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Added revised prepare_control_image() that leverages lvmin high quality resizing

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user1 2023-07-19 19:01:14 -07:00
parent 4e3f58552c
commit 3a987b2e72
1 changed files with 57 additions and 4 deletions
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61
invokeai/app/util/controlnet_utils.py
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@ -107,7 +107,6 @@ def np_img_resize(
w: int,
device: torch.device = torch.device('cpu')
):
print("in np_img_resize")
# if 'inpaint' in module:
# np_img = np_img.astype(np.float32)
# else:
@ -192,7 +191,6 @@ def np_img_resize(
# if resize_mode == external_code.ResizeMode.RESIZE:
if resize_mode == "just_resize": # RESIZE
print("just resizing")
np_img = high_quality_resize(np_img, (w, h))
np_img = safe_numpy(np_img)
return get_pytorch_control(np_img), np_img
@ -207,7 +205,6 @@ def np_img_resize(
# if resize_mode == external_code.ResizeMode.OUTER_FIT:
if resize_mode == "fill_resize": # OUTER_FIT
print("fill + resizing")
k = min(k0, k1)
borders = np.concatenate([np_img[0, :, :], np_img[-1, :, :], np_img[:, 0, :], np_img[:, -1, :]], axis=0)
high_quality_border_color = np.median(borders, axis=0).astype(np_img.dtype)
@ -224,7 +221,6 @@ def np_img_resize(
np_img = safe_numpy(np_img)
return get_pytorch_control(np_img), np_img
else: # resize_mode == "crop_resize" (INNER_FIT)
print("crop + resizing")
k = max(k0, k1)
np_img = high_quality_resize(np_img, (safeint(old_w * k), safeint(old_h * k)))
new_h, new_w, _ = np_img.shape
@ -233,3 +229,60 @@ def np_img_resize(
np_img = np_img[pad_h:pad_h + h, pad_w:pad_w + w]
np_img = safe_numpy(np_img)
return get_pytorch_control(np_img), np_img
def prepare_control_image(
# image used to be Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor, List[torch.Tensor]]
# but now should be able to assume that image is a single PIL.Image, which simplifies things
image: Image,
# FIXME: need to fix hardwiring of width and height, change to basing on latents dimensions?
# latents_to_match_resolution, # TorchTensor of shape (batch_size, 3, height, width)
width=512, # should be 8 * latent.shape[3]
height=512, # should be 8 * latent height[2]
# batch_size=1, # currently no batching
# num_images_per_prompt=1, # currently only single image
device="cuda",
dtype=torch.float16,
do_classifier_free_guidance=True,
control_mode="balanced",
resize_mode="just_resize_simple",
):
# FIXME: implement "crop_resize_simple" and "fill_resize_simple", or pull them out
if (resize_mode == "just_resize_simple" or
resize_mode == "crop_resize_simple" or
resize_mode == "fill_resize_simple"):
image = image.convert("RGB")
if (resize_mode == "just_resize_simple"):
image = image.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
elif (resize_mode == "crop_resize_simple"): # not yet implemented
pass
elif (resize_mode == "fill_resize_simple"): # not yet implemented
pass
nimage = np.array(image)
nimage = nimage[None, :]
nimage = np.concatenate([nimage], axis=0)
# normalizing RGB values to [0,1] range (in PIL.Image they are [0-255])
nimage = np.array(nimage).astype(np.float32) / 255.0
nimage = nimage.transpose(0, 3, 1, 2)
timage = torch.from_numpy(nimage)
# use fancy lvmin controlnet resizing
elif (resize_mode == "just_resize" or resize_mode == "crop_resize" or resize_mode == "fill_resize"):
nimage = np.array(image)
timage, nimage = np_img_resize(
np_img=nimage,
resize_mode=resize_mode,
h=height,
w=width,
# device=torch.device('cpu')
device=device,
)
else:
pass
print("ERROR: invalid resize_mode ==> ", resize_mode)
exit(1)
timage = timage.to(device=device, dtype=dtype)
cfg_injection = (control_mode == "more_control" or control_mode == "unbalanced")
if do_classifier_free_guidance and not cfg_injection:
timage = torch.cat([timage] * 2)
return timage