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Ensure images are multiples of 16 for Chroma Radiance

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Add batch dimension and fix channels when necessary in ChromaRadianceImageToLatent node
This commit is contained in:
blepping 2025-08-20 13:14:46 -06:00
parent 0f584ea983
commit d1a3a5d1b7
1 changed files with 15 additions and 2 deletions
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17
comfy_extras/nodes_chroma_radiance.py
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@ -10,8 +10,8 @@ class EmptyChromaRadianceLatentImage:
@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}),
return {"required": { "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
"height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "go"
@ -57,6 +57,19 @@ class ChromaRadianceImageToLatent:
CATEGORY = "latent/chroma_radiance"
def go(self, *, image):
if image.ndim == 3:
image = image.unsqueeze(0)
elif image.ndim != 4:
raise ValueError("Unexpected input image shape")
h, w, c = image.shape[1:]
if h < 16 or w < 16 or not (h / 16).is_integer() or not (w / 16).is_integer():
raise ValueError("Chroma Radiance image inputs must have sizes that are multiples of 16.")
if c > 3:
image = image[..., :3]
elif c == 1:
image = image.expand(-1, -1, -1, 3)
elif c != 3:
raise ValueError("Unexpected number of channels in input image")
latent = image.to(device=self.device, dtype=torch.float32, copy=True)
latent = latent.clamp_(0, 1).movedim(-1, 1).contiguous()
latent -= 0.5