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https://github.com/invoke-ai/InvokeAI
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Martin Kristiansen
@ -17,16 +17,8 @@ from controlnet_aux.util import HWC3, resize_image
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# If you use this, please Cite "High Quality Edge Thinning using Pure Python", Lvmin Zhang, In Mikubill/sd-webui-controlnet.
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lvmin_kernels_raw = [
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np.array([
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[-1, -1, -1],
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[0, 1, 0],
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[1, 1, 1]
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], dtype=np.int32),
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np.array([
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[0, -1, -1],
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[1, 1, -1],
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[0, 1, 0]
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], dtype=np.int32)
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np.array([[-1, -1, -1], [0, 1, 0], [1, 1, 1]], dtype=np.int32),
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np.array([[0, -1, -1], [1, 1, -1], [0, 1, 0]], dtype=np.int32),
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]
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lvmin_kernels = []
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@ -36,16 +28,8 @@ lvmin_kernels += [np.rot90(x, k=2, axes=(0, 1)) for x in lvmin_kernels_raw]
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lvmin_kernels += [np.rot90(x, k=3, axes=(0, 1)) for x in lvmin_kernels_raw]
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lvmin_prunings_raw = [
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np.array([
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[-1, -1, -1],
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[-1, 1, -1],
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[0, 0, -1]
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], dtype=np.int32),
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np.array([
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[-1, -1, -1],
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[-1, 1, -1],
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[-1, 0, 0]
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], dtype=np.int32)
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np.array([[-1, -1, -1], [-1, 1, -1], [0, 0, -1]], dtype=np.int32),
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np.array([[-1, -1, -1], [-1, 1, -1], [-1, 0, 0]], dtype=np.int32),
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]
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lvmin_prunings = []
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@ -99,10 +83,10 @@ def nake_nms(x):
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################################################################################
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# FIXME: not using yet, if used in the future will most likely require modification of preprocessors
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def pixel_perfect_resolution(
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image: np.ndarray,
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target_H: int,
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target_W: int,
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resize_mode: str,
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image: np.ndarray,
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target_H: int,
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target_W: int,
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resize_mode: str,
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) -> int:
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"""
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Calculate the estimated resolution for resizing an image while preserving aspect ratio.
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@ -135,7 +119,7 @@ def pixel_perfect_resolution(
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if resize_mode == "fill_resize":
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estimation = min(k0, k1) * float(min(raw_H, raw_W))
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else: # "crop_resize" or "just_resize" (or possibly "just_resize_simple"?)
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else: # "crop_resize" or "just_resize" (or possibly "just_resize_simple"?)
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estimation = max(k0, k1) * float(min(raw_H, raw_W))
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# print(f"Pixel Perfect Computation:")
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@ -154,13 +138,7 @@ def pixel_perfect_resolution(
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# modified for InvokeAI
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###########################################################################
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# def detectmap_proc(detected_map, module, resize_mode, h, w):
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def np_img_resize(
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np_img: np.ndarray,
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resize_mode: str,
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h: int,
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w: int,
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device: torch.device = torch.device('cpu')
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):
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def np_img_resize(np_img: np.ndarray, resize_mode: str, h: int, w: int, device: torch.device = torch.device("cpu")):
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# if 'inpaint' in module:
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# np_img = np_img.astype(np.float32)
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# else:
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@ -184,15 +162,14 @@ def np_img_resize(
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# below is very boring but do not change these. If you change these Apple or Mac may fail.
