File size: 6,732 Bytes
b4feb07 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 |
import math
import torch
import torch.nn.functional as F
import torchvision.transforms.functional as tvF
import numpy as np
def apply_low_pass_filter(
tensor: torch.Tensor,
filter_type: str,
# Gaussian Blur Params
blur_sigma: float,
blur_kernel_size: float, # Can be float (relative) or int (absolute)
# Down/Up Sampling Params
resize_factor: float,
):
"""
Applies the specified low-pass filtering operation to the input tensor.
Handles 4D ([B, C, H, W]) and 5D ([B, C, F, H, W]) tensors by temporarily
reshaping 5D tensors for spatial filtering.
"""
# --- Early Exits for No-Op Cases ---
if filter_type == "none":
return tensor
if filter_type == "down_up" and resize_factor == 1.0:
return tensor
if filter_type == "gaussian_blur" and blur_sigma == 0:
return tensor
# --- Reshape 5D tensor for spatial filtering ---
is_5d = tensor.ndim == 5
if is_5d:
B, C, K, H, W = tensor.shape
# Flatten frames into batch dimension using view
tensor = tensor.view(B * K, C, H, W)
else:
B, C, H, W = tensor.shape
# --- Apply Selected Filter ---
if filter_type == "gaussian_blur":
if isinstance(blur_kernel_size, float):
kernel_val = max(int(blur_kernel_size * H), 1)
else:
kernel_val = int(blur_kernel_size)
if kernel_val % 2 == 0:
kernel_val += 1
tensor = tvF.gaussian_blur(tensor, kernel_size=[kernel_val, kernel_val], sigma=[blur_sigma, blur_sigma])
elif filter_type == "down_up":
h0, w0 = tensor.shape[-2:]
h1 = max(1, int(round(h0 * resize_factor)))
w1 = max(1, int(round(w0 * resize_factor)))
tensor = F.interpolate(tensor, size=(h1, w1), mode="bilinear", align_corners=False, antialias=True)
tensor = F.interpolate(tensor, size=(h0, w0), mode="bilinear", align_corners=False, antialias=True)
# --- Restore original 5D shape if necessary ---
if is_5d:
tensor = tensor.view(B, C, K, H, W)
return tensor
def get_lp_strength(
step_index: int,
total_steps: int,
lp_strength_schedule_type: str,
# Interval params
schedule_interval_start_time: float,
schedule_interval_end_time: float,
# Linear params
schedule_linear_start_weight: float,
schedule_linear_end_weight: float,
schedule_linear_end_time: float,
# Exponential params
schedule_exp_decay_rate: float,
) -> float:
"""
Calculates the low-pass guidance strength multiplier for the current timestep
based on the specified schedule.
"""
step_norm = step_index / max(total_steps - 1, 1)
if lp_strength_schedule_type == "linear":
schedule_duration_fraction = schedule_linear_end_time
if schedule_duration_fraction <= 0:
return schedule_linear_start_weight
if step_norm >= schedule_duration_fraction:
current_strength = schedule_linear_end_weight
else:
progress = step_norm / schedule_duration_fraction
current_strength = schedule_linear_start_weight * (1 - progress) + schedule_linear_end_weight * progress
return current_strength
elif lp_strength_schedule_type == "interval":
if schedule_interval_start_time <= step_norm <= schedule_interval_end_time:
return 1.0
else:
return 0.0
elif lp_strength_schedule_type == "exponential":
decay_rate = schedule_exp_decay_rate
if decay_rate < 0:
print(f"Warning: Negative exponential_decay_rate ({decay_rate}) is unusual. Using abs value.")
decay_rate = abs(decay_rate)
return math.exp(-decay_rate * step_norm)
elif lp_strength_schedule_type == "none":
return 1.0
else:
print(f"Warning: Unknown lp_strength_schedule_type '{lp_strength_schedule_type}'. Using constant strength 1.0.")
return 1.0
def _generate_crop_size_list(base_size=256, patch_size=32, max_ratio=4.0):
"""generate crop size list (HunyuanVideo)
Args:
base_size (int, optional): the base size for generate bucket. Defaults to 256.
patch_size (int, optional): the stride to generate bucket. Defaults to 32.
max_ratio (float, optional): th max ratio for h or w based on base_size . Defaults to 4.0.
Returns:
list: generate crop size list
"""
num_patches = round((base_size / patch_size) ** 2)
assert max_ratio >= 1.0
crop_size_list = []
wp, hp = num_patches, 1
while wp > 0:
if max(wp, hp) / min(wp, hp) <= max_ratio:
crop_size_list.append((wp * patch_size, hp * patch_size))
if (hp + 1) * wp <= num_patches:
hp += 1
else:
wp -= 1
return crop_size_list
def _get_closest_ratio(height: float, width: float, ratios: list, buckets: list):
"""get the closest ratio in the buckets (HunyuanVideo)
Args:
height (float): video height
width (float): video width
ratios (list): video aspect ratio
buckets (list): buckets generate by `generate_crop_size_list`
Returns:
the closest ratio in the buckets and the corresponding ratio
"""
aspect_ratio = float(height) / float(width)
diff_ratios = ratios - aspect_ratio
if aspect_ratio >= 1:
indices = [(index, x) for index, x in enumerate(diff_ratios) if x <= 0]
else:
indices = [(index, x) for index, x in enumerate(diff_ratios) if x > 0]
closest_ratio_id = min(indices, key=lambda pair: abs(pair[1]))[0]
closest_size = buckets[closest_ratio_id]
closest_ratio = ratios[closest_ratio_id]
return closest_size, closest_ratio
def get_hunyuan_video_size(i2v_resolution, input_image):
"""
Map to target height and width based on resolution for HunyuanVideo
Args:
height (float): video height
width (float): video width
ratios (list): video aspect ratio
buckets (list): buckets generate by `generate_crop_size_list`
Returns:
the closest ratio in the buckets and the corresponding ratio
"""
if i2v_resolution == "720p":
bucket_hw_base_size = 960
elif i2v_resolution == "540p":
bucket_hw_base_size = 720
elif i2v_resolution == "360p":
bucket_hw_base_size = 480
origin_size = input_image.size
crop_size_list = _generate_crop_size_list(bucket_hw_base_size, 32)
aspect_ratios = np.array([round(float(h)/float(w), 5) for h, w in crop_size_list])
closest_size, _ = _get_closest_ratio(origin_size[1], origin_size[0], aspect_ratios, crop_size_list)
target_height, target_width = closest_size
return target_height, target_width |