Spaces:
Runtime error
Runtime error
File size: 8,570 Bytes
2c3674f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 |
import logging
from typing import Optional
import torch
from comfy_api.input.video_types import VideoInput
from comfy_api.latest import Input
def get_image_dimensions(image: torch.Tensor) -> tuple[int, int]:
if len(image.shape) == 4:
return image.shape[1], image.shape[2]
elif len(image.shape) == 3:
return image.shape[0], image.shape[1]
else:
raise ValueError("Invalid image tensor shape.")
def validate_image_dimensions(
image: torch.Tensor,
min_width: Optional[int] = None,
max_width: Optional[int] = None,
min_height: Optional[int] = None,
max_height: Optional[int] = None,
):
height, width = get_image_dimensions(image)
if min_width is not None and width < min_width:
raise ValueError(f"Image width must be at least {min_width}px, got {width}px")
if max_width is not None and width > max_width:
raise ValueError(f"Image width must be at most {max_width}px, got {width}px")
if min_height is not None and height < min_height:
raise ValueError(f"Image height must be at least {min_height}px, got {height}px")
if max_height is not None and height > max_height:
raise ValueError(f"Image height must be at most {max_height}px, got {height}px")
def validate_image_aspect_ratio(
image: torch.Tensor,
min_ratio: Optional[tuple[float, float]] = None, # e.g. (1, 4)
max_ratio: Optional[tuple[float, float]] = None, # e.g. (4, 1)
*,
strict: bool = True, # True -> (min, max); False -> [min, max]
) -> float:
"""Validates that image aspect ratio is within min and max. If a bound is None, that side is not checked."""
w, h = get_image_dimensions(image)
if w <= 0 or h <= 0:
raise ValueError(f"Invalid image dimensions: {w}x{h}")
ar = w / h
_assert_ratio_bounds(ar, min_ratio=min_ratio, max_ratio=max_ratio, strict=strict)
return ar
def validate_images_aspect_ratio_closeness(
first_image: torch.Tensor,
second_image: torch.Tensor,
min_rel: float, # e.g. 0.8
max_rel: float, # e.g. 1.25
*,
strict: bool = False, # True -> (min, max); False -> [min, max]
) -> float:
"""
Validates that the two images' aspect ratios are 'close'.
The closeness factor is C = max(ar1, ar2) / min(ar1, ar2) (C >= 1).
We require C <= limit, where limit = max(max_rel, 1.0 / min_rel).
Returns the computed closeness factor C.
"""
w1, h1 = get_image_dimensions(first_image)
w2, h2 = get_image_dimensions(second_image)
if min(w1, h1, w2, h2) <= 0:
raise ValueError("Invalid image dimensions")
ar1 = w1 / h1
ar2 = w2 / h2
closeness = max(ar1, ar2) / min(ar1, ar2)
limit = max(max_rel, 1.0 / min_rel)
if (closeness >= limit) if strict else (closeness > limit):
raise ValueError(
f"Aspect ratios must be close: ar1/ar2={ar1/ar2:.2g}, "
f"allowed range {min_rel}–{max_rel} (limit {limit:.2g})."
)
return closeness
def validate_aspect_ratio_string(
aspect_ratio: str,
min_ratio: Optional[tuple[float, float]] = None, # e.g. (1, 4)
max_ratio: Optional[tuple[float, float]] = None, # e.g. (4, 1)
*,
strict: bool = False, # True -> (min, max); False -> [min, max]
) -> float:
"""Parses 'X:Y' and validates it against optional bounds. Returns the numeric ratio."""
