mirror of
https://github.com/comfyanonymous/ComfyUI.git
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71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
import torch
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import comfy.model_management
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import nodes
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class EmptyChromaRadianceLatentImage:
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def __init__(self):
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self.device = comfy.model_management.intermediate_device()
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "width": ("INT", {"default": 1024, "min": 2, "max": nodes.MAX_RESOLUTION}),
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"height": ("INT", {"default": 1024, "min": 2, "max": nodes.MAX_RESOLUTION}),
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"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "go"
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CATEGORY = "latent/chroma_radiance"
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def go(self, *, width, height, batch_size=1):
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latent = torch.zeros((batch_size, 3, height, width), device=self.device)
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return ({"samples":latent}, )
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class ChromaRadianceLatentToImage:
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def __init__(self):
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self.device = comfy.model_management.intermediate_device()
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"latent": ("LATENT",)}}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "go"
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CATEGORY = "latent/chroma_radiance"
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def go(self, *, latent):
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img = latent["samples"].to(device=self.device, dtype=torch.float32, copy=True)
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img = img.clamp_(-1, 1).movedim(1, -1).contiguous()
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img += 1.0
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img *= 0.5
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return (img.clamp_(0, 1),)
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class ChromaRadianceImageToLatent:
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def __init__(self):
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self.device = comfy.model_management.intermediate_device()
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"image": ("IMAGE",)}}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "go"
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CATEGORY = "latent/chroma_radiance"
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def go(self, *, image):
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latent = image.to(device=self.device, dtype=torch.float32, copy=True)
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latent = latent.clamp_(0, 1).movedim(-1, 1).contiguous()
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latent -= 0.5
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latent *= 2
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return ({"samples": latent.clamp_(-1, 1)},)
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NODE_CLASS_MAPPINGS = {
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"EmptyChromaRadianceLatentImage": EmptyChromaRadianceLatentImage,
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"ChromaRadianceLatentToImage": ChromaRadianceLatentToImage,
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"ChromaRadianceImageToLatent": ChromaRadianceImageToLatent,
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}
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