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Add resolution multiplier to SDXLEmptyLatentSizePicker #126

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Add resolution multiplier to SDXLEmptyLatentSizePicker #126

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16 changes: 10 additions & 6 deletions misc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -472,17 +472,21 @@ def INPUT_TYPES(s):
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
"width_override": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"height_override": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"resolution_multiplier": ("FLOAT", {"default": 1, "min": 0, "max": 100, "step": 0.5}),
}}

RETURN_TYPES = ("LATENT","INT","INT",)
RETURN_NAMES = ("LATENT","width","height",)
FUNCTION = "execute"
CATEGORY = "essentials/utilities"
FUNCTION = "execute"
CATEGORY = "essentials/utilities"

def execute(self, resolution, batch_size, width_override=0, height_override=0):
def execute(self, resolution, batch_size, width_override=0, height_override=0, resolution_multiplier=1.0):
width, height = resolution.split(" ")[0].split("x")
width = width_override if width_override > 0 else int(width)
height = height_override if height_override > 0 else int(height)
width = width_override if width_override > 0 else int(width)
height = height_override if height_override > 0 else int(height)

width = int(resolution_multiplier * width)
height = int(resolution_multiplier * height)

latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device)

Expand Down Expand Up @@ -571,4 +575,4 @@ def tensorShape(tensor):
"SimpleMathPercent+": "🔧 Simple Math Percent",
"SimpleMathSlider+": "🔧 Simple Math Slider",
"SimpleMathSliderLowRes+": "🔧 Simple Math Slider low-res",
}
}