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47 lines
1.7 KiB
47 lines
1.7 KiB
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": 1.0, "step": 0.001}), |
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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, noise_augmentation): |
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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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n[1]['noise_augmentation'] = noise_augmentation |
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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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n[1]['noise_augmentation'] = noise_augmentation |
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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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