Add support for Chroma Radiance (#9682)

* Initial Chroma Radiance support

* Minor Chroma Radiance cleanups

* Update Radiance nodes to ensure latents/images are on the intermediate device

* Fix Chroma Radiance memory estimation.

* Increase Chroma Radiance memory usage factor

* Increase Chroma Radiance memory usage factor once again

* Ensure images are multiples of 16 for Chroma Radiance
Add batch dimension and fix channels when necessary in ChromaRadianceImageToLatent node

* Tile Chroma Radiance NeRF to reduce memory consumption, update memory usage factor

* Update Radiance to support conv nerf final head type.

* Allow setting NeRF embedder dtype for Radiance
Bump Radiance nerf tile size to 32
Support EasyCache/LazyCache on Radiance (maybe)

* Add ChromaRadianceStubVAE node

* Crop Radiance image inputs to multiples of 16 instead of erroring to be in line with existing VAE behavior

* Convert Chroma Radiance nodes to V3 schema.

* Add ChromaRadianceOptions node and backend support.
Cleanups/refactoring to reduce code duplication with Chroma.

* Fix overriding the NeRF embedder dtype for Chroma Radiance

* Minor Chroma Radiance cleanups

* Move Chroma Radiance to its own directory in ldm
Minor code cleanups and tooltip improvements

* Fix Chroma Radiance embedder dtype overriding

* Remove Radiance dynamic nerf_embedder dtype override feature

* Unbork Radiance NeRF embedder init

* Remove Chroma Radiance image conversion and stub VAE nodes
Add a chroma_radiance option to the VAELoader builtin node which uses comfy.sd.PixelspaceConversionVAE
Add a PixelspaceConversionVAE to comfy.sd for converting BHWC 0..1 <-> BCHW -1..1

comfy/latent_formats.py

@@ -629,3 +629,20 @@ class Hunyuan3Dv2mini(LatentFormat):
 class ACEAudio(LatentFormat):
     latent_channels = 8
     latent_dimensions = 2
+
+class ChromaRadiance(LatentFormat):
+    latent_channels = 3
+
+    def __init__(self):
+        self.latent_rgb_factors = [
+            # R    G    B
+            [ 1.0, 0.0, 0.0 ],
+            [ 0.0, 1.0, 0.0 ],
+            [ 0.0, 0.0, 1.0 ]
+        ]
+
+    def process_in(self, latent):
+        return latent
+
+    def process_out(self, latent):
+        return latent