218 lines
7.3 KiB
Python
218 lines
7.3 KiB
Python
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
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# SPDX-License-Identifier: Apache-2.0
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from dataclasses import dataclass, field
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from sglang.multimodal_gen.configs.sample.sampling_params import SamplingParams
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from sglang.multimodal_gen.configs.sample.teacache import WanTeaCacheParams
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@dataclass
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class WanT2V_1_3B_SamplingParams(SamplingParams):
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# Video parameters
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height: int = 480
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width: int = 832
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num_frames: int = 81
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fps: int = 16
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# Denoising stage
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guidance_scale: float = 3.0
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negative_prompt: str = (
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"Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
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)
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num_inference_steps: int = 50
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teacache_params: WanTeaCacheParams = field(
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default_factory=lambda: WanTeaCacheParams(
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teacache_thresh=0.08,
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ret_steps_coeffs=[
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-5.21862437e04,
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9.23041404e03,
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-5.28275948e02,
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1.36987616e01,
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-4.99875664e-02,
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],
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non_ret_steps_coeffs=[
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2.39676752e03,
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-1.31110545e03,
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2.01331979e02,
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-8.29855975e00,
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1.37887774e-01,
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],
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)
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)
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@dataclass
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class WanT2V_14B_SamplingParams(SamplingParams):
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# Video parameters
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height: int = 720
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width: int = 1280
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num_frames: int = 81
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fps: int = 16
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# Denoising stage
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guidance_scale: float = 5.0
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negative_prompt: str = (
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"Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
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)
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num_inference_steps: int = 50
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teacache_params: WanTeaCacheParams = field(
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default_factory=lambda: WanTeaCacheParams(
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teacache_thresh=0.20,
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use_ret_steps=False,
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ret_steps_coeffs=[
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-3.03318725e05,
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4.90537029e04,
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-2.65530556e03,
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5.87365115e01,
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-3.15583525e-01,
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],
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non_ret_steps_coeffs=[
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-5784.54975374,
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5449.50911966,
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-1811.16591783,
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256.27178429,
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-13.02252404,
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],
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)
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)
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@dataclass
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class WanI2V_14B_480P_SamplingParam(WanT2V_1_3B_SamplingParams):
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# Denoising stage
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guidance_scale: float = 5.0
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num_inference_steps: int = 50
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# num_inference_steps: int = 40
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teacache_params: WanTeaCacheParams = field(
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default_factory=lambda: WanTeaCacheParams(
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teacache_thresh=0.26,
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ret_steps_coeffs=[
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-3.03318725e05,
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4.90537029e04,
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-2.65530556e03,
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5.87365115e01,
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-3.15583525e-01,
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],
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non_ret_steps_coeffs=[
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-5784.54975374,
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5449.50911966,
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-1811.16591783,
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256.27178429,
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-13.02252404,
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],
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)
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)
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@dataclass
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class WanI2V_14B_720P_SamplingParam(WanT2V_14B_SamplingParams):
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# Denoising stage
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guidance_scale: float = 5.0
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num_inference_steps: int = 50
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# num_inference_steps: int = 40
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teacache_params: WanTeaCacheParams = field(
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default_factory=lambda: WanTeaCacheParams(
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teacache_thresh=0.3,
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ret_steps_coeffs=[
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-3.03318725e05,
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4.90537029e04,
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-2.65530556e03,
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5.87365115e01,
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-3.15583525e-01,
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],
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non_ret_steps_coeffs=[
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-5784.54975374,
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5449.50911966,
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-1811.16591783,
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256.27178429,
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-13.02252404,
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],
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)
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)
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@dataclass
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class FastWanT2V480PConfig(WanT2V_1_3B_SamplingParams):
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# DMD parameters
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# dmd_denoising_steps: list[int] | None = field(default_factory=lambda: [1000, 757, 522])
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num_inference_steps: int = 3
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num_frames: int = 61
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height: int = 448
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width: int = 832
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fps: int = 16
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# =============================================
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# ============= Wan2.1 Fun Models =============
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# =============================================
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@dataclass
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class Wan2_1_Fun_1_3B_InP_SamplingParams(SamplingParams):
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"""Sampling parameters for Wan2.1 Fun 1.3B InP model."""
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height: int = 480
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width: int = 832
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num_frames: int = 81
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fps: int = 16
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negative_prompt: str | None = (
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"色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
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)
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guidance_scale: float = 6.0
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num_inference_steps: int = 50
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# =============================================
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# ============= Wan2.2 TI2V Models =============
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# =============================================
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@dataclass
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class Wan2_2_Base_SamplingParams(SamplingParams):
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"""Sampling parameters for Wan2.2 TI2V 5B model."""
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negative_prompt: str | None = (
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"色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
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)
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@dataclass
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class Wan2_2_TI2V_5B_SamplingParam(Wan2_2_Base_SamplingParams):
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"""Sampling parameters for Wan2.2 TI2V 5B model."""
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height: int = 704
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width: int = 1280
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num_frames: int = 121
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fps: int = 24
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guidance_scale: float = 5.0
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num_inference_steps: int = 50
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@dataclass
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class Wan2_2_T2V_A14B_SamplingParam(Wan2_2_Base_SamplingParams):
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guidance_scale: float = 4.0 # high_noise
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guidance_scale_2: float = 3.0 # low_noise
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num_inference_steps: int = 40
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fps: int = 16
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# NOTE(will): default boundary timestep is tracked by PipelineConfig, but
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# can be overridden during sampling
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@dataclass
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class Wan2_2_I2V_A14B_SamplingParam(Wan2_2_Base_SamplingParams):
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guidance_scale: float = 3.5 # high_noise
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guidance_scale_2: float = 3.5 # low_noise
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num_inference_steps: int = 40
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fps: int = 16
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# NOTE(will): default boundary timestep is tracked by PipelineConfig, but
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# can be overridden during sampling
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# =============================================
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# ============= Causal Self-Forcing =============
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# =============================================
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@dataclass
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class SelfForcingWanT2V480PConfig(WanT2V_1_3B_SamplingParams):
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pass
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