Annotated Examples
A growing collection of fragments of example code…
Images and Masks
Load an image
Load an image into a batch of size 1 (based on LoadImage source code in nodes.py)
i = Image.open(image_path)
i = ImageOps.exif_transpose(i)
if i.mode == 'I':
i = i.point(lambda i: i * (1 / 255))
image = i.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
Save an image batch
Save a batch of images (based on SaveImage source code in nodes.py)
for (batch_number, image) in enumerate(images):
i = 255. * image.cpu().numpy()
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
filepath = # some path that takes the batch number into account
img.save(filepath)
Invert a mask
Inverting a mask is a straightforward process. Since masks are normalised to the range [0,1]:
mask = 1.0 - mask
Convert a mask to Image shape
# We want [B,H,W,C] with C = 1
if len(mask.shape)==2: # we have [H,W], so insert B and C as dimension 1
mask = mask[None,:,:,None]
elif len(mask.shape)==3 and mask.shape[2]==1: # we have [H,W,C]
mask = mask[None,:,:,:]
elif len(mask.shape)==3: # we have [B,H,W]
mask = mask[:,:,:,None]
Using Masks as Transparency Layers
When used for tasks like inpainting or segmentation, the MASK’s values will eventually be rounded to the nearest integer so that they are binary — 0 indicating regions to be ignored and 1 indicating regions to be targeted. However, this doesn’t happen until the MASK is passed to those nodes. This flexibility allows you to use MASKs as as you would in digital photography contexts as a transparency layer:
# Invert mask back to original transparency layer
mask = 1.0 - mask
# Unsqueeze the `C` (channels) dimension
mask = mask.unsqueeze(-1)
# Concatenate ("cat") along the `C` dimension
rgba_image = torch.cat((rgb_image, mask), dim=-1)
Noise
Creating noise variations
Here’s an example of creating a noise object which mixes the noise from two sources. This could be used to create slight noise variations by varying weight2.
class Noise_MixedNoise:
def __init__(self, nosie1, noise2, weight2):
self.noise1 = noise1
self.noise2 = noise2
self.weight2 = weight2
@property
def seed(self): return self.noise1.seed
def generate_noise(self, input_latent:torch.Tensor) -> torch.Tensor:
noise1 = self.noise1.generate_noise(input_latent)
noise2 = self.noise2.generate_noise(input_latent)
return noise1 * (1.0-self.weight2) + noise2 * (self.weight2)
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