汪志鹏
06c9d9b463
Signed-off-by: princepride <wangzhipeng628@gmail.com> Signed-off-by: 汪志鹏 <wangzhipeng628@gmail.com>
85 lines
3.4 KiB
Python
85 lines
3.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from dataclasses import dataclass
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# Model-specific polynomial coefficients for rescaling L1 distances
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# These coefficients account for model-specific characteristics in how embeddings change
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# Source: TeaCache paper and ComfyUI-TeaCache empirical tuning
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_MODEL_COEFFICIENTS = {
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# FLUX transformer coefficients from TeaCache paper
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"FluxTransformer2DModel": [
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4.98651651e02,
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-2.83781631e02,
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5.58554382e01,
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-3.82021401e00,
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2.64230861e-01,
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],
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# Qwen-Image transformer coefficients from ComfyUI-TeaCache
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# Tuned specifically for Qwen's dual-stream transformer architecture
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# Used for all Qwen-Image Family pipelines, in general
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"QwenImageTransformer2DModel": [
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-4.50000000e02,
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2.80000000e02,
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-4.50000000e01,
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3.20000000e00,
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-2.00000000e-02,
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],
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# Bagel transformer coefficients
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# Using Qwen's coefficients as reasonable default given shared architecture
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"Bagel": [1.33313129e06, -1.68644226e05, 7.95050740e03, -1.63747873e02, 1.26352397e00],
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# Z-Image transformer coefficients
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# Copied from Qwen-Image, need to be tuned specifically for Z-Image in future
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"ZImageTransformer2DModel": [
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-4.50000000e02,
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2.80000000e02,
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-4.50000000e01,
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3.20000000e00,
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-2.00000000e-02,
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],
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}
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@dataclass
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class TeaCacheConfig:
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"""
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Configuration for TeaCache applied to transformer models.
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TeaCache (Timestep Embedding Aware Cache) is an adaptive caching technique that speeds up
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diffusion model inference by reusing transformer block computations when consecutive
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timestep embeddings are similar.
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Args:
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rel_l1_thresh: Threshold for accumulated relative L1 distance. When below threshold,
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cached residual is reused. Values in [0.1, 0.3] work best:
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- 0.2: ~1.5x speedup with minimal quality loss
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- 0.4: ~1.8x speedup with slight quality loss
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- 0.6: ~2.0x speedup with noticeable quality loss
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coefficients: Polynomial coefficients for rescaling L1 distance. If None, uses
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model-specific defaults based on transformer_type.
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transformer_type: Transformer class name (e.g., "QwenImageTransformer2DModel").
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Auto-detected from pipeline.transformer.__class__.__name__ in backend.
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Defaults to "QwenImageTransformer2DModel".
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"""
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rel_l1_thresh: float = 0.2
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coefficients: list[float] | None = None
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transformer_type: str = "QwenImageTransformer2DModel"
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def __post_init__(self) -> None:
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"""Validate and set default coefficients."""
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if self.rel_l1_thresh <= 0:
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raise ValueError(f"rel_l1_thresh must be positive, got {self.rel_l1_thresh}")
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if self.coefficients is None:
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# Use model-specific coefficients, explicitly check if the type exists or not
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if self.transformer_type not in _MODEL_COEFFICIENTS:
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raise KeyError(
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f"Cannot find coefficients for {self.transformer_type}. "
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f"Supported: {list(_MODEL_COEFFICIENTS.keys())}"
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)
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self.coefficients = _MODEL_COEFFICIENTS[self.transformer_type]
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if len(self.coefficients) != 5:
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raise ValueError(f"coefficients must contain exactly 5 elements, got {len(self.coefficients)}")
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