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46 lines
1.6 KiB
46 lines
1.6 KiB
11 months ago
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import torch
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import nodes
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import comfy.utils
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class SD_4XUpscale_Conditioning:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "images": ("IMAGE",),
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"positive": ("CONDITIONING",),
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"negative": ("CONDITIONING",),
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"scale_ratio": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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# "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}), #TODO
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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/upscale_diffusion"
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def encode(self, images, positive, negative, scale_ratio):
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width = max(1, round(images.shape[-2] * scale_ratio))
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height = max(1, round(images.shape[-3] * scale_ratio))
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pixels = comfy.utils.common_upscale((images.movedim(-1,1) * 2.0) - 1.0, width // 4, height // 4, "bilinear", "center")
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out_cp = []
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out_cn = []
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for t in positive:
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n = [t[0], t[1].copy()]
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n[1]['concat_image'] = pixels
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out_cp.append(n)
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for t in negative:
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n = [t[0], t[1].copy()]
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n[1]['concat_image'] = pixels
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out_cn.append(n)
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latent = torch.zeros([images.shape[0], 4, height // 4, width // 4])
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return (out_cp, out_cn, {"samples":latent})
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NODE_CLASS_MAPPINGS = {
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"SD_4XUpscale_Conditioning": SD_4XUpscale_Conditioning,
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}
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