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import torch
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class InstructPixToPixConditioning:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"vae": ("VAE", ),
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"pixels": ("IMAGE", ),
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}}
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RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT")
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RETURN_NAMES = ("positive", "negative", "latent")
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FUNCTION = "encode"
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CATEGORY = "conditioning/instructpix2pix"
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def encode(self, positive, negative, pixels, vae):
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x = (pixels.shape[1] // 8) * 8
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y = (pixels.shape[2] // 8) * 8
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if pixels.shape[1] != x or pixels.shape[2] != y:
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x_offset = (pixels.shape[1] % 8) // 2
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y_offset = (pixels.shape[2] % 8) // 2
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pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
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concat_latent = vae.encode(pixels)
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out_latent = {}
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out_latent["samples"] = torch.zeros_like(concat_latent)
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out = []
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for conditioning in [positive, negative]:
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c = []
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for t in conditioning:
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d = t[1].copy()
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d["concat_latent_image"] = concat_latent
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n = [t[0], d]
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c.append(n)
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out.append(c)
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return (out[0], out[1], out_latent)
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NODE_CLASS_MAPPINGS = {
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"InstructPixToPixConditioning": InstructPixToPixConditioning,
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}
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