ar = _parse_aspect_ratio_string(aspect_ratio)
_assert_ratio_bounds(ar, min_ratio=min_ratio, max_ratio=max_ratio, strict=strict)
return ar
def validate_video_dimensions(
video: Input.Video,
min_width: Optional[int] = None,
max_width: Optional[int] = None,
min_height: Optional[int] = None,
max_height: Optional[int] = None,
):
try:
width, height = video.get_dimensions()
except Exception as e:
logging.error("Error getting dimensions of video: %s", e)
return
if min_width is not None and width < min_width:
raise ValueError(f"Video width must be at least {min_width}px, got {width}px")
if max_width is not None and width > max_width:
raise ValueError(f"Video width must be at most {max_width}px, got {width}px")
if min_height is not None and height < min_height:
raise ValueError(f"Video height must be at least {min_height}px, got {height}px")
if max_height is not None and height > max_height:
raise ValueError(f"Video height must be at most {max_height}px, got {height}px")
def validate_video_duration(
video: Input.Video,
min_duration: Optional[float] = None,
max_duration: Optional[float] = None,
):
try:
duration = video.get_duration()
except Exception as e:
logging.error("Error getting duration of video: %s", e)
return
epsilon = 0.0001
if min_duration is not None and min_duration - epsilon > duration:
raise ValueError(f"Video duration must be at least {min_duration}s, got {duration}s")
if max_duration is not None and duration > max_duration + epsilon:
raise ValueError(f"Video duration must be at most {max_duration}s, got {duration}s")
def get_number_of_images(images):
if isinstance(images, torch.Tensor):
return images.shape[0] if images.ndim >= 4 else 1
return len(images)
def validate_audio_duration(
audio: Input.Audio,
min_duration: Optional[float] = None,
max_duration: Optional[float] = None,
) -> None:
sr = int(audio["sample_rate"])
dur = int(audio["waveform"].shape[-1]) / sr
eps = 1.0 / sr
if min_duration is not None and dur + eps < min_duration:
raise ValueError(f"Audio duration must be at least {min_duration}s, got {dur + eps:.2f}s")
if max_duration is not None and dur - eps > max_duration:
raise ValueError(f"Audio duration must be at most {max_duration}s, got {dur - eps:.2f}s")
def validate_string(
string: str,
strip_whitespace=True,
field_name="prompt",
min_length=None,
max_length=None,
):
if string is None:
raise Exception(f"Field '{field_name}' cannot be empty.")
if strip_whitespace:
string = string.strip()
if min_length and len(string) < min_length:
raise Exception(
f"Field '{field_name}' cannot be shorter than {min_length} characters; was {len(string)} characters long."
)
if max_length and len(string) > max_length:
raise Exception(
f" Field '{field_name} cannot be longer than {max_length} characters; was {len(string)} characters long."
)
def validate_container_format_is_mp4(video: VideoInput) -> None:
"""Validates video container format is MP4."""
container_format = video.get_container_format()
if container_format not in ["mp4", "mov,mp4,m4a,3gp,3g2,mj2"]:
raise ValueError(f"Only MP4 container format supported. Got: {container_format}")
def _ratio_from_tuple(r: tuple[float, float]) -> float:
a, b = r
if a <= 0 or b <= 0:
raise ValueError(f"Ratios must be positive, got {a}:{b}.")
return a / b
def _assert_ratio_bounds(
ar: float,
*,
min_ratio: Optional[tuple[float, float]] = None,
max_ratio: Optional[tuple[float, float]] = None,
strict: bool = True,
) -> None:
"""Validate a numeric aspect ratio against optional min/max ratio bounds."""
lo = _ratio_from_tuple(min_ratio) if min_ratio is not None else None
hi = _ratio_from_tuple(max_ratio) if max_ratio is not None else None
if lo is not None and hi is not None and lo > hi:
lo, hi = hi, lo # normalize order if caller swapped them
if lo is not None:
if (ar <= lo) if strict else (ar < lo):
op = "<" if strict else "≤"
raise ValueError(f"Aspect ratio `{ar:.2g}` must be {op} {lo:.2g}.")
if hi is not None:
if (ar >= hi) if strict else (ar > hi):
op = "<" if strict else "≤"
raise ValueError(f"Aspect ratio `{ar:.2g}` must be {op} {hi:.2g}.")
def _parse_aspect_ratio_string(ar_str: str) -> float:
"""Parse 'X:Y' with integer parts into a positive float ratio X/Y."""
parts = ar_str.split(":")
if len(parts) != 2:
raise ValueError(f"Aspect ratio must be 'X:Y' (e.g., 16:9), got '{ar_str}'.")
try:
a = int(parts[0].strip())
b = int(parts[1].strip())
except ValueError as exc:
raise ValueError(f"Aspect ratio must contain integers separated by ':', got '{ar_str}'.") from exc
if a <= 0 or b <= 0:
raise ValueError(f"Aspect ratio parts must be positive integers, got {a}:{b}.")
return a / b
|