Fill
0bcd5c3c7c
added the ability to schedule some values in the FX nodes, as well as optimized some code to make them a little faster. Iv also added a % complete print in the console so you can see how far along the processing is =)
82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
import torch
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import numpy as np
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from PIL import Image
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from glitch_this import ImageGlitcher
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import sys
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class FL_Glitch:
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def __init__(self):
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self.seed_index = 0
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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},
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"optional": {
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"glitch_amount": ("FLOAT", {"default": 3.0, "min": 0.1, "max": 10.0, "step": 0.01}),
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"color_offset": (["Disable", "Enable"],),
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"seed": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "glitch"
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CATEGORY = "🏵️Fill Nodes"
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def t2p(self, t):
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if t is not None:
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i = 255.0 * t.cpu().numpy().squeeze()
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p = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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return p
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def s2b(self, v):
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return v == "Enable"
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def glitch(self, images, glitch_amount=1, color_offset="Disable", seed=0):
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color_offset = self.s2b(color_offset)
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g = ImageGlitcher()
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out = []
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total_images = len(images)
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# Convert seed to a list if it's a single value
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if not isinstance(seed, list):
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seed = [seed] * total_images
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for i, image in enumerate(images, start=1):
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p = self.t2p(image)
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# Get the current seed value
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current_seed = seed[i - 1]
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# Ensure current_seed is a single integer value
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if isinstance(current_seed, (int, float)):
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current_seed = int(current_seed)
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elif isinstance(current_seed, (list, tuple)):
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current_seed = current_seed[0]
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else:
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current_seed = current_seed.iloc[0]
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g1 = g.glitch_image(p, glitch_amount, color_offset=color_offset, seed=current_seed)
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r1 = g1.rotate(90, expand=True)
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g2 = g.glitch_image(r1, glitch_amount, color_offset=color_offset, seed=current_seed)
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f = g2.rotate(-90, expand=True)
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o = np.array(f.convert("RGB")).astype(np.float32) / 255.0
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o = torch.from_numpy(o).unsqueeze(0)
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out.append(o)
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# Print progress update
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progress = i / total_images * 100
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sys.stdout.write(f"\rProcessing images: {progress:.2f}%")
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sys.stdout.flush()
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# Print a new line after the progress update
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print()
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out = torch.cat(out, 0)
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return (out,) |