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Add support for Chroma Radiance (#9682)

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* 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
This commit is contained in:
blepping
2025-09-13 15:58:43 -06:00
committed by GitHub
parent e5e70636e7
commit c1297f4eb3
10 changed files with 770 additions and 9 deletions
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14
comfy/model_detection.py
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@@ -174,7 +174,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
dit_config["guidance_embed"] = len(guidance_keys) > 0
return dit_config
if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and '{}img_in.weight'.format(key_prefix) in state_dict_keys: #Flux
if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and ('{}img_in.weight'.format(key_prefix) in state_dict_keys or f"{key_prefix}distilled_guidance_layer.norms.0.scale" in state_dict_keys): #Flux, Chroma or Chroma Radiance (has no img_in.weight)
dit_config = {}
dit_config["image_model"] = "flux"
dit_config["in_channels"] = 16
@@ -204,6 +204,18 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
dit_config["out_dim"] = 3072
dit_config["hidden_dim"] = 5120
dit_config["n_layers"] = 5
if f"{key_prefix}nerf_blocks.0.norm.scale" in state_dict_keys: #Chroma Radiance
dit_config["image_model"] = "chroma_radiance"
dit_config["in_channels"] = 3
dit_config["out_channels"] = 3
dit_config["patch_size"] = 16
dit_config["nerf_hidden_size"] = 64
dit_config["nerf_mlp_ratio"] = 4
dit_config["nerf_depth"] = 4
dit_config["nerf_max_freqs"] = 8
dit_config["nerf_tile_size"] = 32
dit_config["nerf_final_head_type"] = "conv" if f"{key_prefix}nerf_final_layer_conv.norm.scale" in state_dict_keys else "linear"
dit_config["nerf_embedder_dtype"] = torch.float32
else:
dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
return dit_config