jaidaken
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Includes 30 custom nodes committed directly, 7 Civitai-exclusive loras stored via Git LFS, and a setup script that installs all dependencies and downloads HuggingFace-hosted models on vast.ai. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
from nodes import EmptyLatentImage
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from .constants import get_category, get_name
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class RgthreeSDXLEmptyLatentImage:
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NAME = get_name('SDXL Empty Latent Image')
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CATEGORY = get_category()
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@classmethod
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def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring
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return {
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"required": {
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"dimensions": (
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[
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# 'Custom',
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'1536 x 640 (landscape)',
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'1344 x 768 (landscape)',
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'1216 x 832 (landscape)',
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'1152 x 896 (landscape)',
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'1024 x 1024 (square)',
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' 896 x 1152 (portrait)',
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' 832 x 1216 (portrait)',
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' 768 x 1344 (portrait)',
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' 640 x 1536 (portrait)',
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],
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{
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"default": '1024 x 1024 (square)'
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}),
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"clip_scale": ("FLOAT", {
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"default": 2.0,
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"min": 1.0,
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"max": 10.0,
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"step": .5
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}),
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"batch_size": ("INT", {
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"default": 1,
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"min": 1,
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"max": 64
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}),
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},
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# "optional": {
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# "custom_width": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 64}),
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# "custom_height": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 64}),
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# }
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}
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RETURN_TYPES = ("LATENT", "INT", "INT")
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RETURN_NAMES = ("LATENT", "CLIP_WIDTH", "CLIP_HEIGHT")
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FUNCTION = "generate"
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def generate(self, dimensions, clip_scale, batch_size):
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"""Generates the latent and exposes the clip_width and clip_height"""
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if True:
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result = [x.strip() for x in dimensions.split('x')]
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width = int(result[0])
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height = int(result[1].split(' ')[0])
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latent = EmptyLatentImage().generate(width, height, batch_size)[0]
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return (
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latent,
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int(width * clip_scale),
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int(height * clip_scale),
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)
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