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37 lines
1.3 KiB
Python
37 lines
1.3 KiB
Python
import torch
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from nodes import MAX_RESOLUTION
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# diffusers library scale the random noise
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default_vae_scaling_factor = 0.18215
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class NoisyLatentImage:
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def __init__(self, device="cpu"):
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self.device = device
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"vae_scaling_factor": ("FLOAT", {"default": default_vae_scaling_factor, "min": 0.01, "max": 1.1, "step": 0.01}),
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"width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
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"height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
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"batch_size": ("INT", {"default": 1, "min": 1, "max": 64})}}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "generate"
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CATEGORY = "latent"
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def generate(self, seed, vae_scaling_factor, width, height, batch_size=1):
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generator = torch.manual_seed(seed)
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latent = torch.randn([batch_size, 4, height // 8, width // 8], generator=generator, device=self.device) / vae_scaling_factor
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return ({"samples":latent}, )
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NODE_CLASS_MAPPINGS = {
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"Noisy Latent Image": NoisyLatentImage,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"NoisyLatentImage": "Noisy Latent Image"
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}
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