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import logging
from typing import Optional
import torch
from comfy_api.input.video_types import VideoInput
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_aspect_ratio: Optional[float] = None,
max_aspect_ratio: Optional[float] = None,
):
width, height = get_image_dimensions(image)
aspect_ratio = width / height
if min_aspect_ratio is not None and aspect_ratio < min_aspect_ratio:
raise ValueError(
f"Image aspect ratio must be at least {min_aspect_ratio}, got {aspect_ratio}"
)
if max_aspect_ratio is not None and aspect_ratio > max_aspect_ratio:
raise ValueError(
f"Image aspect ratio must be at most {max_aspect_ratio}, got {aspect_ratio}"
)
def validate_image_aspect_ratio_range(
image: torch.Tensor,
min_ratio: tuple[float, float], # e.g. (1, 4)
max_ratio: tuple[float, float], # e.g. (4, 1)
*,
strict: bool = True, # True -> (min, max); False -> [min, max]
) -> float:
a1, b1 = min_ratio
a2, b2 = max_ratio
if a1 <= 0 or b1 <= 0 or a2 <= 0 or b2 <= 0:
raise ValueError("Ratios must be positive, like (1, 4) or (4, 1).")
lo, hi = (a1 / b1), (a2 / b2)
if lo > hi:
lo, hi = hi, lo
a1, b1, a2, b2 = a2, b2, a1, b1 # swap only for error text
w, h = get_image_dimensions(image)
if w <= 0 or h <= 0:
raise ValueError(f"Invalid image dimensions: {w}x{h}")
ar = w / h
ok = (lo < ar < hi) if strict else (lo <= ar <= hi)
if not ok:
op = "<" if strict else "≤"
raise ValueError(f"Image aspect ratio {ar:.6g} is outside allowed range: {a1}:{b1} {op} ratio {op} {a2}:{b2}")
return ar
def validate_aspect_ratio_closeness(
start_img,
end_img,
min_rel: float,
max_rel: float,
*,
strict: bool = False, # True => exclusive, False => inclusive
) -> None:
w1, h1 = get_image_dimensions(start_img)
w2, h2 = get_image_dimensions(end_img)
if min(w1, h1, w2, h2) <= 0:
raise ValueError("Invalid image dimensions")
ar1 = w1 / h1
ar2 = w2 / h2
# Normalize so it is symmetric (no need to check both ar1/ar2 and ar2/ar1)
closeness = max(ar1, ar2) / min(ar1, ar2)
limit = max(max_rel, 1.0 / min_rel) # for 0.8..1.25 this is 1.25
if (closeness >= limit) if strict else (closeness > limit):
raise ValueError(f"Aspect ratios must be close: start/end={ar1/ar2:.4f}, allowed range {min_rel}–{max_rel}.")
def validate_video_dimensions(
video: VideoInput,
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: VideoInput,
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)
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