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y = torch.from_numpy(y)
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y = y.float() / 255.0
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y = rearrange(y, 'h w c -> 1 c h w')
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y = rearrange(y, "h w c -> 1 c h w")
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y = y.clone()
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# y = y.to(devices.get_device_for("controlnet"))
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y = y.to(device)
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y = y.clone()
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return y
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def high_quality_resize(x: np.ndarray,
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size):
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def high_quality_resize(x: np.ndarray, size):
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# Written by lvmin
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# Super high-quality control map up-scaling, considering binary, seg, and one-pixel edges
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inpaint_mask = None
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@ -244,7 +221,7 @@ def np_img_resize(
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return y
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# if resize_mode == external_code.ResizeMode.RESIZE:
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if resize_mode == "just_resize": # RESIZE
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if resize_mode == "just_resize": # RESIZE
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np_img = high_quality_resize(np_img, (w, h))
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np_img = safe_numpy(np_img)
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return get_pytorch_control(np_img), np_img
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@ -270,20 +247,21 @@ def np_img_resize(
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new_h, new_w, _ = np_img.shape
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pad_h = max(0, (h - new_h) // 2)
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pad_w = max(0, (w - new_w) // 2)
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high_quality_background[pad_h:pad_h + new_h, pad_w:pad_w + new_w] = np_img
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high_quality_background[pad_h : pad_h + new_h, pad_w : pad_w + new_w] = np_img
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np_img = high_quality_background
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np_img = safe_numpy(np_img)
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return get_pytorch_control(np_img), np_img
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else: # resize_mode == "crop_resize" (INNER_FIT)
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else: # resize_mode == "crop_resize" (INNER_FIT)
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k = max(k0, k1)
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np_img = high_quality_resize(np_img, (safeint(old_w * k), safeint(old_h * k)))
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new_h, new_w, _ = np_img.shape
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pad_h = max(0, (new_h - h) // 2)
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pad_w = max(0, (new_w - w) // 2)
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np_img = np_img[pad_h:pad_h + h, pad_w:pad_w + w]
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np_img = np_img[pad_h : pad_h + h, pad_w : pad_w + w]
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np_img = safe_numpy(np_img)
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return get_pytorch_control(np_img), np_img
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def prepare_control_image(
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# image used to be Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor, List[torch.Tensor]]
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# but now should be able to assume that image is a single PIL.Image, which simplifies things
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@ -301,15 +279,17 @@ def prepare_control_image(
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resize_mode="just_resize_simple",
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):
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# FIXME: implement "crop_resize_simple" and "fill_resize_simple", or pull them out
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if (resize_mode == "just_resize_simple" or
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resize_mode == "crop_resize_simple" or
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resize_mode == "fill_resize_simple"):
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if (
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resize_mode == "just_resize_simple"
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or resize_mode == "crop_resize_simple"
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or resize_mode == "fill_resize_simple"
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):
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image = image.convert("RGB")
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if (resize_mode == "just_resize_simple"):
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if resize_mode == "just_resize_simple":
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image = image.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
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elif (resize_mode == "crop_resize_simple"): # not yet implemented
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elif resize_mode == "crop_resize_simple": # not yet implemented
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pass
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elif (resize_mode == "fill_resize_simple"): # not yet implemented
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elif resize_mode == "fill_resize_simple": # not yet implemented
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pass
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nimage = np.array(image)
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nimage = nimage[None, :]
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@ -320,7 +300,7 @@ def prepare_control_image(
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timage = torch.from_numpy(nimage)
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# use fancy lvmin controlnet resizing
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elif (resize_mode == "just_resize" or resize_mode == "crop_resize" or resize_mode == "fill_resize"):
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elif resize_mode == "just_resize" or resize_mode == "crop_resize" or resize_mode == "fill_resize":
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nimage = np.array(image)
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timage, nimage = np_img_resize(
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np_img=nimage,
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@ -336,7 +316,7 @@ def prepare_control_image(
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exit(1)
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timage = timage.to(device=device, dtype=dtype)
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cfg_injection = (control_mode == "more_control" or control_mode == "unbalanced")
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cfg_injection = control_mode == "more_control" or control_mode == "unbalanced"
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if do_classifier_free_guidance and not cfg_injection:
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timage = torch.cat([timage] * 2)
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return timage
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