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