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Perlin Power Fractal Latent

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Add Perlin Power Fractal Latent
Patch Constant Number
This commit is contained in:
Jordan Thompson
2023-08-25 13:48:19 -07:00
parent 3f6aae827d
commit 4d41e150ef
2 changed files with 85 additions and 5 deletions
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README.md
+1
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@@ -197,6 +197,7 @@
- Number to Int
- Number to String
- Number to Text
- Perlin Power Fractal Latent: Create a power fractal based latent image. Doesn't work with all samplers (unless you add noise).
- Random Number
- Save Text File: Save a text string to a file
- Samples Passthrough (Stat System): Logs RAM, VRAM, and Disk usage to the console.
WAS_Node_Suite.py
+84 -5
View File
@@ -418,6 +418,12 @@ def get_sha256(file_path):
sha256_hash.update(chunk)
return sha256_hash.hexdigest()
# Batch Seed Generator
def seed_batch(seed, batches, seeds):
rng = np.random.default_rng(seed)
btch = [rng.choice(2**32 - 1, seeds, replace=False).tolist() for _ in range(batches)]
return btch
# Download File
def download_file(url, filename=None, path=None):
if not filename:
@@ -4156,8 +4162,8 @@ class WAS_Image_Perlin_Power_Fractal:
def INPUT_TYPES(cls):
return {
"required": {
"width": ("INT", {"default": 512, "max": 2048, "min": 64, "step": 1}),
"height": ("INT", {"default": 512, "max": 2048, "min": 64, "step": 1}),
"width": ("INT", {"default": 512, "max": 8192, "min": 64, "step": 1}),
"height": ("INT", {"default": 512, "max": 8192, "min": 64, "step": 1}),
"scale": ("INT", {"default": 100, "max": 2048, "min": 2, "step": 1}),
"octaves": ("INT", {"default": 4, "max": 8, "min": 0, "step": 1}),
"persistence": ("FLOAT", {"default": 0.5, "max": 100.0, "min": 0.01, "step": 0.01}),
@@ -4179,7 +4185,78 @@ class WAS_Image_Perlin_Power_Fractal:
image = WTools.perlin_power_fractal(width, height, octaves, persistence, lacunarity, exponent, scale, seed)
return (pil2tensor(image), )
return (pil2tensor(image), )
# PERLIN POWER FRACTAL LATENT
class WAS_Perlin_Power_Fractal_Latent:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"vae": ("VAE",),
"width": ("INT", {"default": 512, "max": 8192, "min": 64, "step": 1}),
"height": ("INT", {"default": 512, "max": 8192, "min": 64, "step": 1}),
"scale": ("INT", {"default": 100, "max": 2048, "min": 2, "step": 1}),
"octaves": ("INT", {"default": 8, "max": 8, "min": 0, "step": 1}),
"persistence": ("FLOAT", {"default": 1.0, "max": 100.0, "min": 0.01, "step": 0.01}),
"lacunarity": ("FLOAT", {"default": 2.0, "max": 100.0, "min": 0.01, "step": 0.01}),
"exponent": ("FLOAT", {"default": 4.0, "max": 100.0, "min": 0.01, "step": 0.01}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 64, "step": 1})
},
}
RETURN_TYPES = ("LATENT",)
RETURN_NAMES = ("latents",)
FUNCTION = "power_fractal_latent"
CATEGORY = "WAS Suite/Latent/Generate"
def power_fractal_latent(self, vae, width, height, scale, octaves, persistence, lacunarity, exponent, seed, batch_size):
WTools = WAS_Tools_Class()
encoder = nodes.VAEEncode()
rgbmix = WAS_Image_RGB_Merge()
if batch_size > 1:
latents = []
seeds = seed_batch(seed, batch_size, 3)
for batch in range(batch_size):
batch_seeds = seeds[batch]
r = WTools.perlin_power_fractal(width, height, octaves, persistence, lacunarity, exponent, scale, batch_seeds[0])
g = WTools.perlin_power_fractal(width, height, octaves, persistence, lacunarity, exponent, scale, batch_seeds[1])
b = WTools.perlin_power_fractal(width, height, octaves, persistence, lacunarity, exponent, scale, batch_seeds[2])
rgb_noise = rgbmix.merge_channels(pil2tensor(r), pil2tensor(g), pil2tensor(b))
encoded_noise = encoder.encode(pixels=rgb_noise[0], vae=vae)
latents.append(encoded_noise[0]['samples'])
latents = torch.cat(latents)
return ({'samples': latents}, )
else:
r = WTools.perlin_power_fractal(width, height, octaves, persistence, lacunarity, exponent, scale, seed)
g = WTools.perlin_power_fractal(width, height, octaves, persistence, lacunarity, exponent, scale, seed+1)
b = WTools.perlin_power_fractal(width, height, octaves, persistence, lacunarity, exponent, scale, seed+2)
rgb_noise = rgbmix.merge_channels(pil2tensor(r), pil2tensor(g), pil2tensor(b))
tensors = rgb_noise
latents = encoder.encode(pixels=tensors[0], vae=vae)
return latents
# IMAGE VORONOI NOISE FILTER
@@ -9918,7 +9995,8 @@ class WAS_Text_to_Conditioning:
CATEGORY = "WAS Suite/Text/Operations"
def text_to_conditioning(self, clip, text):
return ([[clip.encode(text), {}]], )
encode = clip.encode(text)
return ([[encode[0][0][0], encode[0][0][1], {}]], )
# TEXT PARSE TOKENS
@@ -11463,7 +11541,7 @@ class WAS_Constant_Number:
return (float(number), float(number), int(number) )
elif number_type == 'bool':
boolean = (1 if int(number) > 0 else 0)
return (int(boolean), float(boolean), int(boolan) )
return (int(boolean), float(boolean), int(boolean) )
else:
return (number, float(number), int(number) )
@@ -13133,6 +13211,7 @@ NODE_CLASS_MAPPINGS = {
"Number to Seed": WAS_Number_To_Seed,
"Number to String": WAS_Number_To_String,
"Number to Text": WAS_Number_To_Text,
"Perlin Power Fractal Latent": WAS_Perlin_Power_Fractal_Latent,
"Prompt Styles Selector": WAS_Prompt_Styles_Selector,
"Prompt Multiple Styles Selector": WAS_Prompt_Multiple_Styles_Selector,
"Random Number": WAS_Random_Number,