bit 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
Release Wan2.2 T2V A14B q8/BF16 MLX-Gen package
Browse files- README.md +23 -24
- transformer/0.safetensors +1 -1
- transformer/1.safetensors +1 -1
- transformer/2.safetensors +1 -1
- transformer/3.safetensors +1 -1
- transformer/4.safetensors +1 -1
- transformer/5.safetensors +1 -1
- transformer/6.safetensors +1 -1
- transformer/7.safetensors +1 -1
- transformer/model.safetensors.index.json +1 -1
- transformer_2/0.safetensors +1 -1
- transformer_2/1.safetensors +1 -1
- transformer_2/2.safetensors +1 -1
- transformer_2/3.safetensors +1 -1
- transformer_2/4.safetensors +1 -1
- transformer_2/5.safetensors +1 -1
- transformer_2/6.safetensors +1 -1
- transformer_2/7.safetensors +1 -1
- transformer_2/model.safetensors.index.json +1 -1
- vae/0.safetensors +2 -2
- vae/model.safetensors.index.json +2 -2
README.md
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- wan2.2
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- video-generation
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- text-to-video
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- image-to-video
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- wan-a14b
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---
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# wan2.2-t2v-a14b-diffusers-8bit
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This repository contains MLX-Gen saved weights for `Wan-AI/Wan2.2-T2V-A14B-Diffusers`. The checkpoint is designed for local Apple Silicon inference with [`mlx-gen`](https://github.com/lpalbou/mlx-gen).
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It uses the mflux/MLX saved-weight layout
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## Source Model
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- q8 for quantizable Wan transformer attention and feed-forward modules.
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- BF16 for the Wan VAE.
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- BF16 for Wan transformer conditioning/output projection linears, the UMT5 text encoder, scheduler metadata, tokenizer files, norms, and other non-quantizable parameters.
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Wan q4 quality and any possible mixed q4/q8 policy are still under validation. Prefer q8 for publishable Wan checkpoints until the q4 policy is documented.
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## Local Validation
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| This mixed q8/BF16 package | 40 GiB | 20.8 GiB | 31.8 GiB | 110.3 s | Storage and memory; side-by-side quality validation passed. |
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Compared with the original source snapshot, this mixed q8/BF16 package cuts disk usage by about 66%, MLX peak memory by about 37%, and physical peak memory by about 35% in this validation run. It is not byte-identical to BF16, but the validation contact sheet stayed in the same visual family. The prepared q8/BF16 output was byte-identical to running `--quantize 8` from the upstream source snapshot.
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## Compatibility
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Requires `mlx-gen >= 0.18.
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Generated with `mlx-gen 0.18.
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Use the `mlxgen` command and Python import path for new MLX-Gen projects.
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## Usage
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```bash
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python -m pip install -U mlx-gen
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--model AbstractFramework/wan2.2-t2v-a14b-diffusers-8bit \
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--task text-to-video \
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--prompt "Your video prompt here" \
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--width
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--height
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--frames
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--steps
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--guidance 4 \
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--guidance-2 3 \
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--fps
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--seed
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--output video.mp4
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```
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- wan2.2
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- video-generation
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- text-to-video
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- wan-a14b
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---
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# wan2.2-t2v-a14b-diffusers-8bit
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This repository contains MLX-Gen saved weights for `Wan-AI/Wan2.2-T2V-A14B-Diffusers`. The checkpoint is designed for local Apple Silicon inference with [`mlx-gen`](https://github.com/lpalbou/mlx-gen).
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It uses the mflux/MLX saved-weight layout. Quantized checkpoints include MLX quantization tensors. It is not a Diffusers or Transformers `from_pretrained()` checkpoint.
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## Source Model
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- q8 for quantizable Wan transformer attention and feed-forward modules.
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- BF16 for the Wan VAE.
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- BF16 for Wan transformer conditioning/output projection linears, the UMT5 text encoder, scheduler metadata, tokenizer files, norms, convolutions, and other non-quantizable parameters.
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Wan q4 quality and any possible mixed q4/q8 policy are still under validation. Prefer q8 for publishable Wan checkpoints until the q4 policy is documented.
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## Local Validation
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These measurements are validation-sized release checks for this uploaded package. They verify package loading, video integrity, and prompt influence for this profile only; they do not claim full-size `1280x720`, 81-frame, 40-step readiness.
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| Measurement | Value |
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|---|---:|
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| Package disk usage | 39.5 GiB |
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| Validation profile | 384x224, 33 frames, 12 steps, 8.0 fps, seed 4242, `--low-ram` |
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| Prompt pair | scientist scene / red car scene |
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| Video health | 33 / 33 frames decoded, 8.0 fps, nonblank |
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| Mean temporal delta | 5.6 / 3.2 luma |
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| Prompt delta | 102.0 mean abs RGB |
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| Generation time | 162.2 s / 319.6 s |
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## Compatibility
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Requires `mlx-gen >= 0.18.9`.
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Generated with `mlx-gen 0.18.9`.
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Use the `mlxgen` command and Python import path for new MLX-Gen projects.
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## Usage
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The q8 A14B example below is intentionally validation-sized. Do not use this card to claim full-size `1280x720`, 81-frame, 40-step readiness until that exact path has passed video integrity and quality validation.
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```bash
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python -m pip install -U mlx-gen
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--model AbstractFramework/wan2.2-t2v-a14b-diffusers-8bit \
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--task text-to-video \
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--prompt "Your video prompt here" \
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--width 384 \
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--height 224 \
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--frames 33 \
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--steps 12 \
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--guidance 4 \
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--guidance-2 3 \
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--fps 8 \
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--seed 4242 \
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--metadata \
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--output video.mp4
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```
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