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45 lines
1.4 KiB
45 lines
1.4 KiB
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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