91 lines
3.9 KiB
Markdown
91 lines
3.9 KiB
Markdown
# Wan recipes
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<RecipeFamily family="wan" />
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<details >
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<summary>Compare Wan modes and options</summary>
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<h2 id="wan-modes-heading">Supported modes</h2>
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<p>
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FastMetal MLX is T2V in the checked-in examples. Image-to-video and
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TI2V stay on the CUDA recipes. Temporal <code>--fast</code> composes
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with either spatial path. <code>--refine</code> and
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<code>--fast-spatial</code> cannot run together.
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<code>--refine</code> wins if both are set.
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<code>basic_mps.py</code> is the older PyTorch MPS demo and is not a
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FastMetal recipe.
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</p>
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<div >
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<table>
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<thead>
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<tr>
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<th>Mode</th>
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<th>CUDA</th>
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<th>MLX FastMetal</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>T2V</td>
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<td>FastWan2.1 1.3B, Wan2.2 A14B</td>
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<td>1.3B, 5B, and 14B</td>
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</tr>
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<tr>
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<td>I2V</td>
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<td>Wan2.1 14B 480P</td>
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<td>Not in the released examples</td>
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</tr>
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<tr>
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<td>TI2V</td>
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<td>Wan2.2 TI2V 5B</td>
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<td>FastMetal 5B is T2V in <code>mlx_wan22_generate.py</code></td>
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</tr>
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<tr>
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<td>Temporal <code>--fast</code></td>
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<td>No cookbook recipe</td>
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<td>RIFE. Fewer frames, then interpolate to <code>--num-frames</code></td>
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</tr>
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<tr>
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<td>Spatial <code>--fast-spatial</code></td>
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<td>No cookbook recipe</td>
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<td>Denoise and decode at half resolution, then upsample. No second denoise</td>
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</tr>
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<tr>
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<td>Two-pass <code>--refine</code></td>
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<td>No cookbook recipe</td>
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<td>Denoise at base resolution, upsample, re-noise, denoise again. Wins over <code>--fast-spatial</code></td>
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</tr>
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</tbody>
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</table>
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</div>
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</details>
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<details id="cookbook-setup">
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<summary>Setup</summary>
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<div >
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<p>The generated commands expect a local clone:</p>
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<pre><code>git clone https://github.com/hao-ai-lab/FastVideo.git
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cd FastVideo</code></pre>
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<p>Use <a href="../../inference/configuration/">Configuration</a> for supported Python and CLI settings, <a href="../../inference/optimizations/">Optimizations</a> for attention and memory tradeoffs, and the <a href="../../inference/support_matrix/">support matrix</a> for the supported model and optimization surface.</p>
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</div>
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</details>
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<details id="cookbook-troubleshooting">
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<summary>Troubleshooting</summary>
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<div >
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<ul>
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<li>Out of memory on the A14B recipes: the checked-in sources already enable CPU offload; see <a href="../../inference/configuration/">Configuration</a> for the offload surface before reducing resolution or frames.</li>
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<li>The FastWan2.1 recipe requires <code>VIDEO_SPARSE_ATTN</code>; confirm the environment variable in the command was set in the same shell.</li>
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<li>FastMetal MLX: install with the <a href="../../getting_started/installation/mlx/">MLX install guide</a>, then pick a FastMetal recipe in the builder. CUDA FastWan-QAD checkpoints are refused on the MLX runtime.</li>
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<li>FastMetal 5B uses <code>mlx_wan22_generate.py</code>. 1.3B and 14B use <code>mlx_wan_prompt_to_video.py</code>.</li>
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<li>Gated or missing checkpoints: run <code>huggingface-cli login</code> and confirm you accepted the model's license on Hugging Face.</li>
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</ul>
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</div>
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</details>
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<details id="cookbook-evidence">
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<summary>Evidence status</summary>
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<div >
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<p>Every recipe on this page maps to a checked-in FastVideo source. The FastMetal MLX releases include the recorded M4 Max system memory, documented unified-memory floor, and measured peak MLX memory. CUDA entries remain <strong>Source-backed</strong> where the examples record a GPU count but no exact GPU model or VRAM. Unlisted hardware is unknown, not unsupported.</p>
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</div>
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</details>
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