96 lines
3.6 KiB
Python
96 lines
3.6 KiB
Python
from dataclasses import dataclass, field
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from fastvideo.v1.configs.sample.base import SamplingParam
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from fastvideo.v1.configs.sample.teacache import WanTeaCacheParams
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@dataclass
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class WanT2V_1_3B_SamplingParam(SamplingParam):
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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 = "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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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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-5.21862437e+04, 9.23041404e+03, -5.28275948e+02,
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1.36987616e+01, -4.99875664e-02
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],
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non_ret_steps_coeffs=[
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2.39676752e+03, -1.31110545e+03, 2.01331979e+02,
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-8.29855975e+00, 1.37887774e-01
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]))
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@dataclass
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class WanT2V_14B_SamplingParam(SamplingParam):
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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 = "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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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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-3.03318725e+05, 4.90537029e+04, -2.65530556e+03,
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5.87365115e+01, -3.15583525e-01
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],
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non_ret_steps_coeffs=[
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-5784.54975374, 5449.50911966, -1811.16591783, 256.27178429,
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-13.02252404
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]))
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@dataclass
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class WanI2V_14B_480P_SamplingParam(WanT2V_1_3B_SamplingParam):
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# Denoising stage
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guidance_scale: float = 5.0
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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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-3.03318725e+05, 4.90537029e+04, -2.65530556e+03,
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5.87365115e+01, -3.15583525e-01
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],
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non_ret_steps_coeffs=[
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-5784.54975374, 5449.50911966, -1811.16591783, 256.27178429,
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-13.02252404
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]))
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@dataclass
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class WanI2V_14B_720P_SamplingParam(WanT2V_14B_SamplingParam):
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# Denoising stage
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guidance_scale: float = 5.0
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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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-3.03318725e+05, 4.90537029e+04, -2.65530556e+03,
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5.87365115e+01, -3.15583525e-01
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],
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non_ret_steps_coeffs=[
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-5784.54975374, 5449.50911966, -1811.16591783, 256.27178429,
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-13.02252404
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]))
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