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Added pixel_perfect_resolution() method to controlnet_utils.py, but not using yet. To be usable this will likely require modification of ControlNet preprocessors

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user1 2023-07-20 00:38:20 -07:00
parent c2b99e7545
commit 09dfcc4277
1 changed files with 55 additions and 1 deletions
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56
invokeai/app/util/controlnet_utils.py
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@ -94,12 +94,66 @@ def nake_nms(x):
return y
################################################################################
# copied from Mikubill/sd-webui-controlnet external_code.py and modified for InvokeAI
################################################################################
# FIXME: not using yet, if used in the future will most likely require modification of preprocessors
def pixel_perfect_resolution(
image: np.ndarray,
target_H: int,
target_W: int,
resize_mode: str,
) -> int:
"""
Calculate the estimated resolution for resizing an image while preserving aspect ratio.
The function first calculates scaling factors for height and width of the image based on the target
height and width. Then, based on the chosen resize mode, it either takes the smaller or the larger
scaling factor to estimate the new resolution.
If the resize mode is OUTER_FIT, the function uses the smaller scaling factor, ensuring the whole image
fits within the target dimensions, potentially leaving some empty space.
If the resize mode is not OUTER_FIT, the function uses the larger scaling factor, ensuring the target
dimensions are fully filled, potentially cropping the image.
After calculating the estimated resolution, the function prints some debugging information.
Args:
image (np.ndarray): A 3D numpy array representing an image. The dimensions represent [height, width, channels].
target_H (int): The target height for the image.
target_W (int): The target width for the image.
resize_mode (ResizeMode): The mode for resizing.
Returns:
int: The estimated resolution after resizing.
"""
raw_H, raw_W, _ = image.shape
k0 = float(target_H) / float(raw_H)
k1 = float(target_W) / float(raw_W)
if resize_mode == "fill_resize":
estimation = min(k0, k1) * float(min(raw_H, raw_W))
else: # "crop_resize" or "just_resize" (or possibly "just_resize_simple"?)
estimation = max(k0, k1) * float(min(raw_H, raw_W))
# print(f"Pixel Perfect Computation:")
# print(f"resize_mode = {resize_mode}")
# print(f"raw_H = {raw_H}")
# print(f"raw_W = {raw_W}")
# print(f"target_H = {target_H}")
# print(f"target_W = {target_W}")
# print(f"estimation = {estimation}")
return int(np.round(estimation))
###########################################################################
# Copied from detectmap_proc method in scripts/detectmap_proc.py in Mikubill/sd-webui-controlnet
# modified for InvokeAI
###########################################################################
# def detectmap_proc(detected_map, module, resize_mode, h, w):
@staticmethod
def np_img_resize(
np_img: np.ndarray,
resize_mode: str,